The core insight is behavioral. Tang separates what a design is claimed to do from what it actually does to the people navigating it. A verification step travels along two analytically distinct pathways. In the claimed-mechanism pathway, the design is hypothesized to verify information, induce deliberation, deter misuse, or screen applicants. In the experienced-burden pathway, the same design produces subjective effort, delay, anxiety, and failed recovery, and these costs depend on users’ cognitive, administrative, physical, linguistic, and digital resources. Crucially, the claimed mechanism does not mediate the experienced burden. A three-minute task for one user may mean device replacement, document retrieval, travel, or translation for another. Observed dropout is therefore behaviorally ambiguous: it may signal deterrence, confusion, inability to comply, or failed recovery, and treating everyone screened out as a fraudulent or erroneous applicant converts an outcome into an unsupported program-integrity claim.

To discipline these claims, BRFF builds a five-layer ontology. The first layer is design form, described at a granularity fine enough to change independently; multifactor authentication is too broad to evaluate, while entering a one-time code for every low-value interaction from a trusted device is evaluable. The second layer is the claimed mechanism, the pathway through which the design is expected to alter the target risk. The third is experienced burden, measured as users actually experience it rather than as the nominal length of a requirement. The fourth is behavioral outcome, split into protection-side measures such as residual fraud and unprevented error, and user-side measures such as blocked legitimate access, abandonment, delay, and failed recovery. The fifth layer is normative status: justified for the current review period, evidence-uncertain, a correctable misfit, or sludge. The object of evaluation is explicitly conditional, indexed to one friction, one risk, one service stage, one user group, and one review period, conditional on the available evidence and the feasible set of alternatives.

The framework then runs this object through a legal precondition and three behavioral gates. Gate 0, which sits outside the trivalent evidence structure, records the governing jurisdiction, legal basis, legitimate purpose, and binding rights constraints; without authority, the sequence stops. Gate 1 tests functional fit: whether the friction reduces a specified risk relative to an explicit counterfactual. Evidence is graded from A to D, where Grade A reflects credible randomized or quasi-experimental estimates of incremental protection, Grade B bounds material rival explanations with strong observational designs, Grade C supports only mechanism plausibility, and Grade D is an asserted purpose with no performance evidence. A Gate 1 Pass ordinarily requires Grade A or strong, independently scrutinized Grade B evidence. Grade C can support only narrow provisional use under a precautionary rule with safeguards, evidence milestones, and constrained renewal, and Grade D supports neither a Pass nor provisional use, regardless of how severe the target harm may be.

Gate 2 imports the logic of non-inferiority testing from clinical trials. Before any results are observed, the institution prespecifies a primary protection outcome and every material secondary protection outcome, including plausible displacement across channels or harm types, and sets a protection-loss margin for each. A lower-burden alternative qualifies only if it stays within every prespecified margin; a favorable primary outcome cannot compensate for material deterioration in a secondary one. A candidate that clears every margin then enters an intermediate routing state, pending a distributional assessment, and if that assessment passes and the candidate demonstrably lowers the prespecified burden outcome, Gate 2 is finalized as a failure of the current design, because the alternative dominates it. Gate 3, in its two labeled applications, applies access, recovery, and rights floors to every materially affected group, and these floors are non-compensable: no aggregate fraud reduction can license a procedure that predictably blocks a protected group without effective remedy.

The framework’s life cycle converts justification from a verdict into a monitored state. Every friction classified as justified carries a review date, monitoring indicators, and explicit triggers for reassessment, covering protection effect, abandonment, delay, recovery, group disparities, habituation, strategic circumvention, and the emergence of new lower-burden alternatives. What expires automatically is the positive justificatory status, not necessarily the control itself. Four statuses capture the possible states: Justified for the Current Review Period, Evidence-Uncertain, Correctable Misfit, and Sludge. Unknown is a result, Evidence-Uncertain is its status, and provisional use is an action; Fail and Unknown never share a default status. Even a manual fallback deployed during an emergency becomes a distinct evaluation unit that must re-enter the framework at Gate 0 and earn its own status, rather than inheriting the displaced control’s justification.

