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I show that hedge funds react to unrealized losses on their passive positions by engaging with the management. The hedge fund managers' psychological response is consistent with cognitive dissonance: They blame the firms' management and switch to activism. The loss, which is hedge fund-investment specific, is distinct from economic factors such as the firm's industry-adjusted performance. Loss-driven activism is more likely to be unfocused on specific issues and results in worse firm performance. This study shows that an overlooked consequence of unrealized losses is to trigger an active engagement with the firm. [ABSTRACT FROM AUTHOR]

The interview with Silicon Valley venture capitalist Mark Coopersmith explores the current landscape of privately funded fusion energy efforts. Coopersmith discusses the challenges and optimism surrounding the commercialization of fusion energy, highlighting the different perspectives of physicists, engineers, entrepreneurs, and investors. The interview delves into the role of venture capital in funding fusion startups, the need for government support in basic research, and the potential spin-off technologies that could emerge from advancements in fusion science. The discussion also touches on the intersection of Silicon Valley culture and the portrayal of tech startups in popular media. [Extracted from the article]

This article uses FAS 123R regulation to examine how reduction in CEO compensation incentives affects managerial "playing it safe" behavior. Using proxies reflecting deliberate managerial efforts to change firm risk, difference-in-difference tests show that affected firms drastically reduce both systematic and idiosyncratic risks, leading to an 8% decline in total firm risk. These reductions in risk are achieved by shifting to safer, but low-Q, segments while closing the riskier ones, without significant changes in investment levels. Our findings suggest that decrease in risk-taking incentives provided by option compensation, when not compensated for by alternative incentives or governance mechanisms, exacerbates risk-related agency problem. [ABSTRACT FROM AUTHOR]

Renewable hydrogen is widely considered a key technology to achieve net zero emissions in industrial production processes. This paper presents a structured bibliometric analysis, examining current and future applications of hydrogen as feedstock and fuel across industries, quantifying demand for different industrial processes, and identifying greenhouse gas emissions reduction potential against the context of current fossil-based practices. The findings highlight significant focus on hydrogen as feedstock for steel, ammonia, and methanol production and its use in high-to medium-temperature processes, and a general emphasis on techno-economic and technological evaluations of hydrogen applications across industries. However, gaps exist in research on hydrogen use in sectors like cement, glass, waste, pulp and paper, ceramics, and aluminum. Additionally, the analysis reveals limited attention in the identified literature to hydrogen supply chain efficiencies, including conversion and transportation losses, as well as geopolitical and raw material challenges. The analysis underscores the need for comprehensive and transparent data to align hydrogen use with decarbonization goals, optimize resource allocation, and inform policy and investment decisions for strategic deployment of renewable hydrogen. • Detailed overview of renewable hydrogen use in industrial processes •Reviewed articles mainly focus on hydrogen in steel, ammonia, and methanol •Limited evidence from hydrogen in heat, cement, glass, and ceramics •Supply chain efficiency, geopolitics, and raw material challenges underexplored •Improved data transparency and cross-sectoral analysis for informed decision making [ABSTRACT FROM AUTHOR]

The development of China's National Carbon Market has strengthened the inherent link between the carbon market and the broader energy market, providing a potential for cross-market risk transmission resonance. Studying the risk spillover effects between China's National Carbon Market and the crude oil futures market is of significant practical importance, both in terms of carbon market development and carbon risk management. Based on the Maximal Overlap Discrete Wavelet Transform (MODWT), the price series are decomposed across multiple scales, and the risk spillover effects between the carbon market and the crude oil futures market are examined from both the time domain and the frequency domain. Methods such as wavelet energy decomposition, wavelet correlation, lead-lag analysis, and wavelet coherence are used to explore the mean spillover effects and volatility spillover effects (collectively referred to as risk spillover effects) across various scales. The study finds that China's National Carbon Market exhibits a clear compliance-driven effect, with relatively low market liquidity. The crude oil futures market experiences frequent price fluctuations, primarily driven by long-term factors. In the time domain, the risk transmission resonance between the carbon market and the crude oil futures market is high, with significant positive correlations observed at the D1 to D4 scales, and noticeable mean spillover effects. In the frequency domain, at the D3 to D4 scales, the carbon market and the crude oil futures market exhibit similar volatility frequencies, indicating strong volatility spillover effects. Based on these findings, it is recommended that the trading volume of the carbon market be gradually increased to improve market liquidity. Furthermore, the risk monitoring and early warning mechanisms of China's National Carbon Market should be improved. For carbon-emitting companies, enhancing awareness of carbon asset management and making informed investment and hedging decisions based on the correlation between the two markets is crucial. [ABSTRACT FROM AUTHOR]

Transportation engineers face challenging safety investment decisions, particularly for highway-railway grade crossings (HRGCs), where rare collision occurrences and incomplete historical records complicate the assessment of countermeasure cost-effectiveness. This study introduces an empirical Bayes (EB) observational before–after approach to address these challenges, specifically examining the impact of a widely used safety countermeasure: flashing lights, bells, and gates (FLBG). The research covers a total 171 FLBG crossings with pre-implementation conditions varying between standard reflective crossing signs and flashing lights and bells, utilizing collision data from 2000 to 2012 for analysis. This study is the first to substantiate quantitatively the effectiveness of FLBG treatment in improvising the safety of HRGCs and show that the effect is dependent of crossing characteristics, such as track angle, pavement conditions, and train speed limits. These findings can provide valuable support to decision-makers, enabling them to make more effective decisions for grade crossing safety improvement projects. [ABSTRACT FROM AUTHOR]

