Search Results (341)

Software startups operate in environments characterized by rapid change, high uncertainty, and limited resources, resulting in high failure rates and challenges such as premature scaling and cash flow mismanagement. Prior research on pre-MVP validation has largely measured activity by volume rather than by the strength of evidence produced, leaving open whether evidence type, rather than quantity, is associated with startup performance. This study addresses that gap by investigating how early-stage software startups validate their initial idea before building their first Minimum Viable Product (MVP). Through 29 semi-structured interviews with founders from 16 software startups, pre-MVP validation activities were extracted and inductively coded into a six-level Validation Canvas spanning three validation stages identified in the literature: problem validation, problem-solution fit, and product-market fit. Startup performance was assessed through a composite ranking across funding, revenue, profitability, and runway indicators, and validation activities were analyzed thematically to derive the six evidence levels. No clear relationship was observed between the number of validation events and startup performance. Instead, stronger-performing startups tended to reach higher levels of evidence—particularly securing contingent investment commitments (Level 5) or paying customers (Level 6) before full MVP development. Level 6—paying customers before the full product exists—is identified as the strongest form of pre-MVP market evidence, as it directly validates willingness-to-pay without relying on investor confidence. In this study, product-market fit is operationalised as demonstrated commercial viability through external financial commitments rather than interest signals or free sign-ups alone. Based on these exploratory findings, the study proposes the Hierarchy of Validation: a staged, bidirectional process model in which bottom-up traversal from informal interest signals (L1) toward paying customers (L6) emerged as the primary pattern among stronger-performing startups. A top-down direction, in which experienced founders begin at higher evidence levels and work downward, is proposed as a hypothesis for future research. To our knowledge, this is among the first accounts of pre-MVP validation that differentiates strength of evidence rather than volume of activity, contributing the Hierarchy of Validation as an original, exploratory framework for early-stage software startups. These findings remain exploratory and require validation in larger and more diverse samples. Full article

Green mergers and acquisitions (M&A) may enable firms to acquire external environmental technologies, assets, and organizational capabilities. However, whether green M&A is associated with subsequent green innovation, how this association evolves across innovation types and time horizons, and whether prior carbon information disclosure conditions this process remain unclear. Using 40,923 firm year observations of Chinese A-share-listed firms from 2012 to 2024, this study combines licensed green M&A data from Zhixing Data Analytics, green patent data from CNRDS, and carbon disclosure, financial, and corporate governance data from CSMAR. The baseline treatment identifies firm years in which at least one green M&A transaction first announced during the year was subsequently recorded as completed. The analysis employs firm and year fixed-effects models, common-sample distributed-lag specifications, formal cross-type coefficient comparisons, forward-outcome tests, propensity score matching, entropy balancing, and alternative measures and specifications. Green M&A is positively associated with total green patenting. The baseline coefficient of 0.045 implies an approximately 4.65% increase in one plus the number of total green patent applications. The contemporaneous association is stronger for green utility model patenting than for green invention patenting, whereas the association with invention patenting becomes more evident in subsequent periods. Prior carbon information disclosure positively moderates the association between green M&A and one-year-ahead invention patenting (β = 0.220, p = 0.018), while the corresponding moderation estimates for total and utility model patenting are not statistically significant. The findings are supported by observable selection adjustments and several alternative measurements and specifications, although fixed-effects PPML estimates using the original patent counts are not statistically significant. This study provides an integrated framework for understanding how innovation timing and prior information governance shape the green M&A dilemma. The results suggest that regulators and investors should assess green acquisitions using credible pre-acquisition carbon disclosure and post-acquisition innovation trajectories rather than relying on environmental transaction labels alone. Full article