The paper demonstrates the framework’s bite with a worked application drawn from a real randomized field experiment involving approximately 65,000 Supplemental Nutrition Assistance Program applicants in Los Angeles County. The experiment held eligibility, documentation, and interview requirements constant but allowed treated applicants to complete the mandatory interview by phone at a convenient time. Thirty-day approval rose by 6.2 percentage points, from roughly 49 to 55 percent, and participation within five months rose by 2.2 points. Yet the study did not independently estimate fraud, erroneous payment, or false acceptance. Tang shows how BRFF prevents this measured access gain from becoming an unmeasured integrity claim: the fixed-time scheduling requirement becomes a separate evaluation unit, flexible scheduling becomes a plausible lower-burden candidate whose comparative protection effect remains unknown, and the missing protection comparison is converted into a specified test, safeguards, and an expiry condition rather than an open-ended inference.

Two explicitly hypothetical records illustrate the completed paths. In the Justified record, a 24-hour hold and independent callback after a high-value benefit recipient changes bank details passes Gate 1 with strong Grade B evidence showing confirmed payment misdirection of 0.4 percent with the hold versus 1.0 percent without it. An instant risk-tiered verification candidate fails the prespecified protection-loss margin, no other qualifying candidate exists, and rights floors are satisfied, so the hold is justified for a twelve-month review period with monitoring. In the Sludge record, mandatory in-person proof of life for pension verification detects confirmed ineligibility at 0.20 percent in person versus 0.21 percent through an authorized death-register match, while producing 8 percent non-completion among mobility-limited older users against 1 percent among others, with no timely restoration. Functional failure alone assigns the Sludge status, and the register-plus-escalation design must be assessed as a new unit from Gate 0.

The framework also yields empirically testable implications. Task–resource interaction predicts that greater mismatch between task demands and users’ resources should increase experienced burden and, through it, access error and abandonment, with accessible assistance attenuating the disparity. Ordeal informativeness predicts that increasing ordeal cost improves screening only when the capacity or willingness to endure the cost predicts ineligibility or target risk more strongly than genuine need, a prediction that existing screening economics has already complicated; research on health-insurance auto-enrollment, for example, estimated that replacing automatic enrollment with required active plan choice reduced new enrollment by approximately 33 percent and disproportionately excluded economically disadvantaged eligible adults, without measuring fraud. Conditional targeting predicts that risk-tiered friction lowers total burden within protection margins only when risk heterogeneity is large relative to classifier error, manipulation, privacy cost, and disparate impact, and that controls scheduled for revalidation should be less likely to remain dominated than comparable unreviewed controls.

Tang is careful about the framework’s limits. BRFF is an audit discipline, not an algorithm, and not a validated scale; inter-rater reliability is unknown. It does not replace legal judgment, supply universal thresholds, or solve fragmented data ownership, limited evaluation budgets, or the political cost of withdrawing visible controls. Evidence is asymmetric across domains, with benefit studies often omitting fraud outcomes and security studies omitting socioeconomic effects, so early audits may produce many evidence-uncertain controls, which is useful only if institutions resolve rather than warehouse that uncertainty. But the conceptual shift is clear: the relevant claim in policy debate is no longer that authentication or verification is generally beneficial or burdensome, but that a specified design, for a specified risk and group at a specified time, has passed an auditable sequence of causal, comparative, distributive, and temporal tests. Justified friction, in this view, is a monitored conclusion that a design may earn, lose, and regain, never a permanent property of the design itself.

Subject of Research: A behavioral risk–friction fit framework for justifying administrative frictions in digital public services

Article Title: Justification as a revisable state: a behavioral risk–friction fit framework for digital public services

Article References: Tang, K. (2026). Justification as a revisable state: a behavioral risk–friction fit framework for digital public services. Frontiers in Behavioral Economics, 5, Article 1936872. https://doi.org/10.3389/frbhe.2026.1936872

Image Credits: AI Generated

DOI: 10.3389/frbhe.2026.1936872

Keywords: administrative burden, behavioral economics, digital public services, sludge, proportionality, fraud prevention, digital exclusion, screening, SNAP, public policy, identity verification, evidence standards

The research was conducted by Dr. Katie Matt, Assistant Professor of Accounting at SUNY Polytechnic Institute, in collaboration with Daniel Hebert of Syracuse University. Their article, titled “Governance and Fraud Prevention in Federal Relief Programs: Insights from the Paycheck Protection Program,” analyzes the governance failures that allowed an estimated $64 billion in fraudulent claims, a figure reported by the U.S. Small Business Administration Inspector General. Rather than relying on statistical estimates alone, the researchers grounded their analysis in publicly prosecuted fraud cases, using court records to trace how specific schemes unfolded and which structural weaknesses made them possible.

