Firm Performance and the Factors That Influence the Adoption of Digital Innovations
DOI:
https://doi.org/10.29070/9ny0sg13Keywords:
Digital Capability, Technology, Firm, Performance, InnovationAbstract
Despite the growing importance of digital innovation—defined as new digital products and services that allow digital transformation of firms across industries, empirical research on digital innovation drivers is still limited, resulting in a knowledge gap. This study looks at how digital capacity and digital orientation affect digital innovation as well as how digital innovation functions as a mediator in the link among digital orientation and digital capability and firm performance. This study centres on IT firms that operate within the ICT sector of India and also utilized a quantitative methodology to evaluate the connections between digital innovation, orientation, capability and firm performance, specifically employing a sample size of 105 SMEs. The findings indicate a noteworthy positive influence of digital capability and digital orientation on digital innovation. Furthermore, it was discovered that digital innovation serves as a mediator in the connection between digital orientation and firm performance as well as between digital capability and firm performance. These results emphasize the significance of cultivating a culture that prioritizes digitalization and enhancing digital competencies in order to stimulate digital innovation, thereby resulting in enhanced financial and non-financial performance for IT firm operating in ICT sector. In order to ensure that the workforce is ready for the digital age, policymakers are urged to fund rescaling programmes and introduce digital education at an early age.
Downloads
References
1. Ageron, B., Bentahar, O., & Gunasekaran, A. (2020, July). Digital supply chain: challenges and future directions. In Supply Chain Forum: An International Journal (Vol. 21, No. 3, pp. 133-138). Taylor & Francis.
2. Andal-Ancion, A., Cartwright, P. A., & Yip, G. S. (2003). The digital transformation of traditional business. MIT Sloan Management Review, 44(4), 34.
3. Aubert, J. E. (2005). Promoting innovation in developing countries: a conceptual framework (Vol. 3554). World Bank Publications.
4. Bagozzi, R. P., & Yi, Y. (1990). Assessing method variance in multitrait-multimethod matrices: The case of self-reported affect and perceptions at work. Journal of Applied Psychology, 75(5), 547.
5. Bisht, D., Singh, R., Gehlot, A., Akram, S. V., Singh, A., Montero, E. C., ... & Twala, B. (2022). Imperative role of integrating digitalization in the firms finance: A technological perspective. Electronics, 11(19), 3252.
6. Bogers, M., Zobel, A. K., Afuah, A., Almirall, E., Brunswicker, S., Dahlander, L., ... & Ter Wal, A. L. (2017). The open innovation research landscape: Established perspectives and emerging themes across different levels of analysis. Industry and Innovation, 24(1), 8-40.
7. Bughin, J., & Van Zeebroeck, N. (2017). The best response to digital disruption. MIT Sloan management review.
8. Dobni, C. B. (2011). The relationship between innovation orientation and organisational performance. International Journal of Innovation and Learning, 10(3), 226-240.
9. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
10. Gatignon, H., & Xuereb, J. M. (1997). Strategic orientation of the firm and new product performance. Journal of marketing research, 34(1), 77-90.
11. Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T., & Potapov, D. (2020). Digital analytics: Modeling for insights and new methods. Journal of Interactive Marketing, 51(1), 26-43.
12. Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European business review, 26(2), 106-121.
13. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Advanced diagnostics for multiple regression: A supplement to multivariate data analysis. Advanced Diagnostics for Multiple Regression: A Supplement to Multivariate Data Analysis.
14. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (Vol. 20, pp. 277-319). Emerald Group Publishing Limited.
15. Heredia, J., Castillo-Vergara, M., Geldes, C., Gamarra, F. M. C., Flores, A., & Heredia, W. (2022). How do digital capabilities affect firm performance? The mediating role of technological capabilities in the “new normal”. Journal of Innovation & Knowledge, 7(2), 100171.
16. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50.
17. Kopalle, P. K., Kumar, V., & Subramaniam, M. (2020). How legacy firms can embrace the digital ecosystem via digital customer orientation. Journal of the Academy of Marketing Science, 48, 114-131.
18. Lewis, J., Wright, P. C., & Geroy, G. D. (2004). Managing human capital: The study of a self‐managed group venturing into the digital economy. Management Decision, 42(2), 205-228.
19. Lyytinen, K., Yoo, Y., & Boland Jr, R. J. (2016). Digital product innovation within four classes of innovation networks. Information Systems Journal, 26(1), 47-75.
20. Madanmohan, T. R., Kumar, U., & Kumar, V. (2004). Import-led technological capability: a comparative analysis of Indian and Indonesian manufacturing firms. Technovation, 24(12), 979-993.
21. Maldonado-Guzmán, G., Garza-Reyes, J. A., Pinzón-Castro, S. Y., & Kumar, V. (2018). Innovation capabilities and performance: are they truly linked in SMEs?. International Journal of Innovation Science, 11(1), 48-62.
22. Miller, D., & Le Breton-Miller, I. (2005). Managing for the long run: Lessons in competitive advantage from great family businesses. Harvard Business Press.
23. Paladino, A. (2007). Investigating the drivers of innovation and new product success: a comparison of strategic orientations. Journal of Product Innovation Management, 24(6), 534-553.
24. Pencarelli, T. (2020). The digital revolution in the travel and tourism industry. Information Technology & Tourism, 22(3), 455-476.
25. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
26. Prakash, Y., & Gupta, M. (2008). Exploring the relationship between organisation structure and perceived innovation in the manufacturing sector of India. Singapore management review, 30(1), 55-77.
27. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior research methods, 40(3), 879-891.
28. Rigby, D. (2011). The future of shopping. Harvard business review, 89(12), 65-76.
29. Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (software). SmartPLS, Hamburg, available at: www. smartpls. de (accessed January, 7, 2014.
30. Seethamraju, R. (2015). Adoption of software as a service (SaaS) enterprise resource planning (ERP) systems in small and medium sized enterprises (SMEs). Information systems frontiers, 17, 475-492.
31. Singh, V., Kumar, A., & Singh, T. (2018). Impact of TQM on organisational performance: The case of Indian manufacturing and service industry. Operations Research Perspectives, 5, 199-217.
32. Swaans, K., Boogaard, B., Bendapudi, R., Taye, H., Hendrickx, S., & Klerkx, L. (2014). Operationalizing inclusive innovation: lessons from innovation platforms in livestock value chains in India and Mozambique. Innovation and Development, 4(2), 239-257.
33. Zhou, K. Z., Yim, C. K., & Tse, D. K. (2005). The effects of strategic orientations on technology-and market-based breakthrough innovations. Journal of marketing, 69(2), 42-60.
This work is licensed under a 