Search Results (2,390)

Purpose: This study systematically reviews research on six emerging technologies in external auditing, Big Data, Blockchain, Machine Learning, Deep Learning, Artificial Intelligence (AI), and Robotic Process Automation (RPA), to clarify what is currently known and to identify where the main gaps remain. Rather than treating each technology in isolation, this study brings them together under a single integrative review to provide a consolidated reference point for scholars assessing their impact on external audit practices. Design/Methodology/Approach: Following a structured systematic review protocol, searches were conducted in Scopus, ScienceDirect and SpringerLink (2000–2024) using technology-related keywords combined with “audit”, “auditor” and “auditing”. After applying explicit inclusion and exclusion criteria, 471 records were reduced to 32 ABS-listed journal articles, which were analysed thematically. Findings: The review shows that research on emerging technologies in external auditing is still fragmented, with substantial variation in the depth and maturity of evidence across the six technologies. The strongest empirical base is concentrated in Big Data analytics and ML-based predictive models (including more advanced Deep Learning variants), whereas Blockchain and RPA work remains predominantly conceptual or confined to small-scale design-science implementations. Across technologies, most studies are single-country and either rely on auditors’ self-reported perceptions of adoption and impact or evaluate model performance without tracing effects on audit strategies and engagement outcomes, which limits external validity and construct measurement. Very few articles explicitly integrate the Audit Risk Model or other formal theories, and almost no work examines multi-technology “audit stacks” or generative AI, leaving substantial gaps in understanding how these tools jointly reshape inherent, control and detection risk across the audit cycle. Originality/Value: By integrating six technologies within a single external audit framework, the review offers a technology-specific evidence map and a targeted future research agenda that can guide scholars, audit firms and regulators in designing studies and policies aligned with actual gaps in the current literature. Full article

The transition toward Circular Economy (CE) models has become a central challenge for organizations seeking to enhance sustainability performance and long-term value creation. While existing research has extensively examined technological, regulatory, and operational drivers of CE adoption, limited attention has been paid to the internal organizational orientations that enable firms to implement circular practices in a coherent and sustained manner. Addressing this gap, this study examines the role of Sustainable Marketing Orientation (SMO) as an organizational driver of CE adoption. Drawing on survey data collected from 368 micro-, small-, and medium-sized enterprises operating in environmentally relevant sectors in an emerging economy context, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test a hierarchical model in which SMO is conceptualized as a multidimensional organizational orientation composed of ethical capabilities, social commitment, and strategic integration. The results demonstrate that SMO significantly and positively influences CE adoption, explaining 41.5% of the variance. Among the dimensions of SMO, social commitment and strategic integration emerge as particularly influential in supporting circular economy practices. The findings contribute to the literature by empirically validating SMO as a higher-order organizational orientation and by identifying it as a key antecedent of CE adoption. Beyond theoretical contributions, the study offers practical insights for managers and policymakers, highlighting the importance of integrating sustainability into organizational strategy, stakeholder relationships, and performance measurement systems to facilitate circular economy transitions. Overall, the results position sustainable marketing orientation as a critical organizational mechanism supporting systemic sustainability and socio-economic transitions. Full article

Underground bauxite comprehensive mechanized mining has attracted growing attention, yet regional differences in bauxite characteristics challenge its applicability and economic efficiency. The conical pick, a key cutting tool for this mining method, has cutting load characteristics that directly impact the cutting mechanism’s efficiency and reliability. Three typical bauxite samples from different mining areas were selected as research objects. After testing their composition and firmness coefficients, a linear cutting test bench was used to measure their tri-axial cutting forces at various cutting depths. Regression analysis and the linear fitting of cutting coefficients were conducted to study cutting force and normal force (two core cutting loads). The results show that power functions effectively describe the relationships between the cutting force, normal force, fluctuation coefficient and cutting depth. As the bauxite firmness coefficient and cutting depth rise, the cutting force peak value, fluctuation amplitude and frequency all increase. Excessively high or low cutting coefficients reduce the cutting efficiency; only when both the cutting coefficient and the depth are in an optimal range can the cutting efficiency reach its maximum. Full article

