Evidence overview · Technology adoption and digital consumers

What drives technology adoption and online purchase intention? Evidence from Indian consumers, SMEs and a mandatory LMS

Across five exploratory studies, Vajjhala and colleagues found that adoption and online purchase intention were explained more by attitudes, emotions and trust than by demographics. Among 63 young online shoppers in Andhra Pradesh and Telangana, India, satisfaction with the Internet context alone correctly classified 90.5% of respondents as likely or unlikely purchasers (Vajjhala & Strang, 2018, AMCIS 2018) (Strang & Vajjhala, 2020, IJESMA), and among 89 consumers in the USA and India, trust in social media and government information combined with feeling controlled by the e-commerce system discriminated purchasers (Wilks' lambda = 0.595, effect size about 41%) (Vajjhala & Strang, 2019, ISSM 2019). In a TAM3 study of 65 US supply chain students, behavioral intention predicted actual course grade in a mandatory learning management system (LMS) (Strang & Vajjhala, 2017, JOEUC), while managers of 40 Indian SMEs saw Web 3.0 as offering service integration but raising privacy, financial and organizational challenges (Potluri & Vajjhala, 2018, JAFEB). All samples are small, so the results are exploratory.

A summary of 5 peer-reviewed studies by Narasimha Rao Vajjhala and co-authors, set against the literature those studies cite. Each study links to its own page with the full abstract and DOI.

Key takeaways

  1. Among 63 young Indian online shoppers, consumers satisfied with the Internet context were almost 28 times more likely to plan an online purchase than unsatisfied ones (Vajjhala & Strang, 2018, AMCIS 2018).
  2. A discriminant model using satisfaction alone correctly classified 90.5% of the Indian respondents as likely or unlikely to buy online, although the sample was only 63 (Vajjhala & Strang, 2018, AMCIS 2018) (Strang & Vajjhala, 2020, IJESMA).
  3. In a USA-India sample of 89 young consumers, gender, age and income did not predict online smartphone purchase, whereas social media trust, government trust and feeling controlled by the e-commerce system did (Vajjhala & Strang, 2019, ISSM 2019).
  4. In a TAM3 study of a mandatory Moodle LMS, behavioral intention predicted students' actual course grade, while computer self-efficacy, computer anxiety and subjective norm predicted neither intention nor grade (Strang & Vajjhala, 2017, JOEUC).
  5. Managers of 40 Indian SMEs linked Web 3.0 to integration of data and services and new functionalities, but also to privacy and security, financial and technological, and organizational challenges (Potluri & Vajjhala, 2018, JAFEB).

Context: technology acceptance, technology resistance and online consumer behavior

Technology acceptance research asks why people take up, resist or under-use new information systems. Strang and Vajjhala cite estimates that 65%-75% of new information technology initiatives are considered failures, mostly for people-related rather than technical reasons[1] (Strang & Vajjhala, 2017, JOEUC). Their LMS study builds on the Technology Acceptance Model (TAM), in which perceived usefulness and perceived ease of use were correlated with self-reported system use[2] and the usefulness-intention link held across meta-analyses[3]. TAM3, cited as explaining 70% of the variance in behavioral intention[4], and UTAUT2, which added hedonic motivation, price value and habit[5], supply the constructs tested.

The consumer studies take a psychological rather than a technical approach to online purchase intention. They draw on a review noting that most online consumer behavior models assume behavior follows from beliefs, attitudes, intentions and emotions[12], on the split between utilitarian and hedonic shopping motivation, the latter centred on enjoyment, satisfaction and happiness[13], and on the pleasure-arousal-dominance description of emotional response[14] (Vajjhala & Strang, 2018, AMCIS 2018) (Vajjhala & Strang, 2019, ISSM 2019). India is presented as under-studied: 75% of its Internet users were reported to be aged 25-35[16], and factors significant in Western samples were not relevant in Thailand and India samples[15] (Strang & Vajjhala, 2020, IJESMA).

