AUT Research Repository (Auckland Univ. of Technology)
Not a member yet
14707 research outputs found
Sort by
Indigenous Communities and Marine Mammal Tourism Management: Incorporating the Perspectives of the Indigenous Māori People of Aotearoa/New Zealand
No full textArtificial Intelligence in Automation of Community Disaster Resilience Measurement
No full textOver time, numerous frameworks have emerged to gauge a community's resilience, which is the community’s ability to get back to function, in the face of disasters. These frameworks vary in complexity and scope, often encompassing both quantitative and qualitative metrics. The dichotomy between quantitative and qualitative measurements underscores a critical limitation in current resilience frameworks. While quantitative metrics excel in providing tangible data points and statistical analyses, they often overlook the intricate social dynamics and cultural factors that profoundly influence a community's resilience. In contrast, qualitative assessments offer a more holistic understanding by capturing the lived experiences, perceptions, and narratives of community members. This qualitative data, comprising approximately 80 percent of the information relevant to resilience, provides invaluable insights into the adaptive strategies, cultural norms, and social networks that shape a community's capacity to withstand and recover from disasters.
The pursuit of capturing richer data through qualitative methods, particularly open-ended interviews, stands as a cornerstone of this study. By delving into the nuanced perspectives and experiences of individuals, such methods offer a depth of understanding that quantitative approaches often struggle to achieve. However, despite their potential to yield valuable insights, qualitative methods are not without their limitations. One significant challenge inherent to open-ended interviews is the potential for inconsistency of capturing community’s resilience. Unlike structured surveys or questionnaires, which provide standardized prompts and response options, open-ended interviews allow participants to express themselves freely. While this flexibility can unearth unexpected insights and perspectives, it can also lead to variability in results, making it difficult to establish clear patterns or draw definitive conclusions. This inconsistency can stem from variations in interviewers' probing techniques causing the interview to follow a different direction. Another limitation of open-ended interviews is the risk of bias. Human subjectivity inevitably influences every stage of data collection in an interview. In the context of open-ended interviews, bias may manifest in various forms and types, which in this study, gender bias of interviewer is targeted. Interviewers' preconceived notions or personal beliefs can inadvertently shape the direction of the interview, influencing the topics explored and the interpretation of responses. Another significant aspect is the integration of automation and repeatability into the process of conducting disaster resilience interviews, particularly in mitigating the impact of variables such as inconsistency and gender bias. Automation streamlines data collection and analysis, reducing the potential for human error and enhancing the efficiency of the research endeavor. This standardization is crucial for mitigating inconsistencies in responses, as it promotes uniformity across interviews and facilitates the comparison of findings.
Additionally, automation brings validity by ensuring interviews can be streamlined in an accurate repeatable process. In tackling the challenges in obtaining holistic data from open-ended interviews, this research adopts a systematic approach comprising five distinct steps. The steps involved identifying a practical measurement approach to effectively quantify inconsistency and gender bias, and then addressing these issues. In the final step, an automation approach is developed to not only assist interviews in maintaining consistency and impartiality but also enable the process to be repeatable. Building upon these foundational insights, the research proceeds to devise innovative solutions aimed at mitigating the impact of these variables on data collection. Methods of measurement were developed through the utilization of simulated interviews to generate numerical representations. Besides, by leveraging the latest advancements in Artificial Intelligence (AI), novel solutions to address key variables are identified through a structured three-phase design encompassing content analysis, exploratory analysis, and comparative analysis.
The AI-driven methods also pave the way for the automation in conducting open-ended interviews. The variable of inconsistency was quantified using the Interview’s Inconsistency Mark (IIM), ranging from 0 to 13, where higher scores denoted increased inconsistency levels. To mitigate this issue, a solution was devised through natural language processing techniques, specifically sentence embedding. This method retrieved consistent information from a knowledge base housing peer-reviewed papers, resulting in significantly reduced inconsistency levels compared to the benchmark interviews, with observed values of 5.13 and 1.35. Gender bias was assessed using the Practical Measurement of Gender Bias (PMGB) index, represented as a percentage, where higher values indicated greater bias. An approach was developed employing natural language processing methodologies, particularly word embedding, to identify gender-sensitive language. Utilizing a deep learning model named Claude 3 Sonnet helped in replacing gender-sensitive terms with neutral gender equivalents. Consequently, the solution successfully eliminated gender-sensitive values, reflected by a PMGB of zero, in contrast to simulated interviews yielding higher values of 12% and 10%. Automation was achieved by developing a Decision Support System integrating both inconsistency and gender bias resolution components. Additionally, two complementary components were included: a speech recognition system modeled after SpeechStew for input reception and a follow-up question generator based on Claude 3 Sonnet. This integrated system enables the automation of open-ended interviews, promising high-quality outcomes based on predefined metrics.
