Business, Economics and Management

Abstract: This study examines the relationship between sustainable development, insecurity, policy uncertainty, institutional quality, and agricultural development in Nigeria and Niger State from 2000 to 2024. Using the Autoregressive Distributed Lag (ARDL) model, it analyzes how these factors influence sustainable economic performance. Correlation analysis shows significant relationships among the variables at the 1% level, highlighting their interconnectedness. The ARDL results reveal differences between Nigeria and Niger State: in Nigeria, policy uncertainty positively impacts sustainable development (GDP), while insecurity notably reduces economic performance by weakening investment confidence, productivity, and stability. Conversely, the rule of law fosters growth by enhancing institutional effectiveness. In Niger State, policy uncertainty and insecurity negatively impact sustainable development (GDP), while the rule of law and overall development positively influence economic performance. Based on the CRS Cobb–Douglas production framework, the study underscores that strong institutions enhance productivity and economic resilience, whereas insecurity and uncertainty reduce total factor productivity. By integrating insecurity, policy uncertainty, and agricultural development into a single framework, the study builds on existing research and stresses the importance of institutional reforms, stronger security measures, and predictable policies to achieve sustainable and inclusive economic growth.

Abstract: This empirical study looks at how green marketing driven by artificial intelligence (AI) affects sustainable tourism behavior in regions facing crisis, focusing on Port Sudan, Sudan, from 2023 to 2026. After national conflict caused a sudden influx of displaced people, local coastal ecosystems and vulnerable marine reserves — especially Sanganeb National Park — have faced severe strain. To explore potential interventions, we used an exploratory sequential mixed-methods design. We first did in-depth qualitative interviews with 10 leaders from the hospitality and environmental sectors, and then ran a field experiment with 284 visitors who were randomly assigned to either control or experimental groups. Our Partial Least Squares Structural Equation Modeling (PLS-SEM) shows that AI-driven green marketing has a strong, direct positive effect on sustainable tourism behavior (β = 0.42, p < 0.001). Additionally, digital trust in what the algorithms recommend partially mediates this connection (β = 0.24, p < 0.001). During the field experiment, visitors who saw personalized, real-time AI nudges followed ecological guidelines much better than those who only got standard green marketing messages. These results prove that flexible digital tools can work as cheap resilience mechanisms to encourage sustainable habits, even when local institutions and infrastructure face heavy constraints.

Abstract: Despite growing interest in artificial intelligence (AI) as a sustainability capability, evidence of its association with distinct environmental, social, and governance (ESG) outcomes remains inconclusive. Grounded in the Resource-Based View, Natural Resource-Based View, and stakeholder and institutional perspectives, this study examines IMD AI-maturity scores and Refinitiv ESG ratings for 119 large firms across ten industries. Hierarchical ordinary least squares regressions adjusted for industry, with HC3 robust inference, multiple-testing corrections, influence diagnostics, and industry interaction tests. AI maturity showed no statistically significant association with overall ESG (B = 0.121, p = 0.087) or Governance (B = −0.002, p = 0.986). Positive Environmental (B = 0.222, p = 0.019) and Social (B = 0.200, p = 0.022) associations did not survive Holm correction (adjusted p = 0.077); the Environmental finding was sensitive to an influential observation. Industry moderation was not detected (F(9,99) = 0.494, p = 0.876), although small sector samples limit precision. The study contributes by distinguishing aggregate from pillar-specific AI–ESG relationships and testing their sensitivity to alternative inference procedures. Theoretically, the findings qualify expectations that digital capabilities uniformly translate into sustainability advantages; practically, they favor evaluating AI investments using distinct, directly observed sustainability indicators rather than presuming broad ESG gains.

