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The Coronavirus (COVID-19) is one of the major pandemic diseases caused by a newly discovered virus that has been directly affecting the human respiratory system. Because of the gradually increasing magnitude of the COVID-19 pandemic across the world, it has been sparking emergencies and critical issues in the healthcare systems around the world. However, predicting the exact amount of daily reported new COVID cases is the most serious issue faced by governments around the world today. So, the purpose of this current study is to propose a novel hybrid grey exponential smoothing model (HGESM) to predicting transmission dynamics of the COVID-19 outbreak properly.

As a result of the complications relates to the traditional time series approaches, the proposed HGESM model is well defined to handle exponential data patterns in multidisciplinary systems. The proposed methodology consists of two parts as double exponential smoothing and grey exponential smoothing modeling approach respectively. The empirical analysis of this study was carried out on the basis of the 3rd outbreak of Covid-19 cases in Sri Lanka, from 1st March 2021 to 15th June 2021. Out of the total 90 daily observations, the first 85% of daily confirmed cases were used during the training, and the remaining 15% of the sample.

The new proposed HGESM is highly accurate (less than 10%) with the lowest root mean square error values in one head forecasting. Moreover, mean absolute deviation accuracy testing results confirmed that the new proposed model has given more significant results than other time-series predictions with the limited samples.

The findings suggested that the new proposed HGESM is more suitable and effective for forecasting time series with the exponential trend in a short-term manner.

Psychological and entrepreneurial traits have been widely studied as explicative variables of encouraging entrepreneurial behavior, while their impact on innovative activity is less explored. This study examines whether, how and why psycho-entrepreneurial traits and social networks effect innovativeness among women firm owners.

Analysis of data collected from 304 Saudi women entrepreneurs accompanied by business accelerators provides a wide support with some notable exceptions. We use Structural Equation Modeling technique to estimate how different constructs interact with each other and jointly affect directly or indirectly women's innovativeness behavior in Saudi Arabia.

Findings point out that innovativeness is positively and significantly affected by emotional intelligence, internal locus of control, entrepreneurial alertness and entrepreneurial self-efficacy. The construct of entrepreneurial self-efficacy mediates the relationship between both business and personal networks and innovativeness. However, professional forums and mentors have no significant effect on innovativeness.

The sample selection is limited to two entrepreneurial support structures especially business accelerator and business incubator. Expanding the context to other support structures can reinforce the implications and provide more valuable results.

The findings are likely to be of applicability for improving women entrepreneurship by entrepreneurial support structures.

This research is original in the sense that it investigated useful insights of innovativeness among Saudi female entrepreneurs.