The Paycheck Protection Program was administered through the Small Business Administration but delivered largely by private lenders, many of them financial technology companies that moved applications through automated pipelines at extraordinary speed. The researchers found that this delivery model, combined with limited verification requirements and minimal upfront screening, meant that applications could be approved with little more than self-reported figures. Borrowers attested to their payroll costs, employee counts, and business status, but in many cases no independent documentation was demanded before funds were disbursed. The design traded verification for velocity, and the study documents the consequences of that trade in detail.

Among the most common fraud patterns identified in the prosecuted cases were falsified financial records. Individuals inflated payroll expenses to qualify for larger loans, fabricated tax forms and bank statements to support their applications, and in some instances submitted the same fabricated documents to multiple lenders simultaneously. Because lenders were not systematically cross-checking applications against one another, duplicate and inflated requests could move through the system unnoticed. The researchers emphasize that these were not isolated acts of opportunism but recurring schemes that exploited predictable gaps in the program’s architecture.

Equally striking was the prevalence of fictitious businesses. In prosecuted cases, applicants created shell companies that existed only on paper, complete with fabricated employer identification numbers, invented employee rosters, and forged state registration documents. Because the program’s screening procedures were too weak to distinguish a legitimate small business from a convincing fabrication, these phantom enterprises received millions of dollars in federal funds. The study notes that once the money was disbursed, recovery became difficult, as proceeds were often spent quickly on personal purchases, real estate, luxury vehicles, and other expenditures far removed from payroll support.

The researchers argue that the root of the problem was not simply individual dishonesty but governance design. Emergency programs face an inherent tension: the faster aid must flow, the less time there is for verification, and the weaker the controls that can realistically be imposed. The PPP resolved that tension almost entirely in favor of speed, relying on after-the-fact audits and prosecutions to catch wrongdoing. The study shows that this retrospective approach caught many offenders, but only after billions of dollars had already left the treasury, and only against a fraction of the total fraud that investigators believe occurred.

From this analysis, Matt and Hebert derive a set of practical recommendations for policymakers who will inevitably design future disaster and economic relief programs. First, they call for improved applicant verification, including systematic cross-checking of applications against existing federal and state databases before disbursement. Second, they recommend centralized fraud prevention efforts, so that intelligence about suspicious applications is shared across lenders rather than trapped within individual institutions. The fragmented structure of the PPP meant that a fraudulent application rejected by one bank could simply be resubmitted elsewhere, a pattern the prosecuted cases illustrate repeatedly.

The recommendations also extend to the composition of the oversight workforce and the technology used to screen applications. The researchers advocate greater involvement of forensic accountants, whose training in detecting fabricated documents and tracing illicit fund flows is directly suited to the fraud patterns observed in the PPP cases. They further call for enhanced oversight of financial technology lenders, which processed a large share of program volume but operated under less traditional banking supervision. Finally, the study explores how artificial intelligence could strengthen future programs, using machine learning models to flag suspicious applications in real time by identifying anomalies in reported figures, duplicate documentation, and patterns inconsistent with a business’s stated industry or size.

The researchers are careful to frame these safeguards as complements to, rather than substitutes for, rapid aid delivery. Their central argument is that the choice between speed and accountability is a false one when controls are designed in advance. Automated identity verification, database cross-referencing, and AI-based anomaly detection can operate at the pace of an emergency rollout, catching the most blatant fraud without slowing legitimate applications. The lesson of the PPP, in their view, is that governance must be built into the architecture of a relief program from the start, not bolted on through prosecutions after the damage is done.

As federal agencies continue to recover funds and pursue cases stemming from pandemic-era programs, the study offers a timely evidence base for reform. The $64 billion in fraudulent claims represents taxpayer money intended for struggling businesses and their employees, and the researchers argue that preventing even a fraction of similar losses in future emergencies would justify the investment in stronger verification, centralized coordination, forensic expertise, and intelligent screening. Their work underscores a principle that extends well beyond one program: in national crises, the design of financial controls is not a bureaucratic afterthought but a determinant of whether aid reaches the people it was meant to protect.

