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The purpose of this work is to honor the wisdoms of Anishinaabe Elders, community and culture by interweaving these teachings with my own (first author) Anishinaabe experiences and a research project. Ceremonies are an important health practice for Anishinaabe people. This project aimed to gain a clearer conceptualization of the protective role of Anishinaabe puberty ceremonies on health in adolescence and across the lifespan.

Spiritual offerings guided this project at every stage including inviting Elders and community members into shared spaces of storytelling and teaching elicitation and grounding me as I carefully adopted the use of a western tool (research) in sacred community spaces. Elders were invited to share their experiences and perspectives. Three community members engaged with the interview transcripts on their own before coming together to discuss themes, patterns and insights that arose for them. This group coding discussion constructed the structural foundation of the findings.

An Anishinaabe perspective on youth development emerged. Key aspects of this model included a foundation of ceremonial experiences that spiritually prepares a child for adulthood and impending life’s challenges. As one transitions into adulthood, they accept the responsibilities of being caretakers of their families and communities and gain new tools to contribute to Anishinaabe society. Ideally, this society prioritizes Anishinaabe spirituality, language and way of life.

Frameworks of health, grounded in unique community wisdoms and worldviews, are imperative to repair spiritual and community relationships damaged in a history of colonialism. An Anishinaabe perspective on youth development may shed light on shared Indigenous experiences of cultural restoration and continuity.

The purpose of this study is to explain why people respond to toxic leadership in different ways. The toxic triangle was applied as a lens and extended followership by investigating unsusceptible followers and susceptible followers.

This study employed Q methodology to illustrate the subjective viewpoints of 31 employees. Participants sorted 41 statements ranging from “most uncharacteristic” to “most characteristic” according to their beliefs using a forced distribution. We used qualitative data from the survey and follow-up interviews to document participant motivations.

Findings from this Q study demonstrated three distinct perceptions of responses to toxic leadership: Suffer in Silence (Perspective 1), Confront and Advocate (Perspective 2) and Quiet yet Concerned (Perspective 3). This study found that Perspectives 1 and 3 helped to explain differences in susceptible followership, whereas Perspective 2 helped to explain unsusceptible followership. Our research supports the need for organizations to provide safe whistleblowing channels for reporting unethical behavior by adopting clear policies for handling unethical behaviors and sharing those policies with all constituents within the organization.

Our research supports the need for organizations to provide safe whistleblowing channels for reporting unethical behavior by adopting clear policies for handling unethical behaviors and sharing those policies with all constituents within the organization.

Our study adds to the developing literature on followership by building a conceptual framework for response types that better explains the motivation and subsequent actions of susceptible and unsusceptible followers. This framework helps us identify new ways to combat toxic leadership by providing a more nuanced view of how employees perceive and respond to toxic leadership.

The study aims to explore the role of ChatGPT, an artificial intelligence (AI) language model, in the field of management education. Specifically, the goal is to evaluate ChatGPT's effectiveness in facilitating active learning, promoting critical thinking, and fostering creativity among students. Additionally, the study seeks to investigate the potential of ChatGPT as a novel tool for enhancing traditional teaching methods within the framework of management education.

This research systematically explores ChatGPT's impact on student engagement in management education, considering AI integration benefits and limitations. Ethical dimensions, including information authenticity and bias, are scrutinized, alongside educators' roles in guiding AI-augmented learning.

The study reveals ChatGPT's effectiveness in engaging students, nurturing critical thinking, and fostering creativity in management education. Ethical concerns regarding information authenticity and bias are addressed. Insights from student and teacher perceptions offer valuable pedagogical implications for AI's role in management education.

While this study offers valuable insights into the role of ChatGPT in management education, it is essential to acknowledge certain limitations. Firstly, the research primarily focuses on a specific AI model (ChatGPT), and findings may not be generalized to other AI language models. Additionally, the study relies on a specific set of educational contexts and may not fully capture the diverse landscape of management education globally. The duration of the research and the sample size could also impact the generalizability of the findings.

The findings of this study hold practical significance for educators and institutions engaged in management education. The integration of ChatGPT into teaching strategies has the potential to improve active learning, critical thinking, and creativity. Educators can utilize this AI tool to diversify instructional methods and accommodate diverse learning styles. However, the practical implementation of AI in the classroom necessitates meticulous consideration of infrastructure, training, and ongoing support for both educators and students. Furthermore, institutions should proactively tackle ethical concerns and establish guidelines for the responsible use of AI in education.

The incorporation of AI, such as ChatGPT, in management education carries broader social implications. The study underscores the significance of addressing ethical concerns associated with AI, including issues related to information authenticity and bias. As AI becomes more widespread in educational settings, there is a necessity for societal discussions on the role of technology in shaping learning experiences. This research advocates for a thoughtful approach to AI adoption, emphasizing the importance of transparency, accountability, and inclusivity in the development and deployment of AI technologies within the educational sphere. The findings prompt reflections on the societal impact of AI-driven education and the potential consequences for students' skills, employment prospects, and societal values.

