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Fear of missing out and online scam vulnerability: a sequential interlayering multimethod approach

Youngjin Park ; Juyoung Kang (2026) — Telematics and Informatics 105, 102377

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DOI: 10.1016/j.tele.2026.102377 (opens in a new tab)

Country:   South Korea
Tags:   AI Scam Prevention, Digital Ethnography, Experimental Vignette Method, Fear Of Missing Out, Heuristic Systematic Processing, Message Framing, Online Scam Vulnerability, Smishing
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Synopsis

            
This study investigates how Fear of Missing Out (FOMO), message framing, and information processing tendencies shape vulnerability to online scams. The authors propose the SC-HIP framework, an integration of the C-HIP model with Heuristic-Systematic Processing (HSM) and hyperpersonal communication theory, to explain how scammers orchestrate relational persuasion in digital environments. They conduct a sequential interlayering multimethod program: Study 1 uses digital ethnography to observe real-time scam interactions, coding 210 messages across seven confirmed scam accounts and identifying a three-stage progression—approach, trust-building, and exploitation—and the use of FOMO cues alongside hedonic and utilitarian framing. Study 2 then tests the resulting constructs in a controlled experiment (3 FOMO levels × 2 framing types, N = 273), measuring behavioral intention to engage with scams and tracking heuristic versus systematic processing tendencies. The findings indicate that FOMO exerts a main effect on scam vulnerability and interacts with message framing to shape processing routes. Specifically, hedonic framing elevates vulnerability most at medium FOMO, while utilitarian framing increases vulnerability as FOMO intensifies. Both heuristic and systematic processing tendencies contribute to this effect, with partial mediation observed for heuristic processing and a separate mediation pathway for systematic processing. The results support viewing online scams as relationship-based persuasion occurring within hyperpersonal communication contexts, where affective states and message design jointly steer information processing toward greater susceptibility. The authors discuss implications for proactive AI-based detection and tailored digital literacy interventions, outlining how adaptive warning systems could respond to affective cues and framing in real time, while acknowledging the study’s limitations and avenues for future research.

          

        
                  

            
Identified Gaps

            
Prior research relies heavily on victim retrospection and inadequately explains real-time persuasion. Evidence on scammers’ linguistic and psychological FOMO cues is limited, as is validation of the interaction between FOMO and message framing. Existing prevention systems are described as reactive, with insufficient understanding of how cognitive and affective factors combine to create vulnerability.

          

        
                  

            
Methods

            
Sequential exploratory-confirmatory multimethod design. Study 1 used 12 months of digital ethnography, analyzing 210 smishing messages and sustained interactions with seven confirmed scam accounts using thematic, pattern, and frequency analysis. Study 2 used a preregistered-not-stated online 3 × 2 experimental vignette design with 273 panel participants, manipulating FOMO level and hedonic versus utilitarian framing. Analyses included manipulation checks, exploratory factor analysis, two-way ANCOVA controlling social-media use, and bootstrapped PROCESS mediation models.

          

        
                  

            
Limitations

            
Text-based chat vignettes did not represent multimodal scams involving voice, video, images, or deepfakes. The study did not directly test learning effects or changes in resistance over time. Its social-media-user sample came from a specific cultural context, limiting generalizability. It also gave limited attention to platform-level factors such as interface design, notifications, visual trust cues, and recommender systems. Behavioral intention rather than actual victimization was the primary outcome.

          

        
                  

            
Future Work

            
Use multimodal VR, AI-agent, or immersive scam simulations; conduct longitudinal or field research on learning and resistance; test multicultural and multinational samples; examine platform design factors and adaptive real-time warnings; assess metacognitive awareness as a moderator; and test dual-pathway interventions combining immediate behavioral inhibition with trust-calibration training.

          

              

    
    

        
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