Subject of Research: Fraud prevention and governance weaknesses in the federal Paycheck Protection Program

Article Title: Dr. Katie Matt examines fraud prevention in federal relief programs

Article References: Dr. Katie Matt examines fraud prevention in federal relief programs. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Paycheck Protection Program, fraud prevention, federal relief programs, COVID-19, small business loans, forensic accounting, artificial intelligence, government oversight, SUNY Polytechnic Institute, Small Business Administration, financial technology lenders, public accountability

The study, led by Ali Ala of TU Dublin and Saveetha Institute of Medical and Technical Sciences, together with Behnam Malmir, Hamed Baziyad, and Vladimir Simic, frames blockchain adoption not as a simple technology selection problem but as a cognitive reasoning challenge. Supply chain managers must judge interdependent criteria under uncertainty, weighing incomplete information and conflicting priorities. To capture that mental process, the researchers collected linguistic judgments from a panel of twenty experts—academics and industry practitioners, most with more than a decade of supply chain experience—and converted their assessments into triangular fuzzy numbers, a mathematical device that preserves the vagueness inherent in human language rather than forcing it into artificial precision.

The Fuzzy-DEMATEL method, an extension of the Decision-Making Trial and Evaluation Laboratory technique, then transforms those judgments into a total-relations matrix that captures both direct and indirect influences among criteria. From this matrix, the team computed two key statistics for each criterion: R, the total influence it exerts on the others, and C, the total influence it receives. The difference, R minus C, separates net causes from net effects, while the sum, R plus C, measures overall prominence within the system. The result is an impact relationship map—a visual, explainable picture of how experts believe the adoption ecosystem is wired.

The headline finding concerns smart contracts: self-executing, code-driven agreements that trigger transactions and certifications without intermediaries. Smart contracts recorded the highest outgoing influence of any criterion, at R = 3.46, and the strongest net causal effect, R minus C = +0.78. In plain terms, the experts judged that nearly every other capability in a blockchain deployment depends on contract logic. Smart contracts determine when a traceability record is written, under what conditions a payment settles, and what happens when a condition fails. Intervening on smart contracts changes what the system does; intervening on almost anything else changes only how much of it is visible.

Food traceability, by contrast, emerged as the most prominent criterion overall, with a combined influence score of R + C = 6.40, yet its net causality was nearly neutral at +0.06. The authors interpret this as the signature of a hub rather than a driver. Traceability simultaneously enables fraud detection and waste identification downstream while relying on smart contracts to generate records, secure storage to retain them, and payment events to populate them. It is the integration point of the system, not its origin. Alongside smart contracts, the analysis classified payment transactions, fraud prevention, and permanent secure storage as net causes, while food waste prevention, carbon emission detection, and power consumption fell firmly into the net-effect camp.

That last classification carries perhaps the most consequential practical implication. Food waste prevention and carbon emission detection sent no influence edges at or above the analytical threshold, marking them as pure receivers in the causal network. The authors argue this reflects a fundamental distinction between capabilities and measured outcomes: a blockchain deployment executes contracts, records provenance, and settles payments—things it does—whereas waste reduction and verified emissions are things it reveals or achieves. An outcome cannot be a precondition for the mechanism that produces it. For companies tempted to launch their first blockchain pilot around a sustainability story, the message is sobering: such pilots are likely to disappoint for reasons unrelated to the technology itself, because the capabilities they depend on have not yet been built.

The study’s robustness checks strengthen its claims considerably. Kendall’s coefficient of concordance showed strong agreement among the twenty experts (W = 0.72, p < 0.001), and a matrix-convergence test confirmed the stability of the aggregated judgments. Sensitivity analyses varied both the edge threshold and the prominence cutoff used to classify criteria; the cause-effect partition held across every setting for seven of the eight criteria. The lone exception was fraud prevention, which showed a slightly negative net causality at the lower bound of the fuzzy uncertainty range. Rather than dismissing this as noise, the authors read it as substantively meaningful: fraud prevention only operates where records are already trustworthy, so its causal power is conditional on prior infrastructure. They describe it as a conditional cause—high priority, but contingent on deployment maturity.