Abstract: Halloween is a transnational seasonal market for costumes, confectionery, decorations, social displays, and deliberately frightening experiences. This concise paper introduces the “fright economy” as the conversion of ritualized fear, identity performance, and collective participation into economic value. It selectively examines a Scopus export of 1,171 records retrieved using “Halloween” and two associated VOSviewer visualizations. A transparent metadata screen provisionally classifies 111 records as directly focused on the holiday and another 52 as related to horror and media; most other records concern unrelated scientific meanings of the term or incidental uses. The network maps reveal why unfiltered bibliometrics distort the study of festive consumerism. As an illustrative market indicator, the National Retail Federation projected U.S. Halloween spending of US$13.5 billion in 2026. This paper links this scale to staged experiences, cultural adaptation, and social and environmental costs. Our results are intentionally reported at the aggregate level; source-level rankings, detailed NLP cluster estimates, and full expenditure decompositions are outside this brief account.

Abstract: Introduction: Broad-Based Black Economic Empowerment (BBBEE) certification and compliance evaluation are intended to support economic transformation, broaden participation and improve opportunities for historically disadvantaged enterprises in South Africa. Yet certification status is not necessarily equivalent to demonstrated transformation or MSME development. This review examined how BBBEE certification and compliance evaluation relate to evidential value for economic transformation and MSME development, including associated opportunities, risks, implementation conditions and evidence gaps. Methods: The researcher-selected databases and information sources were Google Scholar, ERIC, OpenAlex, Crossref. The eligibility protocol restricted publication years to 2013–2023. COSTAQDA recorded source-level retrieval through OpenAlex (75 retrieved; 660 available), Crossref (75 retrieved; 2656 available). Where researcher-selected sources did not expose a suitable direct bulk-search connector, COSTAQDA used its OpenAlex/Crossref discovery bridge while preserving the researcher-selected source names in the PRISMA record. The identification run recorded 150 records before cross-source deduplication and 144 unique records after deduplication. Results: Five higher-order conclusions emerged. BBBEE compliance provided signals of opportunity but not consistent proof of MSME development; market access was a principal linking mechanism; outcomes depended on implementation capacity, support and contextual fit; fronting, corruption and unequal distribution weakened evidential value; and the available evidence was fragmented and insufficient for certificate-level causal evaluation. Evidence included positive perceptions, procurement access, enterprise and supplier development, skills initiatives and employee inclusion, alongside adverse or uncertain business effects and implementation failures. Discussion/Conclusion: BBBEE certification is best interpreted as an access, compliance and accountability signal rather than as standalone evidence that economic transformation or MSME development has occurred. Stronger conclusions require transparent, longitudinal and outcome-linked analysis connecting certification records with firm, beneficiary and distributional outcomes.

Abstract: Introduction: South Africa’s Broad-Based Black Economic Empowerment (B-BBEE) framework seeks to widen economic participation, yet the supplied evidence indicates that empowerment benefits may remain concentrated among relatively advantaged groups. Concerns therefore persist regarding elite capture, unequal benefit distribution, weak accountability, and limited evidence on how Enterprise and Supplier Development (ESD) screening and targeting operate in practice.Methods: The researcher-selected databases and information sources were Google Scholar, ERIC. The eligibility protocol restricted publication years to 1994–2026. The exact calendar date of search execution was not recorded in the verified identification provenance; this is distinct from the publication-year eligibility range. A systematic review was conducted using Google Scholar and ERIC, with eligibility restricted to English-language peer-reviewed studies published from 1994 to 2026.Results: The synthesis indicates that B-BBEE benefits are reported as insufficiently broad-based and vulnerable to elite concentration. Transparency, accountability, public participation, civil-society involvement, co-production, and partnerships are presented as safeguards, but direct evidence that these mechanisms prevent capture in ESD programmes is limited. Finance, information, training, and skills development may widen participation, although beneficiary experiences and business-performance effects are mixed. Direct evidence on eligibility verification, conflict-of-interest controls, appeals, audits, and procurement-level anti-capture mechanisms is substantially limited.Discussion/Conclusion: The evidence supports a governance and inclusion framework for reducing capture but does not establish that existing ESD screening or targeting practices have demonstrably prevented elite capture. Conclusions are constrained by heterogeneous evidence, limited South African coverage, methodological uncertainty, and a PRISMA identification discrepancy requiring verification.