This study addresses shipping companies' compliance with the International Maritime Organization's 2020 sulfur emission limits, providing guidance for investment decisions in desulfurization projects. The paper uses a novel RCFM model, integrating real option theory (R), shipping cycle theory (C), fuzzy set theory (F), and Monte Carlo simulation (M). Key findings are: (1) During high oil price volatility, traditional binary tree models show computational distortions corrected by Monte Carlo simulation, whereas in lower volatility, the fuzzy binary tree model offers reliable investment intervals. (2) Investment in desulfurization depends on remaining vessel life and fuel price differences. For example, during the recovery period, for vessels with a remaining life of less than 5 years, investing in a desulfurizer is profitable if the fuel price difference exceeds $550/t and using low-sulfur fuel is advisable if below $400/t. For vessels of remaining life of 5-9 years, invest in a desulfurizer if the difference exceeds $500/t and use low-sulfur fuel if below $300/t. For over 9 years, invest in a desulfurizer if the difference exceeds $450/t and use low-sulfur fuel if below $200/t. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore the interplay between CO2 emissions, financial development (FD) and foreign direct investment (FDI) in Asia-Pacific and Oceania. It also aims to understand short- and long-term impacts, emphasizing the role of FDI, FD and FD's moderating effect on the FDI–CO2 relationship. Design/methodology/approach: Using a 21-year panel data set (2000–2020) from 44 countries, the study employs the pooled mean group-autoregressive distributed lag (PMG-ARDL) model supplemented by the Dumitrescu–Hurlin panel causality test. This method assesses the complex dynamics and offers a robust analysis of short- and long-term effects in the Asia-Pacific and Oceanian context. Findings: Long-term results indicate that FDI coupled with FD and FD's moderating effect on FDI significantly contributes to CO2 emissions. Short-term relationships are more complex and lack statistical significance. FD positively moderates the FDI–CO2 relationship in the long run. Practical implications: For investors, policymakers and stakeholders in Asia-Pacific and Oceania, the study highlights the importance of considering environmental impacts in investment decisions. The insights into the role of FDI and FD help craft policies and strategies for environmental sustainability. Social implications: Socially, this research emphasizes the necessity of a balanced approach to economic development, considering the potential long-term environmental consequences. Policymakers and stakeholders may use these findings to guide discussions and actions to achieve sustainable and socially responsible development in this dynamic region. Originality/value: The findings contribute original insights into the essential relationships among FDI, FD and CO2 emissions in a diverse region like Asia-Pacific, enhancing the understanding of environmental implications in regions experiencing rapid economic growth. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the consistency between firms' stakeholder-friendly responses to the COVID-19 pandemic and their environmental, social and governance (ESG) ratings. Consistent firms are those with high prior ESG ratings that actively support stakeholders during the COVID-19 crisis. Design/methodology/approach: The authors use data from JUST Capital, which tracks Russell 1000 firms' actions in response to the pandemic, to examine the relationship between pre-pandemic ESG ratings and their COVID responses towards employees, customers and communities. The authors also analyse the impact of firms' consistency between pre-pandemic ESG ratings and stakeholder-friendly COVID responses on ESG ratings and stock returns. Findings: This study finds that firms with higher pre-pandemic ESG ratings are more likely to support their stakeholders during the pandemic. The authors also find that firms with high ESG ratings before the pandemic experience a decline in their ESG ratings if they do not actively support their communities during the COVID-19 crisis, although insufficient employee/customer support does not impact their ESG ratings. Finally, the authors find that firms with higher pre-pandemic ESG ratings that continue to uphold their ESG commitments through community assistance during the pandemic achieve higher stock returns compared to inconsistent firms. Practical implications: The results reveal gaps in how comprehensively ESG agencies assess firms' crisis responses, highlighting areas for rating improvements. The findings contribute to sustainable development by revealing the importance of firms upholding their ESG commitments during crises to maintain stakeholder trust and drive long-term value creation. Social implications: The findings underscore the need for responsive, transparent ESG rating processes to support the integration of sustainability considerations into corporate practices and investment decisions, particularly during evolving societal expectations during crises. Originality/value: To the best of the authors' knowledge, this study is the first to investigate how pre-pandemic ESG ratings explain firms' stakeholder-friendly responses during the COVID-19 pandemic and analyse the integration of these responses and pandemic risks into ESG ratings during the crisis. [ABSTRACT FROM AUTHOR]

Pension fund must consider some significant issues when making renewable energy project investment decisions. It is necessary to determine the most important factors and prioritize the indicators. Accordingly, the purpose of this study is to conduct a priority analysis of the determinants of investment in renewable energy projects by pension funds. This study constructs a novel fuzzy decision-making model. First, five indicators for this process are weighted using an entropy methodology based on sine trigonometric Pythagorean fuzzy sets. The CRITIC methodology is also considered to make a comparative evaluation. Second, five different clean energy investment alternatives for pension funds are ranked using the RATGOS methodology. Similarly, this ranking analysis is also made by considering TOPSIS technique to check the reliability of the results. The main contribution of this study is the creation of a new and comprehensive fuzzy decision-making model to identify the most important factors in renewable energy project investments for pension funds. The proposed model uses the RATGOS technique to rank clean energy investment alternatives for pension funds. By considering the geometrical mean in the RATGOS calculation process, criticisms related to existing ranking techniques can be overcome. The use of sine trigonometric Pythagorean fuzzy numbers provides significant benefits to the quality of the proposed decision-making model. The defuzzification process can be implemented appropriately using these sets. Therefore, this study's findings pave the way for investors to make investment decisions under these circumstances. It is concluded that the most important criterion is risk minimization. Effective regulations are another critical issue. Furthermore, the ranking results indicate that the most suitable renewable energy alternative is green bonds. The comparative results with STPFY-TOPSIS show that the proposed model generates coherent and reliable findings. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the amount and direction of economic policy uncertainty (EPU) spillover among six emerging market economies (EMEs), and to also ascertain arguments on the increased volatilities of uncertainty in most EMEs. Design/methodology/approach: This study adopts a recent methodology developed by Baruník and Krehlík's (2018) methodology to measure pairwise, composite and net spillover. This methodology helps investigate the size and direction of EPU spillover in EMEs. The unique feature of this methodology is its ability to capture frequency domain as well as time-frequency dynamics. Findings: Inter-country static spillover connectedness among the EPU of the selected EMEs show that Korea-EPU is the main transmitter and recipient of spillover shocks among the EMEs across all frequency bands. The findings from this study also show evidence of spillover between EPU, GDP and SPX across the EMEs. The time-varying total spillover index analysis shows evidence of overall connectedness across the selected EMEs. Overall connectedness is highest in the short term. We document that global economic and financial events intensify the volatility of the total spillover across the selected EMEs. Originality/value: This study extends the literature on studies conducted on EMEs as studies on EPU spillover has mainly focused on advanced economies. To address the limitation of previous empirical studies that were unable to address the amount and direction of spillover from a country to other countries, this study offers new insight on country-specific spillover amounts and causal patterns "to" and "from" the selected EMEs. The findings throw more light on the network connectedness across EMEs and hence aids investors to undertake precise investment decisions and intelligently plan their portfolio diversification strategies. We then introduce two new variables to the analysis and record evidence of high connectedness between EPU, gross domestic product and share price index in all the frequency bands. [ABSTRACT FROM AUTHOR]

