This paper studies whether financial technology (FinTech) changes loan contract design by reducing banks’ reliance on collateral in corporate lending. Using loan-level data on Chinese listed firms from 2007 to 2023 and exploiting the People’s Bank of China’s 2019 FinTech Development Plan as a quasi-natural experiment, we find that banks with stronger pre-policy FinTech capability significantly reduce secured lending after the policy shock. In the benchmark specification, the probability that a loan is secured falls by 1.64 percentage points, or about 2.7% relative to the baseline secured-loan share. The result is robust to alternative loan classifications, matching procedures, alternative measures of FinTech adoption, aggregated lending outcomes, and alternative inference procedures. The pattern is more pronounced among small and medium-sized enterprises, lower-tier branches, and branches located outside bank headquarters’ cities, where borrower information is likely to be more limited. Supplementary analyses are consistent with FinTech reducing banks’ information-production costs and suggest that technological proximity to FinTech-active peers may amplify the collateral-reducing effect. Overall, the evidence indicates that FinTech can enhance banks’ screening capacity and shift lending decisions away from reliance on asset-based guarantees toward information-based credit assessment.

Technology firms are often financed on narratives about products, contracts, customers, and technological progress well before these developments appear in accounting statements. We ask when such narratives become economically informative. Our central idea is that narratives should matter more once they can be linked to later verifiable outcomes rather than treated as stand-alone text.Using listed Chinese technology firms, we develop a validated corporate narrative framework for bank-affiliated investment, a setting in which investors must screen with soft information ex ante and then monitor hard realization and downside risk ex post. We use GPT-5.1 to extract business claims from management discussion, investor-relations records, exchange Q&A, and earnings-roadshow materials, and to label later claim–evidence pairs as support, partial support, conflict, duplicate, or irrelevant. We then connect these labels to official announcements, procurement awards, permits, project updates, and negative-event disclosures to construct a validated firm-month signal. The broad merged panel contains 592 firms and 30,169 firm-month observations; the main return tests use 576 firms and 18,230 firm-month observations over 2022–2024. A simple production rule that combines a low-narrative-premium component with hard-narrative and hard-event anchors, together with a separate downside-risk gate, delivers an implementable annualized long-short return of 8.93% in bank-invested firms after trading costs. The signal is much weaker in non-bank firms, predicts future gross-margin improvement more strongly than future ROE, and improves downside screening.

The parking reservation systems (PRS), as an intelligent system, was adopted to address urban parking difficulties. However, the parking reservation system has not been widely adopted in China due to the reasons such as the imperfect system and the uncontrollable parking behaviour of users. This study examines the impact of users' non-compliant behaviour on intentions to reuse PRS. Non-compliant behaviours include not arriving or leaving parking spaces not as scheduled, negatively affecting subsequent users. The Technology Acceptance Model (TAM) was expanded by adding perceived risk, social influence, and behavioral attitude. A survey involving 702 PRS users from multiple Chinese cities was conducted. Structural Equation Modelling (SEM) was used for analysis Results show that perceived risk—such as occupied reserved spaces and extra fees from time deviations—significantly reduces behavioral attitudes and reuse intentions. Conversely, perceived usefulness, ease of use, and social influence positively influence both attitudes and reuse intentions. Importantly, the findings highlight that behavioral uncertainty, particularly users’ deviations from scheduled parking times, is a critical source of perceived risk that undermines trust and long-term engagement. Effectively managing this uncertainty through improved system flexibility and reliability is therefore essential to promoting the sustained adoption of PRS.

This paper investigates how environmental regulation induces firm digitalization. We construct a digital index based on textual analyses and find that after the implementation of the program, pilot firms' digitalization increased relative to that of a group of carefully matched control firms, which is opposite to the findings in the extant literature on technology adoption. This increase cannot be fully explained by regional unobservables, firms' own innovation, firm selection, or other policies. The results are robust when we consider firm subsidiaries. The increase in digitalization is not due to regulatory arbitrage, and the industry-level concentration of digitalization changes little.

Despite strong GDP growth and a favourable demographic profile, India faces an impending jobs crisis. A large share of the workforce remains employed in low-productivity agriculture, while many new labour market entrants are absorbed into the persistently large informal sector. By contrast, China’s rapid ascent was driven by manufacturing-led, export-oriented industrialisation, underpinned by large inflows of foreign direct investment and sustained technology transfer. India’s manufacturing base remains modest in contrast. The bulk of well-paid, formal employment continues to be concentrated in the high-skill services sector. This paper contrasts the development trajectories of these two economies and identifies several underutilised jobs-growth levers in India: manufacturing, goods exports, manufacturing-oriented foreign direct investment and innovation. All of these remain underdeveloped, yet together they offer a pathway to more labour-absorbing, durable growth. Leveraging them effectively would be central to achieving India’s ‘Viksit Bharat 2047’ ambition of attaining high-income status. The scale of India’s challenge to employ eight to ten million labour-market entrants per year implies that job creation must become an explicit policy priority. This calls for greater trade openness, particularly with Asia and Europe, to integrate India into Asia-centric global supply chains as an alternative to China. Labour market reform is equally critical, making the effective implementation of the new labour codes essential. Strengthening innovation ecosystems and realigning education and skills policies to support industrialisation are also key. Without these structural shifts, India’s current pattern of jobless growth risks transforming its demographic dividend into a long-term liability.