Findings: predictors of online purchase intention among young consumers in India and the USA

The India study surveyed 63 technology-literate consumers who had shopped online at least once (61.3% male, mean age 33.6). Age was negatively correlated with purchase intention (Spearman rho = -0.410, p = .001, r2 = 0.16) and income level was related to it (Pearson chi-square = 4.281, p = 0.04). Satisfaction (rho = 0.67) and happiness (rho = 0.621) were strongly correlated with intention; satisfied consumers were almost 28 times more likely to plan a purchase (odds ratio 27.9, 95% CI 4.43-175.89). Positive feelings (r2 = 52.5%) and pleasant feelings (r2 = 48.2%) predicted intention, excitement did not (r2 = 9.5%), and with demographic controls only happiness and satisfaction remained significant (r2 = 47.9%). A two-factor discriminant model classified 87.3% (55 of 63) correctly; satisfaction alone classified 90.5% (57 of 63) (Vajjhala & Strang, 2018, AMCIS 2018).

The 2020 article on e-service consumption in a BRICS region reports the same 63-respondent sample and statistics, framed around BRICS markets (Strang & Vajjhala, 2020, IJESMA). Both reports note caveats: goodness-of-fit tests could not confirm that the logistic models adequately represented the hypotheses, happiness and satisfaction were highly correlated, and the gender test, although described as accepted, had p = .927 and an effect size the authors call too small to support inferences (Vajjhala & Strang, 2018, AMCIS 2018) (Strang & Vajjhala, 2020, IJESMA).

A cross-cultural study of 89 senior students in New York State and Andhra Pradesh and Telangana found that gender, age and income were not related to the decision to buy a $100 smartphone online, and ethnicity had a very small effect (chi-square(5) = 4.281, p = .05, Cramer's V2 = 0.00795). Social media trust was the strongest single discriminator (effect size 0.343), followed by feeling controlled by the e-commerce system (0.221) and government trust (0.182) (Vajjhala & Strang, 2019, ISSM 2019). The age and income results therefore differ between the India-only and the combined USA-India samples.

Findings: mandatory LMS resistance (TAM3) and Web 3.0 adoption in Indian SMEs

In the LMS study, 65 senior supply chain management students at SUNY had to use a new Moodle LMS, with course grade as the measure of actual behavior. Perceived usefulness, enjoyment, voluntariness and results demonstrability captured 52.3% of the variance in behavioral intention; perceived resources, perceived ease of use, enjoyment, voluntariness and behavioral intention captured 48.6% of the variance in grade, and intention predicted grade (beta = .052, T = 2.44, p = .02). Computer self-efficacy, perceptions of external control, computer anxiety, subjective norm, image, job relevance and output quality were not causally related to either outcome. All-factor models captured 88.3% and 85.5% of the variance but were judged theoretically weak (Strang & Vajjhala, 2017, JOEUC).

The Web 3.0 study interviewed managers of 40 SMEs in five economic sectors in Andhra Pradesh and Karnataka, coding transcripts in NVivo 11. It identified five key themes and 12 subthemes: integration of data and services, creation of new functionalities, privacy and security, financial and technological challenges, and organizational challenges (Potluri & Vajjhala, 2018, JAFEB). It defines Web 3.0 through sources describing intelligent machines that read, interrelate and manipulate data[10], the symbiosis of web technologies and knowledge representation[9], and opportunities such as process automation and faster information production[8].

How the findings fit the wider technology adoption literature

The LMS results partly match TAM: usefulness predicted intention, as the TAM literature leads one to expect[2][3]. They depart from it in finding no role for computer self-efficacy, anxiety or perceived control[4], which the authors attribute to 'hedonistic' students whose enjoyment of the technology offset those concerns, an interpretation close to UTAUT2's hedonic motivation[5] (Strang & Vajjhala, 2017, JOEUC). Among consumers, the weight of satisfaction and happiness is consistent with the hedonic motivation account[13] (Vajjhala & Strang, 2018, AMCIS 2018).

On demographics, the literature cited is itself mixed. One study reports that younger users are more comfortable shopping online[17], while a review found both positive and negative effects of age[18] (Vajjhala & Strang, 2019, ISSM 2019); the two Vajjhala and Strang samples reproduce that split. On culture, Yoon found uncertainty avoidance and long-term orientation, but not power distance or individualism, moderated e-commerce acceptance[11]; the India papers use Hofstede scores to interpret results without testing culture (Vajjhala & Strang, 2018, AMCIS 2018). The LMS paper cites evidence that culture affects technology acceptance across countries[6] and that the technology-organization-environment framework has been applied to SMEs[7], and calls for culture to be tested directly (Strang & Vajjhala, 2017, JOEUC).

