Massey Research Online


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Relationship between the three dimensions of paternalistic leadership, cognitive and affective trust and organizational citizenship behavior: a multilevel mediational pathway
(Emerald Publishing Limited, 2025-02-25) Lee MCC
Purpose The current study aims to explore the three dimensions of paternalistic leadership (i.e. moral leadership, benevolent leadership and authoritarian leadership) and their dual pathways of positive and negative influences on employees’ organizational citizenship behavior through the two aspects of trust (i.e. cognitive and affective trust). Design/methodology/approach Given that trust is pertinent in any human relationship, especially in Asian countries where bonding plays an important role, the current study investigated the relationship of each leadership style within paternalistic leadership on employees’ cognitive and affective trust in their leaders, employees’ organizational citizenship behavior and the processes involved. The current study employed a cross-sectional multilevel approach with 435 employees from 85 workgroups participating in the study. Findings As hypothesized, benevolent and moral leadership styles (but not the authoritarian leadership style) had a positive effect on employees’ cognitive and affective trust in their leaders and on employees’ organizational citizenship behavior. Cognitive and affective trust also mediated the relationships of benevolent and moral leadership styles with organizational citizenship behavior. Originality/value The study’s findings urge practitioners and human resources personnel to be aware of the dual effects that a paternalistic leader has on employees. To be specific, benevolent and moral leadership styles are conducive to employees’ work outcomes, whereas the authoritarian leadership style has a non-significant role in employees’ work outcomes.
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Multimodal Deep Learning for Android Malware Classification
(MDPI (Basel, Switzerland), 2025-02-28) Arrowsmith J; Susnjak T; Jang-Jaccard J; Buccafurri F
This study investigates the integration of diverse data modalities within deep learning ensembles for Android malware classification. Android applications can be represented as binary images and function call graphs, each offering complementary perspectives on the executable. We synthesise these modalities by combining predictions from convolutional and graph neural networks with a multilayer perceptron. Empirical results demonstrate that multimodal models outperform their unimodal counterparts while remaining highly efficient. For instance, integrating a plain CNN with 83.1% accuracy and a GCN with 80.6% accuracy boosts overall accuracy to 88.3%. DenseNet-GIN achieves 90.6% accuracy, with no further improvement obtained by expanding this ensemble to four models. Based on our findings, we advocate for the flexible development of modalities to capture distinct aspects of applications and for the design of algorithms that effectively integrate this information.
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Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies for Literature Review Automation
(MDPI (Basel, Switzerland), 2025-03-26) Sujau M; Wada M; Vallée E; Hillis N; Sušnjak T; Verspoor K
As climate change transforms our environment and human intrusion into natural ecosystems escalates, there is a growing demand for disease spread models to forecast and plan for the next zoonotic disease outbreak. Accurate parametrization of these models requires data from diverse sources, including the scientific literature. Despite the abundance of scientific publications, the manual extraction of these data via systematic literature reviews remains a significant bottleneck, requiring extensive time and resources, and is susceptible to human error. This study examines the application of a large language model (LLM) as an assessor for screening prioritisation in climate-sensitive zoonotic disease research. By framing the selection criteria of articles as a question–answer task and utilising zero-shot chain-of-thought prompting, the proposed method achieves a saving of at least 70% work effort compared to manual screening at a recall level of 95% (NWSS 95%). This was validated across four datasets containing four distinct zoonotic diseases and a critical climate variable (rainfall). The approach additionally produces explainable AI rationales for each ranked article. The effectiveness of the approach across multiple diseases demonstrates the potential for broad application in systematic literature reviews. The substantial reduction in screening effort, along with the provision of explainable AI rationales, marks an important step toward automated parameter extraction from the scientific literature.
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Nutrient-Level Evaluation of Meals Provided on the Government-Funded School Lunch Program in New Zealand
(MDPI (Basel, Switzerland), 2022-12) de Seymour J; Stollenwerk Cavallaro A; Wharemate-Keung L; Ching S; Jackson J; Maeda-Yamamoto M
Approximately 1 in 6 children in New Zealand are living in households facing poverty and 14% of the population is food insecure. The Ka Ora, Ka Ako|Healthy School Lunches program aims to reduce food insecurity by providing access to a nutritious lunch every school day. This study analyzed the nutritional content of Ka Ora, Ka Ako meals and compared them to national and international standards. Meals were selected at random from approved menus. The suppliers covered by the 302 meals analyzed provide 161,699 students with a lunch (74.9% of students on the program). The meals were analyzed using Foodworks 10 nutrient analysis software. The nutrient content was compared against the New Zealand/Australia Nutrient Reference Values (NRVs) and to nutrient-level standards for international school lunch programs. A total of 77.5% of nutrients analyzed exceeded 30% of the recommended daily intakes. Protein, vitamin A and folate met the NRV targets and a majority of the international standards (55/57). Energy, calcium, and iron were low compared to NRVs and international standards (meeting 2/76 standards). Carbohydrates were low compared to international standards. The findings have been used to inform the development of revised nutrition standards for the program, which will be released in 2022.