Hsiu-Mei Lee

Ms. Hsiu-Mei Lee

orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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12 December 2023 in Corporate Social Responsibility and Environmental Management
Verified
Journal Article
Closed access
ACAriana Chang
Ariana Chang
TLTian‐Shyug Lee
Tian‐Shyug Lee
HLHsiu-Mei Lee
Hsiu-Mei Lee

3 February 2023 in Sustainability
Verified
Journal Article
Open access
PDF file
ACAriana Chang
Ariana Chang
Tian-Shyug LeeTian-Shyug Lee
Tian-Shyug Lee
HLHsiu-Mei Lee
Hsiu-Mei Lee
JWJing Wang
Jing Wang
The COVID-19 pandemic has taken business uncertainty to an unprecedented level and put business agility and leadership under harsh tests, confronting a diverse and complicated stakeholder reality. Through original interview data and case studies from the hospitality industry in Taiwan collected and compiled since the beginning of the COVID-19 pandemic in 2020, the present research explores how strategic agility can be built, fostered, and enhanced through responsible leadership and CSR initiatives during a time of grand challenges in the hospitality industry. We also discuss and highlight the role played by one stakeholder, namely, the local government, in contributing to strategic agility through reinforcing and possibly enabling responsible leadership during hardships. This paper sheds light both empirically and theoretically on the roles played by business leaders in contributing to strategic agility, which, in turn, helps to build a more socially responsible organization.

21 October 2024 in Mathematics
Verified
Journal Article
Open access
PDF file
KHKai-Bin Huang
Kai-Bin Huang
Tian-Shyug LeeTian-Shyug Lee
Tian-Shyug Lee
JLJonathan Lee
Jonathan Lee
JWJy-Ping Wu
Jy-Ping Wu
LLLeemen Lee
Leemen Lee
HLHsiu-Mei Lee
Hsiu-Mei Lee
The steady rise in carbon emissions has significantly exacerbated the global climate crisis, posing a severe threat to ecosystems due to the greenhouse gas effect. As one of the most pressing challenges of our time, the need for an immediate transition to renewable energy is imperative to meet the carbon reduction targets set by the Paris Agreement. Buildings, as major contributors to global energy consumption, play a pivotal role in climate change. This study diverges from previous research by employing multi-task deep learning techniques to develop a predictive model for electricity load in commercial buildings, incorporating auxiliary tasks such as temperature and cloud coverage. Using real data from a commercial building in Taiwan, this study explores the effects of varying batch sizes (100, 125, 150, and 200) on the model’s performance. The findings reveal that the multi-task deep learning model consistently surpasses single-task models in predicting electricity load, demonstrating superior accuracy and stability. These insights are crucial for companies aiming to enhance energy efficiency and formulate effective renewable energy procurement strategies, contributing to broader sustainability efforts and aligning with global climate action goals.