Search Results (401)

The rise of fintech has disrupted traditional financial services, prompting banks and asset managers to respond strategically, often through mergers and acquisitions. This study investigates the short-term market reaction to M&A announcements involving fintech targets by incumbent financial institutions. Using an event study methodology centered on different event windows and cumulative abnormal returns computed via the market model, the analysis incorporates regression models with bidder-, deal-, and target-level variables to identify the drivers of performance. The results show that, on average, financial institutions experience negative abnormal returns around announcement dates, suggesting limited short-term value creation. Higher market-to-book ratios and tax rates are positively associated with CARs, while lower profit margins are linked to better market reactions. Subsample comparisons reveal that U.S. acquirers underperform their European peers, commercial banks fare worse than asset managers and investment banks, and pre-COVID-19 deals yield more favorable returns than post-COVID-19 ones. Robustness checks using different market benchmarks demonstrate that key patterns—especially those related to geography and timing—are sensitive to benchmark selection. Overall, this study highlights market skepticism toward fintech acquisitions by traditional financial institutions, particularly in specific contexts, and emphasizes the importance of controlling for structural factors when interpreting abnormal returns. Full article

This study aims to investigate the impact of digital transformation on corporate tax avoidance. In fact, this revolution has pervasively affected firms in different aspects and represents a significant opportunity to modernize their internal processes, bringing alongside a set of challenges that they must overcome. One hypothesis posits that digitalization enhances information transparency and internal control, reducing tax avoidance, while the other one suggests that the increase in digitalization leads to more complex and opaque transactions, leaving avenues for more aggressive tax strategies. This paper uses data of listed firms in the Casablanca Stock Exchange from 2020 to 2024, excluding the financial sector due to its specific tax regulation, leaving a final sample of 56 companies and 272 firm-year observations. It applies an OLS regression to assess the relation between the two variables, controlling for a set of firm and governance characteristics. The aim of the article is to address the scholarly debate by providing insights into an emerging economy where there is little research on the subject. The findings reveal that digital transformation contributes to the decrease in corporate tax avoidance in conjunction with governance variables like the presence of independent directors on the board and the duality of a CEO position, strongly supporting the first hypothesis. Notably, the OLS regression results show that an increase in digitalization by 1 point is associated with a decrease of 40.4755 in the book-tax differences, significant at the 5% level. The results provide high support for firms to invest in technologies in order to optimize their internal processes and improve their data quality; it also calls for tax authorities to strengthen their digital audit capacities and integrate data-driven tools to detect and interpret signals of potential tax-aggressive strategies. Full article

In the digitally saturated hospitality environment, research on digital transformation remains dominated by macro-level adoption trends and user-generated content, while the potential of micro-level web-behavioural data remains largely untapped. Recent systematic reviews highlight a fragmented body of literature and note that hospitality studies seldom address first-party behavioural data or big-data analytics capabilities. To address this gap, we collected clickstream, navigation and booking-funnel data from five luxury hotels in the Mediterranean and employed big-data analytics integrated with simulation modelling—specifically fuzzy cognitive mapping (FCM)—to model causal relationships among digital touchpoints, managerial actions and customer outcomes. FCM is a robust simulation tool that captures stakeholder knowledge and causal influences across complex systems. Using a case-study methodology, we show that first-party behavioural data enable real-time insights, support knowledge-based decision-making and drive digital service innovation. Across a 12-month panel, visitor volume was strongly associated with search traffic and social traffic, with the total-visitors model explaining 99.8% of variance. Our findings extend digital-transformation models by embedding micro-level behavioural data flows and simulation modelling. Practically, this study offers a replicable framework that helps managers integrate web-analytics into decision-making and customer-centric innovation. Overall, embedding micro-level web-behavioural analytics within an FCM framework yields a decision-ready, replicable pipeline that translates behavioural evidence into high-leverage managerial interventions. Full article