Abstract: Introduction: Broad-Based Black Economic Empowerment (B-BBEE) is intended to advance participation, enterprise development, supplier development, skills development and transformation in South Africa. Its contribution to the growth, performance and sustainability of micro, small and medium enterprises (MSMEs), however, remains contested. The supplied literature includes evidence of perceived benefits, developmental opportunities, resource constraints, compliance burdens and weak institutional implementation. Methods: The researcher-selected databases and information sources were Google Scholar and ERIC. The eligibility protocol restricted publication years to 2016–2026. The exact calendar date of search execution was not recorded in the verified identification provenance; this is distinct from the publication-year eligibility range. The review searched Google Scholar and ERIC using a predefined Boolean strategy covering MSMEs, B-BBEE mechanisms and enterprise outcomes, with publication eligibility restricted to 2016–2026. Results: B-BBEE appears most enabling when enterprise and supplier development is embedded in productive value chains, knowledge transfer, skills development and inter-organisational collaboration. Benefits are constrained by limited finance, collateral, infrastructure, market access, technical knowledge, planning and management capability. Legislative change, compliance complexity, weak communication, fragmented institutional support, corruption and fronting risks can displace substantive transformation. Perceptions of benefit coexist with weakly demonstrated direct effects on MSME growth and sustainability. Evidence is heterogeneous and direct longitudinal outcome evidence is limited. Discussion/Conclusion: B-BBEE should be understood as conditionally effective rather than uniformly successful or unsuccessful. Its developmental contribution depends on contextual inclusion, measurable outcomes, coordinated implementation, enterprise capability building and accountability. Stronger longitudinal and MSME-specific evaluations are required to establish causal effects on revenue, employment, productivity, survival and sustainability.

Abstract: Accurate and accessible information can help people navigating overseas employment, while generative artificial intelligence (AI) creates new questions about the quality and verification of migration-related advice. This study examines responses to statements about AI literacy and migrant-worker-rights information in 385 adult, AI-aware survey records from Bangladesh. The mixed sample includes law students, graduates, legal professionals and other respondents; prospective overseas workers are not identified in the workbook. The questionnaire asks about self-reported ability to locate rights information with AI, recognition of the need to verify migration information, awareness of misleading recruitment information and support for pre-departure AI-literacy training. It does not test knowledge of specific migrant-worker rights or observe use of an AI migration service. Item distributions, joint endorsement, paired comparisons and exploratory ordinal correlations are reported. Of 385 respondents, 256 (66.5%) endorsed the ability-to-locate statement, 341 (88.6%) endorsed the need to verify AI-generated migration information, and 267 (69.4%) supported AI-literacy training in pre-departure preparation. A total of 231 respondents (60.0%) endorsed both locating information and the need for verification; 110 endorsed verification without endorsing locating information. These answers do not establish actual verification or training effectiveness. The article distinguishes locating information, verifying its authority, knowing a legal right and obtaining an effective remedy. Future research should identify prospective workers, use rights-specific scenarios and document recruitment and consent procedures.

Abstract: This article examines how managers in the Norwegian construction industry—represented by bachelor’s students in construction site management—perceive and understand the Norwegian construction industry’s development towards the concept of Industry 5.0 (I5.0). The study focused on the participants’ reflections and expectations regarding these emerging frameworks. Using qualitative data, the research draws on insights from 70 part time students with full time industry positions, organised into 15 interdisciplinary groups. The findings show that the participants believe that the Norwegian construction industry is prepared for the concept of I5.0, especially in light of the human-oriented approach. The empirical material indicates that the Norwegian construction industry has a strong focus on employees, supported by trade unions that help steer the industry towards the goal of collaboration between technology and humans and I5.0. This research shows that the construction industry has invested only modestly in robotics within the construction process. Participants highlighted the advantages of the Norwegian working life model, noting its compatibility with the human centred principles of I5.0. The article contributes to understanding how prepared the Norwegian construction industry is for a development towards the concept of I5.0. Construction managers interpreted these concepts and offered an analysis of resilience, including vulnerability and capacity, within a Norwegian context.