The rapid development of digital finance is reshaping the business model of the traditional bank industry and bringing challenges to it as well. Based on an unbalanced panel of data constructed by 36 banks in Malaysia from 2006 to 2020, this study examines the impact of financial technology on banks' stability and efficiency. We find that, compared with Islamic banks, FinTech innovation significantly improves the stability of commercial banks. Additionally, it improves the entire sample banks' efficiency calculated by the data envelopment analysis‐Malmquist method, which can capture the efficiency changes from a dynamic perspective. These baseline results are affirmed by the generalized method of moment approach to mitigate potential endogeneity issues. Furthermore, the impacts of FinTech innovation on banks are heterogeneous. The high‐profit banks enjoy the benefits of improving their stability level from FinTech development. However, for the small‐sized and low‐profit banks, FinTech innovation contributes more to improving their efficiency. Our analysis provides empirical evidence for Malaysia and similar developing countries that are receptive to FinTech development but have relatively less advanced technology infrastructure. It can also shed light on the FinTech investment decisions of bank management. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the association between corporate risk-taking and cash holdings, and whether financial constraints moderate this association. Design/methodology/approach: Regression analyses were applied to 606 firm-year observations from Saudi Arabia from 2011 to 2020. Findings: Firms with higher risk-taking exhibit higher cash holdings, whereas financially constrained firms have lower cash holdings. The positive association between corporate risk-taking and cash holdings is weaker for financially constrained firms than for nonconstrained firms. Practical implications: For investors, investment decisions that include the cash holding assessment would also consider the firm-level uncertainty surrounding the firms. Originality/value: This study explores the joint effect of corporate risk-taking and financial constraints on cash holding, and hence considers the strategic adaptations to navigate uncertainty in strategies related to cash holdings in Saudi Arabia. [ABSTRACT FROM AUTHOR]

Purpose: Millennials are a vital generational cohort of the Indian population, and understanding their motivation to participate in the stock market is crucial. This study aims to understand the investment decision-making behavior among millennials in the Indian Stock Market. Design/methodology/approach: Using a cross-sectional research design that entails in-depth personal interviews, this study aims to understand the equity investment behavior of millennials. Verbatim texts from interview transcripts were used to analyze the content and arrive at themes. Findings: The study investigated the motivation to enter the stock market and gained insights into how individuals make equity investment decisions considering economic and behavioral dimensions. The basis for stock selection was predominantly on the self-analysis of investors. Multiple stock selection priorities are also discussed. In addition, informants ensured asset diversification and exercised various strategies to overcome emotions. Furthermore, they suffered from various behavioral biases. Practical implications: Individual investors are the least informed and most impacted stakeholders in the stock markets; therefore, this study contributes fresh insights to enhance their financial security. The paper also examines some noticeable behavioral tendencies retail investors exhibit and gathers helpful strategies for mitigating behavioral biases. Originality/value: The uniqueness of the research lies in its adoption of a qualitative methodology that uses the investment experience of millennial investors to reveal the components of decision-making behavior and investor psychology. The findings are thereby unique and have significant managerial implications. [ABSTRACT FROM AUTHOR]

Purpose: The crypto market is growing quickly, marked by a lack of fundamentals, and the risks are not yet fully comprehended by participants. Our goal is to investigate overconfidence in this market and analyze the role that risk propensity and certain demographics play. Design/methodology/approach: We conducted a survey in Brazil and Portugal, leveraging an online questionnaire disseminated via social media channels to engage a diverse adult population. We collected a total of 826 responses, addressing ethical considerations throughout the process. The data analysis was conducted using SPSS statistical software and logit regression modeling. Findings: Our study reveals that overconfidence is a notable bias that distinguishes individuals who invest in cryptocurrencies from those who do not. Although overconfidence and risk propensity are closely linked, they originate from distinct personal characteristics. Furthermore, our findings indicate that age and market experience positively correlate with overconfidence and negatively correlate with risk propensity. Financial knowledge, interestingly, did not prove to be a significant factor for cryptocurrency investment. Originality/value: Our research augments the existing literature on overconfidence, delving into this phenomenon in a new subdomain, and in doing so, it enriches our comprehension of the unique and still relatively under-researched cryptomarket. Moreover, we illuminate individual factors that sway the decision to invest in cryptocurrencies and should be considered by market participants. Highlights: Pioneering work examining the presence of overconfidence bias among crypto-investors, using a robust data set collected from a binational survey. Verifies the relations among overconfidence, risk propensity, and demographics. Examines the influence of age and experience on investment decisions, revealing a positive relationship with overconfidence and a negative correlation with risk propensity. Logistic regression is used to determine the combined effect of overconfidence, risk propensity, and demographics on the decision to invest in cryptocurrencies. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines the volatility spillover effects from traditional financial assets to cryptocurrency markets and vice versa. It aims to provide insights into the dynamic interconnectedness of these markets. Design/methodology/approach: This paper employs the time-varying parameter vector autoregression technique to examine the volatility spillover among the crypto markets (across leading cryptocurrencies such as Bitcoin (BTC), USD Tether, NEAR Protocol (NEAR), Immutable and Dogecoin) and traditional financial instruments (Treasury Bills (TBILL) and Volatility Index). Findings: The results reveal significant bidirectional volatility spillovers between cryptocurrencies and traditional financial assets. NEAR and BTC act as a major transmitter of volatility, both influencing others significantly (71.63 and 68.17%, respectively) and being influenced by others (54.74 and 62.3%, respectively). TBILL and Grayscale Bitcoin Trust ETF are the largest net receivers of volatility, indicating a higher dependency on other assets' volatility. Practical implications: Understanding the volatility spillover dynamics can aid investors in portfolio diversification and risk management. The findings provide actionable insights for constructing portfolios that include both cryptocurrencies and traditional financial assets, allowing for more informed investment decisions under volatile market conditions. Originality/value: This paper contributes to the literature by analyzing volatility spillovers among traditional financial markets and various major cryptocurrencies. It offers a framework for assessing how shocks in one market or cryptocurrency can propagate to others, thereby enhancing the understanding of interconnectedness between markets. This understanding improves our ability to risk manage modern portfolios, which increasingly include significant alternative assets like cryptocurrencies. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to identify individuals' biases while making investment decisions and explore how these biases can be incorporated into a robo-advisory platform to help mitigate these biases. This paper identifies eight investment-related behavioral biases: mental accounting, gambler's fallacy, hindsight, regret aversion, disposition, trend-chasing, loss aversion and herding. Design/methodology/approach: This study uses primary data from 263 respondents across various age groups, of which approximately 50 were wealth management professionals in the UAE. A random sampling method from probability sampling is employed to gather the primary data. The identified biases serve as dependent variables; the age and income of individuals serve as the independent variables. Findings: Age and income are significantly related to mental accounting, herding, gambler fallacy and loss aversion. Existing studies on behavioral finance demonstrate that individuals who make investment decisions are susceptible to cognitive fallacies, leading to nonrational investment decisions. Practical implications: By studying these biases affecting individuals of varying ages and income levels, wealth management professionals can tailor their financial robo-advisory services to address these biases and help clients build wealth with consistent investment. Originality/value: This study uses survey-based sampling in the context of the UAE; hence, the data and analysis represent originality. [ABSTRACT FROM AUTHOR]