Corporate net-zero pledges have proliferated across the Middle East, yet whether capital markets distinguish credible decarbonization strategies from symbolic ones remains unexamined in energy-exporting emerging markets, a gap this study addresses. This omission matters because the six Gulf Cooperation Council (GCC) economies in our sample face acute stranded-asset exposure amid national diversification programmes such as Saudi Arabia’s Vision 2030 and the UAE’s Operation 300bn. We test whether net-zero credibility is associated with firm valuation among 167 listed firms across seven Middle Eastern countries (2020–2025, 1002 firm-year observations), constructing a five-dimension Credibility Index (pathway specificity, capex alignment, verification, policy compliance, and track record) and applying event-study, pooled panel regression, mediation, moderation, and firm/year fixed-effects analysis. The index is internally consistent (Cronbach’s α = 0.92) and its estimated valuation association is invariant to equal, alternative and leave-one-out weighting schemes. Credibility is positively associated with all four valuation multiples (p < 0.001, firm-clustered standard errors); a one-standard-deviation increase corresponds to a 0.133 increase in Tobin’s Q (95% CI [0.116, 0.149]). High-credibility announcements earn cumulative abnormal returns 5.95 percentage points above low-credibility announcements over (−5, +5) trading days (95% CI [4.79, 7.11]), a gap robust to nonparametric tests, placebo dates and strictly pre-announcement credibility scoring. The association survives firm and year fixed effects (within-firm β = 0.119 per SD, p < 0.001). No moderator—foreign ESG fund, domestic institutional or sovereign wealth fund ownership, regulatory stringency, carbon intensity or financial health—reaches significance after correction for multiple testing, so we find no evidence of investor-type or jurisdictional heterogeneity in this sample. To the authors’ knowledge, this is the first Middle East-calibrated, capex-verified credibility index and the first evidence on the pricing of transition authenticity across GCC markets and Egypt, informing regulators, boards, and ESG investors. Full article

This paper assesses the impact of uncertainty for investors on the economic valuation of medical innovation projects for orphan drugs or rare diseases. Conventionally, investor evaluation uses the deterministic discounted cash flow (DCF) method with an appropriate sensitivity analysis that captures some level of uncertainty. In healthcare, and particularly for rare diseases, the levels of uncertainty in financial outcomes (return on investment and net present value (NPV)) are broader than the ones normally captured by the DCF formula. Uncertainties include R&D costs, the approval process (level and timing) for obtaining reimbursement, sales, the production cost, and the failure probabilities of the clinical trial phases, to name a few. Additionally, there is not only one type of investor to consider, but different investors exposed to different levels of risk management of their investment. Our analysis tried to capture those two dilemmas (higher levels of uncertainty and different investor types) in two ways. One way was to identify a better method to enhance the different levels of uncertainty. The real option method of evaluation was proposed instead of DCF. For instance, the real option method better captures the uncertainty of the different phases of product development. The other way is to differentiate the investor types through their level of risk assessment perspectives. Small investors and start-up companies may see more benefit in applying the real option methodology to estimate their NPVs at different time points during product development. In summary, our evaluation identified various types of uncertainty when assessing an investment, along with methods to manage their effect on the economic/financial outcomes of medical innovations. Given the high uncertainty associated with early-stage drug development, such as orphan drugs for rare diseases, the real options approach is preferable to traditional DCF models. The analysis also showed that there is not just a single investor perspective to consider but specific perspectives that enhance the prime use of the real option methodology. Full article

As financial markets are characterized by the increasing incidence and complexity of fraudulent activities, generating substantial losses for companies while posing serious risks to potential investors, financial fraud is a constantly debated issue, particularly with regard to its prevention and detection. As classical methods for detecting financial fraud have proven inefficient and time-consuming, many researchers have turned to artificial-intelligence-based methods. The use of AI-based methods for fraud detection is currently a widely debated topic, particularly regarding fraudulent financial statements. This study aims to determine the extent to which fuzzy logic contributes to financial statement fraud risk assessment. The target population comprises Romanian companies listed on the Bucharest Stock Exchange. Following analysis, a sample of 62 listed companies was selected. The analysis covers the last seven completed financial years (2018–2024). The dependent variable is represented by financial statement fraud risk (FSF), measured using the modified F-score model, under the influence of some independent variables that are defined by a series of financial ratios: return on assets, return on equity, net profit margin, leverage and working capital. Additionally, taking into account the audit opinion type and advanced statistical methods for data analysis, the research results demonstrated the importance of using fuzzy logic in fraud risk assessment by improving the process of detecting fraud in financial statements, based on specific financial ratios and statistical associations between these ratios, as fuzzy rules. Full article

