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Measuring innovation effectiveness: a SEM-based cross-lagged analysis

Tahseen Anwer Arshi (Department of Management, American University of Ras Al Khaimah, Ras al Khaimah, United Arab Emirates)
Venkoba Rao (Faculty of Business Management, Majan University College, Ruwi, Oman)
Sumithra Viswanath (Management Department, Gulf College, Maabelah, Oman)
Vazeerjan Begum (Department of Management, American University of Ras Al Khaimah, Ras al Khaimah, United Arab Emirates)

International Journal of Innovation Science

ISSN: 1757-2223

Article publication date: 25 January 2021

Issue publication date: 13 October 2021




The study aims to develop measures for innovation effectiveness impacting organizational performance outcomes. Substantial evidence suggests that measuring innovation effectiveness (IE) continues to be challenging because of the use of different measures across innovation’s broad spectrum. The purpose of this study is to overcome it by examining multiple drivers of IE in emerging market economies (EMEs) and predicting their impact on financial and nonfinancial performance outcomes.


Through a two-wave panel design, firms from India, Oman and the United Arab Emirates participated in the study with a time lapse of 12 months (T1n = 417, T2n = 403). Four cross-lagged competing models are tested for autoregressive, causal, reversed and reciprocal effects using structural equation modeling (SEM).


The findings show that the synergistic effect of multiple innovation characteristics, such as innovation degree, cost, frequency and speed determines its endogenous effectiveness. The exogenous effectiveness of innovation is further established through its impact on financial and nonfinancial performance outcomes. Furthermore, readiness for innovation (RFI) is a critical factor that moderates the relationship between drivers and IE.

Practical implications

The study’s findings could inform practitioners in emerging market economies about the appropriate measures of IE. It will guide managerial decisions on making an investment, evaluation, accountability and strategic choices related to innovation.


It is one of the first studies that use a time-based lens to examine IE in EMEs. It posits that given the innovation’s complexity, IE needs to be measured at multiple levels. The study explains how evolutionary dynamics in different sociocultural contexts can bring a new perspective into theory of diffusion of innovation. The moderating role of RFI brings new insights into the IE process and emphasizes its importance in objective-driven and performance-focused innovation efforts.



Arshi, T.A., Rao, V., Viswanath, S. and Begum, V. (2021), "Measuring innovation effectiveness: a SEM-based cross-lagged analysis", International Journal of Innovation Science, Vol. 13 No. 4, pp. 437-455.



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