Abstract: Employee surveys and documentary records are seldom examined together, so it is rarely known whether the people who work in a public utility describe it in the way its own records do. This study addresses that question for Himachal Pradesh State Electricity Board Limited (HPSEBL), an Indian state distribution utility, through a convergent mixed-methods design. A stratified survey of 414 employees, analysed by partial least squares structural equation modelling, was set beside documentary indicators for 2014-15 to 2023-24 from the utility's accounts, regulatory orders and national performance ratings, and each construct and each of twelve hypothesised paths was graded against its documentary counterpart under criteria fixed in advance. Both sources show weak finances alongside operations that compare well with national averages. Agreement was strong for financial sustainability, partial or bounded for governance, operations, well-being and sector development, and limited to context for commitment and for six paths that the records cannot observe. In 2022-23 the regulator recorded a surplus of ₹136.44 crore against an accounting loss of ₹1,436.71 crore, and employees' ratings of the finances appear to follow the accounting position more closely than the regulatory one. The study offers a transparent protocol for comparing employee perceptions with documentary records.

Abstract: Link predictors that support decisions, such as suggesting a director to a startup, must explain why a link is or is not recommended. We propose the Multiplicative View-Gated GNN (MG-GNN), which scores a pair as the weighted product of gates, each restricted to one view of the evidence: attributes, structural position, neighbourhood overlap and proximity. Its attributions are exact, and a single failing view caps the score, which certifies robustness to the manipulation of any one view. We prove that, under a conjunctive mechanism, each gate recovers its true condition, whereas additive attributions are not identified. We also prove that disjoint view inputs stop one view from absorbing the vetoes of the others, at a measurable cost. An out-of-sample Vuong test tells the analyst whether the conjunctive reading is warranted, and it identifies the mechanism in all ten synthetic graphs. On six benchmarks, a heterogeneous graph and a new national graph of 13,765 Italian innovative firms and 25,918 directors, MG-GNN matches strong GNN baselines. Its explanations reach 0.97–0.99 of oracle fidelity and are far more stable than Shapley values. In the case study, MG-GNN predicts the 2025–2026 appointments from 2024 data and outperforms the platform’s hand-crafted index.

Abstract: Automatic stabilizers dampen macroeconomic fluctuations without legislative action, but whether flat or progressive personal income tax (PIT) schedules stabilize more strongly over the business cycle remains open. We examine real gross domestic product (GDP) growth volatility in 11 Central and Eastern European economies over 1995–2024, combining rolling-window volatility measures, two-way fixed-effects regressions and direct estimates of the cyclical responsiveness of fiscal balances, revenue and expenditure ratios. Unconditionally, five-year output volatility is 1.16 percentage points higher under flat PIT regimes, but the difference does not survive country and year fixed effects and lagged covariates. Interacted with the 2008–2010 global financial crisis the differential widens by 2.42 percentage points, an effect that rests on the three Baltic economies, whereas the COVID-19 interaction is indistinguishable from zero. A one-percentage-point rise in real GDP growth improves the fiscal balance by 0.265 percentage points of GDP under progressive regimes and by 0.242 under flat regimes, a difference that is not significant. In levels, a weaker revenue response under flat regimes is largely offset by a stronger expenditure response. The results do not support a simple ranking: stabilization capacity depends on the whole tax–benefit system, fiscal institutions and the nature of the shock.

Abstract: As AI-enabled workplace monitoring expands, organizations may increasingly have the technical capacity to classify prosocial conduct, assign scores, and link those scores to managerial incentives. This paper treats the governance implications as an AI-enabled management control and accounting governance question, not an engineering scoring study; reward crowding-out is borrowed evidence, not a new finding here. It makes two contributions. First, it explains how accounting measurement, target setting, budgeting, disclosure, and compensation design turn a fleeting motivational reaction into a durable arrangement reproduced across reporting periods. Second, it analyzes the asymmetry between auditable behavioral indicators and moral costs that resist measurement, and why those costs stay out of ordinary records. A four-arm governance design—management control, financial reporting, internal audit, compensation—extends these contributions in practice. Three layers and four falsifiable propositions follow, offered for later testing, not validated causal claims.