Overall, this thesis contributes significantly to the advancement of knowledge in disaster resilience for both existing and future frameworks by offering novel insights and perspectives on data collection. While the study encountered several limitations such as the lack of transparency in existing frameworks utilizing open-ended interviews for data collection, particularly within New Zealand and the challenges in automating aspects of the data collection process, these constraints underscore the indispensable role of human engagement and qualitative insight in certain research contexts. Furthermore, financial barriers associated with testing and utilizing certain AI models were identified. However, with upcoming advancements in AI, this study provides a robust foundation for future enhancements. Integrating the data collection solution into existing and upcoming frameworks, along with longitudinal observations, will enable future studies to gain better insights. By rigorously applying the solution in additional real-world scenarios, its performance can be more comprehensively evaluated, allowing future researchers to fine-tune it to address specific needs and improve its efficacy
Feasibility and Effectiveness of Consciousness, Relaxation, Attention, Fulfillment, and Transcendence (CRAFT) in Enhancing Tertiary Student Musicians’ Well-Being and Academic Experience
No full textTertiary student musicians experience complex well-being concerns and challenges (music performance anxiety, psychological distress, musculoskeletal disorders) as they navigate through higher education and attempt to meet their highly physical, emotional, and cognitive music-making demands. Yoga, mindfulness, positive psychology, and emotional intelligence offer various practices and principles that may help tertiary student musicians prevent and self-regulate these stressors while satisfying elite performance levels. Grounded in these four disciplines and/or fields of knowledge, Consciousness, Relaxation, Attention, Fulfillment, and Transcendence (CRAFT) is a newly developed program for self-actualization, happiness, and well-being, that could comprehensively address this population’s demands. Building on previous limitations and gaps of prior yoga and mindfulness-based research conducted in music contexts, the purpose of the current PhD investigation was to examine the feasibility and effectiveness of CRAFT to enhance tertiary student musicians’ well-being and academic experience.
This investigation involved two phases including studies conducted during the COVID-19 pandemic (Phase 1; Studies 1-3) and studies undertaken before and after the pandemic (Phase 2; Studies 4-6). In both phases, participants were tertiary student musicians at two higher conservatories in Spain who followed either CRAFT, Alexander Technique, or music curricular instruction, once a week for 60-90 min during 2017-2023. In a quasi-experimental controlled trial (Study 1) and qualitative investigation (Study 2), the applicability and perceived benefits of CRAFT to improve tertiary student musicians’ well-being during the lockdown were explored. Subsequently, the potential occurrence of a CRAFT-induced response shift phenomenon and the extent to which it could bias participants’ self-reported changes in mindfulness was examined through the then-test (Study 3). Through a single-arm pre-post trial (Study 4) and mixed-methods investigation (Study 5), a comprehensive assessment of the feasibility of CRAFT in terms of its applicability, perceived benefits, and preliminary effectiveness to improve tertiary student musicians’ well-being and academic experience was conducted. Lastly, a three-arm non-randomized controlled trial comparing CRAFT participants with active and inactive controls (Study 6), was implemented to examine the effectiveness of CRAFT in improving tertiary student musicians’ dispositional mindfulness, music performance anxiety, emotional regulation, psychological distress, well-being, as well as lower body flexibility and balance.