Purpose: We analyze the role of trust as a driver of speculative investment decisions in technology firms. Design/methodology/approach: Structural Equation Modeling analysis in the context of blockchain technology supports our hypotheses. Findings: Our findings indicate that a general propensity to trust technology leads to trusting beliefs in a service based on technology and that trusting beliefs in a technological service leads to a higher propensity to invest in any firm associated with that service. In addition, we show that in a non-technological context, there is no evidence for such an effect of trusting beliefs in a service on investment decisions. These results support the notion that trusting beliefs are facilitators of speculative investment in technology firms. Research limitations/implications: The research advances knowledge about the influence of trust in technology on investment decisions; its findings can help build new theoretical models regarding investment decisions using Fintech. Practical implications: For investors, it is important to realize the potential bias identified in this study, so they can actively avoid adhering to it, thus avoiding exposure to unnecessary risk. Further, beyond individual investors, investment firms take active measures to avoid biases in their own decision-making. Banks and investment firms can help guide their clients about trust-based bias when building their investment portfolio. Originality/value: Although trust in information systems has been studied extensively, research on the relationship between trust in technology and decisions to invest in technology-related firms is limited. [ABSTRACT FROM AUTHOR]

Pension fund managers face challenges in acquiring, analysing, and synthesising external information to make informed investment decisions. Text-mining and sentiment analysis can aid fund managers integrate contrasting news, financial data, and market speculation into their decision rubric. Considering Armenia pension funds as the research object, we perform sentiment analysis on Yahoo Finance data, transform the information into numerical data, add confidence weights, and apply the Black–Litterman model using supervised deep learning to predict how information affects portfolio management. Our method using the Black–Litterman model is practical to quantify how external information impacts on fund portfolio management, and using the method leads to an increase in portfolio performance. [ABSTRACT FROM AUTHOR]

In the context of global climate change and sustainable development, enterprise digital transformation has become key to improving efficiency and competitiveness. Digital twin technology, as an emerging tool, enables real-time monitoring, prediction, and optimization by creating dynamic virtual models of real-world processes. This paper explores the impact of digital twin-based transformation on renewable energy investment decisions. Through empirical analysis of over 200 companies globally, the study finds that companies using digital twin technology exhibit higher accuracy and efficiency in renewable energy investment decisions. These companies show improved forecasting of energy consumption and investment returns, gaining a competitive edge. On average, these companies experience a 15% ROI increase for their renewable energy investments and enjoy a 20% acceleration in the decision-making process. Furthermore, the study delves into how the adoption of digital twin technology differs across various company sizes and industries, providing actionable insights and guidance for enterprises embarking on their digital transformation journey. [ABSTRACT FROM AUTHOR]

In this study, we assess the effect of the European Union's novel food regulations on firms' incentives to invest in such products. We adopt a conceptual framework based on real option value theory, which underpins an empirical analysis of a detailed dataset comprising 326 applications submitted under both the 1997 EU novel food regulation and its 2018 replacement. We investigate the dynamics of novel food applications under these regulations and disentangle the determinants of successful cases. Our results show a relatively stable number of applications over the years, with a spike after the introduction of the 2018 regulation, which sought to simplify and centralise the approval process. This upsurge can be interpreted as a reduction in the real option value of postponing investments, attributable to the introduction of a transitional regime and of 5‐year data protection measures. However, the new regulation did not shorten the authorisation process, with the expected benefits of centralisation compromised by operational bottlenecks and a lower chance of approval. Finally, we find that approvals under the 2018 regulation are more likely when applicants are private entities from non‐EU countries and have substantial experience with novel foods. Our empirical evidence suggests that the new regulation may be insufficient to speed up and streamline the novel food assessment process, which is inevitably constrained by EU food safety principles. This, in turn, may discourage future investments. [ABSTRACT FROM AUTHOR]

The transition towards climate-neutral industry is a challenge, particularly for heavy industries like steel and basic chemicals. Existing models for assessing industrial transformation often lack spatial resolution and fail to capture individual investment decisions. Consequently, the spatial interplay between industry transformation, energy availability, infrastructure availability, and the dynamics of discrete investments is inadequately addressed. Here we present a site-specific approach that considers individual industrial sites to simulate discrete investment decisions. The investment decision is modelled as a discrete choice among alternative technologies with their total cost of ownership as the main decision criterion. Process costs depend on the scenario-specific assumptions, such as energy carrier prices, policy instruments and local infrastructures. The age of production units and their reinvestment cycles are considered the main restrictions on the dynamics of the transition. The results provide high spatial resolution to capture the spatial and temporal dynamics of industry transition under varying process and policy assumptions. The presented model and its results can be coupled with energy system models to assess the implications of site-specific industry transition on energy system related research questions. We conduct an exemplary case study for a transformation pathway of the European primary steel production. [ABSTRACT FROM AUTHOR]

This study explores the factors influencing Informal Investment Intentions among potential investors, focusing on Risk Propensity and Subjective Norms. It examines the mediating effect of Entrepreneurial Alertness and the moderating role of gender, aiming to understand how these elements shape investment decisions among management students in India. A survey-based cross-sectional research design is followed to evaluate a 340 cross-sectional sample. Global Entrepreneurship Monitor (GEM) database, SEM, and the PROCESS macro have been utilized to understand the synchronization between the variables. CFA, RMSEA, GFI, and TLI tests are employed to check the fitness and validity of the model, and CMB, together with PFA, have been applied for variance testing. The study found that Subjective Norms significantly influence Informal Investment Intentions, mediated by Entrepreneurial Alertness. Gender exuberates the relationship between Risk Propensity and Informal Investment Intentions through Self-Efficacy, with stronger effects observed among male students. Findings offer insights for policymakers and educators to design interventions fostering Informal Investment Intentions by enhancing Entrepreneurial Alertness and addressing gender-specific differences, particularly among management students. This study adds to the literature by examining gender's moderating role in the relationship between Risk Propensity and Informal Investment Intentions, providing a nuanced understanding of informal investment behavior in an Indian context. [ABSTRACT FROM AUTHOR]

