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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.

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.

Existing business model frameworks show weak conceptual unification, a paucity of measurement focus and limitations when applied in emerging economies. The study proposes a new business model framework – “Start-up Evaluation Calculus Using Research Evidence” (SECURE). The purpose of this study is to allow the measurement of the impact of business model design on start-up performance in emerging economies.

Data collected from 713 entrepreneurs in select cities of India, Oman and the United Arab Emirates is analyzed through structural equation modeling. The study uses measurement and structural models to examine the validity of measures and additionally tests the five hypothesized relationships proposed in the study.

The SECURE’s components comprising desirability, marketability, feasibility, scalability and viability showed validity and reliability. They synergistically demonstrated a statistically significant effect on a mix of financial and non-financial start-up performance outcomes. An alternative structural relationship that examined the impact of SECURE on only financial performance outcomes showed a weaker model fit. The findings indicate that a business model framework is useful when its ex ante measures show a positive causal effect on the desired performance outcomes.

The scores obtained by the SECURE framework serve as an evaluative tool that informs entrepreneurs and start-ups on the readiness of their proposed, incubated or existing start-ups.

Replacing subjective judgments with objective assessment criteria, SECURE is one of the first quantitative and performance-driven business model frameworks that contain measures from all functional domains of a start-up business. Start-ups can evaluate their business models against the SECURE model’s research-driven quantitative criteria and assess their impact on start-up performance.

Considerable evidence suggests that although they overlap, entrepreneurial and employee stressors have different causal antecedents and outcomes. However, limited empirical data explain how entrepreneurial traits, work and life drive entrepreneurial stressors and create entrepreneurial strain (commonly called entrepreneurial stress). Drawing on the challenge-hindrance framework (CHF), this paper hypothesises the causal effect of hindrance stressors on entrepreneurial strain. Furthermore, the study posits that entrepreneurial stressors and the resultant strain affect entrepreneurial behaviour.

The study adopts an SEM-based machine-learning approach. Cross-lagged path models using SEM are used to analyse the data and train the machine-learning algorithm for cross-validation and generalisation. The sample consists of 415 entrepreneurs from three countries: India, Oman and United Arab Emirates. The entrepreneurs completed two self-report surveys over 12 months.

The results show that hindrances to personal and professional goal achievement, demand-capability gap and contradictions between aspiration and reality, primarily due to unique resource constraints, characterise entrepreneurial stressors leading to entrepreneurial strain. The study further asserts that entrepreneurial strain is a significant predictor of entrepreneurial behaviour, significantly affecting innovativeness behaviour. Finally, the finding suggests that psychological capital moderates the adverse impact of stressors on entrepreneurial strain over time.

This study contributes to the CHF by demonstrating the value of hindrance stressors in studying entrepreneurial strain and providing new insights into entrepreneurial coping. It argues that entrepreneurs cope effectively against hindrance stressors by utilising psychological capital. Furthermore, the study provides more evidence about the causal, reversed and reciprocal relationships between stressors and entrepreneurial strain through a cross-lagged analysis. This study is one of the first to evaluate the impact of entrepreneurial strain on entrepreneurial behaviour. Using a machine-learning approach is a new possibility for using machine learning for SEM and entrepreneurial strain.