Deciphering Pico-Solar Product Adoption : A Random Forest Approach

Authors

DOI:

https://doi.org/10.17010/ijom/2026/v56/i1/175432

Keywords:

complexity, pico-solar products, random forest, compatibility, solar energy adoption, rural consumers, relative advantage, affordability, renewable energy marketing.
Publication Chronology: Paper Submission Date : August 24, 2025 ; Paper sent back for Revision : December 22, 2025 ; Paper Acceptance Date : December 29, 2025 ; Paper Published Online : January 15, 2026

Abstract

Purpose : This paper examined the adoption of pico-solar products in rural India through the lens of diffusion of innovation (DOI). This study aimed to explore the effect of relative advantage, compatibility, complexity, and affordability on the adoption of pico-solar products and identify key determinants boosting the adoption.

Methodology : Primary data were collected from 568 users and potential users of pico-solar products in the rural areas of the Pune district of India through a structured questionnaire. In order to better capture the non-linear relationships among the variables, a machine learning (ML) method – Random Forest was employed. The model performance was measured using the metrics MAE, RMSE, R2, and cross-validated R2. SHAP analysis, feature importance, and partial dependence plots (PDPs) were used for the analyses.

Results : SHAP analysis and feature importance evaluation showed that relative advantage is most important in driving the adoption, followed by affordability, compatibility, and complexity. PDPs verified that the individual’s relative advantage perception, affordability, and compatibility enhanced adoption, and increased complexity decreased adoption likelihood.

Practical Implications : The investigation showed that solar choice is not only determined by financial factors, but it also considers easy product usability, compatibility with rural lifestyles, and advantages over conventional energy. These implications would guide policymakers, solar enterprises, agencies, and firms regarding the development of marketing strategies focusing on financial incentives, easy product designs, and consumer training programs to boost pico-solar product adoption.

Value : This work is a pioneering study to combine DOI and random forest modeling in the field of pico-solar products adoption.

Downloads

Download data is not yet available.

Downloads

Published

2026-01-15

How to Cite

Nahar, K., & Ranade, M. (2026). Deciphering Pico-Solar Product Adoption : A Random Forest Approach. Indian Journal of Marketing, 56(1), 10–28. https://doi.org/10.17010/ijom/2026/v56/i1/175432

Issue

Volume 56, Issue 1, January 2026

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

1) Aggarwal, A. K., Syed, A. A., & Garg, S. (2021). Diffusion of RT solar PV in suburbs of Delhi/NCR, India: Triggers of architect recommendation intent. Vision, 25(3), 285–299. https://doi.org/10.1177/0972262919894144

2) Agyenim, F. B., Dzamboe, P. D., Mohammed, M., Bawakyillenuo, S., Okrofu, R., Decker, E., Agyemang, V. K., & Nyarko, E. H. (2020). Powering communities using hybrid solar–biogas in Ghana, a feasibility study. Environmental Technology & Innovation, 19, Article ID 100837. https://doi.org/10.1016/j.eti.2020.100837

3) Ahmed, Y. A., Rashid, A., & Khurshid, M. M. (2022). Investigating the determinants of the adoption of solar photovoltaic systems—Citizen's perspectives of two developing countries. Sustainability, 14(18), 11764. https://doi.org/10.3390/su141811764

4) Bauner, C., & Crago, C. L. (2015). Adoption of residential solar power under uncertainty: Implications for renewable energy incentives. Energy Policy, 86, 27–35. https://doi.org/10.1016/j.enpol.2015.06.009

5) Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238

6) Biau, G., & Scornet, E. (2016). A random forest guided tour. TEST, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7

7) Bose, D., Saini, D. K., Yadav, M., Shrivastava, S., & Parashar, N. (2021). Review of sustainable grid‐independent renewable energy access in remote communities of India. Integrated Environmental Assessment and Management, 17(2), 364–375. https://doi.org/10.1002/ieam.4373

8) Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

9) Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005

10) Chen, Z.-Y., Fan, Z.-P., & Sun, M. (2012). A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. European Journal of Operational Research, 223(2), 461–472. https://doi.org/10.1016/j.ejor.2012.06.040

