Dr M. Awais Gulzar
Lecturer in Finance (ZUCC, China)
Qualifications: PhD Finance (HUST), MBA-Finance (IUB)
Contact DetailsEmail: [email protected]
Room: New Zealand University of Waikato Institute (NZUWI) at Zhejiang University City College, China
About M. Awais
Dr. Gulzar joined Waikato Management School in August 2019 as a lecturer in finance. Prior to this, he served as a teaching fellow of finance at Waikato Management School's overseas programme in China, and as an assistant professor at Zhejiang University City College (ZUCC), China.
He is also an adjunct professor at Zhejiang University, ESSCA School of Management Shanghai and Shanghai Normal University. Since receiving his PhD from Huazhong University of Science and Technology, Dr Gulzar has been working in China.
Dr. Gulzar's teaching and research focuses on corporate finance, behavioural finance, corporate governance, corporate social responsibility, family businesses, earnings management and low carbon economics. He has published several articles in leading international journals.
Corporate finance; Behavioural finance; Corporate governance; Corporate social responsibility; Low carbon economics, Family businesses, Earnings management.
Pan, W., Gulzar, M. A., & Hassan, W. (2020). Synthetic evaluation of China’s regional low-carbon economy challenges by driver-pressure-state-impact-response model. International Journal of Environmental Research and Public Health, 17(15), 5463. doi:10.3390/ijerph17155463
Hwang, J., Kim, I., & Gulzar, M. A. (2020). Understanding the eco-friendly role of drone food delivery services: Deepening the theory of planned behavior. Sustainability (Switzerland), 12(4), 1-12. doi:10.3390/su12041440
Gulzar, M. A., Asghar, H., Hwang, J., & Hassan, W. (2020). China’s pathway towards solar energy utilization: Transition to a low-carbon economy. International Journal of Environmental Research and Public Health, 17(12), 1-11. doi:10.3390/ijerph17124221
Gulzar, M. A., Abid, M., Nazir, H. Z., Zahid, F. M., & Riaz, M. (2020). On enhanced estimation of population variance using unconventional measures of an auxiliary variable. Journal of Statistical Computation and Simulation, 90(12), 2180-2197. doi:10.1080/00949655.2020.1767620
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