Recently, the People’s Bank of China introduced a series of quantitative easing measures, including lowering the reserve requirement ratio and implementing swap facilities to support the stock market. These actions aim to address challenges in economic growth, prevent deflation, and inject liquidity to stimulate the stock market, countering investors’ pessimism. However, despite strong stock market performance, the real economy faces challenges, with industrial profits dropping nearly 17% and consumer confidence declining steadily.

Over the last decade, researchers and practitioners have become increasingly interested in how firms can leverage managerial responses (MRs) to capitalize on positive reviews and mitigate the potential harm caused by negative reviews. Intuitively, companies should respond to negative reviews (MR-N) to mitigate the damage they cause, while offering more general responses to positive reviews (MR-P). However, the proportion of MR-P on TripAdvisor has consistently increased since 2006 and has surpassed the percentage of MR-N since 2013. Two key questions remain: Given the constraints of limited resources, should managers prioritize soothing unsatisfied customers or pleasing satisfied ones? What are the

Will generative artificial intelligence (AI) become the next technology bubble? Or is it simply a short-term market adjustment? The rapid development of AI has attracted significant investment, yet many experts are raising alarms about a potential dotcom bubble. Some argue that AI companies are overvalued, with limited actual gains, urging investors to adopt a rational and objective approach to the sector’s development.

Are environmental and social (ES) funds really voting in support of their claims? Each year, asset managers vote on various proposals from the companies in their investment portfolio on behalf of fund investors, who expect strong support for ES initiatives from these companies. However, in a recent research conducted by the author, Prof. Roni Michaely, along with Prof. Guillem Calafi-Ordonez, and Prof. Silvina Rubio, a surprising pattern has been identified. These ES funds often support proposals that are expected to pass or fail easily. However, when it comes to close votes—where their decision really matters—they tend to vote against these

The upcoming US presidential election is poised to be a historic one, with its outcome closely tied to economic performance. Recent events, including the Russia-Ukraine war, soaring inflation, and the US-China tech rivalry, have created a turbulent global landscape. Voters are primarily concerned with economic issues, employment, healthcare, and immigration.

The gig economy is rapidly growing, raising critical questions for policymakers about balancing workers’ rights, business interests, and technological advancement. By 2027, gig workers are expected to make up 51% of the US workforce. Hong Kong faces similar challenges, necessitating improvements in its traditional labour protection system.

Since ChatGPT was introduced, large language models (LLMs) have quickly become a focus in the global tech competition. LLMs are being applied in various fields, presenting ample opportunities for AI development. The U.S. currently leads in technology development and innovation, with its models excelling at the technological forefront. In contrast, Chinese models focus on optimizing for local languages and practical applications. Striking a balance of competition and cooperation between the two countries will be crucial in the coming years.

Over the past 5 years, the US government’s fiscal deficit has persistently exceeded $1 trillion, positioning the country among global leaders in debt levels. Economists and international institutions are concerned that the government’s fiscal deficit has consistently exceeded 3% of GDP. Currently, US public debt stands close to 100% of GDP, projected to rise to 116% by 2034, posing a significant burden on the country’s long-term economic development.