Sirat Mahmuda | The Australian National University (original) (raw)

Papers by Sirat Mahmuda

Business Research, 2020

The rise of the sharing economy has had transformative impacts on extant service delivery models,... more The rise of the sharing economy has had transformative impacts on extant service delivery models, with wide ranging implications for existing firms, regulators, and the workforce at large. This paper draws upon firm-level data to better understand how new forms of service delivery have accompanied the diffusion of the sharing economy. Unlike previous waves of technological innovation, sharing economy firms have emerged as digital intermediaries rather than direct service providers driven by shifting consumer practices and attitudes. We apply an innovation diffusion model to trace the development trajectory of the sharing economy across 1000 firms. Our model segments the evolution of the sharing economy into three distinct and overlapping phases, comprising an Embryonic Stage (1995–2008), an Early Growth Stage (2007–2015), and most recently a Late Growth Stage (2014–present). Analysis of the 1000 firms reveals that the sharing economy has rapidly gained momentum across all industry s...

Additional file 5. Week 14 (ending April 1st) comparison of standardised coefficients at 25th, 50... more Additional file 5. Week 14 (ending April 1st) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Additional file 7. Correlogram and Multicollinearity Diagnostics.

Additional file 6. Week 15 (ending April 8th) comparison of standardised coefficients at 25th, 50... more Additional file 6. Week 15 (ending April 8th) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Additional file 4. Week 13 (ending March 25th) comparison of standardised coefficients at 25th, 5... more Additional file 4. Week 13 (ending March 25th) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Additional file 2. Week 11 (ending March 11th) comparison of standardised coefficients at 25th, 5... more Additional file 2. Week 11 (ending March 11th) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Additional file 3. Week 12 (ending March 18th) comparison of standardised coefficients at 25th, 5... more Additional file 3. Week 12 (ending March 18th) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Additional file 1. Week 10 (ending April 4th) comparison of standardised coefficients at 25th, 50... more Additional file 1. Week 10 (ending April 4th) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Australian Planner, 2016

ABSTRACT Urban heat island (UHI) describes the higher temperature that occurs in built-up areas c... more ABSTRACT Urban heat island (UHI) describes the higher temperature that occurs in built-up areas compared to surrounding natural landscapes. The article identifies the evidence of UHI in the Australian Capital Territory (ACT) and the extent to which the Territory is addressing this through urban planning and climate adaptation regimes. Remotely sensed data were used to retrieve land surface temperature to outline the spatial form of surface heat hotspots. It was found that established town centres in the Territory have hotspots. Hotspots were also found in grasslands, bare ground and dry forests. Research from other jurisdictions provides insights on potential UHI responses at city, neighbourhood and building scales. Whilst current ACT Government planning and adaptation regimes do not address UHI explicitly, they do promote urban forests, integrated open space and urban water bodies which, along with various building standards and guides, can help ameliorate UHI. The ACT has adopted the ‘compact city’ as a future development strategy. Urban intensification will occur in areas that are already identified as hotspots. This, along with the potential impacts of climate change, will require more explicit and increased focus on UHI in the future.

A Modern Guide to the Urban Sharing Economy

Globalization and Health

Background COVID-19 is an emergent infectious disease that has spread geographically to become a ... more Background COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. Results The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human developmen...

Drafts by Sirat Mahmuda

Business Research, 2020

The rise of the sharing economy has had transformative impacts on extant service delivery models,... more The rise of the sharing economy has had transformative impacts on extant service delivery models, with wide ranging implications for existing firms, regulators, and the workforce at large. This paper draws upon firm-level data to better understand how new forms of service delivery have accompanied the diffusion of the sharing economy. Unlike previous waves of technological innovation, sharing economy firms have emerged as digital intermediaries rather than direct service providers driven by shifting consumer practices and attitudes. We apply an innovation diffusion model to trace the development trajectory of the sharing economy across 1000 firms. Our model segments the evolution of the sharing economy into three distinct and overlapping phases, comprising an Embryonic Stage (1995–2008), an Early Growth Stage (2007–2015), and most recently a Late Growth Stage (2014–present). Analysis of the 1000 firms reveals that the sharing economy has rapidly gained momentum across all industry s...

Additional file 5. Week 14 (ending April 1st) comparison of standardised coefficients at 25th, 50... more Additional file 5. Week 14 (ending April 1st) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Additional file 7. Correlogram and Multicollinearity Diagnostics.

Additional file 6. Week 15 (ending April 8th) comparison of standardised coefficients at 25th, 50... more Additional file 6. Week 15 (ending April 8th) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Additional file 4. Week 13 (ending March 25th) comparison of standardised coefficients at 25th, 5... more Additional file 4. Week 13 (ending March 25th) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Additional file 2. Week 11 (ending March 11th) comparison of standardised coefficients at 25th, 5... more Additional file 2. Week 11 (ending March 11th) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Additional file 3. Week 12 (ending March 18th) comparison of standardised coefficients at 25th, 5... more Additional file 3. Week 12 (ending March 18th) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Additional file 1. Week 10 (ending April 4th) comparison of standardised coefficients at 25th, 50... more Additional file 1. Week 10 (ending April 4th) comparison of standardised coefficients at 25th, 50th, 75th and 90th quantiles and the mean function.

Australian Planner, 2016

ABSTRACT Urban heat island (UHI) describes the higher temperature that occurs in built-up areas c... more ABSTRACT Urban heat island (UHI) describes the higher temperature that occurs in built-up areas compared to surrounding natural landscapes. The article identifies the evidence of UHI in the Australian Capital Territory (ACT) and the extent to which the Territory is addressing this through urban planning and climate adaptation regimes. Remotely sensed data were used to retrieve land surface temperature to outline the spatial form of surface heat hotspots. It was found that established town centres in the Territory have hotspots. Hotspots were also found in grasslands, bare ground and dry forests. Research from other jurisdictions provides insights on potential UHI responses at city, neighbourhood and building scales. Whilst current ACT Government planning and adaptation regimes do not address UHI explicitly, they do promote urban forests, integrated open space and urban water bodies which, along with various building standards and guides, can help ameliorate UHI. The ACT has adopted the ‘compact city’ as a future development strategy. Urban intensification will occur in areas that are already identified as hotspots. This, along with the potential impacts of climate change, will require more explicit and increased focus on UHI in the future.

A Modern Guide to the Urban Sharing Economy

Globalization and Health

Background COVID-19 is an emergent infectious disease that has spread geographically to become a ... more Background COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. Results The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human developmen...