Artificial intelligence (AI) has advanced dramatically in recent years. In particular, deep learning and generative AI models have gained widespread popularity at both organizational and individual levels, with a range of innovative and viable applications being developed.

Environmental, Social, and Governance (ESG) investing is rapidly gaining traction in global financial markets, aiming to integrate environmental protection and social responsibility into investment decisions to promote sustainable development. Despite its wide support, ESG investing faces criticism, particularly in the United States, where some state governments and the energy sector express concerns about its potential negative impact on the economy and business competitiveness.

Environmental protection is traditionally seen as a state responsibility, and the private market is meant to focus on creating shareholder value rather than preserving nature. Still, market-based solutions such as green investing have emerged. The research addresses whether green investment funds can influence firm-level environmental performance in relation to the presence of state environmental- and shareholder protection policies.

Does a “free lunch” really exist? Some online shopping platforms let consumers disclose if they received products for free or at a discount when writing reviews. Although these reviews for free products are not the majority, they can energize online communities where reviews are limited. The lack of objectivity in free product reviews can trigger self-regulation within online communities, prompting more users to provide fair feedback. Voluntary disclosure policies can effectively boost the number of reviews at a lower cost than hiring experts to write positive reviews.

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