Effective production prediction is vital for optimizing energy resource management, designing efficient extraction strategies, minimizing operational risks, and informing strategic investment decisions within the energy sector. This paper introduces a Dual-Stage Attention Temporal Convolutional Network (DA-TCN) model to enhance the accuracy and efficiency of gas production forecasting, particularly for wells in tight sandstone reservoirs. The DA-TCN architecture integrates feature and temporal attention mechanisms within the Temporal Convolutional Network (TCN) framework, improving the model's ability to capture complex temporal dependencies and emphasize significant features, resulting in robust forecasting performance across multiple time horizons. Application of the DA-TCN model to gas production data from two wells in Block T of the Sulige gas field in China demonstrated a 19% improvement in RMSE and a 21% improvement in MAPE compared to traditional TCN methods for long-term forecasts. These findings confirm that dual-stage attention not only increases predictive accuracy but also enhances forecast stability over short-, medium-, and long-term horizons. By enabling more reliable production forecasting, the DA-TCN model reduces operational uncertainties, optimizes resource allocation, and supports cost-effective management of unconventional gas resources. Leveraging existing knowledge, this scalable and data-efficient approach represents a significant advancement in gas production forecasting, delivering tangible economic benefits for the energy industry. [ABSTRACT FROM AUTHOR]

As photovoltaic technologies are being promoted throughout the country, the widespread installation of distributed photovoltaic systems in rural areas in rural regions compromises the safety and stability of the distribution network. Distributed photovoltaic clusters can be configured with energy storage to increase photovoltaic local consumption and mitigate the impact of grid-connected photovoltaic modes. Against this background, this paper focuses on rural areas, combines typical operation modes of distributed photovoltaic clusters, and constructs the two-stage energy storage optimization configuration model for rural distributed photovoltaic clusters. Taking a Chinese village as an example, the proposed model is optimized with an improved particle swarm optimization algorithm. Given different combinations with and without energy storage and demand response, comparative analyses are conducted on photovoltaic local consumption and the economic benefits of independent operators in various scenarios. Simulations indicate that the photovoltaic local consumption proportion of distributed photovoltaic clusters with energy storage reaches 62.64%, which is 34.02% more than the scenario without energy storage. The results indicate that configuring energy storage for rural distributed photovoltaic clusters significantly improves the photovoltaic local consumption level. Meanwhile, implementing demand response can achieve the same photovoltaic local consumption effect while reducing the energy storage configuration, and the life-cycle economic benefits are appreciable. The simulation results show that participating in demand response can reduce the energy storage system cost by 7.15% at a photovoltaic local consumption proportion of 60%. This research expands application channels of rural distributed photovoltaic clusters and provides references for investment and operation decisions of distributed photovoltaic energy storage systems. [ABSTRACT FROM AUTHOR]

AbstractCommunity renewable energy is gaining popularity globally, and private investment in photovoltaic is considered a promising path toward energy transition. This study attempts to explore which attributes are more attractive for the public to invest in community photovoltaics, especially in the context of the increasing popularity of photovoltaics in China. Based on a conjoint experiment with urban and rural residents (N = 973), this study finds that the public values (economic, environmental, social) generated by the photovoltaic project and the project’s attributes are important factors of investment decisions. The results of the conjoint experiment confirm previous findings in the literature, particularly the positive effect of the economic value of increased local employment on citizen investment. In addition, this study finds that government regulation plays the most critical role in facilitating citizen investment. The findings can contribute to more effective photovoltaic diffusion, transcending the role of the citizen as a single consumer and achieving substantial public participation and value co-creation. [ABSTRACT FROM AUTHOR]

To clarify the role of agricultural technological progress in the process of rural revitalization, this paper uses the agricultural panel data of 31 provinces in China from 2007 to 2020 to measure the Total Factor Productivity of agriculture, analyzes the impact direction and spatial spillover effect of agricultural technological progress on China's rural revitalization through the spatial Durbin model, and analyzes the threshold mechanism of agricultural technological progress on China's rural revitalization by using the panel threshold model. The results are as follows: (1) The spatial and geographical agglomeration of interprovincial rural revitalization in China has gradually weakened, and the regional imbalance has improved; (2) The progress in agricultural technology plays a positive role in promoting China's interprovincial rural revitalization, and the overall nonlinear characteristics of "first inhibiting and then promoting" are presented, and the conclusion is still robust after fully considering the factors of time, region and economic distance; (3) Further analysis shows that the impact of agricultural technological progress on China's interprovincial rural revitalization is based on the threshold constraints of land-scale operation and agricultural-industry agglomeration, and shows significant spatial heterogeneity. The inherent reason is that whether the land-scale operation entity adopts long-term investment decisions such as new agricultural technology depends on the expectation of land management risk stability, while agricultural-industry agglomeration hinders the diffusion and spillover of agricultural technology due to the exclusive characteristics of agricultural production geographical locations. Therefore, in the future of China's rural revitalization, the government should guide the large-scale operation of land and agricultural production according to local conditions to give full play to the positive spillover effect and spatial radiation capacity of agricultural technology. [ABSTRACT FROM AUTHOR]

Accurate stock prediction plays an important role in financial markets and can aid investors in making well-informed decisions and optimizing their investment strategies. Relationships exist among stocks in the market, leading to high correlation in their prices. Recently, several methods have been proposed to mine such relationships in order to enhance forecasting results. However, previous works have focused on exploring the correlations among stocks while neglecting the causal characteristics, thereby restricting the predictive performance. Furthermore, due to the diversity of relationships, existing methods are unable to handle both dynamic and static relationships simultaneously. To address the limitations of prior research, we introduce a novel stock trend forecasting framework capable of mining the causal relationships that affect changes in companies' stock prices and simultaneously extracts both dynamic and static features to enhance the forecasting performance. Extensive experimental results in the Chinese stock market demonstrate that the proposed framework achieves obvious improvement against multiple state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

It is not uncommon that supply chain partners carry out cooperative advertising in green production involving green and dirty products (i.e., substitute products). Besides the advertising decisions, they need to jointly make many other decisions, such as substitute products' production quantities, wholesale prices, and retail prices. Practice and literature have shown that manufacturers-retailers' joint decision-making is of paramount importance yet challenging. This decision-making difficulty is compounded by governments' carbon tax policies and financial subsidies. To facilitate firms in making decisions, this study examines the joint decision-making mechanism involving local governments' carbon taxes and subsidies. To overcome the limitations of the relevant literature addressing one product and relatively fewer decisions, we include both dirty and green products and consider diverse decisions, including technology selection, production quantities, wholesale prices, and retail prices for both products. Additionally, we consider the retailers' advertising investment decisions for both products and the manufacturers' ratios of advertising investment paid to retailers. Capitalizing on decision interactions, we develop a Stackelberg game-based bilevel optimization model. Caused by the large number of decisions and their interactions, solving the game model analytically is barely possible. Consequently, we propose an algorithm of nested particle swarm optimization (NPSO). We perform numerical examples to show how the game model and the NPSO can help firms make complex joint decisions with many interactions. We also carry out sensitivity analysis based on which managerial insights are drawn. [ABSTRACT FROM AUTHOR]