Property condition assessment is a critical step in residential real estate investment underwriting, motivating after-repair value (ARV) estimates and rehabilitation cost projections. Traditional approaches rely on in-person inspections or manual photo review by experienced investors—processes that are time-consuming, subjective, and do not scale. Prior computer vision work on building analysis has focused on structural defect detection using convolutional neural networks but has not addressed the holistic, room-level condition assessment needed for residential investment decision-making. This paper presents a per-room analysis framework that leverages multimodal large language models (MLLMs) to assess the condition of residential properties from photographs. The framework analyzes each photo independently at the room level—detecting the room type, condition category, condition score, material features, and visible issues. Condition output is intended to feed a separate downstream rehabilitation cost and ARV estimation model that is outside the scope of this paper; the present empirical evaluation is restricted to per-photo condition assessment and inter-rater agreement with human experts. I evaluate the framework on two complementary datasets: (i) a primary per-image condition evaluation on 57 photographs from 14 real off-market properties in the Memphis, TN MSA, spanning three condition tiers (Fixer, Outdated, Standard), with independent labels from two experienced real estate investors; (ii) a secondary room classification evaluation on the public REI Dataset (51 attempted, 39 successful, 12 HTTP-503 failures). The room classification accuracy was 76.5% intention-to-analyze on REI (100% per-protocol on the 39 successful calls; 23.5% API failure rate) and 82.5% on the concierge dataset. The inter-rater agreement on the concierge dataset, with 95% bootstrap CIs (5000 resamples) and Spearman’s $\rho$ as primary score statistic, was as follows: Cohen’s $\kappa =0.773$ ($95\%$ CI $[0.64,0.90]$) between Labeler A and the MLLM (weighted $\kappa =0.853$ $[0.76,0.94]$; $\rho =0.906$); and $\kappa =0.502$ $[0.35,0.66]$ between Labeler B and the MLLM ($\rho =0.858$); both bracket the human–human reliability of $\kappa =0.590$ $[0.42,0.74]$ ($\rho =0.807$). The MLLM’s $\kappa$ asymmetry across the two labelers is statistically significant ($\Delta \kappa =0.271$, $95\%$ bootstrap CI $[0.115,0.429]$, $p=0.0004$), which I attribute to plausible training distribution and labeling style differences. A blind re-labeling sensitivity analysis on a stratified 15-image subsample yields anchoring-corrected $\kappa$ estimates of approximately $0.65$ (Labeler A) and $0.35$ (Labeler B); the headline anchored values therefore sit at the upper bound of plausible blind-equivalent agreement. Failure modes concentrate at the Outdated tier and at the Outdated–Standard boundary, where humans themselves disagree most, indicating intrinsic taxonomy ambiguity rather than a model artifact. I make no claim to multi-market generalization and present multi-market extension as ongoing work. Full article

Driven by “dual-carbon” strategic goals, green innovation is one of the main directions for China’s economic transformation and upgrading. Shareholders are the main decision-makers of enterprises, exerting considerable influence on enterprises’ green transformation. How ownership concentration, as the main indicator of shareholders’ power structure, affects the green innovation performance of “Specialized, Refined, Differentiated, and Innovative” (SRDI) enterprises is a critical question that remains to be addressed. Based on the principal-agent theory, this study takes into account the two main kinds of agency conflicts from the principal-agent theory. Panel data on the listed SRDI companies from 2019 to 2024 are adopted. The study empirically examines the effect of ownership concentration on the enterprises’ green innovation performance through a Poisson regression model and further examines the moderating effect of institutional investors on the relationship between ownership concentration and their green innovation performance. The results show that ownership concentration has a significant promoting effect on the green innovation performance of SRDI enterprises. Institutional investors positively moderate this relationship. Heterogeneity analysis shows that the effect of ownership concentration on promoting the green innovation performance of the enterprise is larger in small-scale companies and smaller in large-scale ones. This research reveals the facilitating effect of ownership concentration on green innovation performance of SRDI enterprises and the boundary condition, providing a theoretical basis and practical guidance for optimizing equity structure and enhancing enterprises’ innovation capability from the two types of agency problems. Full article

Sustainable innovation constitutes the cornerstone of firms’ long-term competitive edge, yet the underlying mechanisms via which patient capital facilitates corporate sustainable innovation remain understudied. Based on a sample of Chinese A-share listed firms spanning 2013 to 2024, this study operationalizes patient capital through two proxies: relational debt and stable institutional ownership. We systematically investigate the impact of patient capital on sustainable innovation, alongside the mediating pathway of internal control quality and the moderating role of climate policy uncertainty. The empirical outcomes indicate that both forms of patient capital exert a significant positive effect on sustainable innovation, with internal control quality serving as a partial mediator in this relationship. Additionally, climate policy uncertainty reinforces the promotional influence of patient capital on sustainable innovation. We further stratify heterogeneity analyses into two dimensions: firm-inherent heterogeneity and external environmental heterogeneity. From the perspective of endogenous firm attributes, the innovation-stimulating effect of patient capital differs markedly across enterprises with distinct ownership types, life-cycle stages, and total asset sizes. Externally, the observed positive impact varies considerably conditional on industrial factor intensity and the regional marketization degree of the firm’s location. These findings expand the existing literature concerning long-term capital and sustainable innovation, and yield actionable implications for corporate management, institutional investors, and policymakers. Full article

