Journal article · 2020
Predictors of e-service Consumption in a Highly Productive Brazil-Russia-India-China-South Africa Region Sample
International Journal of E-Services and Mobile Applications, 12(1), pp. 39–56 · Published
Summary
What question does this paper answer?
Which social, demographic and emotional factors predict e-service (online) purchase intention among young educated consumers in a highly productive, highly populated BRICS region of India?
What did the study find?
In a sample of 63 young educated Indian consumers, satisfaction with e-services, happiness, positive feelings and pleasant feelings predicted intention to buy a $100 smartphone online, while excitement did not. A two-factor model (happiness and satisfaction) correctly classified 87.3% of consumers, and satisfaction alone correctly classified 90.5%; satisfied consumers were almost 28 times more likely to intend to purchase (odds ratio 27.91).
Why does it matter?
BRICS countries account for 40% of the world’s population and about a third of world GDP, and India has a rapidly growing young online population whose culture differs from the Western samples most e-commerce studies use. The findings give marketing managers a simple go/no-go predictor — consumer satisfaction with e-services — for forecasting online purchases among emerging technology-literate consumers in India.
This study is part of the evidence overview What drives technology adoption and online purchase intention? Evidence from Indian consumers, SMEs and a mandatory LMS
Key findings
- The study surveyed 63 young educated e-service consumers in the Indian states of Andhra Pradesh and Telangana (38.7% female, 61.3% male; mean age 33.6 years, SD = 9.5; median 31) about intention to buy a $100 smartphone online.
- Age was negatively related to online purchase intention among the Indian consumers, with younger participants more likely to intend to buy (Spearman rho = −0.410, p = .001, r2 = 0.16).
- Consumer satisfaction with the Internet context strongly correlated with online purchase intention (rho = 0.67, p < .001), and satisfied consumers were almost 28 times more likely to intend to purchase (odds ratio 27.91, 95% CI 4.43–175.89).
- Positive feelings about the Internet predicted purchase intention among the Indian e-service consumers (simple regression r2 = 52.5%; logistic odds ratio 94, 95% CI 9.88–894.45), and pleasant feelings were also significant (r2 = 48.2%).
- Consumer excitement about the Internet did not reliably predict online purchase intention (r2 = 9.5%; logistic goodness-of-fit deviance χ2(61) = 53.03, p = .756), the only one of eight hypotheses rejected.
- A two-factor discriminant model using happiness and satisfaction correctly classified 87.3% (55 of 63) of the Indian consumers as likely or unlikely to purchase online.
- Satisfaction alone correctly classified 90.5% (57 of 63) of the consumers, and 94.2% (49 of 52) of those willing to make an Internet purchase.
Source: Strang & Vajjhala (2020), International Journal of E-Services and Mobile Applications, 12(1), pp. 39–56. DOI: 10.4018/IJESMA.2020010103
Study at a glance
| Research question | Which social and demographic factors can predict e-service consumption in a highly productive, highly populated BRICS region of India? |
|---|---|
| Design | Exploratory, post-positivist quantitative study testing eight hypotheses with a structured questionnaire administered in participants’ own language. |
| Sample | 63 young educated e-service consumers in Andhra Pradesh and Telangana, India (38.7% female, 61.3% male; mean age 33.6, SD = 9.5). |
| Methods | Chi-square tests, Spearman correlation, simple and binary logistic regression, and discriminant analysis classification in SPSS 22; binary dependent variable = intention to buy a $100 smartphone. |
| Main result | Seven of eight hypotheses supported (excitement rejected); satisfaction and happiness correctly classified 87.3% of consumers, satisfaction alone 90.5%. |
| Implication | Consumer satisfaction with the Internet context is the strongest single predictor marketing managers can use to forecast online purchasing in India. |
| Citation | Strang & Vajjhala (2020) · DOI 10.4018/IJESMA.2020010103 |
Abstract
The authors investigated consumer e-commerce behavior in a Brazil-Russia-India-China-South-Africa (BRICS) region from a socio-cultural perspective. BRICS countries are important to study because they have a large population representative of other global e-services markets, they account for 40% of the world’s population, 26% of the world’s land and approximately a third of the world’s gross domestic economic e-commerce production, plus residents are habitual consumers of mobile technology like smartphones. A binary logistic regression model revealed that young educated consumer satisfaction with e-services, e-service happiness, positive feelings and e-service pleasant feelings, but not e-service excitement, could predict purchase behavior. The model correctly classified 87.3% of the e-commerce consumers using two factors and a second model with one factor correctly categorized 90.5% of them. These results are important for managers and academics to consider.
Abstract as published in International Journal of E-Services and Mobile Applications.
Keywords: Brazil-Russia-India-China-South-Africa; Empirical Predictive Model Study; Mobile E-Services; Online Purchase Behavior; Socio-Cultural Factors
Key terms
- BRICS
- The group of emerging economies Brazil, Russia, India, China and South Africa, which the paper notes account for 40% of the world’s population and 26% of its land area.
- Binary logistic regression
- A regression technique that uses maximum likelihood to estimate the odds of a two-valued outcome (here, will purchase versus unlikely to purchase) from a set of predictors.
- Discriminant analysis
- A classification technique that assigns cases to predefined groups using linear discriminant functions of the predictor variables.
Limitations
- The sample was very small (N = 63), so results should not be assumed to generalize to all young technology-literate consumers in India; replication with much larger samples is needed.
- The factors tested are not well recognized in the extant literature.
- The study is limited by the honesty of participants’ responses and the amount of time available to conduct the study.
- Goodness-of-fit tests could not confirm that the single- and two-factor logistic models adequately represented the hypotheses, and happiness and satisfaction were highly correlated.
How to cite
Strang, K. D., & Vajjhala, N. R. (2020). Predictors of e-service Consumption in a Highly Productive Brazil-Russia-India-China-South Africa Region Sample. International Journal of E-Services and Mobile Applications, 12(1), 39–56. https://doi.org/10.4018/IJESMA.2020010103
BibTeX
@article{strang2020e,
title = {Predictors of e-service Consumption in a Highly Productive Brazil-Russia-India-China-South Africa Region Sample},
author = {Strang, Kenneth David and Vajjhala, Narasimha Rao},
journal = {International Journal of E-Services and Mobile Applications},
volume = {12},
number = {1},
pages = {39--56},
year = {2020},
publisher = {IGI Global},
doi = {10.4018/IJESMA.2020010103},
url = {https://doi.org/10.4018/IJESMA.2020010103}
}Related research