This paper examines how companies in Slovakia and Poland perceive AI tool utilization and report changes in selected performance indicators after AI adoption (annual turnover, BIT, and employee error rates), and whether these assessments differ across firm demographics (country, company size, and length of operation). Using a CAWI survey of 865 firms and a contingency-table framework with Pearson’s chi-square tests and Cramer’s V effect sizes, we observe statistically significant—yet predominantly weak—associations between firm demographics and both AI utilization and self-reported performance changes. The findings provide actionable implications for managers and policy-support institutions seeking to accelerate AI adoption and value realization in central Europe, while acknowledging the limitations of cross-sectional self-reported data. Full article

This study aims to determine the effects of incorporating melon and watermelon kernel oils into ice cream formulations on the textural profile, mineral richness, and antioxidant activity of the product, and to investigate how oil addition optimizes critical quality parameters such as melting characteristics and viscosity of ice cream. The parameters analyzed include dry matter percentage, first drop, meltdown, overrun, antioxidant content, color and textural characteristics, total phenolic content, and mineral matter content. Among the samples, the highest first drop, meltdown, and overrun values were determined to be 31.67 s, 122.08 s, and 33.34%, respectively, in ice cream samples produced with 0.3% melon kernel oil addition, and the highest DPPH, ABTS, FRAP, and TPC in samples produced with a 0.3% addition of watermelon kernel oil, with values of 81.88%, 9.90 µmol TE/g, 2.06 µmol TE/g, and 128.72 mg GAE/100 g, respectively. Likewise, the lowest firmness, highest consistency, cohesiveness, and viscosity index values (15.53 g, 456.34 g.s, −21.50 g.s, and −8.16) were also found in the same ice cream samples. P, Mg, Ca, Na, K, Fe, and Zn contents increased with increasing addition of seed oil, and P showed the highest increase among the samples, followed by Na, K, and Ca, respectively. The samples demonstrating the most significant increase in mineral content were those produced with 0.3% melon kernel oil. Full article

Organizations communicate across many channels, yet official websites remain a controlled, authoritative space where firms articulate identity and strategy. This study examines how Croatia’s top enterprises (n = 100) describe themselves on their websites and which emotional tones they use to signal strategic intent. Our goal is to identify recurring strategic signifiers and map distinct sentiment profiles in corporate narratives. We compiled company descriptions from official sites; texts were originally in Croatian and machine-translated into English, and all analysis was conducted on the English corpus. Using lexicon-based sentiment methods (AFINN, Bing, NRC), we quantified polarity and discrete emotions, aggregated scores at the firm level, and applied k-means clustering to normalized emotion vectors. Results show a consistent emphasis on mission–vision–values language and a dominance of positive emotions—especially trust and anticipation. We interpret, based on cluster exemplars, that higher trust/anticipation tones can function as soft governance cues, while transparency about negatives characterizes an issue-addressing regime without eroding overall positivity. Cluster analysis reveals three stable profiles: optimistic consumer-oriented narratives, transparent issue-addressing messaging, and low-affect technical descriptions. We conclude that sentiment profiling offers a practical audit tool for aligning website copy with stakeholder expectations and governance communication, supporting benchmarking, and future tests linking narrative tone to investor behavior and firm performance. Full article

Utilising data from the selected companies listed on the Johannesburg Stock Exchange and using the Panel Autoregressive Distributed Lag (ARDL) specifically employing the Pooled Mean Group approach, this study examines the cointegrating and causal relationships among firm liquidity, performance and firm leverage. The results reveal a negative and significant long-run and short-run relationship between profitability and leverage. Conversely, higher leverage is found to diminish firm performance, consistent with trade-off theory implications regarding financial distress costs. On liquidity, results revealed a bidirectional long-run relationship among liquidity, leverage, and firm value as measured by Tobin’s Q. Also, liquidity plays a pivotal moderating role, where firms with stronger liquidity and profitability exhibit reduced reliance on external debt, highlighting the interplay between financial health and capital structure decisions. Additionally, a positive bidirectional relationship between Tobin’s Q and leverage suggests that growth opportunities and market valuation influence firms’ debt utilisation. The error correction terms confirm stable long-run equilibrium and moderate adjustment speeds. These results contribute to the understanding of optimal capital structure by integrating liquidity and performance factors and provide practical insights for corporate financial management and policy formulation. Full article