In this paper, we address the confidence of the agricultural producers in their prosperity as a driver of development. The agricultural sector development is highly dependent on short term investment decisions for sowing area, breeding, among others, which in turn is mediated by confidence. Agricultural producer confidence is continually measured in Argentina by CREA, a non-profit organisation composed of groups of producers spread across the country striving for innovation and best practices diffusion. We leverage this measurement methodology to propose a structural equation model to explain confidence by an exhaustive collection of factors affecting the business at company, sector or economy level, providing critical insight about the range of policies that would enable agriculture to grow. High regulation and tax burdens prevailing in the market during the past decades have been identified as relevant hindrance factors to confidence. On the enablers side are the respect for property rights and the organisation effectiveness actively fostered by CREA. [ABSTRACT FROM AUTHOR]

[Display omitted] • Using DEMATEL for causal relationship analysis of determinants in digital twin performance. • Synergy of Quantum picture fuzzy rough sets & Quantum theory to enhance digital twin policies for renewables. • Criteria list based on TBL technique for evaluating social, environmental, & economic impacts. • Importance of analyzing climatic conditions for risk identification & sustainable results. • Strong infrastructure & up-to-date tech crucial for accurate data analysis in digital twins. This study aims to identify optimal digital twin policies for enhancing renewable energy projects. Through a comprehensive analysis, the research evaluates the potential of digital twins in the renewable energy sector while considering triple bottom line perspectives. The study's main goal is to prioritize digital twin policies that can effectively boost renewable energy projects. The research aims to demonstrate the practical application and reliability of a proposed evaluation model. Nine criteria, derived from literature review and triple bottom line viewpoints, are selected. Using the decision-making trial and evaluation laboratory (DEMATEL) methodology and Quantum picture fuzzy rough sets, criteria weights are determined. Quantum picture fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) evaluates sustainable industrial internet of things strategies in new-gen energy investments. VIsekriterijumska optimizcija i KOmpromisno Resenje (VIKOR) methodology enables a comparative assessment, and sensitivity analysis is conducted across nine cases. Consistent outcomes across various methods validate the model's reliability. Ecosystem preservation carries the highest weight (0.1147), followed by resource policy optimization with digital twins (0.1139). Distributed energy resilience ranks first (RCi 0.576), closely followed by energy efficiency optimization (RCi 0.542). This study underscores ecosystem preservation and efficient resource policies as pivotal for successful digital twin deployment in renewable energy projects. The findings highlight digital twins' potential contribution to environmental protection and ecosystem sustainability, emphasizing resource efficiency through their effective use. [ABSTRACT FROM AUTHOR]

At Ontario International Airport (ONT), elevating the customer experience goes beyond increasing operational efficiency. By using a comprehensive, data-driven approach to creating a customer journey map, ONT gains a clear and comprehensive understanding of a passenger's experience throughout the airport. This paper identifies key problems, areas of success and opportunities for improvement, leading to informed decision making and strategic investments that elevate overall customer satisfaction. Readers will learn the purpose and application of a customer journey map, including how to identify critical touchpoints and visualise the user experience from start to finish. The paper also introduces behavioural science tools, such as retina-scanning glasses, which help benchmark current experiences and uncover gaps in service. Additionally, it highlights the role of data-driven insights in influencing future design strategies, considering spatial features, emerging trends, policy changes and technology integration. The paper describes how to analyse and prioritise recommendations across each touchpoint, fostering cross-organisational consensus and guiding strategic investments in enhancing the airport experience. This knowledge is vital for professionals looking to innovate in service and infrastructure improvements, ensuring a world-class experience for airport passengers. [ABSTRACT FROM AUTHOR]

Social costs of construction projects – systematic literature review and recommendations for action The world's population is still growing. The need for housing, educational and care facilities, logistics and infrastructure in inner‐city areas is increasing. The associated construction measures affect neighborhoods, the use of the district and urban traffic and cause social costs for the surrounding structures (so‐called social cost). However, as the focus of investment decisions by developers is currently predominantly on investment costs, social costs are rarely taken into account or internalized in construction projects. Consequently, they have to be borne by society. Studies on the social costs of construction projects already exist internationally. An up‐to‐date, holistic and systematic overview is still lacking. However, international regulations such as the Sustainable Development Goals (SDG) and the EU taxonomy (ESG criteria) as well as national regulations such as CO2 pricing and the Climate Protection Act in Germany are increasing the importance of sustainable measures in the construction process, which also influence the social costs of construction projects. In order to be able to provide an economic assessment of the consequences of these regulations, a systematic literature review of the social costs is carried out in this article. This is pursued with the aim of promoting the use of more sustainable measures in the construction process and demonstrating their added value for society in a social context. The article focuses on building construction projects. [ABSTRACT FROM AUTHOR]

Stress testing provides a powerful tool for investors to understand the implications of climate change for their portfolios. Investors and regulators are increasingly demanding that investment portfolios be stress tested rigorously for the effects of climate change. Current stress tests have generated varied economic outcomes to severe climate changes. This article adopts a simple robust framework for assessing how well stress tests capture climate-related tail risks and offers a robust tool for investors to incorporate these risks into their investment decisions. [ABSTRACT FROM AUTHOR]

The reduction in energy consumption in shipping is a crucial issue to achieve a more sustainable sector. Nevertheless, investments in energy efficiency are inhibited by barriers. Consequently, under a Principal-Agent approach, this study aims to analyze the factors affecting the investment preference for either technical or operational measures. To date, the research problem has barely been addressed from a similar approach. This work further integrates agency theory with the identification of barriers and drivers, as well as the cost–benefit ratio from both an environmental and a financial perspective. This makes it possible to consider shipping management from a more comprehensive perspective. The study sample is current and representative (658 individual bulk carriers). The research was carried out utilizing two binominal logistic models that provide similar results when testing the proposed hypotheses. The outcomes show that regulatory factors, such as the distance of a vessel's technical emissions from EEDI requirements (standardized coefficients: −2.8352 and −2.5069), and Principal-Agent problems, such as split incentives (standardized coefficients: −1.0059 and −0.9828), have the greatest influence on investment preferences. As a consequence of Principal-Agent problems, vessels operating under Time Charter contracts are less likely to invest in technical measures than in operational ones. Verified information and activity promote technical measures. Maritime regulation promotes technical measures in younger vessels, especially those meeting only the minimum requirements. Better knowledge can help achieve a more environmentally responsible shipping sector. The role of shipowners and charterers should be highlighted, and transparency should be promoted to enable well-informed decisions to be made. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Business Studies is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, due to the spike in natural gas spot prices, gas-fired power corporations' operating costs have skyrocketed. Traditional power generation corporations have gradually been withdrawing from gas power generation investment, replaced by oil and gas enterprises with upstream resources. The development of gas-fired power plants helps to maintain the stability of the power grid and has a positive effect on the realization of carbon neutrality goals. At present, most of the financial evaluation methods for gas power generation projects tend to focus on the static tariffs of the project itself and lack consideration for the overall contribution to the industry chain and the latest "gas–electricity price linkage" mechanisms in China, leading to oil and gas enterprises reducing investment in gas-fired power plants due to yield constraints. In this paper, a financial evaluation methodology for gas power generation projects based on the industrial chain and the "gas–electricity price linkage" mechanism was proposed. The investment return characteristics of specific gas power generation projects under the "gas–electricity price linkage" mechanism in different provinces were revealed through this methodology. Considering the characteristics and industrial development trends in major provinces in China, investment and operation strategies for gas power generation were proposed. These studies provide oil and gas enterprises with references and suggestions for future investment decisions for new gas power generation projects. [ABSTRACT FROM AUTHOR]