11) Dash, G., Akmal, S., & Chakraborty, D. (2023). A study on adoption of mobile learning apps (MLA): Development of an integrated framework in a multinational context. Indian Journal of Marketing, 53(5), 8–24. https://doi.org/10.17010/ijom/2023/v53/i5/172724

12) Dash, G., Akmal, S., & Chakraborty, D. (2022). A study on adoption of e-health services: Developing an integrated framework in a multinational context. Indian Journal of Marketing, 52(5), 25–40. https://doi.org/10.17010/ijom/2022/v52/i5/169415

13) Desai, A., Mukhopadhyay, I., & Ray, A. (2023). Solar PV-hydropower enhanced Picogrid as a sustainable energy model for hilly remote areas: Analytics and prospects thereof. Energy Informatics, 6, Article no. 24. https://doi.org/10.1186/s42162-023-00294-z

14) Dumlao, S. M., & Ogata, S. (2025). Japan's local consumption of solar energy: The role of energy demand in residential and small-scale solar projects. Solar Energy, 287, Article ID 113175. https://doi.org/10.1016/j.solener.2024.113175

15) El Mrabet, Z., Sugunaraj, N., Ranganathan, P., & Abhyankar, S. (2022). Random forest regressor-based approach for detecting fault location and duration in power systems. Sensors, 22(2), 458. https://doi.org/10.3390/s22020458

16) Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

17) Ganesan, K., Palanisamy, S., Krishnasamy, V., Salau, A. O., Rathinam, V., & Nayakkar, S. G. (2024). Hybrid photovoltaic/thermal performance prediction based on machine learning

algorithms with hyper-parameter tuning. International Journal of Sustainable Energy, 43(1), Article no. 2364226. https://doi.org/10.1080/14786451.2024.2364226

18) Glowik, M., Chwialkowska, A., & Bhatti, W. A. (2024). Global solar photovoltaic industry network dynamics 2007–2023. Inter-organizational relationships as a source of competitive advantage? Journal of Cleaner Production, 467, Article ID 142921. https://doi.org/10.1016/j.jclepro.2024.142921

19) Hair Jr., J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624

20) Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). Chapter 12: Using partial least squares path modeling in advertising research: Basic concepts and recent issues. In T. L. Goodrich (ed.), Handbook of research on international advertising. Edward Elgar Publishing. https://doi.org/10.4337/9781781001042.00023

21) Hirmer, S., & Cruickshank, H. (2014). Making the deployment of pico-PV more sustainable along the value chain. Renewable and Sustainable Energy Reviews, 30, 401–411. https://doi.org/10.1016/j.rser.2013.10.018

22) Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

23) Jager, J., Putnick, D. L., & Bornstein, M. H. (2017). More than just convenient: The scientific merits of homogeneous convenience samples. Monographs of the Society for Research in Child Development, 82(2), 13–30. https://doi.org/10.1111/mono.12296

24) Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert scale: Explored and explained. Current Journal of Applied Science and Technology, 7(4), 396–403. https://doi.org/10.9734/BJAST/2015/14975

25) Joshi, R., Mishra, S., Pardeshi, A., & Bhatia, M. K. (2024). Hyper-personalization in motor insurance: Understanding telematics insurance adoption using the extended technology acceptance model. Indian Journal of Marketing, 54(6), 47–64. https://doi.org/10.17010/ijom/2024/v54/i6/173946

26) Kalinić, Z., Marinković, V., Kalinić, L., & Liebana-Cabanillas, F. (2021). Neural network modeling of consumer satisfaction in mobile commerce: An empirical analysis. Expert Systems with Applications, 175, Article ID 114803. https://doi.org/10.1016/j.eswa.2021.114803

27) Kapoor, K. K., & Dwivedi, Y. K. (2020). Sustainable consumption from the consumer's perspective: Antecedents of solar innovation adoption. Resources, Conservation and Recycling, 152, Article ID 104501. https://doi.org/10.1016/j.resconrec.2019.104501

28) Kizilcec, V., & Parikh, P. (2020). Solar home systems: A comprehensive literature review for Sub-Saharan Africa. Energy for Sustainable Development, 58, 78–89. https://doi.org/10.1016/j.esd.2020.07.010