This study investigates the relationship between corporate reporting practices and the value relevance of accounting information by analyzing 100 publicly listed non-financial European firms between 2015 and 2019. Drawing on the Ohlson valuation framework, the analysis combines random effects with Driscoll–Kraay standard errors and System GMM estimations to assess the role of financial and non-financial disclosures. Materiality and stakeholder engagement were scored through content analysis of corporate reports, while ESG performance data were obtained from Refinitiv Eikon. The results show that financial fundamentals remain the most robust determinants of firm value, consistent with Ohlson’s model. Among qualitative disclosures, materiality demonstrates a strong and statistically significant positive association with market value in the random effects specification, while stakeholder engagement and ESG scores do not attain statistical significance. In the dynamic panel model, lagged market value is highly significant, confirming the persistence of valuation, while the effect of materiality and stakeholder engagement diminishes. Interaction models further indicate that materiality strengthens the relevance of earnings but reduces the role of book value, underscoring its selective contribution. Overall, the findings provide partial support for the claim that Integrated Reporting enhances the value relevance of accounting information. It suggests that the usefulness of IR depends less on adoption per se and more on the quality and substance of disclosures, particularly the integration of financial material ESG issues into corporate reporting. This highlights IR’s potential to improve transparency, accountability, and investor decision making, thereby contributing to more effective capital market outcomes. Full article

This article examines the creation of Dr. John E. Esslemont’s seminal work Bahá’u’lláh and the New Era (1923), the first comprehensive introductory book in English on the Bahá’í Faith. Drawing particularly on the extensive correspondence between Esslemont and Luṭfu’lláh Ḥakím, the article traces Esslemont’s journey from his initial encounter with the Bahá’í teachings in 1914 to the book’s publication and subsequent global impact. The unique involvement of ‘Abdu’l-Bahá and Shoghi Effendi in reviewing and correcting the manuscript is highlighted, along with Esslemont’s collaboration with prominent early Bahá’ís. Rather than specifically addressing the book’s content, this paper examines its preparation and publication, its rapid translation and worldwide dissemination in multiple languages. Finally, Esslemont’s legacy is considered, both through his book and his personal example as a pioneering Western adherent of the Bahá’í Faith. Esslemont’s original spelling of Bahá’í names and terminology in his correspondence has been maintained. Full article

Developing secure and well-structured web applications using Spring Boot presents a significant challenge, as it requires developers to manage multiple layers, employ framework-specific annotations, and ensure authentication, authorization, and compliance with architectural standards. These complexities often lead to errors among students and novice developers. Although current low-code platforms reduce coding effort, they frequently compromise clarity, modularity, and maintainability. This paper introduces XBoot, a lightweight framework that utilizes a straightforward XML-based domain-specific language (DSL) to automatically generate modular and secure Spring Boot applications. By providing concise specifications for entities, services, routes, and user roles, XBoot generates database entities, service classes, controllers, user interface templates, and integrated security rules. Validation rules are directly enforced from the DSL, and built-in Swagger documentation facilitates interactive API testing. The evaluation was conducted in two phases. Initially, XBoot was validated by generating applications for student–course and flight-booking domains, where less than 50 lines of DSL resulted in 950–1350 lines of Java and HTML code, complete with security and documentation. Subsequently, 10 undergraduate students utilized XBoot in practice. All participants successfully generated and deployed applications within 2–20 min (average ≈ 7), compared to 45–120 min for manual implementation. On a 5-point Likert scale, students rated the reinforcement of layered architecture at an average of 4.0. These findings suggest that XBoot effectively eliminates common structural and security errors, reduces boilerplate complexity through concise DSL specifications, and maintains modularity and transparency-limitations often observed in traditional coding and other low-code platforms. Full article

Background/Objectives: Lichen Planus Pemphigoides (LPP) represents a rare variant of Oral Lichen Planus in which the typical pemphigoid-associated antibodies, BP180 and BP230, are present. The objectives of this Systematic Review are to analyze the data currently available in the literature on this rare condition, with the aim of laying the groundwork for future investigations and research. Methods: This Systematic Review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under the registration number CRD420251133018. Subsequently, a search was conducted on PubMed/Medline, Scopus, and Ovid using specific keywords combined with Boolean operators. Articles published up to 2025 were included. The following types of studies were considered eligible: case reports, clinical conferences, clinical studies, clinical trials, controlled clinical trials, letters, multicenter studies, observational studies, randomized controlled trials, and human-based studies. Book chapters, systematic reviews, narrative reviews, in vitro studies, and animal models were excluded. Results: A total of 67 articles were initially identified; following thorough review and exclusion, 20 articles were retained. The patient data extracted from these selected studies were used to construct a table in which patients were categorized according to both qualitative and quantitative variables. The results highlight that LPP is a condition requiring a complex diagnostic process involving both histological examination and serological testing (Immunofluorescence and Enzyme-Linked Immunosorbent Assay—ELISA). Conclusions: Furthermore, with the advent of immunotherapy, an increasingly well-documented new category of drug-induced LPP has emerged, associated with PD-1 and PD-L1 inhibitors. Full article

