Model Statements are designed to modify an interviewee's expectation of the amount of details required during an interview. This study examined tailored Model Statements, emphasising either spatial (Spatial-MS), or temporal (Temporal-MS) details, compared to a control condition (no-MS). 126 participants (63 liars, 63 truth-tellers), were randomly allocated to one of three interviewing conditions. Truth-tellers honestly reported a 'spy' mission, whereas liars performed a covert mission and lied about their activities. The Spatial-MS elicited more spatial details than the control, particularly for truth-tellers. The Temporal-MS elicited more temporal details than the control, for truth-tellers and liars combined.Results indicate that the composition of different Model Statements increase the amount of details provided and, regarding spatial details, affect truth-teller's and liar's statements differently. Thus, Model Statements can be constructed to elicit information and magnify cues to deceit.

Liars can, when prompted, provide detailed statements. Ideally, interview protocols to improve lie-detection should (a) encourage forthcoming verbal strategies for truth tellers and (b) encourage withholding verbal strategies for liars. Previous research has investigated (a) but not (b). We designed an Asymmetric Information Management (AIM) instructioninforming interviewees, inter alia, that more detailed statements are easier to accurately classify as genuine or fabricated -to encourage truth tellers to be verbally forthcoming and to encourage liars to be verbally withholding. Truth tellers (n = 52) and liars (n = 52) took part in one of two counterbalanced missions, and were assigned to either the AIM or control interviewing condition. Truth tellers provided (and liars withheld) more information in the AIM condition (compared to the control condition), and thus, discriminant analysis classificatory performance was improved. Therefore, a simple instruction can simultaneously modify the respective strategies of liars and truth tellers.

Forensic interviewing involves gathering information from a suspect or eyewitness. Administering a model statement during an interview results in greater information elicitation, which can enhance lie detection. Typically, a model statement is a highly detailed statement, on an unrelated topic to that of the interview. This study examined the effect of manipulating the modality of the MS, either by allowing participants to listen to (Audio‐MS), or read (Written‐MS) a model statement. A total of 162 (81 truth tellers, 81 liars) participants were randomly allocated to one of three interviewing conditions where they received either the Audio‐MS, Written‐MS, or No‐MS (control condition). Truth tellers honestly reported a “spy” mission, whereas liars performed a covert mission and lied about their activities. Results showed both model statements were equally more effective at eliciting information and facilitating lie detection, compared with a control condition. Theoretical and practical implications are discussed.

Investigators need to elicit detailed statements from interviewees to find potential leads, whilst simultaneously judging if a statement is genuine or fabricated. Researchers have proposed that the Model Statement (MS) can both (a) increase information elicitation from interviewees and (b) amplify the verbal differences between liars and truth tellers, thereby enhancing lie‐detection accuracy. Based upon a critical analysis of the MS literature, we argue that this tool is not currently ready for practical usage, as its utility has not been fully established. We highlight a diverse range of existing MS scripts, and a greater diversity in the dependent measures examined in conjunction with this tool. More robust replications of these procedures are needed. We also highlight why some measures of verbal content may not be suitable as outcome measures and suggest that new research could use the well‐established reality monitoring criteria to allow for standardisation across studies.

Converged security risk management is an approach that addresses interdependencies between security-related business functions that have traditionally been managed by separate departments within organizations. It is a more effective means of addressing organizational security risks and threats than tackling physical and information security challenges separately, given that the boundaries between the two are frequently blurred. However, fully converged security remains the exception rather than the rule, leaving organizations increasingly vulnerable as their adoption and reliance on digital technologies accelerates. Through interviews with eight senior security professionals, this research identified key factors critical to effective converged security risk management, expressed as ‘drivers,’ ‘barriers,’ and ‘facilitators.’ The practitioners’ accounts illuminated how the modern threat landscape continues to drive further the need for such an approach, while the traditional separation of corporate security departments from the information security function in organizations remains a barrier. A greater focus on training and education, as well as soft skills, were identified as key priorities in the drive for an effective converged approach.