29) Kumar, A., Kabra, G., Mussada, E. K., Dash, M. K., & Rana, P. S. (2019). Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention. Neural Computing and Applications, 31(Suppl 2), 877–890. https://doi.org/10.1007/s00521-017-3047-z

30) Kumar, V., & Kaushik, A. K. (2022). Solar rooftop adoption among Indian households: A structural equation modeling analysis. Journal of Social Marketing, 12(4), 513–533. https://doi.org/10.1108/jsocm-07-2021-0170

31) Kumar, V., Kaushik, A. K., & Singh, G. (2022). Modeling the Indian households' intention to adopt the solar net metering system. Built Environment Project and Asset Management, 12(6), 956–972. https://doi.org/10.1108/BEPAM-10-2021-0129

32) Kumar, P., & Usman, M. U. (2024). Analyzing the application of UTAUT2 model in predicting the adoption of electronic shopping in Nigeria. Indian Journal of Marketing, 54(3), 61–81. https://doi.org/10.17010/ijom/2024/v54/i3/170942

33) Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management, 37(2), 14–24. https://doi.org/10.1016/j.ijinfomgt.2016.10.008

34) Liu, Z., Wu, D., Liu, Y., Han, Z., Lun, L., Gao, J., Jin, G., & Cao, G. (2019). Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction. Energy Exploration & Exploitation, 37(4), 1426–1451. https://doi.org/10.1177/0144598718822400

35) Meher, B. K., Anand, A., Kumar, S., Birau, R., & Singh, M. (2024). Effectiveness of random forest model in predicting stock prices of solar energy companies in India. International Journal of Energy Economics and Policy, 14(2), 426–434. https://doi.org/10.32479/ijeep.15581

36) Minas, A. M., García-Freites, S., Walsh, C., Mukoro, V., Aberilla, J. M., April, A., Kuriakose, J., Gaete-Morales, C., Gallego‐Schmid, A., & Mander, S. (2024). Advancing sustainable development goals through energy access: Lessons from the global south. Renewable and Sustainable Energy Reviews, 199, Article ID 114457. https://doi.org/10.1016/j.rser.2024.114457

37) Mohamad, M., Chowdhury, D., Ahmad, A., Jyawali, H., & Khan, A. (2025). The diffusion of predictive analytics for innovative interactive marketing strategy in international e-fashion commerce. Indian Journal of Marketing, 55(10), 30–52. https://doi.org/10.17010/ijom/2025/v55/i10/175612

38) Mukherjee, S. C. (2020). Boosting renewable energy technology uptake in Ireland: A machine learning approach (Working Paper No. WP20/27). UCD Centre for Economic Research. https://www.ucd.ie/economics/t4media/WP20_27.pdf

39) Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430–445. https://doi.org/10.1037/0033-2909.105.3.430

40) Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625–632. https://doi.org/10.1007/s10459-010-9222-y

41) Nygaard, I., Hansen, U. E., & Larsen, T. H. (2016). The emerging market for pico-scale solar PV systems in Sub-Saharan Africa: From donor-supported niches toward market-based rural electrification. UNEP DTU Partnership. https://unepccc.org/wp-content/uploads/2018/10/market-pico-solar-web.pdf

42) Ossai, W., & Fagbola, T. M. (2025). Machine learning-based predictive modelling of renewable energy adoption in developing countries. Energy Reports, 14, 66–84. https://doi.org/10.1016/j.egyr.2025.05.066

43) Parsad, C., Mittal, S., & Krishnankutty, R. (2020). A study on the factors affecting household solar adoption in Kerala, India. International Journal of Productivity and Performance Management, 69(8), 1695–1720. https://doi.org/10.1108/ijppm-11-2019-0544

44) Pathania, A. K., Goyal, B., & Saini, J. R. (2017). Diffusion of adoption of solar energy – A structural model analysis. Smart and Sustainable Built Environment, 6(2), 66–83. https://doi.org/10.1108/SASBE-11-2016-0033

45) Rajendran, B., Babu, M., & Anandhabalaji, V. (2025). A predictive modelling approach to decoding consumer intention for adopting energy-efficient technologies in food supply chains. Decision Analytics Journal, 15, Article ID 100561. https://doi.org/10.1016/j.dajour.2025.100561

46) Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press. https://www.scirp.org/reference/referencespapers?referenceid=3275637

47) Rogers, E. M., Singhal, A., & Quinlan, M. M. (2009). Diffusion of innovations. In D. W. Stacks, & M. B. Salwen (eds.), An integrated approach to communication theory and research. Routledge.

