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The purpose of this paper is to explore the status of university accelerators (UAs) in Australia, expanding a similar paper on related entrepreneurship education (EE) in 2019. The aim is to review neoteric global best practice UA, aligning context and specific inference to the impact of UAs in Australia.

The authors introduce an iterative and emergent inquiry into multi-method research, including a quantitative examination of Australian UAs, Leximancer algorithmic analyses of entrepreneurial strategic intent and narratives from best practice applications.

The paper highlights the sparse and inconsistent distribution across UAs in Australia, further characterized by significant symbolic motives of operation. Furthermore, the integration of EE evidenced on global UA is not as evident in Australia, highlighting outcomes more specific to the success of nascent (student) startups as opposed to educational outcomes.

Limitations include the availability and accuracy of online documents and data, although implications have been mitigated using multi-method research design.

Despite the provision of critical grounding for practitioners and researchers in developing UAs, further research is recommended regarding the efficacy and impact of these accelerators.

This study is the first multi-methods emergent inquiry into UAs in Australia, coupled with integration of EE. The authors provide guidelines and inferences for researchers, educators, policymakers and practitioners alike as they seek to explore and act upon the impact of UAs.

The fertilizer industry (FI) is well known for its high energy needs, reliance on limited natural resources, and negative environmental impacts (EIs). The consumption of 14.2 billion tons (BT) of materials and the extraction of 1,580 tons of resources per acre are solely attributed to the FI. Because of FI's resource and energy-intensive nature, it becomes crucial for FI to adopt a Circular Economy (CE) to improve efficiency, energy, and resource reuse. However, FI needs to strengthen its progress toward CE adoption. The proposed study comprehends and examines the barriers that inhibit the adoption of CE in FI.

A total of 15 barriers obstructing the CE in FI are identified and categorized into seven different categories. The barriers were identified by performing a comprehensive literature review and expert input. The study employs the DEMATEL approach to analyze the barriers and establish a causal relationship between them.

The study reveals that the most significant challenge to implementing CE in FI is governmental restrictions, which are followed by a lack of awareness and understanding and a need for a steady supply of bulk materials. The results comprehensively comprehend the pivotal factors that jeopardize the CE in FI and furnish a robust foundation for the methodology and tactics to surmount the barriers to CE adoption.

The literature review encompasses the barriers to the transition to CE and offers management and policy perspectives that help the FI's policy and decision-makers surmount these barriers with future research endeavors.

The study aims to identify Green Lean Six Sigma (GLSS) barriers in the context of Higher Education Institutions (HEIs) and prioritize them for executing the GLSS approach.

A systematic literature review (SLR) was used to identify a total of 14 barriers, which were then verified for greater relevance by the professional judgments of industrial personnel. Moreover, many removal measures strategies are also recommended in this study. Furthermore, this work also utilizes Gray Relational Analysis (GRA) to prioritize the identified GLSS barriers.

The study reveals that training and education, continuous assessment of SDG, organizational culture, resources and skills to facilitate implementation, and assessment of satisfaction and welfare of the employee are the most significant barriers to implementing this approach.

The present study provides an impetus for practitioners and managers to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers. In this case, the outcomes of this research, and in particular the GRA technique presented by this work, can be used by managers and professionals to rank the GLSS barriers and take appropriate action to eliminate them.

The ranking of GLSS barriers gives top officials of HEIs a very clear view to effectively and efficiently implementing GLSS initiatives. The outcomes also show training and education, sustainable development goals and organizational culture as critical barriers. The findings of this study provide an impetus for managers, policymakers and consultants to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers.

The GLSS barriers in HEIs may significantly affect the society. HEIs can lessen their environmental effect by using GLSS practices, which can support sustainability initiatives and foster social responsibility. Taking steps to reduce environmental effect can benefit society as a whole. GLSS techniques in HEIs can also result in increased operational effectiveness and cost savings, which can free up resources to be employed in other areas, like boosting student services and improving educational programs. However, failing to implement GLSS procedures in HEIs could have societal repercussions as well. As a result, it is critical for HEIs to identify and remove GLSS barriers in order to advance sustainability, social responsibility and operational effectiveness.

GLSS is a comprehensive methodology that facilitates the optimum utilization of resources, reduces waste and provides the pathway for sustainable development so, the novelty of this study stands in the inclusion of its barriers and HEIs to prioritize them for effective implementation.

The purpose of this paper is to explore and expand the concept of a marketing system for developing a more dynamic and nuanced understanding of marketing. The purpose of the proposed framework is to extend this literature by making salient and explicit how context, market system and value creation are theoretically interrelated. To accomplish this objective, the authors use the framework proposed by Layton (2019) as the theoretical foundation to acquire insights into the market. Particularly, they investigate how four distinct marketing systems (i.e. anarchy, structured, emergent and purposeful market systems) operate in a developing economy. In addition, the study explores the market's effects of technological advancement, sociocultural influences, historical background and political institutions, as well as the responses of political entities, firms and consumers. Also, the positive and negative effects of the various marketing systems are analyzed. Finally, the authors investigate the changing marketplace in various industrial sectors (e.g. home appliances, food, apparel/fashion and transportation) to provide marketing researchers and practitioners with insights. In essence, the study focuses on the sectors related to everyday consumption.

This analysis uses a theoretical approach to extend the understanding concept of marketing. To examine the numerous market systems in India, the authors use an approach developed by Layton (2007). This theoretical approach is intended to sensitize scholars to critical processes rather than a hypothetico-deductive analysis with a prediction goal (Turner, 1986). Epistemologically, this analysis can be classified as a form of discovery-oriented theory development (Wells, 1993).

Although all four systems (e.g. autarchic, emergent, purposeful and structured) are ingrained in India, their functionality differs from the Western system and among industries. For example, the apparel sector appears more autarchic, but the food industry is more purposeful. How the home appliance market operates demonstrates the transition from an autarchic to an emergent system. The authors also uncover additional environmental factors that impact the four types of marketing systems and moderator roles of governate agencies and nonprofit organizations. The externality and positive outcomes also emerged throughout the analysis.

This study articulates the four types of marketing systems and illustrates the environmental factors/antecedents and outcomes for the exchange and value creation. Most importantly, it adds value to the literature by emphasizing the role of government agencies and unrestricted institutions in the mechanism. It also uncovers cultural elements such as spirituality as a catalyst for exchange and value creation.

The analysis provides practitioners with insights into operating the firm in India by articulating the industrial differentiations and the exchange/value creation. Specifically, it provides a blueprint for strategic analysis that can be used prior to market entry to increase the likelihood of market entry success by understanding the nuanced differences that lead to significant operational difficulties if not properly prepared for and managed.

This study adds to our existing knowledge of marketing from a systemic standpoint. It also broadens and explicates marketing system theory by assessing the uniqueness of developing markets.

The study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.

In this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.

The experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.

Experiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.