Traditional production function theory treats executive characteristics as exogenous fac-tors, failing to capture how managerial heterogeneity dynamically influences productiv-ity. This study integrates Upper Echelon Theory and Dynamic Capabilities Theory to develop an endogenous production function framework where executive attributes serve as dynamic parameters reshaping capital and labor elasticities. Using fixed effect-s panel-data models, we analyze Chinese listed companies, examining how executives’ education, experience, and compensation interact with production parameters. Re-sults show executive characteristics significantly moderate factor productivity: higher education and board independence enhance capital productivity, while compensation intensity transforms labor elasticity from negative to strongly positive values. Hetero-geneity analysis reveals effects vary across ownership types and technology intensity, with technology-intensive firms showing strongest responsiveness. This research recon-ceptualizes executives as active architects of production processes, provides a rigorous framework for endogenizing management factors, and offers evidence-based guidance for corporate governance and talent strategy optimization.

Robo-advisors have been hailed as financial innovations that combine Artificial Intelligence (AI) and low-cost advisory services, with the potential to democratize stock market participation and improve financial inclusion, especially in less developed countries. However, to date their adoption has been slower than expected and existing research that has attempted to understand this puzzle focuses exclusively on existing users of robo-advisors. In this paper, we study the intention to adopt robo-advisors as an antecedent of actual adoption. Using data from a survey of 1,277 Chinese adults, a country with one of the highest saving rates in the world but also very low stock market participation rate, we find that financial literacy and technology acceptance strongly influence the intention to adopt robo-advisors. A one-unit increase in financial literacy (technology acceptance) is associated with a 5.69% (4.74%) increase in the probability of adopting robo-advisors. Importantly, financial confidence partially mediates the literacy-adoption link, highlighting a key psychological mechanism in improving stock market participation rates. Our results shed light on the underlying drivers that facilitate financial inclusion.

We exploit a unique financial regulatory tool with data-governance functions as a quasi-natural experiment to explore the determinants of bank risk-taking. The paper finds that Examination Analysis System Technology (EAST) reduces bank risk-taking. This result is more pronounced in banks with higher capital adequacy ratios and higher liquidity levels. We also find that the inhibitory effect of EAST on bank risk is more significant for banks in eastern regions and listed banks. Our findings highlight the positive impact of data regulation on promoting financial stability.

This study proposes an innovative CPO-VMD-PConv-Informer framework to forecast the KBW Nasdaq Financial Technology Index (KFTX). The framework comprehensively incorporates the effects of eight representative uncertainty indicators on KFTX price predictions, including the Economic Policy Uncertainty Index (EPU) and the Geopolitical Risk Index (GPR). The empirical findings are as follows: (1) The proposed CPO-VMD-PConv-Informer framework demonstrates superior predictive performance across the entire sample period, achieving R² values of 0.9681 and 0.9757, significantly outperforming other commonly used traditional machine learning and deep learning models. (2) By integrating VMD decomposition and CPO optimization, the model effectively enhances its adaptability to extreme market volatility, maintaining stable predictive accuracy even under structural shocks such as the COVID-19 outbreak in 2020. (3) Robustness tests show that the proposed model consistently delivers strong predictive performance across different training-testing data splits (9:1, 8:2, and 6:4), with the MAPE remaining below 2%. These findings provide methodological advancements for forecasting in the KFTX market, offering both theoretical value and practical significance.

How enterprises can leverage frontier technologies to achieve synergy between environmental governance and high-quality development has become a critical issue amid the deepening global push for sustainable development and the green economic transition. Based on micro-level data of Chinese enterprises from 2009 to 2023, this study systematically examines the impact of artificial intelligence (AI) on corporate green governance performance and explores the underlying mechanisms. The findings reveal that AI significantly enhances green governance performance at the enterprise level, and this effect remains robust after accounting for potential endogeneity. Mechanism analysis shows that AI empowers green transformation through a dual-path mechanism of “cognition–behavior,” by strengthening environmental tendency and increasing environmental investment. Further heterogeneity analysis indicates that the positive effects are more pronounced in nonheavy polluting industries and state-owned enterprises, suggesting that industry characteristics and ownership structure moderate the green governance impact of AI. This study contributes to the theoretical foundation of research at the intersection of digital technology and green governance, and provides empirical evidence and policy insights to support AI-driven green transformation in practice.