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Impact of Organic Labelling on Consumer Buying Behaviour in Australian Food Products
(2026-05-27) Maharjan, Anina
This paper discusses the workings of organic labelling as a sustainable marketing tool and its effect on the purchasing decision-making process of consumers in Australia. The paper will be guided by the increasing need for sustainable goods and the continuous gap between consumers' stated intentions to purchase versus actual purchases. The research will examine how price, quality, awareness, availability, and value influence consumers to purchase products that carry the label of sustainability. The conceptual framework adopted for the research is grounded on the Theory of Planned Behaviour (TPB). According to TPB, attitudes and perceptions of control determine the behaviour of consumers. In the current study, awareness, quality, and value are considered attitudinal determinants, while price and availability are measures of the perception of control. Data collection was conducted using a quantitative approach, and structured questionnaires were distributed to 275 respondents aged between 18 and 50 in Melbourne. Awareness, quality, value, and availability all appear to have significant positive correlations with purchase behaviour, whereas price is less influential. Quality and value emerge as the critical determinants of consumer purchase behaviour, followed by awareness and availability. The role of price, although not insignificant, is relatively minor compared to the other variables. In summary, the outcomes imply that consumers are more concerned about the advantages of organic products, such as quality, value, and trust, than price. Although the price may act as a deterrent initially, its significance wanes as individuals become more familiar and comfortable with organic products. This investigation makes a significant contribution to the body of literature since TPB is applied within the Australian context.
Maharjan, Anina
The Impact of Artificial Intelligence on Recruitment Efficiency and Fairness in the Australian IT sector
(2026-05-13) Davuluri, Nagarjuna
This thesis examines the role of Artificial Intelligence (AI) in increasing efficiency and equity of the recruitment process in the Australian Information Technology (IT) industry, the sector that has been facing skills shortage and high turnover rates due to the ongoing competition for talent. Although AI-based recruitment tools are actively employed to accelerate the recruitment process and enhance the matching of applicants, the questions of fairness, transparency, and confidence in automated decisions are still present. This paper will look at the question of whether AI can bring quantifiable recruitment advantages without compromising ethics. The studies employ a long version of the Technology Acceptance Model (TAM) to examine the impact of the perception of AI usefulness, ease of use, and ethical concerns on recruiters on the outcome of the recruitment process. The quantitative research design was based on the survey results of 184 HR professionals and recruiters, based in Melbourne and Sydney, who regularly use AI-based recruitment systems. The statistical analysis, regression, mediation, and moderation testing were aimed at evaluating the effect of AI on the efficiency and fairness of the recruitment process. The results indicate that AI has a great impact on operational efficiency, including shorter time-to-hire, less administrative work of administration, and less effort to screen. Efficiency is not a guarantee of responsible adoption. The sense of fairness and transparency has a strong impact on the recruiter's trust and continued use of AI tools. The association between efficiency gains and adoption decisions is partially mediated by ethical issues, in particular, the presence of algorithmic bias and non-explicability. Especially, these effects are controlled by ethical AI training, which enhances trust and increases perceptions of fairness. This study adds to the theory by generalising TAM to incorporate ethical aspects in the adoption of recruitment technology. In practice, it can provide evidence-based advice to Australian IT organisations, recruitment agencies, and policymakers on the equilibrium between efficiency and fairness. The research finds that to make AI adoption in the recruitment process sustainable, technological capacity is not enough but should be coupled with ethical regulation, human control, and responsibility to promote a fair and comprehensive approach to recruitment
Davuluri, Nagarjuna
MIMO-Based Optimisation of Solar Photovoltaic Orientation
(2026-05-13) Shrestha, Nabin
This thesis presents a MIMO-based optimisation framework for improving solar photovoltaic (PV) panel orientation under varying environmental and seasonal conditions. Instead of relying on hardware-based tracking systems with sensors and actuators, the study uses a dataset-driven MATLAB simulation to determine optimal tilt and azimuth angles. The framework incorporates key parameters such as global, direct, and diffuse irradiance, ambient temperature, and wind speed to model PV performance more accurately. A Multi-Input Multi-Output (MIMO) approach analyses interactions among these variables and optimises panel orientation across annual, seasonal, and monthly timescales. The methodology combines solar geometry, irradiance modelling, thermal analysis using the NOCT model, and a two-stage optimisation algorithm. Practical constraints, including wind protection and thermal derating, are also considered. Evaluation using meteorological data from Kathmandu, Pokhara, and Nepalgunj shows significant improvements in energy output. Dual-axis tracking increased annual generation by up to 50.1% in Kathmandu, with gains of 33.4% and 34.4% in Pokhara and Nepalgunj compared to fixed systems. Results also indicate that optimal tilt angles vary seasonally. Overall, the study demonstrates that data-driven optimisation can enhance PV efficiency and offers a cost-effective alternative to complex tracking systems, with potential applications in smart grids and energy management.