Historically, practitioners and researchers have used selected count station data and survey-based methods along with demand modeling to forecast vehicle miles traveled (VMT). While these methods may suffer from self-reporting bias or spatial and temporal constraints, the widely available connected vehicle (CV) data at 3 s fidelity, independent of any fixed sensor constraints, present a unique opportunity to complement traditional VMT estimation processes with real-world data in near real-time. This study developed scalable methodologies and analyzed 238 billion records representing 16 months of connected vehicle data from January 2022 through April 2023 for Indiana, classified as internal combustion engine (ICE), hybrid (HVs) or electric vehicles (EVs). Year-over-year comparisons showed a significant increase in EVMT (+156%) with minor growth in ICEVMT (+2%). A route-level analysis enables stakeholders to evaluate the impact of their charging infrastructure investments at the federal, state, and even local level, unbound by jurisdictional constraints. Mean and median EV trip lengths on the six longest interstate corridors showed a 7.1 and 11.5 mile increase, respectively, from April 2022 to April 2023. Although the current CV dataset does not randomly sample the full fleet of ICE, HVs, and EVs, the methodologies and visuals in this study present a framework for future evaluations of the return on charging infrastructure investments on a regular basis using real-world data from electric vehicles traversing U.S. roads. This study presents novel contributions in utilizing CV data to compute performance measures such as VMT and trip lengths by vehicle type—EV, HV, or ICE, unattainable using traditional data collection practices that cannot differentiate among vehicle types due to inherent limitations. We believe the analysis presented in this paper can serve as a framework to support dialogue between agencies and automotive Original Equipment Manufacturers in developing an unbiased framework for deriving anonymized performance measures for agencies to make informed data-driven infrastructure investment decisions to equitably serve ICE, HV, and EV users. [ABSTRACT FROM AUTHOR]

Copyright of Accounting Perspectives is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we study the decision-making and coordination problem of a two-tier fresh food supply chain consisting of a supplier and a retailer. Considering the influencing factors of consumers' information preference, freshness, and misrepresentation, we construct a centralized decision-making model and a decentralized decision-making Stackelberg game model. We also analyze the changes in the equilibrium solution of the supply chain before and after the input of blockchain technology, identify the conditions for the investment in blockchain technology, and design a "cost-sharing + benefit-sharing" combination contract for the coordination of the blockchain. The results are as follows: Firstly, under decentralized decision-making, if the fresh supplier misreports the freshness of the product, it will mislead the retailer to increase the order quantity, and its own profit will rise. Therefore, the fresh supplier has the motivation to misreport freshness. However, the backlog of fresh products will eventually damage the retailer's profit, and the overall profit of the supply chain will also be damaged. Therefore, the increase in the profit of the fresh supplier is at the expense of the overall interests and stability of the supply chain. Second, when the investment cost of blockchain technology is within a certain threshold, it is feasible to invest in blockchain technology. Consumers' preference for traceable fresh products will encourage the fresh supply chain to improve the level of information traceability and increase investment in blockchain technology. Finally, there are double marginal effects in the fresh supply chain under decentralized decision-making. The combined contract of "cost-sharing + revenue-sharing" can coordinate the overall revenue of the supply chain to the level of centralized decision-making. When the contract parameters meet certain conditions, Pareto improvement in revenue can be achieved for all parties involved in the fresh supply chain. The willingness of retailers to invest in blockchain technology will change with the change in contract parameters. When the proportion of retailers' costs and the proportion of shared income are higher, the level of retailers' investment in blockchain technology will decrease. Therefore, the interests of supply chain members need to be balanced in the process of contract coordination. [ABSTRACT FROM AUTHOR]

The assumption that better institutions are favoured in multinational enterprises' (MNEs) location choices has been questioned in terms of whether emerging market MNEs might have different capabilities associated with the institutional conditions in their home country that they may exploit when internationalizing. They might seek to expand both to better institutional environments (institutional substitution) and to other emerging market environments (institutional complementarity). We examine if elements of institutional complementarity and substitution are evident in the internationalization decisions of Indian MNEs, and whether there are boundary conditions or limits attached to the benefits of these two effects. Our arguments are tested on a sample of Indian MNEs' cross-border acquisitions between 2002 and 2021. The study differentiates between institutional distance effects in terms of both magnitude and direction, and institutional quality. We raise the notion of "institutional ranges or thresholds" (different points on the institutional profile distribution representing varying levels of institutional quality) and explicitly seek to identify such thresholds where the effects of institutional complementarity and substitution may set in and/or disappear, and why that may be the case. [ABSTRACT FROM AUTHOR]

The editorial in the IPPR Progressive Review discusses the importance of transport on both personal and national levels. It highlights the disparities in the current transport system, which often favors commuters and business travelers, leading to limited access for others. The article emphasizes the need for a new approach to transport decision-making that aligns public money with a vision for future transport to improve quality of life, local prosperity, and the environment. Various experts offer diverse perspectives on transport issues, from decarbonization to transport inequity, aiming to inspire policymakers to create a more inclusive, healthy, and green transport system. [Extracted from the article]

This study investigates the financial behavior of Thai investors in equity crowdfunding (ECF), focusing on the factors that influence their investment intentions. Drawing upon the Information System Success Model (ISSM), the Theory of Diffusion of Innovations, and the Technology Acceptance Model 3 (TAM3), the research examines the platform quality (PQ), platform characteristics (PC), and social influence (SI) as independent variables, with the perceived usefulness (PU) and perceived ease of use (PEOU) acting as mediators. Data were gathered from 275 Thai investors and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that the PU significantly influences investment decisions both directly and indirectly through the PEOU, which also directly affects investment intention. Furthermore, SI, PC, and PQ have indirect effects on investment decisions via the PU and PEOU, with SI being the most influential factor. This study provides valuable insights into optimizing ECF platform design, fostering investor trust, and enhancing regulatory frameworks to facilitate financial inclusion and innovation in the Thai crowdfunding landscape. [ABSTRACT FROM AUTHOR]