Originality/Values: This research contributes to the academic discourse by systematically examining the role of ChatGPT in management education, providing insights into both its advantages and potential ethical challenges. The study offers original perspectives on the use of AI in educational settings, paving the way for well-informed decision-making that can shape the future of management education in the evolving landscape of technological progress.

The notion of using a generative artificial intelligence (AI) engine for text composition has gained excessive popularity among students, educators and researchers, following the introduction of ChatGPT. However, this has added another dimension to the daunting task of verifying originality in academic writing. Consequently, the market for detecting artificially generated content has seen a mushroom growth of tools that claim to be more than 90% accurate in sensing artificially written content.

This research evaluates the capabilities of some highly mentioned AI detection tools to separate reality from their hyperbolic claims. For this purpose, eight AI engines have been tested on four different types of data, which cover the different ways of using ChatGPT. These types are Original, Paraphrased by AI, 100% AI generated and 100% AI generated with Contextual Information. The AI index recorded by these tools against the datasets was evaluated as an indicator of their performance.

The resulting figures of cumulative mean validate that these tools excel at identifying human generated content (1.71% AI content) and perform reasonably well in labelling AI generated content (76.85% AI content). However, they are perplexed by the scenarios where the content is either paraphrased by the AI (39.42% AI content) or generated by giving a precise context for the output (60.1% AI content).

This paper evaluates different services for the detection of AI-generated content to verify academic integrity in research work and higher education and provides new insights into their performance.

Higher education institutions (HEIs) offer courses and programmes focusing on sustainability in economics, as courses on sustainable development (SD), which examine the economic, social and environmental dimensions of SD. This paper aims to examine sustainability integration in economics degree programmes.

Through an extensive literature review in Web of Science (WoS) and information search in Google, conducting to 28 relevant case studies, this paper elucidates the emphasis given to sustainability as part of economics degree programmes in HEIs.

The results suggest that, whereas the inclusion of sustainability components in this field is a growing trend, much still needs to be done to ensure that matters related to SD are part of the routine of university students studying economics.

It is worth noting that the literature review conducted in WoS was primarily aimed at assisting in the selection of university case studies. The 28 university case studies scrutinised in this study may lack sufficient representation from numerous developing countries.

This study highlights challenges in integrating the SD into economics degree programmes, suggesting the need for curriculum adjustments as underscoring operational issues, acting as barriers. The inclusion of sustainability in economics programmes must navigate operational issues stemming from packed timetables and busy schedules, requiring innovative solutions.

As far as the authors are aware, this study holds substantial importance in its emphasis on implementing sustainability within HEIs’ economics programmes, assisting in pursuing SD.

The novelty of this study lies in addressing sustainability with the specific economics focus programmes within the HEIs context.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

This paper focuses on First Peoples Founders of for-profit entities in Australia and the role of the Indigenous Economic Development Agencies (IEDAs). We explore the challenges facing First Peoples enterprises, influenced by historical exclusion from white settler society, and the practices of the IEDAs from the perspectives of Founders and agencies.

A qualitative study utilising Indigenous Standpoint Theory and Indigenous research methods, elevating Founder perspectives, in the Yaruwu language - the Nilangany Ngarrungunil, owners of knowledge, to that of research collaborators.

The First Peoples economic landscape is continually evolving with IEDAs contributing to that evolution despite contentious identity ownership definitions. Founders secure in their own identity, are focused on self-determination and opportunities provided by IEDAs, government and corporate sector policies. However, opportunities are undermined by ongoing racism, discrimination and prevailing stereotypes leading to homogeneity, invisibility and exclusion. Founders question organisational commitments to overcoming systemic exclusion in particular their commitment to building respectful relationships and understanding First Peoples ways of working. Instead, Founders focus on building a sustainable First Peoples economic ecosystem through relationship-based practices rather than transactional reconciliation which ignores the reality of the lived experience of everyday racism.

This study extends the scholarly discourse on First Peoples for-profit enterprise success written with an Indigenous voice. We demonstrate how this Founder generation are strengthened by culture with identity infused in organisational practices underpinning their aspirations of economic self-determination.

State agencies responsible for wildlife management and habitat preservation have historically relied on revenues generated from traditional sources, such as hunting and fishing licenses (consumptive users). This conventional funding model tends to overlook the shift in demographics and preferences toward non-consumptive activities like wildlife watching and nature tourism, as well as the indirect benefits from such activities. To address this disproportionate representation, innovative funding mechanisms are being explored throughout the Rocky Mountain West to provide avenues for inclusive conservation funding that incorporates non-consumptive users, such as recreational fees, conservation-oriented merchandizing, and co-branding partnerships with small businesses. Inspired by this methodology, initiatives like the University of Wyoming’s, “WYldlife for Tomorrow” (WFT) have been developed as an innovative approach that fosters collaboration between state agencies, businesses, educational institutions, and local communities for the purpose of creating sustainable funding streams for wildlife and habitat conservation. By responding to the evolving trends in hunting and fishing interests, this collaborative effort holds the potential to establish a sustainable model for wildlife management programs.