Value investing remains grounded in intrinsic value estimation, margin-of-safety reasoning, and disciplined fundamental analysis, but its practical execution is increasingly constrained by the scale, heterogeneity, and velocity of modern financial information. Recent advances in artificial intelligence (AI), particularly large language models and automated information-extraction systems, create new opportunities to accelerate financial analysis; however, their outputs remain probabilistic, context-dependent, and potentially error-prone, making governance and verification essential. This article proposes an AI-assisted value investing framework that integrates automated extraction, valuation modeling, explainability, and human-in-the-loop (HITL) supervision into a unified decision-support architecture. The framework is organized into three layers: (i) a data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling layer for automated key performance indicator (KPI) computation, forecasting support, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer for traceability, verification, model-risk control, and analyst oversight. A central contribution of the paper is the operational characterization of prompt literacy as a determinant of analytical reliability, showing that structured, context-aware prompts materially affect extraction correctness, usability, and verification effort. The framework is evaluated through a case study using Rivanna AI on three large U.S. beverage firms—namely, The Coca-Cola Company, PepsiCo, and Keurig Dr Pepper—selected as a controlled, information-rich setting for comparative analysis. The results indicate that the proposed workflow can reduce end-to-end analysis time from approximately 25–40 h in a traditional manual process to approximately 8–12 h in an AI-assisted setting, including citation/source verification, unit and period reconciliation, and review of key valuation assumptions. Rather than eliminating analyst effort, AI shifts it from manual information processing toward verification, adjudication, and interpretation. Overall, the findings position AI not as an autonomous decision-maker, but as a governed reasoning accelerator whose effectiveness depends on structured human guidance, traceability, and disciplined validation. In value investing, a discipline traditionally grounded in labor-intensive fundamental analysis and disciplined intrinsic value estimation, AI introduces the potential to scale analytical coverage and accelerate evidence synthesis. However, AI systems in financial contexts are probabilistic, context-sensitive, and inherently dependent on human interaction, raising critical questions about reliability, governance, and operational integration. This article proposes a structured framework for AI-driven value investing that preserves the foundational principles of intrinsic value, margin of safety, and economic reasoning, while redesigning the analytical workflow through automation, explainability, and human-in-the-loop (HITL) supervision. The proposed architecture integrates three layers: (i) an AI-enabled data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling and valuation layer combining automated KPI computation, machine learning forecasting, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer ensuring traceability, verification, and model risk control. A central contribution of this work is the operational characterization of prompt literacy, namely the ability to formulate structured, context-aware requests to AI systems, as a critical determinant of system reliability and analytical correctness. Through a focused case study using an AI-assisted analysis platform (Rivanna AI) on three U.S. beverage firms, we provide evidence that structured prompt formulation can improve extraction consistency, reduce verification overhead, and increase workflow efficiency in a human-supervised setting. In this setting, analysis time decreased from a manual range of approximately 25–40 h to 8–12 h with AI assistance and HITL validation, while preserving traceability and decision accountability. The reported hour savings should be interpreted as conservative estimates from the initial deployment phase; additional efficiency gains are expected as operational maturity increases, driven by learning-economy effects. The findings position AI not as an autonomous decision-maker but as a probabilistic reasoning accelerator whose effectiveness depends on structured human guidance, verification discipline, and prompt-driven interaction. These results redefine the role of the financial analyst from manual data processor to reasoning architect, responsible for designing, guiding, and validating AI-assisted analytical workflows. Full article