Phase 1 results indicated that CRAFT was perceived by participants as an applicable, beneficial, and transformative program, while Phase 2 findings substantiated those from Phase 1 and supported CRAFT as a feasible and effective program to holistically improve tertiary student musicians’ well-being and academic experience. This was evidenced by CRAFT participants’ higher levels of proactivity and perceived benefits than controls in terms of implementing practices to improve their well-being during the lockdown (Studies 1 & 2); positive evaluations of the program's practicality, implementation, integration, and viability (Studies 4 & 5); significant improvements in dispositional mindfulness, music performance anxiety, emotional regulation, psychological distress, well-being, as well as lower body flexibility and balance (Studies 4 & 6); larger enhancements in mindfulness after adjusting for response shift (Study 3); and multidimensional well-being-, humanistic-, and music- related benefits mirroring CRAFT’s theoretical framework that confirmed, clarified, and expanded the aforementioned results (Studies 2 & 5). Notwithstanding some limitations, this PhD investigation afforded relevant contributions to the field of yoga- and mindfulness-based research conducted with the target population by bolstering CRAFT as a feasible and effective intervention to comprehensively enhance tertiary student musicians’ well-being and academic experience. Large multi-arm mixed methods investigations with a higher frequency of delivery are recommended to substantiate these findings and extend them to other settings and populations
Support for and Likely Impacts of Endgame Measures in the Smokefree Aotearoa Action Plan: Findings From the 2020-2021 International Tobacco Control New Zealand (EASE) Surveys
No full textAIM: In February 2024, the Aotearoa New Zealand Government repealed legislation to mandate very low nicotine cigarettes (VLNCs), greatly reduce the number of tobacco retailers and disallow sale of tobacco products to people born after 2008 (smokefree generation). We investigated acceptability and likely impacts of these measures among people who smoke or who recently (≤2 years) quit smoking.
METHOD: We analysed data from 1,230 participants from Wave 3 (conducted in late 2020 and early 2021) and 615 participants from Wave 3.5 (conducted in June/July 2021) of the New Zealand arm of the International Tobacco Control (ITC) Policy Evaluation Project. Data were weighted to represent the national population of people who smoke and who recently quit smoking.
RESULTS: Support (excluding "Don't know" responses) was 82.7% (95% confidence interval 77.9, 86.6) for a smokefree generation, 75.0% (95% CI 71.4, 78.3) for mandated VLNCs and 35.2% (95% CI 31.7, 38.9) for retailer reduction. Support was mostly similar by ethnicity, gender, age and evidence of financial hardship, but was higher among people who had recently quit smoking. Around half of the participants who smoked anticipated quitting completely, switching to vaping or cutting down the amount they smoke if mandated VLNCs or substantial retailer reductions were introduced. If VLNCs were mandated, 19% of people who smoked stated they would carry on smoking like they do now and find a way to get the cigarettes or tobacco they want to smoke.
CONCLUSION: Support for and anticipated actions in response to the smokefree legislation measures call into question the Government's decision to repeal them
Exploring How Physiotherapists Consider Falls Risk During the Clinical Management of People With Osteoarthritis
No full textIntroduction. Falls and the number of people diagnosed with osteoarthritis are on the rise. In Aotearoa New Zealand (AoNZ), physiotherapists are well-positioned to incorporate falls screening of people with osteoarthritis into their routine practice. Early identification of falls risk presents an opportunity to implement preventive measures that could reduce the incidence of future falls. The literature suggests a link between osteoarthritis and an increased risk of falls, but the connection is not yet fully understood. Despite this, people with osteoarthritis have been consistently shown to be at risk of falls; therefore, considering falls in assessment and offering appropriate treatment should be routine. I wanted to explore how physiotherapists in AoNZ conceptualised the relationship between falls risk and osteoarthritis.
Objective. To explore how physiotherapists consider falls risk during the clinical management of people with osteoarthritis, if they are screening for falls, or if they include falls-prevention strategies in their practice.
Methods. I used a qualitative descriptive approach, collecting data via 10 semi-structured interviews and analysing them using directed content analysis. Participants were eligible if they were physiotherapists in AoNZ who worked in musculoskeletal practice and commonly managed people with lower limb osteoarthritis.
Results. Three themes were conceptualised from the data. Theme one: ‘What is the risk of a fall?’, this theme explores the physiotherapists reasoning about falls risk. Theme two: ‘Making assumptions about patients’ explores physiotherapists’ assumptions when deciding whether a person with osteoarthritis is at risk of a fall. Theme three: ‘Gatekeeper of treatment’ looks at factors influencing behaviour behind assessment which direct the treatment plan of people with osteoarthritis.