This study investigates the impact of CEO characteristics—specifically CEO age, founder status, and family membership—on investment efficiency in Jordanian non-financial companies, with a focus on the moderating role of political connections. Drawing on the existing literature, we identify conflicting views regarding how these characteristics influence investment decisions. Some studies suggest that younger CEOs may adopt more aggressive investment strategies, while older CEOs tend to be conservative, leading to balanced resource allocation. Similarly, CEOs with founder status and family membership are thought to have an emotional attachment to the company, theoretically resulting in cautious investment behavior. However, empirical evidence remains mixed. By using data from 62 non-financial firms listed on the Amman Stock Exchange (ASE) from 2019 to 2023, this study employs regression analysis to explore these relationships. The findings reveal that CEO age contributes to investment efficiency by mitigating both over- and under-investment. Contrary to expectations, CEO founder status shows no significant effect on investment efficiency. Additionally, family-member CEOs exhibit a tendency toward under-investment, driven by a desire to preserve family wealth. Political connections further complicate these dynamics, encouraging riskier investment strategies while diluting the positive effects of CEO characteristics. These results provide new insights into the intricate interplay between CEO traits and political networks, contributing to the discourse on corporate governance in emerging markets. The study concludes with practical implications for policymakers and company boards, emphasizing the need for balanced leadership selection strategies to optimize investment efficiency. [ABSTRACT FROM AUTHOR]

The 1/N investment strategy, characterized by equally allocating wealth among available investment options, has garnered significant scholarly attention. Simultaneously, risk assessment and management play a critical role in financial decision-making, leading to the development of a diverse array of risk measure models. This paper aims to synthesize these two strategies and propose a novel approach for constructing a naive diversification strategy by incorporating commonly employed risk measures in financial analysis. The research involves an in-depth exploration of various risk measures utilized by financial professionals to enhance effective risk management. These measures include Mean-Variance (MV), Mean Absolute Deviation (MAD), Semi Standard Deviation (MSV), Value at Risk (VaR), Conditional Value at Risk (CVaR), Entropic Value at Risk (EVaR), Drawdown at Risk (DaR) of uncompounded cumulative returns, Conditional Drawdown at Risk (CDaR) of uncompounded cumulative returns, and Entropic Drawdown at Risk (EDaR) of uncompounded cumulative returns. To validate the efficacy of the proposed model, a real-world empirical case study utilizing the annual financial statements of the NASDAQ-100 (NDX) database is conducted. The empirical findings from this study carry practical implications for investors and risk managers engaged in portfolio management. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between neuroticism and panic-selling behavior among investors, particularly during market downturns. Building on the theoretical framework of behavioral finance, we hypothesize that higher levels of neuroticism are positively associated with an increased likelihood of panic selling. The data for this research were derived from a comprehensive survey titled Survey on Life and Money, which was conducted by Rakuten Securities in collaboration with Hiroshima University in November and December 2023, with a total sample size of 189,524 participants. Our results reveal that 9.46% of the respondents fully or partially panic-sold their stocks during market volatility. Additionally, the respondents demonstrated a tendency toward neuroticism, with an average score of 2.95 out of 5 on the neuroticism scale. Using a probit regression analysis, we examined the dependent variable of panic selling in relation to neuroticism as the independent variable, controlling for various demographic, socioeconomic, and behavioral characteristics. Our findings robustly support this hypothesis, indicating that individuals with higher neuroticism scores are more likely to engage in panic selling during periods of market volatility, with significance at the 5% level. This study contributes to the behavioral finance literature by highlighting the significant role of personality traits in investment decision making and underscores the importance of understanding investor psychology in financial markets. This study emphasizes the need for a nuanced understanding of how individual psychological factors, particularly neuroticism, drive market behavior and influence broader economic stability. [ABSTRACT FROM AUTHOR]

The study examines links between automated advisory services and irrational individual investors' behaviour based on dual-process theory. The study contributes to the literature on active vs. passive digital nudges in robo-advisors and the disposition effect. Self-reporting online experiment revealed that robo-advisors help investors to resolve cognitive dissonance, with passive robo-advisors being more efficient. We contribute by introducing and indicating the moderating role of financial literacy. Though moderating feminine gender role was not supported, the need for customized automated advisory services is highlighted. The results are important for individual welfare, as demographic shifts and evolving retirement systems transfer investment decisions to individuals. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is transforming entire industries at an unprecedented pace. Yet, established technology adoption theories offer limited tools for characterizing business AI integration and analyzing its effects. These primarily focus on the factors facilitating or hindering adoption, rather than on adoption patterns and impacts. This paper introduces a novel conceptual framework to address this key gap and applies it to the case of the ESG rating industry. ESG raters play a pivotal role in sustainable finance, providing metrics that guide investment decisions globally. However, little is known about the extent and nature of their AI usage and its implications. Through a mixed‐methods approach combining the analysis of job postings, patent filings, research publications, and corporate websites, we examine AI adoption among major ESG raters. Our investigation explores the specific AI technologies employed, their functional applications, the innovations developed, the intensity of AI integration, and the potential impacts of raters' AI adoption. Our results reveal widespread and growing AI adoption across the industry. Our findings show that raters extensively leverage Natural Language Processing to streamline data collection, processing, and analysis. Furthermore, they have pioneered Machine Learning innovations that significantly expand their sustainability assessment capabilities in various domains. These findings mark a considerable departure from prior academic and gray literature that characterized major ESG raters as having minimal AI use, prompting critical questions regarding the implications of this technological transformation for ESG ratings' reliability, transparency, and potential biases. [ABSTRACT FROM AUTHOR]

Reduced-impact logging is a key aspect of sustainable forest management in the Brazilian Amazon. Real Options Analysis (ROA) improves the evaluation of certified projects, increases their value, and supports investor confidence. This study evaluates the economic viability of a forest management investment project in certified areas of the Brazilian Amazon, using ROA to incorporate uncertainty and managerial flexibility into the decision-making process. The analysis focused on an Ombrophilous Dense Forest in its first cutting cycle, with 30 species of diameter at breast height ≥ 50 cm and a projected activity period of 30 years. Timber value was modeled using the mean reversion motion, and a binomial decision model was applied, incorporating options such as deferral, abandonment, and interruption. The deferral option should be exercised with a 100% probability, both in the joint calculation scenario and in separate analyses, highlighting the significant value of this managerial flexibility. In addition, when analyzed using ROA, the value of the project increased by 105% (USD 91,784,176) compared to the traditional net present value approach. The forest management investment project in the Brazilian Amazon is economically viable and the application of ROA reveals significant value added, demonstrating the importance of incorporating flexibility into investment decisions. [ABSTRACT FROM AUTHOR]