The present contribution provides a systematic review of the international scientific literature on the relationship between contaminated sites and real estate market dynamics. The objective is to investigate whether and to what extent the presence of environmental risk sources—both active or decommissioned—affects the value of surrounding residential properties. In particular, the review is focused on an examination of the methods commonly used in relevant studies to measure, interpret, and represent this impact across different geographical contexts, identifying the main magnitude ranges found in the selected contributions. Several studies consistently confirm a statistically significant negative relationship between proximity to polluting sites and real estate values, although the relevance of this effect varies considerably across case studies. Other records highlight non-notable impacts or even positive effects following remediation and redevelopment interventions. The evidence suggests that this relationship is complex and influenced by factors such as site type, contamination severity, specificities of the local urban context and community perception. Moreover, the findings underscore regional variations in the extent and nature of price impacts, reflecting diverse regulatory frameworks and remediation efforts. The outcomes of the literature review provide a robust foundation for developing more effective evaluation tools able to support decision-making processes, enabling policymakers, planners, and investors to promote sustainable urban regeneration, improve environmental justice, and reduce spatial inequalities. Ultimately, this study highlights the critical need for integrating environmental, social, and economic dimensions to fully capture the multifaceted effects of contaminated sites on property markets, thereby orienting more informed and equitable urban development strategies worldwide. Full article

Flooding poses a significant threat to commercial real estate investment, disrupting business operations, escalating maintenance costs, and heightening investment uncertainty, particularly in coastal and low-lying urban environments. This study examines the social dimensions of climate vulnerability by investigating how flood risk shapes stakeholders’ decisions to invest in commercial properties within flood-prone urban areas, with a focus on Lekki Phase 1, Lagos, Nigeria. A quantitative survey design was adopted. Data were collected from 87 commercial property investors through a structured questionnaire (FIIFRZQ) measured on a four-point Likert-type scale. The instrument demonstrated acceptable overall internal consistency (Cronbach’s α = 0.72), with subscale α values ranging from 0.62 to 0.81. Multiple regression analysis was used to assess the joint and individual contributions of seven factor categories (environmental, legal, economic, neighbourhood, structural, locational and behavioural) to investors’ willingness to invest in commercial property that is at risk of flooding. The seven predictors collectively explained 61.2% of the variance in investment willingness (R² = 0.612; F(7, 79) = 17.91; p < 0.001). Five factors, namely legal, environmental, structural, economic, and locational, were statistically significant contributors to investment willingness, while neighbourhood and behavioural factors were not. Johnson’s relative weights analysis confirmed legal and environmental considerations as the dominant drivers. The findings illuminate the interplay between climate vulnerability and investor behaviour in urban real estate markets, with actionable implications for policymakers, real estate practitioners, and investors navigating decision-making in flood-exposed urban environments. Full article

This study examines how assurance level and assurer type jointly influence non-professional investors’ reactions to sustainability disclosures in an emerging market context. It employs a controlled 2 × 2 mixed-design experiment that manipulates assurance level (limited vs. reasonable) and assurer type (audit firm vs. non-audit firm). Data were collected from MBA and DBA students in Egypt as proxies for non-professional investors. Investor reaction is captured through multiple measures, including perceived sustainability performance, reliance on sustainability information, investment intention, stock valuation, and decision confidence. Non-parametric statistical techniques are used to test hypotheses, complemented by exploratory machine learning using SHAP values. The results provide strong and consistent evidence that the assurance level is the dominant factor shaping investor reactions. Reasonable assurance significantly enhances investor judgments across all key measures, whereas the type of assurer does not have a statistically significant independent effect. Additional analyses reveal that reasonable assurance from a non-audit firm elicits more favorable reactions than limited assurance from an audit firm, underscoring the primacy of assurance strength over provider identity. Exploratory findings further indicate that assurance influences investment decisions primarily through perceived sustainability performance and reliance on information. This study contributes to the literature by clarifying the relative roles of assurance level and assurer type and providing novel evidence from an emerging market setting (i.e., Egypt). The findings offer important implications for firms, assurance providers, and regulators seeking to enhance the credibility and decision usefulness of sustainability reporting. Full article

