Skip to main content

“Elder Scam” Risk Profiles: Individual and Situational Factors of Younger and Older Age Groups’ Fraud Victimization

Katalin Parti (2022) — International Journal of Cybersecurity Intelligence & Cybercrime

Citation activity

Total citations · Google Scholar
Unavailable
Average per calendar year
Citation count unavailable

Counts reflect Google Scholar’s coverage and do not measure research quality. Source, calculation and limitations

What do these research terms mean?
Preprint
A manuscript shared before formal peer review and publication. Check whether a later published version is available.
Dataset
A collection of data or examples for others to inspect or reuse. It can appear in Library search and topic mapping, but RSRC does not use dataset records as evidence in Research Insights.
Dissertation or thesis
Research submitted for an academic degree. This describes its format, not its reliability.
Journal article
An article published in a journal. This label alone does not establish peer review, study quality, or how well the findings apply elsewhere.
Qualitative research
Examines experiences, meanings, or processes, often through interviews or observations. It can explain how something happens without estimating how common it is.
Quantitative research
Uses numerical measurements to describe patterns or test relationships. A relationship between two measurements does not by itself show that one causes the other.
Systematic review
Uses a planned, documented method to find and assess research addressing a question. Its conclusions still depend on the included studies and what the search covered.
Meta-analysis
Statistically combines results from multiple studies. Combining studies does not remove weaknesses in their design or make unlike populations interchangeable.
Not classified
This record has no recognized label in this filter. It does not mean the publication used no method, or that no research exists.

Definitions draw on DataCite resource types; Cochrane review methods; NLM: association and causation. RSRC’s dataset and classification rules are explained in our methodology.

Citation tools


              
              

Transparency

Evidence and review status

This page contains AI-generated content. No human content review or subject-matter-expert review is recorded.

Source basis
Downloaded PDF
Source updates
No notice found at last check
AI-generated page content
Yes
Automated checks
Passed
Administrative approval
Yes
Human content review
Not recorded
Subject-matter-expert review
Not recorded
How this was prepared
Source basis

RSRC downloaded and privately stored a copy of the paper for internal analysis. The PDF is not offered to viewers from this page.

  • PDF added to RSRC:
Source updates

No incoming update notice was found in the dated Crossref response. Coverage is incomplete, particularly for corrections and expressions of concern; this is not a guarantee that the source is valid or unchanged.

  • Last source-status attempt:
AI-generated page content

AI-generated research notes displayed on this page: Synopsis, Identified gaps, Methods, Limitations, Future work. The paper itself is not described as AI-generated.

  • Document analysis recorded:
  • Synopsis generation recorded:
  • Page record updated:
Automated checks

The current, source-bound synopsis passed the recorded versioned publication checks.

  • Checks completed:
View passed checks (3)
  • Length, completeness, repetition, refusal, boilerplate, and active-markup screening
  • Numerical claims checked against the available source text
  • English-source lexical grounding check
Administrative approval

An authenticated administrator approved the bibliographic record for public Library display. This is not a review of every research claim.

  • Approved for public display:
Human content review

No human review is recorded for the AI-generated content displayed on this page.

Subject-matter-expert review

RSRC has not recorded review of this content by a subject-matter or methods expert.

Review-state definitions
Found a possible error? Request a correction.

Synopsis

The article investigates how individual characteristics and daily activities relate to online fraud victimization, and whether these patterns differ between younger (18–54) and older (55+) adults. The study combines the General Theory of Crime with Lifestyle-Rifestyle Routine Activities Theory to test how self-control, online lifestyle factors, and guardianship influence six scam types drawn from the FBI elder fraud list. A national sample of 2,558 U.S. adults, representative by age, sex, and race, was analyzed, with participants divided into two age groups for separate logistic regression analyses. The dependent variables are six binary indications of scam victimization (private info, IT support, grandparent/romance, company impersonation, advance-fee, and other impersonation scams) experienced in the past year, with additional harm outcomes recorded for those reporting victimization. The findings indicate that low self-control and certain routine-online activities predict victimization across the online-scam outcomes, though the strength and significance of effects vary by age and scam type. In general, self-control showed a stronger predictive pattern for older adults, while one exception was the grandparent scam, which showed a different pattern by age. The analysis also examined lifestyle-exposure and guardianship factors, including hours online, use of multiple online services, perceived computer knowledge, and the use of protective technologies or nontechnical safeguards. The study highlights that employment can function as a protective factor for older individuals in some online fraud scenarios, and that older adults report greater reluctance to seek help or report fraud than younger adults. The authors note that reporting behavior differs by age, which has implications for understanding and preventing elder fraud. They call for further research to refine LRAT measures for scams and to explore how guardianship and help-seeking behaviors interact with age to influence victimization. The article does not provide policy recommendations beyond these suggested research directions.

Identified Gaps

Existing lifestyle-routine activities measures may not capture scams initiated by phone contact and sustained through emotional manipulation. The timing and effects of reporting or help-seeking on repeat victimization are unknown. The study identifies a need for scam-specific measures of motivated offenders, suitable targets, and capable guardians, plus research on trust, mental health, cognitive decline, education, and employment-related protection.

Methods

A 2020 online survey of 2,558 U.S. adults, nationally representative by age, sex, and race, compared adults aged 18–54 and 55+. Separate logistic regressions modeled six past-year online scam scenarios using self-control, lifestyle-routine activities, guardianship, demographics, and reporting/help-seeking variables. Respondents were classified as victims only when they reported harmful consequences, such as financial loss or distress.

Limitations

The survey could not determine whether reporting or help-seeking occurred after an initial or repeated scam, so causal or preventive effects cannot be inferred. It was not designed to measure subsequent victimization. Standard online-activity and guardianship variables may inadequately measure sophisticated, emotionally manipulative scams, many of which began by phone contact.

Future Work

Develop scam-specific measures for sophisticated, emotionally manipulative scams; identify technical and nontechnical guardians that prevent such fraud; test whether reporting and help-seeking reduce repeat victimization; examine trust, mental health, cognitive decline, education, employment-related risks, secure workplace networks, and age-appropriate employee training.

See how this publication connects to RSRC's living evidence syntheses through current citations and research-topic mapping.