The heterogeneous empirical evidence in the vast literature on capital structure determinants is puzzling to scholars and practitioners. Various leverage measurements, in conjunction with the inconclusiveness of the significant firm-related capital structure determinants, complicate comparability. Practitioners also find it challenging to determine optimal financing strategies with real precision. This paper provides an integrative position that consolidates firm-related capital structure determinants with their respective measurements and suggests a preferred proxy for capital structure. A qualitative design has been applied, which is rarely done in the context of capital structure. This paper also offers a methodological contribution by utilising a combination of documentary analysis with PRISMA and forward-looking citation analysis, named the adapted documentary analysis. Capital structure determinant studies were targeted from inception until 2023. The synthesis of the results from 335 articles identified the six most prominent capital structure determinants: profitability, tangibility, growth proxied by the market-to-book value of equity (MTB), firm size, non-debt tax shield (NDTS), and business risk. Capital structure book value measurements seem more reliable than market-based measures. Profitability, MTB, and tangibility are the key firm-related determinants informing practitioners’ financing decisions. A consolidated list of the most prominent capital structure determinants, with their associated measurements, and a reliable proxy for capital structure are novel contributions that enable comparability in capital structure research across companies, industries, and countries. It creates a consolidated, integrative platform that adds to the academic debate and assists practitioners in their capital structure decision-making. Full article

Despite growing interest in circular economy practices, limited empirical research explains how circular-oriented HR systems translate into measurable sustainability outcomes, particularly within manufacturing SMEs operating under resource constraints. This study investigates whether and under what conditions Circular Human Resource Management (CHRM) contributes to corporate sustainability by examining the mediating role of green thinking and the moderating role of environmental management investment (EMI). Drawing on the Natural Resource-Based View and Institutional Theory, the study addresses the gap between symbolic adoption of circular HR practices and their substantive sustainability impact. Data were collected from 616 senior and middle managers in environmentally exposed manufacturing SMEs in Turkey and analyzed using confirmatory factor analysis and moderated mediation techniques. The findings indicate that CHRM is positively associated with corporate sustainability, both directly and indirectly through green thinking, although the strength of these effects depends on the level of environmental investment. Specifically, sustainability gains are significantly stronger in firms that complement cognitive capabilities with tangible environmental infrastructure. These results suggest that circular HR practices alone are insufficient without supportive organizational investment. The study contributes by clarifying the conditional mechanisms through which employee-centered circular capabilities generate sustainability value and by delineating boundary conditions in SME contexts. Full article

Fish roe is consumed in different forms, e.g., caviar. The large and firm spherical roe from giant sea catfish (GSC, Arius thalassinus), which have a high price, are popular in some countries, like Thailand. However, the information on their nutrition and properties is scarce. Roe of different sizes from GSC, including medium (GSC-M), large (GSC-L), and extra-large (GSC-XL) sizes, were rich in protein (29.52–32.70%), fat (4.07–5.65%), and essential amino acids, particularly leucine and lysine. Vitelline was the major protein in GSC roe. Polyunsaturated fatty acids, including eicosapentaenoic acid and docosahexaenoic acid, were abundant, although GSC-M showed lower PUFA content (21.91%) than GSC-L and GSC-XL (25.56–25.94%). No significant differences in texture property were found between sizes, despite the microstructural and histological differences. Larger voids and strands were found with augmenting size, while GSC-L showed greater membrane thickness (133.55 µm). FTIR spectra confirmed the presence of peptide and ester bonds associated with proteins and triacylglycerols, respectively. GSC-L had the highest cholesterol content (651.2 mg/100 g), whereas GSC-M showed the highest α-tocopherol level (1.64 mg/kg). Phosphorus was the dominant mineral (3473–3894 mg/kg), followed by calcium and other minerals. Hence, the roe from GSC, regardless of size, possess high nutritive value and could be used as a wholesome marine food or functional ingredient. Full article

Jojoba oil is a well-established skin-beneficial liquid wax with high value in topical formulations. Bigels, as preferred semi-solid dosage forms, serve as versatile platforms by incorporating hydrogels and oleogels to leverage their advantages and address their limitations. In this study, jojoba oil bigels were developed using sorbitan monostearate (20%, w/w) as an oleogelator and different hydrophilic bases, 1% Carbomer 940, 6% methylcellulose, or 20% Poloxamer 407 gel, with all concentrations expressed relative to the corresponding phase. Nine bigels were obtained by varying hydrogel-to-oleogel ratios (90:10–70:30). They were evaluated in terms of their organoleptic, microstructural, and textural characteristics. Both the hydrogel matrix type and the phase proportion impacted the studied properties. Carbomer bigels displayed the highest spreadability, methylcellulose formulations showed the greatest adhesiveness, and poloxamer systems exhibited maximum firmness and cohesiveness, with a comparatively more homogeneous phase distribution. The increase in oleogel content enhanced firmness and cohesiveness while modulating spreadability and adhesiveness in a hydrogel-dependent manner. Moreover, all designed formulations remained physically stable after centrifugation, but only those containing 80% carbomer gel or 70% or 80% poloxamer gel preserved their mechanical characteristics without significant changes after freeze-thawing. Besides identifying three promising biphasic dermal drug delivery platforms, these findings reinforce the tunability of bigels through the careful component selection. Full article