This study examines the relationship between Environmental, Social, and Governance (ESG) performance and financing and pricing outcomes in green and conventional bond markets in China over the period of 2017–2024. Drawing on signaling theory, information asymmetry theory, and market segmentation theory, the study argues that the role of ESG performance differs across bond types because green and conventional bonds operate within different institutional and informational environments. Using a comparative analysis of green and conventional bonds, the findings show that ESG performance is more strongly and consistently associated with conventional bond characteristics, particularly in relation to issuance amount, yield to maturity, and credit spreads. In contrast, ESG effects in green bonds are weaker and less consistent, suggesting that investors place greater emphasis on certification mechanisms, environmental project objectives, and sustainability-related bond characteristics than on broader issuer-level ESG disclosures. The findings also suggest that ESG information does not affect all debt instruments in the same way or always functions as a purely risk-reducing signal. In the Chinese market, stronger ESG exposure may also be associated with transition risks, regulatory pressures, and sector-specific sustainability challenges, particularly in conventional bond markets. Overall, the results indicate that the financial relevance of ESG performance depends not only on firm characteristics but also on the institutional and informational environment of the financial instrument itself. The findings remain robust across alternative model specifications and sensitivity analyses, providing additional confidence in the reported differences between green and conventional bond markets. The study contributes to the sustainable finance literature by showing that the pricing relevance of ESG information is instrument-specific rather than uniform across debt markets. It also provides practical implications for regulators, investors, and issuers by highlighting the importance of disclosure quality, transparency standards, and external verification mechanisms in strengthening investor confidence and reducing potential greenwashing risks in sustainable finance markets. Full article

Generative artificial intelligence (GAI) is increasingly embedded in personal finance, yet little is known about how models make recommendations using financial information and demographic cues. This study audits three frontier GAI models, GPT 5.5, Gemini 3.1 Pro, and Claude Opus 4.7, using a conjoint experiment in which each model evaluated the same hypothetical investor profiles and selected among standardized conservative, balanced, and aggressive portfolios. Investor profiles systematically varied attributes, including risk tolerance, time horizon, goal type, income, and age, gender, ethnicity, marital status, and employment type. Ordered logistic regressions and matched-profile comparisons show that all three models base recommendations primarily on financial attributes, especially risk tolerance and time horizon. Age and marital status shift recommendations towards conservatism in all models, conversely only Claude conditions on gender and employment type. Ethnicity exerts no detectable influence on the recommendations of ChatGPT or Claude, but is a small, statistically significant predictor for Gemini, with non-White profiles receiving slightly more conservative recommendations than otherwise identical White profiles. Overall, we find that the models are not interchangeable: they differ significantly in overall risk appetite and in how they translate risk tolerance, time horizon, goal type, and age into portfolio choices, with economically meaningful differences in predicted recommendations for identical clients. These findings suggest that contemporary GAI investment advice is driven mainly by financially relevant attributes, but that demographic sensitivity may appear in model-specific and statistically nuanced ways, alongside a distinct form of platform risk arising from model-specific advisory logic. Full article

This study examines the role of cadastral data quality in shaping risks within investment processes, focusing on the Polish Land and Buildings Register as a key information source for surveying, planning, and design activities. The research is based on an empirical analysis of cadastral data from twelve study areas representing different settlement types, using datasets obtained in 2022 and 2026. Data quality was evaluated in terms of completeness, accuracy, and currency, with particular emphasis on boundary points, land use classifications, and building records. The results reveal significant spatial disparities, with higher data quality observed in large urban areas and lower quality in smaller towns and rural municipalities, where incomplete, inconsistent, or data derived from raster maps are more prevalent. At the same time, a noticeable improvement in data quality was identified over the analysed period, particularly in less urbanised areas. Despite this progress, inconsistencies in attribute definitions, limited standardisation, and gaps in data documentation persist, reducing reliability and interoperability. The findings demonstrate that inadequate cadastral data quality generates legal, technical, temporal, and financial risks, primarily manifested in delays and increased costs due to additional surveying work. A plan has been drawn up to optimise the investor’s activities, taking into account the risks identified during the research. Full article

This study explores the informativeness of forward-looking disclosures in managers’ speeches in U.S. quarterly earnings conference calls, focusing on time-frame specificity—whether statements provide precise temporal horizons. Using a keyword search, forward-looking statements (FLSs) in managers’ speeches in U.S. quarterly earnings conference calls are classified into those with and without specific time frames, and tests of their determinants, market responses, and implications for firms’ future performance are conducted. First, uncertainty is positively associated only with FLSs without time frames, likely because managers find it more difficult to specify time frames under uncertainty or are less willing to be held accountable. Second, investors respond more quickly to FLSs with time frames and more slowly to those without, while analysts use both types to improve forecasts; however, FLSs without time frames increase forecast dispersion, whereas those with time frames reduce it, suggesting greater information processing difficulty. Third, larger changes in future earnings and discretionary accruals are associated with more FLSs without time frames, while capital investment increases only with more FLSs with time frames. Collectively, these findings indicate that time-frame specificity conveys differential informational value. Full article