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
Strang and Vajjhala (2020) surveyed 63 young educated consumers in Andhra Pradesh and Telangana, India, in an exploratory study of which social, demographic and emotional factors predict e-service (online) purchase intention. Satisfaction with e-services, happiness, positive feelings and pleasant feelings predicted intention to buy a $100 smartphone online, while excitement did not. Satisfaction alone correctly classified 90.5% of consumers, and satisfied consumers were almost 28 times more likely to intend to purchase (odds ratio 27.91), but the very small sample means results should not be assumed to generalize.
Practical implication. For marketing managers, satisfaction with e-services was a simple go/no-go predictor of online purchase intention in a very small sample of 63 consumers in India; results should not be assumed to generalize.
- The Problem
- Online retailers targeting fast-growing BRICS markets such as India know little about which social, demographic and emotional factors drive young consumers’ e-service purchase intentions.
- The Methodology
- Exploratory post-positivist quantitative survey of 63 young educated consumers in Andhra Pradesh and Telangana, India: chi-square tests, Spearman correlation, binary logistic regression and discriminant analysis in SPSS.
- The Core Finding
- Among 63 young educated Indian consumers, satisfaction, happiness, positive feelings and pleasant feelings predicted online purchase intention while excitement did not. Satisfaction alone correctly classified 90.5% of consumers, and satisfied consumers were almost 28 times more likely to intend to buy (odds ratio 27.91).
- The Citation
- 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
The summary, key findings, methods, implications and relevance notes on this page are this website’s own description of the paper, written from the published abstract and text. The official abstract and citation details are given further down.
Key findings
- Finding 1. 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.
- Finding 2. 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).
- Finding 3. 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).
- Finding 4. 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%).
- Finding 5. 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.
- Finding 6. 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.
- Finding 7. 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
The study in detail
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 detail?
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
Methods and data
| 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 |
Definitions and 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. — as used in Strang & Vajjhala (2020)
- 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. — as used in Strang & Vajjhala (2020)
- Discriminant analysis
- A classification technique that assigns cases to predefined groups using linear discriminant functions of the predictor variables. — as used in Strang & Vajjhala (2020)
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
Limitations and open questions
- 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.
Open questions
- Do satisfaction and happiness remain strong predictors of online purchase intention in much larger and more regionally diverse Indian samples?
Open questions from the evidence overview Technology adoption and digital consumers.
Practical implications
- Marketing managers forecasting online purchasing in India can use satisfaction alone as a predictor, since it correctly classified 90.5% (57 of 63) of the consumers in this sample.
- For online retailers, consumer e-service excitement did not reliably predict online purchase intention; the excitement hypothesis was the only one of eight rejected.
- Online retailers may find younger consumers more likely to intend to buy online: age was negatively related to online purchase intention in this Indian sample (Spearman rho = −0.410, p = .001).
- Managers and academics should not assume the results generalize to all young technology-literate consumers in India, because the sample was very small (N = 63) and replication with much larger samples is needed.
Drawn from Strang & Vajjhala (2020), International Journal of E-Services and Mobile Applications, 12(1), pp. 39–56.
When this research may be relevant
This article may be relevant to researchers studying online purchase intention and e-service adoption in India and other BRICS economies, emotional and satisfaction-based predictors of e-commerce behaviour, mobile commerce, and small-sample classification models of consumer intention.
Research topics addressed: e-services; e-commerce; online consumer behavior; purchase intention; BRICS; India; consumer satisfaction; socio-cultural factors; binary logistic regression; discriminant analysis; mobile e-services; young consumers; emerging markets
How to cite
- APA 7
- 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
- MLA 9
- Strang, Kenneth David, and Narasimha Rao Vajjhala. “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, vol. 12, no. 1, 2020, pp. 39–56. https://doi.org/10.4018/IJESMA.2020010103.
- IEEE
- K. D. Strang and N. R. Vajjhala, “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, vol. 12, no. 1, pp. 39–56, Jan. 2020, doi: 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}
}RIS
TY - JOUR TI - Predictors of e-service Consumption in a Highly Productive Brazil-Russia-India-China-South Africa Region Sample AU - Strang, Kenneth David AU - Vajjhala, Narasimha Rao JO - International Journal of E-Services and Mobile Applications PY - 2020 DA - 2020/01/01 VL - 12 IS - 1 SP - 39 EP - 56 PB - IGI Global DO - 10.4018/IJESMA.2020010103 UR - https://www.narasimharao.net/research/e-service-consumption-predictors-brics-india-consumers/ LA - en ER -
Links and files
Related work
Related work: the literature this study cites
India is presented as under-studied: 75% of its Internet users were reported to be aged 25-35[1], and factors significant in Western samples were not relevant in Thailand and India samples[2]. The study draws on a review noting that most online consumer behavior models assume behavior follows from beliefs, attitudes, intentions and emotions[3], on the split between utilitarian and hedonic shopping motivation, the latter centred on enjoyment, satisfaction and happiness[4], and on the pleasure-arousal-dominance description of emotional response[5]. Among consumers, the weight of satisfaction and happiness is consistent with the hedonic motivation account[4]. On culture, Yoon found uncertainty avoidance and long-term orientation, but not power distance or individualism, moderated e-commerce acceptance[6]; the study uses Hofstede scores to interpret results without testing culture.
- [1] Khare, A. (2016). Consumer Shopping Styles and Online Shopping: An Empirical Study of Indian Consumers. Journal of Global Marketing, 29(1), 40–53. https://doi.org/10.1080/08911762.2015.1122137
- [2] Nittala, R. (2015). Factors Influencing Online Shopping Behavior of Urban Consumers in India. International Journal of Online Marketing, 5(1), 38–50. https://doi.org/10.4018/IJOM.2015010103
- [3] Hwang, Y., & Jeong, J. (2016). Electronic Commerce and Online Consumer Behavior Research: A Literature Review. Information Development, 32(3), 377–388. https://doi.org/10.1177/0266666914551071
- [4] Huseynov, F., & Yildirim, S. O. (2015). Behavioral Issues in Ecommerce. Information Development, 32(5), 1343–1358. https://doi.org/10.1177/0266666915599586
- [5] Mehrabian, A., & Russell, J. A. (1974). The Basic Emotional Impact of Environments. Perceptual and Motor Skills, 38(1), 283–301. PMID:4815507 https://doi.org/10.2466/pms.1974.38.1.283
- [6] Yoon, C. (2009). The Effects of National Culture Values on Consumer Acceptance of Ecommerce: Online shoppers in China. Information & Management, 46(1), 294–301. https://doi.org/10.1016/j.im.2009.06.001
From the evidence overview What drives technology adoption and online purchase intention? Evidence from Indian consumers, SMEs and a mandatory LMS, which cites only works that appear in the studies’ own reference lists. This is a selection, not the full reference list of Strang & Vajjhala (2020).
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