Methods and limitations

The quantitative studies used structured questionnaires with a binary purchase-intention outcome, analysed with chi-square tests, Spearman correlation, binary logistic regression and discriminant analysis (Vajjhala & Strang, 2018, AMCIS 2018) (Vajjhala & Strang, 2019, ISSM 2019), or an adapted TAM3 survey analysed with hierarchical, surface response and multiple regression (Strang & Vajjhala, 2017, JOEUC). The Web 3.0 study used qualitative content analysis (Potluri & Vajjhala, 2018, JAFEB). Samples ranged from 40 to 89, came from a few Indian states and one US university, and relied on self-report except for course grade. The SME findings describe managerial perceptions rather than measured adoption, and may be biased toward medium-sized enterprises (Potluri & Vajjhala, 2018, JAFEB).

Evidence table

StudySetting / dataMethodKey result
Vajjhala & Strang (2018)
Twenty-fourth Americas Conference on Information Systems (AMCIS 2018), New Orleans
63 online shoppers, Andhra Pradesh and Telangana, IndiaSurvey; Spearman, binary logistic regression, discriminant analysisSatisfaction OR 27.9; satisfaction alone classified 90.5% (57 of 63); excitement not predictive
Strang & Vajjhala (2020)
International Journal of E-Services and Mobile Applications
Same 63-respondent Indian sample, framed as BRICS e-servicesStructured questionnaire; logistic regression, discriminant classificationTwo-factor model 87.3% correct; satisfaction alone 90.5%; 7 of 8 hypotheses supported
Vajjhala & Strang (2019)
Proceedings of the 2019 International Conference on Information System and System Management (ISSM 2019), Rabat, Morocco
89 senior students, New York State (USA) and Andhra Pradesh/Telangana (India)Chi-square, Spearman, logistic regression, discriminant analysisGender, age, income unrelated; trust/control model Wilks' lambda = 0.595, effect size about 41%
Strang & Vajjhala (2017)
Journal of Organizational and End User Computing
65 senior supply chain students using a new mandatory Moodle LMS, SUNYAdapted TAM3 survey; hierarchical and multiple regression; grade as actual behavior52.3% variance in intention, 48.6% in grade; intention predicted grade (beta = .052, p = .02)
Potluri & Vajjhala (2018)
The Journal of Asian Finance, Economics and Business
Managers of 40 SMEs in five sectors, Andhra Pradesh and KarnatakaQualitative interviews; content analysis in NVivo 115 key themes, 12 subthemes: integration, new functionalities, privacy/security, financial, organizational

Questions researchers ask

What best predicted online purchase intention among young consumers in India?
Satisfaction with the Internet context was the strongest predictor: satisfied respondents were almost 28 times more likely to plan a purchase, and satisfaction alone correctly classified 90.5% of 63 respondents (Vajjhala & Strang, 2018, AMCIS 2018). Happiness also contributed, while excitement did not reliably predict intention (Strang & Vajjhala, 2020, IJESMA).
Do demographics such as gender, age and income predict online smartphone purchases?
Evidence is mixed. In the India-only sample, age was negatively and income positively related to intention, while the gender test was not significant (p = .927) (Vajjhala & Strang, 2018, AMCIS 2018); in the 89-person USA-India sample, gender, age and income were unrelated to purchase decisions (Vajjhala & Strang, 2019, ISSM 2019).
Does behavioral intention predict actual use of a mandatory learning management system?
In a TAM3 study of 65 supply chain students, behavioral intention predicted actual course grade (beta = .052, T = 2.44, p = .02) (Strang & Vajjhala, 2017, JOEUC). The authors caution that the convenience sample from one American university limits generalization (Strang & Vajjhala, 2017, JOEUC).
What challenges do Indian SMEs see in adopting Web 3.0 technologies?
Managers of 40 SMEs cited privacy and security, financial and technological constraints, and organizational challenges, alongside benefits from integrating data and services and creating new functionalities (Potluri & Vajjhala, 2018, JAFEB). The study is qualitative and limited to two Indian states (Potluri & Vajjhala, 2018, JAFEB).

Open questions

  • Do satisfaction and happiness remain strong predictors of online purchase intention in much larger and more regionally diverse Indian samples?
  • Why did age and income relate to purchase intention in the India-only sample but not in the combined USA-India sample?
  • Does national culture predict technology resistance when tested directly with multicultural samples of students and young employees, for example in ERP internships?
  • How cost-effective is Web 3.0 adoption for SMEs in developing countries with limited financial and human resources, and do perceived benefits translate into measured productivity gains?