Buber’s analysis of Job, offering four responses to the theodical questions that innocent suffering raises, has been highly influential since its publication in 1942, as has been Steven Kepnes’s more recent application of Buber’s analysis to the Nazi Holocaust. Each of Buber’s specific responses has been, at various points in history and today, taken up by sufferers and by those pondering suffering’s theodical implications. Yet each response retains troubling features. This article, building on Buber and Kepnes, offers an alternate reading of the Book of Job, centering on two points: (i) a possible covenantal, rather than retributive, nature of the God-Job relationship and (ii) an understanding of God’s response to Job as covenantal. This is not to suggest that such a response “solves” suffering, but it is comprehensible to Job from his own knowledge and experience of the world—an important aspect of the covenantal relationship—and is in some measure helpful. The article first reviews Kepnes’s reading of Buber, discusses four aspects of it that remain theodically troubling, and then sketches a covenantal reading of Job, drawing on the work of Edward Greenstein, Moshe Greenberg, David Burrell, Bill McKibben, Susannah Ticciati, and other more recent Jobian scholars. This discussion explores the following: (i) covenant in the Wisdom literature, (ii) a universalist reading of covenant entailing human integrity, reasoned dialogue, and the validity of human knowledge within natural, human capacity, (iii) why Job’s request to God is covenantal, and (iv) why God’s response is as well. Full article

Background: Pre-retrofit auditing and pre-demolition auditing (PRA/PDA) are important in material reuse, waste reduction, and regulatory compliance in the building sector. An emphasis on sustainable construction practices has led to a higher requirement for PRA/PDA. However, traditional auditing processes demand substantial time and manual effort and are more easily to create human errors. As a developing technology, artificial intelligence (AI) can potentially assist PRA/PDA processes. Objectives: This scoping review aims to review the potential of AI in assisting each sub-stage of PRA/PDA processes. Eligibility Criteria and Sources of Evidence: Included sources were English-language articles, books, and conference papers published before 31 March 2025, available electronically, and focused on AI applications in PRA/PDA or related sub-processes involving structured elements of buildings. Databases searched included ScienceDirect, IEEE Xplorer, Google Scholar, Scopus, Elsevier, and Springer. Results: The review indicates that although AI has the potential to be applied across multiple PRA/PDA sub-stages, actual application is still limited. AI integration has been most prevalent in floor plan recognition and material detection, where deep learning and computer vision models achieved notable accuracies. However, other sub-stages—such as operation and maintenance document analysis, object detection, volume estimation, and automated report generation—remain underexplored, with no PRA/PDA specific AI models identified. These gaps highlight the uneven distribution of AI adoption, with performance varying greatly depending on data quality, available domain-specific datasets, and the complexity of integration into existing workflows. Conclusions: Out of multiple PRA/PDA sub-stages, AI integration was focused on floor plan recognition and material detection, with deep learning and computer vision models achieving over 90% accuracy. Other stages such as operation and maintenance document analysis, object detection, volume estimation, and report writing, had little to no dedicated AI research. Therefore, although AI demonstrates strong potential in PRA/PDA, particularly for floor plan and material analysis, broader adoption is limited. Future research should target multimodal AI development, real-time deployment, and standardized benchmarking to improve automation and accuracy across all PRA/PDA stages. Full article