Abstract: Artificial intelligence (AI) may assist legal professionals in organising information, but its use in legal decision support raises questions about human authority, explanation and procedural safeguards. This study examines responses to related statements in 385 adult, AI-aware survey records from Bangladesh. The mixed sample includes 250 law students, 73 law graduates, 36 advocates or legal professionals and 26 other respondents; it is not presented as a representative survey of practising lawyers or judges. Individual questionnaire items are analysed using response distributions, joint endorsement, paired comparisons and exploratory ordinal correlations. Of 385 respondents, 247 (64.2%) agreed that AI decision-support tools could help legal professionals organise relevant information, 243 (63.1%) endorsed use only when a human decision-maker retains final authority, and 321 (83.4%) agreed that clear rules would increase their acceptance. A total of 158 (41.0%) endorsed both the information-organisation and human-authority statements. Within the 36 recorded advocates or legal professionals, 26 endorsed the former and 18 the latter; the subgroup is too small for a representative professional estimate. The survey does not measure actual use of a judicial system or demonstrate that oversight or rules improve outcomes. The article distinguishes perceived assistance from conditions of use and recommends direct testing of legal information quality, meaningful human review and opportunities to challenge AI-supported recommendations.

Abstract: The purpose of this study is to determine the influence of good corporate Governance and structure on sustainability disclosure for SDGs. This study is a report on industrial sector companies listed on the Indonesia Stock Exchange for 2020-2024. This research is quantitative, with good corporate Governance as the independent variable. Corporate Governance consists of an audit committee, an independent board of commissioners, and a board of directors. Ownership structure variables include managerial, institutional, and foreign ownership. The dependent variable is sustainable development. The population in this study was industrial sector companies listed on the Indonesia Stock Exchange for the 2020-2024 period. The sampling method used was purposive sampling. The type of data used in this study was secondary data. The secondary data used were sustainability reports. Annual reports and reports for the Industrial sector companies listed on the Indonesia Stock Exchange in the 2020-2024 period—data analysis using Structural Equation Modeling-Partial Least Squares (SEM PLS ). The results of data processing found that good corporate Governance has a significantly positive influence on sustainability report disclosure and the structure of ownership. No influence on sustainability report disclosure.

Abstract: The US goat industry has changed as consumer demand has increased, but little is known about how goat characteristics and market variables affect auction prices. This study examines the drivers of goat auction prices in the Maryland goat auction market using 2,416 observations from weekly USDA auction reports from November 2018 to September 2025. We use a hedonic pricing model to estimate the price relationships among goat weight, lot size, selection grade, class, and market timing variables. The findings suggest that observable goat characteristics play an essential role in price formation. Compared with Selection 3 goats, Selection 1, Selection 1–2, and Selection 2 goats had estimated price premiums of 93.47%, 73.90%, and 48.92%, respectively. Premiums for bucks, kids, wether kids, and wethers were 20.31%, 29.95%, 24.81%, and 25.04%, respectively, compared to nannies. The relationship between live weight and price per pound is nonlinear, with expected prices increasing with weight up to around 172.9 pounds and then decreasing. Market time also influences price fluctuations. Eid al-Adha and Ramadan festivals had a favorable impact on prices across different specifications. Prices are also higher during periods of relatively limited supply. The findings indicate that goat prices at the Maryland goat auction market are determined by a combination of observable goat characteristics, class, size, and market conditions. The study provides market-specific evidence to help producers and auction participants understand price differentials and make better marketing decisions, while also providing the foundation for future research on goat price formation in other US auction markets.

Abstract: Higher education institutions are increasingly expected to act as drivers of the bioeconomy, yet strategic planning in this field routinely produces action portfolios far larger than the resources available to implement them, and the choice of what to implement first is usually made implicitly rather than on a traceable basis. This paper applies the Analytic Hierarchy Process to the prioritisation of twenty-one bioeconomy research action plans at a private university in Serbia. Seven criteria and the candidate actions were generated in a Quadruple Helix expert workshop combined with SWOT and TOWS analysis; sixteen institutional respondents completed pairwise comparisons, which were aggregated by the geometric mean method and screened with the consistency ratio. At criterion level, priorities concentrate on sustainable biomass use and circularity integration (18.3%) and environmental and climate contribution (18.0%), while researcher career support ranks last (10.0%). At action level, the highest global priority belongs to an internal technology transfer and commercialization system (7.14%), which sits under the criterion ranked only fifth. Two robustness checks qualify that result: correcting for the unequal number of alternatives per criterion moves this action from first to ninth place, and a sensitivity analysis shows that the leading five actions change in half of the scenarios tested. Sustainability-oriented actions remain stable under both checks.

