Research into practice. Judgment into stewardship.
A disciplined reading path for families who govern capital. Each attributed research edition sits beside an original practical companion, with a separate passage agent for each work.
Family-office research is complemented by relevant studies of family businesses, capital markets, property and financial decisions. Each article states the setting actually studied. An application to a family office is an editorial inference, not a claim that every original study sampled family offices.
Provides a structured follow-through process for family-enterprise retirement education and transitions, with economic constraints kept distinct from motivation.
Retirement planning is a process that must survive competing demands, not a document completed once. This companion proposes a ninety-day review for family-enterprise employees and participating family members, separating knowledge, confidence, economic constraints and completed actions. It draws on a longitudinal Spanish worker study without importing its findings as individualized advice.
Tomasz Szopiński; Michał Buszko; Małgorzata Porada-Rochoń
PLOS One · 2026-03-19
Supports a carefully bounded AI financial-learning service for beneficiaries and next-generation family members without conflating engagement with competence.
Positive attitudes toward an AI assistant are not evidence that it improves financial judgment. This companion proposes a limited learning pilot that measures comprehension, source checking and appropriate escalation alongside adoption. It uses a study of consumer acceptance to distinguish a service people like from a service they can use responsibly.
Elisabeth Carter; Vasileios Karagiannopoulos; Annie Kirby; Mark Button
Security Journal · 2026-10-15
Informs respectful incident disclosure, cross-generational support and practical fraud learning without assigning blame or overriding decision-making authority.
How a family responds to suspected fraud can determine whether the affected person shares the next warning sign. This companion proposes a nonjudgmental disclosure and learning protocol grounded in qualitative research on older adults’ perceptions. It protects autonomy, distinguishes support from authority and keeps causal claims within the source’s limits.
Helps offices challenge a vendor’s customer segmentation claims and protect beneficiaries from propensity scores being mistaken for sound financial recommendations.
A model that predicts who will accept a digital financial service does not establish who should use it. This companion develops a purpose-limited audit for family offices evaluating predictive targeting by banks or technology vendors. It separates marketing propensity, informed consent, appropriateness and observed outcomes.
Adjacent evidence relevant to family-office financial decisions, governance, education or property risk. The original study is not a direct family-office sample; the practical transfer is proposed and limited.
This AI-assisted editorial analysis uses a Chinese household survey to distinguish digital financial literacy, higher consumption and financial wellbeing. It proposes an education protocol that measures understanding and affordability rather than account openings, borrowing or spending targets. The source does not establish that training improves wealth or welfare.
Ryan L. Sriver; Robert J. Lempert; Per Wikman-Svahn; Klaus Keller
PLOS ONE · 2018-02-07
Adjacent evidence relevant to family-office financial decisions, governance, education or property risk. The original study is not a direct family-office sample; the practical transfer is proposed and limited.
This AI-assisted editorial analysis translates a 2018 idealized Port of Los Angeles infrastructure study into a proposed process for examining coastal-property decisions under deep uncertainty. It separates exploratory scenario counts from probabilities and links decision thresholds to current local engineering evidence. Historical model outputs are not current property-risk estimates.
Preeti Sunderaraman; Silvia Chapman; Megan S. Barker; Stephanie Cosentino
PLOS ONE · 2020-07-02
Adjacent evidence relevant to family-office financial decisions, governance, education or property risk. The original study is not a direct family-office sample; the practical transfer is proposed and limited.
This AI-assisted editorial analysis uses a study of 93 healthy adults to separate confidence in financial decisions from demonstrated understanding. It proposes respectful, task-specific clarification and consent records while preserving personal agency. Research measures are not clinical or legal capacity assessments.
Dorien F. Bangma; Anselm B. M. Fuermaier; Lara Tucha; Oliver Tucha; Janneke Koerts
PLOS ONE · 2017-08-09
Adjacent evidence relevant to family-office financial decisions, governance, education or property risk. The original study is not a direct family-office sample; the practical transfer is proposed and limited.
This AI-assisted editorial analysis draws on research finding different age relationships across financial decision tasks. It proposes clearer constraint comparisons, documented support preferences and explicit authority without using chronological age as an individual capacity judgment. Laboratory findings do not establish real-world loss prevention.
Armando Aguayo-Mendoza; Ane Irizar-Arrieta; Diego Casado-Mansilla; Cruz E. Borges
PLOS ONE · 2024-03-21
Adjacent evidence relevant to family-office financial decisions, governance, education or property risk. The original study is not a direct family-office sample; the practical transfer is proposed and limited.
This AI-assisted editorial analysis uses expert elicitation and household survey research to examine energy-investment adoption assumptions beyond price subsidies. It proposes tracking technical fit, perceived control and actual adoption stages. Unvalidated archetypes and inconsistent subgroup statements are disclosed and are not used to target individuals.
This AI-assisted editorial analysis uses a Chinese investor survey to distinguish predicting reported behavior from establishing an individual’s investment needs. The proposed workflow replaces demographic shortcuts with direct preference records, outcome-specific validation and human review. It has not been tested in family offices.
A survey of Malaysian Muslim Islamic unit trust investors links reported intentions with attitudes, subjective norms, financial literacy and fintech self-efficacy. This AI-assisted companion proposes a digital-access and decision-consent process while preserving the distinction between confidence, intention and investment suitability. Reporting inconsistencies in the source are explicitly retained as limitations.
A laboratory lottery experiment found different tendencies to randomize under different payoff structures. This AI-assisted companion translates the study into a proposed audit of decision rules, evidence updates and pattern claims. It does not equate diversified investing with an error or claim the experiment validates a family-office trading strategy.
This expansion has added 208 works toward the requested 4,000: 2,000 source editions and 2,000 practical companions. The 104 works already in the journal are excluded from that target.
5.2% of the requested expansion published.
Article-specific license, metadata and source checks are recorded separately from human peer review. New practical companions are AI-assisted editorial analyses; no independent human peer-review acceptance is asserted. Recorded independent human acceptance for the new companions: 0. Source authors, original journals, publication dates and existing DOIs remain unchanged.
1,778 further metadata-screened leads are retained for investigation. Potential sources only. Publisher/version rights, full text, quality, retraction and related-version checks remain to be completed. They do not count as published articles or agents.
Checked DOI, source-version DOI and normalized title in available repository catalogs. Live submissions and all earlier review queues were not fully available; platform-wide novelty is not claimed.