Big data analytics (BDA) has emerged as a crucial strategic asset for organizations aiming to enhance their sustainable company performance; nevertheless, empirical information elucidating the correlation between analytics and sustainability results is scarce, especially in developing nations. This study examines the influence of big data analytics (BDA) on sustainable firm performance (SFP) within the Libyan telecommunications sector, focusing on the mediating roles of organizational learning (OL) and process-oriented dynamic capabilities (PODCs), utilizing dynamic capability and organizational learning theories. A quantitative, cross-sectional research design was utilized. A systematic questionnaire was used to collect data from personnel at five different managerial and functional levels in the Libyan telecoms sector. There were 354 valid replies from a group of 5400 professionals who worked in the managerial, technical, and strategic areas. We used Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS 4.0 to look at the proposed research model. We used measurement scales from previous investigations. The findings demonstrate that BDA exerts a positive and statistically significant influence on SFP. Nonetheless, this direct effect is quite minor when juxtaposed with the indirect effects conveyed by OL and PODCs. Both organizational learning and process-oriented dynamic capabilities significantly and partially mediate the relationship between big data analytics (BDA) and sustainable performance. This shows that analytics-driven sustainability outcomes depend heavily on a company’s ability to learn from data and change how it does things. This study enhances the Business and Management literature by elucidating the inadequacy of analytics investments in producing robust sustainability outcomes. It emphasizes the essential function of supplementary organizational capabilities in converting data-driven insights into enduring economic, environmental, and social value. From a practical standpoint, the findings indicate that managers and policymakers in developing economies ought to prioritize learning systems and adaptive process capabilities in conjunction with digital investments to fully harness the sustainability potential of big data analytics. Full article

This study examines the relationship between ESG governance maturity and market valuation in the context of Kazakhstan. The analysis is based on a balanced panel of 13 listed companies covering the period 2019–2024. Firm value was measured using Tobin’s Q coefficient, and ESG maturity was assessed using a specific index that describes the extent to which environmental, social, and governance practices are institutionally embedded within an organization. The results of the fixed-effects model suggest that there is a positive relationship between the aggregate ESG score and a firm’s market value, but it is not statistically significant. When considering the sectoral breakdown, a difference is observed: ESG maturity is positively and significantly associated with market valuation for companies in the financial sector, while no such relationship was found for the non-financial sector. No significant relationship was found between ESG maturity and return on equity (ROE). The results obtained show that the impact of ESG factors in the case of Kazakhstan is not equally visible in all sectors and that, at the present stage, its influence is mainly more pronounced in the financial sector. Full article

While AI is widely recognized as an industrial transformation catalyst, how AI translates into green innovation remains insufficiently understood. Drawing on socio-technical systems theory and upper echelons theory, this study investigates how AI adoption influences green innovation and how managerial cognition shapes this relationship. Using data from Chinese A-share listed firms spanning 2012 to 2024, we reveal that AI significantly promotes green innovation by serving as an endogenous technological force. Managerial cognition (green cognition, innovation cognition, long-termism) serves as a critical boundary condition: all three dimensions positively moderate the AI–green innovation nexus, indicating equivalent technological inputs yield divergent outputs depending on executive interpretation frameworks. Mechanism analyses demonstrate AI operates through three channels: information transparency (improving carbon data quality), compliance internalization (embedding requirements into digital systems), and value creation (transforming environmental data into profit sources). Heterogeneity tests show AI’s effect is more pronounced in high-tech industries and under intense market competition. This study reveals the moderating role of managerial cognition—and its multidimensional construct—in the relationship between AI and green innovation. Practically, it provides actionable insights for cultivating managerial cognition to bridge the gap between AI potential and green innovation realization. Full article