48) Sadabadi, A. A., & Rahimirad, Z. (2025). Predictive factors of citizen investment in rooftop solar panels: Evidence from a social innovation project in Iran using machine learning. Journal of Cleaner Production, 529, Article ID 146813. https://doi.org/10.1016/j.jclepro.2025.146813

49) Saini, S., & Bansal, S. (2023). Associating designed information and novelty with purchase intentions for product displays in fashion apparel stores. Indian Journal of Marketing, 53(12), 8–26. https://doi.org/10.17010/ijom/2023/v53/i12/173351

50) Samunderu, E., & Farrugia, M. (2022). Predicting customer purpose of travel in a low-cost travel environment—A machine learning approach. Machine Learning with Applications, 9, Article ID 100379. https://doi.org/10.1016/j.mlwa.2022.100379

51) Satapathy, A., Jain, A. K., & Barthwal, S. (2019). A review of consumer adoption of rooftop solar PV in India and effective frameworks. Indian Journal of Marketing, 49(11), 39–55. https://doi.org/10.17010/ijom/2019/v49/i11/148275

52) Sayed, M. A., Ahmed, M. M., Azlan, W., & Kin, L. W. (2024). Peer-to-peer solar energy sharing system for rural communities. Cleaner Energy Systems, 7, Article ID 100102. https://doi.org/10.1016/j.cles.2023.100102

53) Sharief, Z., & Panghal, A. (2023). Sustainable consumption: Consumer behavior when purchasing sustainability-labeled food products. Indian Journal of Marketing, 53(8), 47–65. https://doi.org/10.17010/ijom/2023/v53/i8/172975

54) Sharma, J., Agarwal, B., & Malati, N. (2024). Examining the factors influencing eWOM through social networking sites and the effect of eWOM on consumers' purchase intention. Indian Journal of Marketing, 54(6), 65–82. https://doi.org/10.17010/ijom/2024/v54/i6/173947

55) Singh, B., & Dagur, A. (2022). Understanding influence of social media marketing of masstige fashion brands on Generation Z female Indian consumers. Indian Journal of Marketing, 52(5), 8–24. https://doi.org/10.17010/ijom/2022/v52/i5/169414

56) Sullivan, G. M., & Artino Jr., A. R. (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education, 5(4), 541–542. https://doi.org/10.4300/JGME-5-4-18

57) Tanaka, K., Higashide, T., Kinkyo, T., & Hamori, S. (2025). A multi-stage financial distress early warning system: Analyzing corporate insolvency with random forest. Journal of Risk and Financial Management, 18(4), 195. https://doi.org/10.3390/jrfm18040195

58) Thakur, A., Bansal, I., & Singla, K. (2025). Interplay among perceived advertisement impact, environmental values, and solar product purchase intention. Indian Journal of Marketing, 55(4), 52–68. https://doi.org/10.17010/ijom/2025/v55/i4/174928

59) van Schaik, P., Clements, H., Karayaneva, Y., Imani, E., Knowles, M., Vall, N., & Cotton, M. (2025). Using machine learning to model the acceptance of domestic low-carbon technologies. Sustainability, 17(15), 6668. https://doi.org/10.3390/su17156668

60) Venkateswaran, J., Solanki, C. S., Werner, K., & Yadama, G. N. (2018). Addressing energy poverty in India: A systems perspective on the role of localization, affordability, and saturation in implementing solar technologies. Energy Research & Social Science, 40, 205–210. https://doi.org/10.1016/j.erss.2018.02.011

61) Wheaton, B., Muthén, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84–136. https://doi.org/10.2307/270754

62) Yadav, N., & Sijoria, C. (2024). Drivers of zero-waste purchase decisions: The mediating role of the theory of planned behavior. Indian Journal of Marketing, 54(9), 37–53. https://doi.org/10.17010/ijom/2024/v54/i9/174396