Abstract: Investors and rating agencies use sustainability reports, yet their linguistic complexity may either conceal unfavourable information or reflect specialised disclosure. This study examines the associations among sustainability report readability, environmental, social, and governance (ESG) scores, and financial performance, and tests whether these asso-ciations are consistent with the obfuscation hypothesis. The sample comprises 101 Eu-ropean companies from the Consumer Goods (n = 56) and Food and Beverage (n = 45) sectors for 2024. Five readability indices were aggregated through principal component analysis, and ESG scores were obtained from the London Stock Exchange Group (LSEG). Ordinary least squares regressions with robust standard errors, sector-specific estimations, and mediation analysis were applied. Contrary to expectations, greater textual complexity was positively associated with ESG scores, particularly in the social and governance pillars, whereas firm size was the strongest determinant of ESG scores. Lower return on equity and higher leverage were associated with less readable reports, but these asso-ciations were sensitive to model specification and limited to the Consumer Goods sector. Report length and sector affiliation were the most consistent determinants of complexity, and readability did not mediate the relationship between financial performance and ESG scores. The results suggest that textual complexity reflects the technical density of dis-closure rather than deliberate obfuscation.

Abstract: Education is widely treated as an enabler of the 2030 Agenda, yet research on interactions between the Sustainable Development Goals (SDGs) shows that such interactions are context-dependent. This study examines whether governance quality (SDG 16) determines whether educational expansion (SDG 4) generates synergies or trade-offs between social and environmental goals. Using a panel of up to 179 countries over 2000–2023, two-way fixed-effects interaction models, panel threshold regressions and a new classification of education-induced SDG interaction profiles, we find that the profile of education’s returns shifts along the governance gradient. In weakly governed countries, education is associated with lower extreme poverty and wider access to clean cooking fuels, but also with higher CO₂ and greenhouse gas emissions, a lower renewable share and forest loss. Above governance thresholds located between the 65th and 82nd percentiles of the 2023 cross-country distribution, the low-carbon and natural-capital associations become favourable. In 2023, about 31% of the world’s population lived in countries where additional schooling implied such a social–environmental trade-off. Governance and income jointly drive this shift; governance-specific moderation is clearest for forests and production-based CO₂. Pursuing SDG 4 and SDG 16 together is therefore a precondition for education to accelerate sustainable development.

Abstract: Artificial intelligence can make migration information easier to access, yet perceived confidence benefits must be distinguished from accurate advice, informed decisions and effective rights protection. This study examines that distinction using 385 adult questionnaire records from Bangladesh. It analyses five migration-information statements separately rather than treating a heterogeneous item block as a validated confidence scale. The analysis combines complete response distributions, four paired endorsement contrasts, joint response configurations and an adjusted proportional-odds model of agreement that accurate AI information could improve migration-related decision confidence. Verification against official or authoritative sources received 87.5% endorsement, compared with 64.2% for the conditional confidence proposition. Their paired difference was 23.4 percentage points, with a whole-record bootstrap 95% interval of 17.9-28.8 points. Verification exceeded endorsement of comparison benefits, the distinction between general information and legal advice, and supplementary use; all four exact paired comparisons remained below p = .001 after Holm correction. Adjusted associations with the conditional confidence outcome were imprecise: comparison benefits, migration-specific verification, the legal-advice boundary and rights-information search all had intervals spanning an odds ratio of one. The results identify a stronger stated verification norm than endorsement of several practical conditions for reliance. They do not establish actual checking, improved decision quality or a causal effect of AI services. The article develops an information-governance account in which confidence is evaluated alongside source traceability, professional referral and usable verification routes. Its contribution is a measurement-specific account of anticipated benefit and verification, with implications for migration-information design and the evaluation of AI-supported legal information.