Conclusion. A physiotherapist’s decision to assess falls risk is influenced by their assumptions about perceived falls risk and beliefs about their clinical role. These factors appear to shape their clinical reasoning and decision-making. My findings suggest that some physiotherapists practice reactively rather than proactively when assessing people with osteoarthritis. They provide assessment and treatment only if the patient indicates a fall or fits a preconceived notion of what constitutes a falls risk. Work is needed to raise physiotherapists’ awareness of falls risk in people with osteoarthritis. Achieving this could include formal teaching and updating guidelines, recommendations, and assessment forms
Corporate Accounting and Accountability for Water in a Chinese Context
No full textWater is one of the most vital natural resources, essential for sustaining ecosystems, human well-being, and economic development. Yet freshwater is exceedingly scarce, with less than 1% of the earth’s water readily accessible for human use. Industrial pollution further strains these limited resources, underscoring the urgency of corporate accountability in water management. Against this backdrop, this thesis investigates water-related information disclosures and corporate water accountability in China. Five research questions guided this study, which related to (1) the extent of water-related disclosure by Chinese companies; (2) motivations for disclosure; (3) stakeholder expectations; (4) whether disclosures meet these expectations; and (5) challenges to corporate water accountability. China provides an ideal context for such an investigation given its global economic role and persistent water issues. A mixed-methods approach was adopted. First, content analysis was used to examine disclosures by 190 listed Chinese companies across their annual reports; corporate social responsibility reports and sustainability reports; and websites. A novel water-specific disclosure index and scoring system were developed to measure the comprehensiveness of the information disclosed. To complement this, 54 semi-structured interviews were subsequently conducted with corporate managers, government officials and policymakers, and a wide range of stakeholders exert influence on corporate actions and disclosures, including shareholders, labour union managers, academics, the media, and an environmental non-governmental organisation (NGO) manager. The findings reveal substantial shortcomings in current water-related disclosures by Chinese companies. Many companies provided only generalised statements lacking quantitative data and detailed explanations. Disclosure quality varied widely, indicating significant room for improvement. The interviews highlighted that stakeholder demands are a key driver of disclosure. Perspectives differed: academics, media representatives and the NGO manager noted a gap between current practices and their expectations, whereas shareholders, regulators, and policymakers generally viewed disclosures as sufficient. Challenges to corporate accountability identified include the absence of standardised reporting frameworks. Without clear guidelines, voluntary disclosures lack consistency, reliability, and credibility. This research contributes to the literature in several ways. First, it analyses the water disclosure practices of 190 Chinese listed companies, filling a gap in corporate water accountability studies. Second, it introduces an innovative water-specific disclosure index, advancing tools for assessing reporting practices. Third, through extensive interviews, it captures diverse stakeholder views, providing nuanced insights into the drivers, expectations, and barriers of corporate water accountability. Practical contributions are equally significant. The proposed index offers Chinese companies a context-specific tool to enhance disclosure comprehensiveness and consistency. It also provides regulators and standard setters with evidence to guide future water reporting policies. By addressing the urgent need for more transparent and reliable water information, this research advances both academic understanding and practical solutions for corporate water accountability in China and beyond
Sustainability Leadership and the Governmentality of Hope: Retheorising Hope in the Context of Environmental Crisis
No full textThis paper employs an affective governmentality approach – one that sees regimes of governmentality as working through affective as well as rational milieus – to explore how sustainability leaders experience, navigate and enact hope. These subjects operate in a highly-charged affective milieu at the intersection of hope for a better world and the confronting realities of environmental crisis. Our study shows how official texts associated with organisations who shape this milieu construct hope as normative for sustainability work. Drawing on interviews with 35 sustainability leaders, it then documents the multiple and sometimes contradictory ways in which these subjects respond to and deploy an imperative to hope in their practices of governing self and others. Our contribution is twofold. Firstly, our explicit attention to affect allows us to extend the existing literature by tracing the complexities, tensions and transgressions in the experience and the practices of subjects who are simultaneously governed and governors. Secondly, our critical understanding of hope as governmentality opens up new possibilities for subjects working in contexts that render hope precarious and even problematic
Integrating Virtual Reality and Artificial Intelligence for Mass Casualty Incident Training
No full textThis thesis explores the integration of Virtual Reality (VR) and Artificial Intelligence (AI) for enhancing pre-hospital triage training in the context of Mass Casualty Incidents (MCIs). Addressing the challenges posed by traditional training methods, which often involve significant costs and logistical complexities due to the infrequent nature of MCIs, this research introduces an innovative VR learning tool designed to realistically simulate emergency scenarios. The primary objectives of this study were to develop the VR learning tool using a Design Science Research methodology and to detail the advanced data analysis methodologies employed in addressing the research questions (RQ1–RQ3.3).