The findings also carry theoretical weight. The criteria were drawn from the Technology-Organization-Environment framework, transaction cost theory, and diffusion of innovation theory, yet the causal map revealed something the standard TOE formulation does not predict: technology-dimension criteria predominantly send influence while environment-dimension criteria predominantly receive it. If this pattern holds across contexts, the authors suggest, the three TOE dimensions may be organized sequentially rather than in parallel—a hypothesis they explicitly offer for future testing rather than a settled conclusion. Transaction cost theory, meanwhile, helps explain why smart contracts, traceability, and fraud prevention dominate: all three address the opportunistic behavior and information asymmetry that plague multi-party food chains.

For practitioners, the study proposes a three-stage implementation sequence. First comes the transactional foundation: define contract logic, data-writing rights, validation rules, and the consensus mechanism before any pilot begins—a stage at which energy concerns can also be largely resolved, since modern permissioned, Byzantine-fault-tolerant systems avoid the power-hungry proof-of-work designs of earlier blockchain generations. Second, once contract execution is reliable across at least two organizational boundaries, organizations should build operational capability in traceability, fraud prevention, and secure storage. Third, only after those layers exist should sustainability outcomes such as waste reduction and emissions verification be pursued. For policymakers, the analysis suggests that mandating sustainability disclosure before traceability infrastructure exists produces reports that cannot be independently verified; regulatory sequencing should mirror the implementation sequence, with data-format and interoperability standards first and verified sustainability reporting second.

The authors are careful about the limits of their evidence. The results derive from expert-elicited perceptions rather than longitudinal field performance data, from a single purposive panel, and from a criterion set deliberately restricted to eight items, excluding factors such as governance arrangements and interoperability with legacy systems. DEMATEL yields perceived directional relationships, not statistically tested causal effects. Still, the convergence between the expert-derived map and real-world deployment studies—including traceability platforms, halal-certification blockchains, and IoT-integrated smart-contract frameworks—lends external plausibility to the prioritization. Future work, the team notes, should test the proposed sequence with field data, larger and more diverse panels, and cross-country comparisons, and should extend the framework to the AI, IoT, and remote-sensing technologies increasingly layered atop distributed ledgers. For now, the study offers food supply chains something they have lacked: an explainable, evidence-based answer to the question of what to build first.

Subject of Research: Cognitive causal modeling of blockchain adoption criteria in agri-food supply chains using Fuzzy-DEMATEL

Article Title: Cognitive Causal Modeling of Blockchain Adoption in Agri-Food Supply Chains: A Fuzzy-DEMATEL Approach

Article References: Ala, A., Malmir, B., Baziyad, H., & Simic, V. (2026). Cognitive Causal Modeling of Blockchain Adoption in Agri-Food Supply Chains: A Fuzzy-DEMATEL Approach. Cognitive Computation, 18(1), Article 114. https://doi.org/10.1007/s12559-026-10660-0

Image Credits: AI Generated

DOI: 10.1007/s12559-026-10660-0

Keywords: blockchain, agri-food supply chain, Fuzzy-DEMATEL, smart contracts, food traceability, multi-criteria decision-making, sustainability, fraud prevention, cognitive computation, expert judgment, impact relationship map, technology adoption

Dr. Katie Matt, Assistant Professor of Accounting, and Dr. Lisa Berardino, Associate Professor of Management, co-authored a peer-reviewed study titled “An Integrated Approach to Developing Students’ Ethical Values for Accounting Fraud Prevention: Aligning Course Learning Objectives with the Elements of Fraud,” published in the Business Education Innovation Journal. Their research confronts a persistent shortcoming in business education: the tendency to treat ethics as a standalone requirement, often delivered in a single isolated course, rather than as a thread woven through every stage of a student’s professional formation. The authors contend that this fragmented approach leaves graduates ill-equipped to recognize and resist the pressures that give rise to fraudulent conduct in real organizational settings.

At the analytical heart of the study lies the Fraud Triangle, one of the most widely used frameworks in accounting research and practice for understanding why fraud occurs. Developed from the work of criminologist Donald Cressey, the model identifies three conditions that typically converge when an individual commits occupational fraud: pressure, opportunity, and rationalization. Pressure refers to the financial or personal stresses that motivate someone to act, such as debt, unrealistic performance targets, or lifestyle expectations. Opportunity describes the perceived ability to carry out and conceal the act, often stemming from weak internal controls or excessive trust placed in a single employee. Rationalization is the psychological process by which the individual justifies the behavior to themselves, convincing themselves that the act is temporary, deserved, harmless, or somehow different from true dishonesty.

