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Research Commons is the University of Waikato's open access research repository, housing research publications and theses produced by the University's staff and students.

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Athlete endorsement and nationality: Is there a world cup effect?
(European Advertising Academy (EAA), 2023) Desmarais, Fabrice; Calderwood, Richard; Mukherjee, Abhishek
This research considers whether attributes such as the nationality of athlete endorsers and their physical and emotional characteristics have an impact on how appealing, credible, and relevant an advertising message is to a country’s national audience. Using rugby athletes, the study assesses the impact of these attributes on respondents’ ad preference and purchasing intention across two distinct periods: during and after the rugby world cup. The results indicate that congruence of national identity between audience and endorser leads to a more positive assessment of the endorsement and a high intention to purchase by the audience across both time periods but that both ad preference and purchase intentions are higher during a mega event period.
Product type associations with hard-sell and soft-sell vocal styles: A cross cultural exploration
(European Advertising Academy (EAA), 2024) Desmarais, Fabrice; Vignolles, Alexandra; Galan, Jean-Phillippe
This study explores whether consumers’ associate hard sell and soft sell vocal styles with specific products The research, conducted across two countries (France and New Zealand), helps us understand whether these associations are universal or differ according to culture. The study shows that assessments of product-advertising vocal style fittingness are mostly consistent across countries but that cultural idiosyncrasies also need to be considered by advertisers.
Response-level identification of Cloud API misconfigurations using large language models
(Scilight Press, 2026) Zahid, Farzana; Krishna, Akshay
Application Programming Interfaces (APIs) are a set of rules that enable communication, data exchange, and automated interactions between applications and services. With the rapid advancement of cloud computing, APIs have evolved from simple data-access interfaces into critical components for managing, configuring, and orchestrating cloud resources. Most modern cloud platforms rely on RESTful APIs for provisioning cloud resources, applying configurations, and maintaining services. As a result, APIs misconfigurations have become a critical cloud security threat that can lead to sensitive data exposure, unauthorized access, or operational disruptions. Identifying these misconfigurations is challenging because traditional rule-based and static analysis methods often fail to capture complex, context-dependent configuration issues and system behaviors. In this study, we investigate the use of Large Language Models (LLMs) to detect security misconfigurations directly from cloud API response data. By treating API responses as representations of a system’s configuration state, we assess whether LLMs can effectively identify potential security risks. We evaluate five LLMs using a unified zero-shot prompting approach and compare their performance with and without Retrieval-Augmented Generation (RAG) to understand the impact of external knowledge on misconfiguration detection. The study not only focuses on each model’s ability to identify configuration components and detect misconfigurations, but also evaluates their capability to accurately determine the number of misconfigurations and generate clear, actionable security explanations. Our preliminary results show that Meta Llama Instruct combined with RAG achieves the reliable performance for identifying security misconfigurations in cloud API responses. This study provides new insights into the practicality of LLM-driven API cloud security analysis and paves the way for future research.
Most versus least: A meta-analysis of stimulus overselectivity
(The University of Waikato, 2026) Simoné; Edwards, Timothy L.
Stimulus overselectivity (OS) occurs when a person's behaviour is controlled by only a limited subset of available cues. This phenomenon is thought to contribute to learning and social challenges in individuals with autism spectrum disorder (ASD). This thesis presents a multilevel meta-analysis quantifying the magnitude of OS in individuals with ASD compared to typically developing (TD) peers. Analyses of 15 studies, producing 41 unique effects, showed a statistically significant population gap (p = .036). The average level of OS was 35.16% for ASD groups and 20.96% for TD groups. Both groups exhibited a general tendency for OS, but the effect was more pronounced in ASD. Notably, this difference was fragile: when the oldest study (Gersten, 1983) was excluded, the gap was no longer statistically significant (p = .129). A major limitation was that only 15 out of 158 relevant studies provided enough data for quantitative analysis, highlighting a widespread reporting gap. These findings were consistent with stimulus control hierarchies, suggesting that the systematic narrowing of control observed in individuals with ASD may be sensitive to non-linear diagnostic shifts within the evolving diagnostic landscape. For practitioners, the findings emphasise the importance of assessing which cues guide a learner’s responses and using targeted strategies to broaden cue control in educational settings. For researchers, these results highlight the need for consistent, standardised reporting of element-level data and variability to advance theoretical understanding of overselectivity across populations.
Indicators of fetal engagement with the environment
(The University of Waikato, 2026) Sheik-Ebrahim, Kousar; Reid, Vincent
Fetal behaviour provides a non-invasive window into early fetal development, reflecting the maturation of sensory and motor systems. This thesis investigates the developmental trajectory of spontaneous and stimulus-evoked behaviours across late gestation (33–37 weeks) to identify behavioural indicators of selective sensory engagement. Secondary analyses were conducted on high-resolution ultrasound recordings using a systematic behavioural measurement framework, incorporating standardised coding of fetal behavioural states (FBS), fetal body movements (FBM), and fetal eye movements (FEM) to enhance observational reliability and analytical rigor. Quantitative analysis, including Dynamic Time Warping (DTW), was employed to distinguish baseline behavioural activity from responses elicited by controlled auditory and visual stimuli. Study 1 (N = 57) investigated patterns of FBM and FEM to determine whether observed behaviours reflected spontaneous activity or environmentally driven engagement. Results indicated that from approximately 33 weeks’ gestation onward, FEM classifications increasingly aligned with behavioural markers of environmental engagement, suggesting progressive maturation of sensory–behavioural integration. Study 2 (N = 24) examined fetal behavioural responses to musical stimuli varying in timbre (string versus piano) and harmonic modality (major versus minor chords). We observed distinct patterns of structured FEM and FBM across auditory conditions, indicating selective behavioural modulation in response to specific acoustic properties. The findings from this cross-sectional study suggest that late-gestation fetuses demonstrate emerging capacities for selective engagement with environmental stimuli before birth. This research advances understanding of prenatal sensory and perceptual processing and supports refinement of fetal behavioural frameworks, with implications for clinical monitoring and foundational research into early perceptual learning.