References

Studies summarised

Other literature cited

  1. [1] Rizzuto, T. E., & Reeves, J. (2007). A multidisciplinary meta-analysis of human barriers to technology implementation. Consulting Psychology Journal: Practice and Research, 59(3), 226–240. doi:10.1037/1065-9293.59.3.226 https://doi.org/10.1037/1065-9293.59.3.226
  2. [2] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340. doi:10.2307/249008 https://doi.org/10.2307/249008
  3. [3] King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management Journal, 43(6), 740–755. doi:10.1016/j.im.2006.05.003 https://doi.org/10.1016/j.im.2006.05.003
  4. [4] Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences Journal, 39(2), 273–315. doi:10.1111/j.1540-5915.2008.00192.x https://doi.org/10.1111/j.1540-5915.2008.00192.x
  5. [5] Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Management Information Systems Quarterly, 36(1), 157–178.
  6. [6] Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management Journal, 33(1), 1–11. doi:10.1016/S0378-7206(97)00026-8 https://doi.org/10.1016/S0378-7206(97)00026-8
  7. [7] Gupta, P., Seetharaman, A., & Raj, J. R. (2013). The usage and adoption of cloud computing by small and medium businesses. International Journal of Information Management, 33(5), 861–874. doi:10.1016/j.ijinfomgt.2013.07.001 https://doi.org/10.1016/j.ijinfomgt.2013.07.001
  8. [8] Rudman, R., & Bruwer, R. (2016). Defining Web 3.0: Opportunities and challenges. The Electronic Library, 34(1), 132-154. doi: 10.1108/EL-08-2014-0140 https://doi.org/10.1108/EL-08-2014-0140
  9. [9] Lassila, O., & Hendler, J. (2007). Embracing Web 3.0. IEEE Internet Computing, 11(3), 90-93.
  10. [10] Garrigos-Simon, F. J., Lapiedra-Alcamí, R., & Ribera, T. B. (2012). Social networks and Web 3.0: Their impact on the management and marketing of organizations, Management Decision, 50(10), 1880-1890. https://doi.org/10.1108/00251741211279657
  11. [11] Yoon, C. (2009). The Effects of National Culture Values on Consumer Acceptance of Ecommerce: Online shoppers in China. Information & Management, 46(1), 294–301. doi:10.1016/j.im.2009.06.001 https://doi.org/10.1016/j.im.2009.06.001
  12. [12] Hwang, Y., & Jeong, J. (2016). Electronic Commerce and Online Consumer Behavior Research: A Literature Review. Information Development, 32(3), 377–388. doi:10.1177/0266666914551071 https://doi.org/10.1177/0266666914551071
  13. [13] Huseynov, F., & Yildirim, S. O. (2015). Behavioral Issues in Ecommerce. Information Development, 32(5), 1343–1358. doi:10.1177/0266666915599586 https://doi.org/10.1177/0266666915599586
  14. [14] Mehrabian, A., & Russell, J. A. (1974). The Basic Emotional Impact of Environments. Perceptual and Motor Skills, 38(1), 283–301. doi:10.2466/pms.1974.38.1.283 PMID:4815507 https://doi.org/10.2466/pms.1974.38.1.283
  15. [15] Nittala, R. (2015). Factors Influencing Online Shopping Behavior of Urban Consumers in India. International Journal of Online Marketing, 5(1), 38–50. doi:10.4018/IJOM.2015010103 https://doi.org/10.4018/IJOM.2015010103
  16. [16] Khare, A. (2016). Consumer Shopping Styles and Online Shopping: An Empirical Study of Indian Consumers. Journal of Global Marketing, 29(1), 40–53. doi:10.1080/08911762.2015.1122137 https://doi.org/10.1080/08911762.2015.1122137
  17. [17] Hernández, B., J. Jiménez, and M.J. Martin, Age, Gender and Income: Do They Really Moderate Online Shopping Behaviour? Online Information Review, 2010. 35(1): p. 113-133.
  18. [18] Akar, E. and V.A. Nasir, A Review of Literature on Consumers' Online Purchase Intentions. Journal of Customer Behavior, 2015. 14(3): p. 215-233.