Inequality of opportunity is a critical issue that significantly impacts the socioeconomic landscape. Understanding the variations in income and educational attainment has become increasingly important, as these disparities are often shaped by social determinants such as individual effort and circumstances, which can affect educational outcomes and income potential throughout life. The aim of this paper is to quantify the inequality of opportunity and to examine how circumstances beyond an individual’s control influence income and level of education in the Central and Eastern European countries. We draw on recent data from the fourth wave of the Life in Transition Survey and employ inequality of opportunity indices, Shapley and Oaxaca decompositions, and econometric models to capture the structure and magnitude of the effects. Our findings reveal that inequality of opportunity in income is mainly due to gender and, to a smaller extent, parental education, while educational attainment is mainly influenced by parental education, books at home and the mother’s occupational sector. The results provide a robust foundation for supporting targeted policies in education, employment, and pay equity, and they indicate the need to tailor strategies to the specific contexts of each Central and Eastern European country. Full article

Technological advancements are reshaping New Mobility Services (NMS) by enhancing trip planning, booking, and payment processes, while also improving fleet management, infrastructure utilization, and data-driven decision-making. Despite these developments, challenges persist in integrating technologies into cohesive and interoperable mobility systems. This study draws insights from 163 stakeholders across the NMS ecosystem to examine both the opportunities and barriers associated with the effective integration of technology into NMS, particularly within urban and metropolitan contexts. Using statistical methods, these responses were analyzed across eight stakeholder groups to determine whether their views converge or diverge. Findings reveal a broad consensus on the technologies expected to have the greatest impact, as well as on the main challenges of integrating these technologies into NMS. Divergences arise in the perceived influence on specific mobility attributes, such as environmental sustainability, security, safety, equity, and social inclusion, and in the services considered most likely to benefit. Notably, investors express a more optimistic view across nearly all technologies, prioritizing shared vehicle services and anticipating the strongest impacts in environmental sustainability. The rest of the stakeholder groups emphasize the potential of technology to enhance modal integration and identify Mobility-as-a-Service (MaaS) as the NMS with the greatest expected benefits. These insights help identify strategic priorities and redirect efforts toward promoting investment in technologies with the highest potential to deliver transformative benefits across the NMS ecosystem. Full article

Background. Length of stay, operationalized here as nights per booking (NPB), is a first-order driver of yield, labor planning, and environmental pressure. The COVID-19 pandemic and the rise of long-stay remote workers (often labeled “slomads”, a slow-travel subset of digital nomads) plausibly altered stay-length distributions, yet national, booking-weighted evidence for the United States remains scarce. Purpose. This study quantifies COVID-19 pandemic-era and post-pandemic shifts in U.S. Airbnb stay lengths, and identifies whether higher averages reflect (i) more long stays or (ii) longer long stays. Methods. Using every U.S. Airbnb reservation created between 1 January 2019 and 31 December 2024 (collapsed to booking-count weights), the analysis combines: weighted descriptive statistics; parametric density fitting (Gamma, log-normal, Poisson–lognormal); weighted negative-binomial regression with month effects; a two-part (logit + NB) model for ≥28-night stays; and a monthly SARIMA$(0,1,1){(0,1,1)}_{12}$ with COVID-19 pandemic-phase indicators. Results. Mean NPB rose from 3.68 pre-COVID-19 to 4.36 during restrictions and then stabilized near 4.07 post-2021 (≈10% above 2019); the booking-weighted median shifted permanently from 2 to 3 nights. A two-parameter log-normal fits best by wide AIC/BIC margins, consistent with a heavy-tailed distribution. Negative-binomial estimates imply post-vaccine bookings are 6.5% shorter than restriction-era bookings, while pre-pandemic bookings are 16% shorter. In a two-part (threshold) model at 28 nights, the booking share of month-plus stays rose from 1.43% (pre) to 2.72% (restriction) and settled at 2.04% (post), whereas the conditional mean among long stays was in the mid-to-high 50 s (≈55–60 nights) and varied modestly across phases. Hence, a higher average NPB is driven primarily by a greater prevalence of month-plus bookings. A seasonal ARIMA model with pandemic-phase dummies improves fit over a dummy-free specification (likelihood-ratio = 8.39, df = 2, p = 0.015), indicating a structural level shift rather than higher-order dynamics. Contributions. The paper provides national-scale, booking-weighted evidence that U.S. short-term-rental stays became durably longer and more heavy-tailed after 2020, filling a gap in the tourism and revenue-management literature. Implications. Heavy-tailed pricing and inventory policies, and explicit regime indicators in forecasting, are recommended for practitioners; destination policy should reflect the larger month-plus segment. Full article