By leveraging VR and AI technologies, the tool aimed to provide emergency healthcare professionals with a dynamic and immersive training environment, enabling them to practice and refine their triage skills without the constraints of traditional physical simulations. A significant portion of the research was dedicated to addressing the limitations of current VR interaction methods by prototyping more interactive and lifelike user experiences through advanced VR controllers. This enhancement allowed for deeper and more comprehensive data collection, including audio metrics, thereby facilitating a nuanced understanding of trainees’ performance and engagement within the simulated environment.
The development process of the VR learning tool, characterized by the integration of AI techniques and statistical methods, reflected the study’s commitment to both innovation and effective assessment of training outcomes. The experimental phase outlined the preparation, execution, and ethical considerations of implementing the VR training, providing insights into the tool’s potential to quantitatively and qualitatively evaluate emergency response skills.
The findings from this study indicated that VR technology can be a supplementary tool in emergency healthcare training, particularly in scenarios involving mass casualty incidents. The analysis of training sessions with 10 participants showed variability in performance during simulated car crash and earthquake scenarios: some participants were quick but less accurate, while others were slower yet more precise, reflecting diversity in emergency response approaches. Survey results showed that participants, predominantly aged 18–24 with varying levels of experience, found the VR training highly immersive and engaging, although some reported physical discomfort, highlighting the need for ergonomic improvements. Additionally, AI-driven analysis of speech data demonstrated improved consistency and accuracy in participants’ communication over time, emphasizing the importance of clear communication in emergencies
How Flavorsome Was That Movie? Using a Bayesian Network Approach to Understand How Audiovisual Stimuli Influence Emotions and Flavor Perception
No full textThis study utilized the temporal check‐all‐that‐apply (TCATA) approach to investigate the impact of viewing video clips on the perception of ice cream. The association between subjectively rated emotions and their electrophysiological correlates was further explored using Bayesian network (BN) modelling. Participants consumed chocolate ice cream under different video conditions, and sweetness, bitterness, milkiness, creaminess, cocoa flavor, and roasted flavor ratings were acquired using the TCATA approach. Additionally, electrophysiological measurements of heart rate (HR), skin conductance (SC), and blood volume pulse (BVP) amplitude were obtained. The results showed that videos that evoked pleasant emotions, such as enjoyment and relaxation, were associated with increased ratings of sweetness and creaminess. Conversely, videos that evoked negative feelings, such as stress, were linked to higher ratings of bitterness. Furthermore, changes in electrophysiological measures were consistent with the variety of affective states evoked by the videos. Arousing videos increased HR and SC, while videos that induced calmness had the opposite effect. The use of BN modelling revealed significant relationships between affective states and electrophysiological responses with flavor perception. The model demonstrated that HR and SC were positively correlated with positive emotions that contributed to the perception of sweetness and milkiness, respectively. On the other hand, BVP amplitude was negatively correlated with arousal and perceptions of cocoa. Additionally, ratings of ‘quiet’ and ‘excited’ emotions were positively correlated to creaminess, while tension was positively correlated to roasted flavor. These findings indicate that understanding the impact of emotions on food perception can facilitate the design of consumer experiences that enhance enjoyment and engagement with food products
Nonlinear PDE Model for Pricing European Options With Transaction Costs Under the 3/2 Non-Affine Stochastic Volatility Model
No full textIn this paper, we study the problem of pricing European options with transaction costs under the 3/2 non-affine stochastic volatility model. First, we derive a nonlinear partial differential equations (PDE) model for pricing European options by using the expectation of transaction costs in a small time interval. It is worth to mention that the nonlinear PDE degenerates into the corresponding pricing PDE under the 3/2 stochastic volatility model when the transaction cost rate is set to zero. Then, we solve the nonlinear PDE numerically by using the finite difference method. Finally, we present numerical simulations and sensitivity analysis to illustrate both the consistency and the impact of transaction costs on option pricing