Matt and Berardino use this tripartite framework not merely to explain fraud after the fact, but as a design blueprint for curriculum development. Their central proposal is that every element of the Fraud Triangle can be addressed through carefully constructed learning objectives embedded across multiple courses. A student’s understanding of pressure can be deepened in courses on organizational behavior and human resource management, where topics such as performance evaluation systems, workplace stress, and incentive structures illuminate how organizations inadvertently cultivate the very stresses that push employees toward misconduct. Opportunity can be examined in accounting and auditing courses through the study of internal controls, segregation of duties, and the systemic vulnerabilities that arise when oversight is weak. Rationalization, perhaps the most psychologically complex element, can be explored through the humanities and strategic management, where students grapple with ethical reasoning, moral disengagement, and the organizational cultures that normalize questionable behavior.

The practical mechanism for achieving this integration, according to the authors, is curriculum mapping, a systematic process by which faculty chart the learning objectives of each course in a program and identify where ethical themes can be introduced, reinforced, and assessed. Rather than relying on a single business ethics course to carry the full moral weight of a student’s education, the integrated model distributes that responsibility across accounting, strategic management, organizational behavior, human resource management, and humanities offerings. In doing so, students encounter ethical questions repeatedly, in varying contexts, and from multiple disciplinary perspectives, mirroring the way ethical dilemmas actually present themselves in professional life, where they rarely arrive neatly labeled or confined to a single domain of knowledge.

The specific learning objectives proposed in the study are grounded in observable, assessable outcomes. Students should be able to evaluate real-world fraud cases, drawing lessons from the documented failures of organizations and the individuals within them. They should be able to recognize common fraud risk indicators, from unusual journal entries and lifestyle changes to reluctance to take vacation leave and overriding of established controls. They should understand how workplace culture shapes decision making, recognizing that an environment tolerating small ethical lapses is far more likely to tolerate large ones. And they should be alert to how emerging technologies, including artificial intelligence, may create entirely new categories of fraud risk, as automated systems, synthetic data, and machine-generated content open novel avenues for deception that traditional controls were never designed to catch.

The emphasis on emerging technology reflects a broader recognition that the fraud landscape is evolving faster than conventional educational approaches. Artificial intelligence tools can now be used to fabricate documents, simulate legitimate transactions, and generate convincing communications at a scale and sophistication that raise the stakes for ethical awareness. The authors’ argument implies that future accounting and business professionals will need not only technical competence with these tools but also the ethical grounding to anticipate their misuse and to question outputs that seem too convenient, too favorable, or too good to scrutinize. Integrating such discussions across the curriculum ensures that technological literacy and ethical judgment develop together rather than in isolation.

The research also speaks to a long-standing debate in professional accounting education. Accrediting bodies and professional organizations, including those that shape the certification pathways for certified public accountants and internal auditors, have repeatedly emphasized the importance of professional skepticism and integrity. Yet studies of business school graduates have frequently found that ethical development does not reliably follow from a single course, particularly one taken early in a program and disconnected from technical coursework. By contrast, an integrated approach creates repeated opportunities for students to practice ethical decision making under increasingly realistic conditions, gradually building what the authors describe as the values and professional integrity needed to navigate difficult workplace situations throughout an entire career, not merely to pass an exam.

The interdisciplinary character of the proposal is equally significant. By involving faculty from management, human resources, strategy, and the humanities in fraud prevention education, the model acknowledges that fraud is not solely an accounting problem. It is a human and organizational problem, shaped by culture, leadership, incentives, and individual psychology. A student who has examined fraud through the lenses of organizational behavior and ethical philosophy, as well as through the technical mechanics of financial reporting, emerges with a richer and more durable set of defenses against it. In an era when the costs of corporate misconduct, both financial and reputational, continue to climb, Matt and Berardino’s work offers business schools a concrete, actionable framework for producing graduates who are not only technically proficient but ethically prepared to recognize fraud risks, resist rationalization, and make principled decisions when it matters most.

Subject of Research: The role of integrated ethics education in preventing accounting fraud among business students

Article Title: College of Business faculty explore role of ethics education in accounting fraud prevention

Article References: College of Business faculty explore role of ethics education in accounting fraud prevention. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: accounting fraud, ethics education, Fraud Triangle, business curriculum, professional integrity, curriculum mapping, organizational culture, rationalization, artificial intelligence, fraud prevention, SUNY Polytechnic Institute, business education