This study aims to bridge the research gap emerging from the relationships between Visual electronic Word-of-Mouth (VeWOM) and brand factors, and their impact on consumers’ behavior by exploring the causal effects of eWOM attributes on hotel brand factor spreading through Brand Awareness (BA) and Brand Perceived Value (BV) and its consequences on Purchase Decisions (PD) in the hospitality context. Attribution Theory was extended to incorporate brand-mediated effects and crisis-specific factors. The study investigates the impact of VeWOM on consumer Purchase Decisions (PD) in terms of hotel room bookings in the British hospitality market, emphasizing the mediating role of brand-related constructs. Drawing on Attribution Theory, the research proposes a structural model to assess both direct and indirect pathways through which VeWOM influences behavioral outcomes. A stratified, non-probability sampling approach yielded 443 valid responses from hotel bookers who engaged with user-generated visual content prior to booking. The Partial Least Squares Structural Equation Model (PLS-SEM) was employed to test the hypothesized relationships. The findings reveal that VeWOM significantly influences Brand Value (BV), eWOM Credibility, and Information Quality, which in turn shape consumer purchase behavior. Crucially, Brand Value emerges as a key mediating variable, bridging VeWOM and Purchase Decisions, while VeWOM alone does not directly affect booking behavior. Moreover, Brand Awareness showed no significant mediating effect. The study underscores the indirect attribution process in visual review contexts, demonstrating that the influence of VeWOM is channeled primarily through brand perception mechanisms rather than direct persuasion. These insights extend Attribution Theory by highlighting the distinct cognitive pathways activated by visual content compared to text-based reviews. Practically, the research suggests that hoteliers should focus on enhancing Brand Value via bundled offerings and relationship-based marketing rather than relying solely on visual appeal or awareness to drive bookings. The study contributes to the growing body of VeWOM literature by clarifying its nuanced effects on decision-making in digital hospitality environments. Full article

This study investigates the bidirectional relationship between biodiversity conservation, an increasingly important dimension of corporate social responsibility (CSR), and corporate financial performance (CFP). Specifically, it compares the manufacturing sector, which has substantial environmental impact and close ties to ecosystems, and the nonmanufacturing sector. The analysis draws on 1079 firm-year observations of Japanese companies from 2017 to 2022, employing the ratio of biodiversity-related expenditures to total environmental costs as the independent variable. CFP is measured by return on assets (ROA) and the price-to-book ratio (PBR). The results show that the effects on ROA significantly differ between manufacturing and nonmanufacturing sectors, with more positive impacts in manufacturing. In contrast, no clear sectoral differences are identified for the PBR. The reverse analysis suggests that, in the nonmanufacturing sector, firms with a higher PBR tend to allocate less to biodiversity conservation, whereas in manufacturing firms, both ROA and the PBR indicate positive effects, although statistical significance was not established. These findings indicate that biodiversity conservation in the manufacturing sector can be regarded as a strategic investment that contributes to profitability, and that its effects differ across industries. The study further suggests that investors and policymakers should consider industry-specific characteristics when evaluating corporate initiatives and designing institutional frameworks. Full article

Traditional book recommendation methods predominantly rely on collaborative filtering and context-based approaches. However, existing methods fail to account for the order of users’ book borrowings and the duration they hold them, both of which are crucial indicators reflecting users’ book preferences. To address this challenge, we propose a book recommendation framework called DPBD, which disentangles preferences based on borrowing duration, thereby explicitly modeling temporal patterns in library borrowing behaviors. The DPBD model adopts a dual-path neural architecture comprising the following: (1) The item-level path utilizes self-attention networks to encode historical borrowing sequences while incorporating borrowing duration as an adaptive weighting mechanism for attention score refinement. (2) The feature-level path employs gated fusion modules to effectively aggregate multi-source item attributes (e.g., category and title), followed by self-attention networks to model feature transition patterns. The framework subsequently combines both path representations through fully connected layers to generate user preference embeddings for next-book recommendation. Extensive experiments conducted on two real-world university library datasets demonstrate the superior performance of the proposed DPBD model compared with baseline methods. Specifically, the model achieved 13.67% and 15.75% on HR@1 and 15.75% and 12.90% on NDCG@1 across the two datasets. Full article