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This case study involves interviews with radiologists of various hospitals and with company personnel. Both primary and secondary data sources have been used. The first-hand perspective from the radiologists highlighted the challenges they face concerning time and the patient load. The company personnel highlighted using machine learning for used cases to make the platform more robust and accurate. This case has been tested with MBA students.

An emerging health-care artificial intelligence (AI) start-up, DeepTek.AI, wants to expand its reach in the radiology market. The company intends to leverage technology to assist radiologists in diagnostics. India's health-care sector faces the challenge of needing more trained doctors and nurses to meet the ever-increasing needs of patients. This case study revolves around the radiologists' concerns about implementing the new technology and its ease of use. The features and benefits of integrating AI in diagnostics are the need of the hour, but the reliability of results needs to be ascertained for adopting it.

This case was written for marketing applications and practices, trends in marketing, marketing strategy and technology adoption in marketing courses at the post-graduate level. Consumer adoption of finance, hospitality, travel and health-care technology is vital for increasing the company's market share and growth prospects. The students will have an opportunity to understand the challenges and the opportunities.

Scientists have been conducting trials to find ways to reduce fuel consumption and enhance heat transfer rates to make heating systems more efficient and cheaper. Adding solid nanoparticles to conventional liquids may greatly improve their thermal conductivity, according to the available evidence. This study aims to examine the influence of external magnetic flux on the flow of a mixed convective Maxwell hybrid non-Newtonian nanofluid over a linearly extending porous flat plate. The investigation considers the effects of thermal radiation, Dufour and Soret.

The mathematical model is formulated based on the fundamental assumptions of mass, energy and momentum conservation. The implicit models are epitomized by a set of interconnected nonlinear partial differential equations, which include a suitable and comparable adjustment. The numerical solution to these equations is assessed for approximate convergence by the Runge−Kutta−Fehlberg method based on the shooting technique embedded with the MATLAB software.

The findings are presented through graphical representations, offering a visual exploration of the effects of various dynamic parameters on the flow field. These parameters encompass a wide range of factors, including radiation, thermal and Brownian diffusion parameters, Eckert, Lewis and Soret numbers, magnetic parameters, Maxwell fluid parameters, Darcy numbers, thermal and solutal buoyancy factors, Dufour and Prandtl numbers. Notably, the authors observed that nanoparticles with a spherical shape exerted a significant influence on the stream function, highlighting the importance of nanoparticle geometry in fluid dynamics. Furthermore, the analysis revealed that temperature profiles of nanomaterials were notably affected by their shape factor, while concentration profiles exhibited an opposite trend, providing valuable insights into the behavior of nanofluids in porous media.

A distinctive aspect of the research lies in its novel exploration of the impact of external magnetic flux on the flow of a mixed convective Maxwell hybrid non-Newtonian nanofluid over a linearly extending porous flat plate. By considering variables such as solar radiation, external magnetic flux, thermal and Brownian diffusion parameters and nanoparticle shape factor, the authors ventured into uncharted territory within the realm of fluid dynamics. These variables, despite their significant relevance, have not been extensively studied in previous research, thus underscoring the originality and value of the authors’ contribution to the field.

Taking the lens of a cue diagnosticity framework and affective primacy theory, this study aims to examine the relative effects of cognitive and affective country image on consumer cognitive judgement, affective evaluation and behavioural tendency in one integrated model. It also explores how the direct effects may vary with the intra-valence nature (ambivalent vs. univalent) of cognition-affect.

The proposed research model was tested using data from a large Chinese sample and consumer responses to products from four countries − the USA, Japan, Brazil and India.

The results show that the relative effects of cognitive and affective country image are complex and differ by the intra-valence nature of cognition-affect. On a general level, cognitive and affective country image exert equal influence on affective evaluation and behavioural tendency. In contrast, cognitive country image demonstrates a more prominent effect than affective country image on cognitive judgement. Compared with univalent, ambivalent cognition-affect strengthens the positive impact of affective country image but does not significantly alter the positive impact of cognitive country image on consumer reactions.

This research contributes to the ongoing debate regarding implications of two focal aspects of macro country image by revealing their relative importance in an integrated framework and enriches country-of-origin research through unveiling the uni/ambivalent cognition-affect as a moderator of the relationship between cognitive/affective country image and consumer reactions. The research findings provide implications as to whether and when marketing strategies should focus on leveraging positive (negative) cognitive or affective country image.

This paper aims to explore the role of individual inner dimensions in fostering sustainable mindsets in accounting students and graduates. Individual inner dimensions such as compassion shape our behaviour and responses to sustainability challenges. Consideration of inner dimensions, in conjunction with sustainability knowledge and skill development, is needed for reshaping the accounting profession towards achieving sustainable futures.

The authors explore the role of individual inner dimensions in accounting and how approaches to cultivating compassion in other disciplinary educational settings could be applied to cultivate and facilitate compassion within accounting education. Approaches to cultivating compassion for human and non-human species within accounting education are presented, highlighting their relevance to accounting decisions and organisational accountability.

Cultivating compassion for human and non-human species within accounting education aligns with the broader role of accounting in social and environmental issues. Embedding compassionate approaches with a problem-solving focus within accounting pedagogies and curricula design could contribute to shaping behaviour and reorienting the mindsets of future accounting professionals.

Cultivating compassion within accounting students enhances connections across species, encourages students to recognise the role of compassion in sustainable decision-making and promotes a sustainable mindset. Enhanced compassion in accounting graduates could provide the motivational force for action-oriented responses from the accounting profession to the unprecedented ecological crisis.

To the best of the authors’ knowledge, this paper presents a first step in exploring potential approaches to cultivating and facilitating compassion within accounting pedagogies and curricula design. This paper extends sustainability accounting education literature by considering individual inner dimensions in shifting mindsets of accounting students, graduates and educators towards sustainability.

Sleep arousal detection is an important factor to monitor the sleep disorder.

Thus, a unique nth layer one-dimensional (1D) convolutional neural network-based U-Net model for automatic sleep arousal identification has been proposed.

The proposed method has achieved area under the precision–recall curve performance score of 0.498 and area under the receiver operating characteristics performance score of 0.946.

No other researchers have suggested U-Net-based detection of sleep arousal.

From the experimental results, it has been found that U-Net performs better accuracy as compared to the state-of-the-art methods.

Sleep arousal detection is an important factor to monitor the sleep disorder. Objective of the work is to detect the sleep arousal using different physiological channels of human body.

It will help in improving mental health by monitoring a person's sleep.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

Traditionally vision-focused, tourism effectively ignores the other senses. With tourists travelling to “see” places, an understanding of how people with vision impairment or blindness (PwVIB) experience tourism is currently lacking. Borrowing from psychology, this viewpoint proposes innovative research approaches to address this knowledge gap, clarifying how PwVIB psychologically experience tourism, enabling tourism professionals to design meaningful and appropriate tourism products and services for this market.

Reviewing extant literature on the tourist experience of PwVIB, this study highlights the existing knowledge gaps regarding understanding how PwVIB experience tourism, proposing alternative theoretical approaches and methodologies for use in future studies.

Alternative research approaches, borrowed from psychology, are proposed to address this knowledge gap and clarify our understanding of how PwVIB experience tourism, serving as a call to action for researchers to attack this issue in innovative ways. An example study, using a mental imagery approach is discussed by way of illustration of how such techniques could be applied.

Highlighting a gap in the understanding of tourist experiences of PwVIB, this viewpoint proposes the adoption of innovative research methods from psychology as a means of delving into the cognitive and conceptual processes involved, offering a new perspective on how to address this important and topical issue and contribute to the design of inclusive and meaningful tourism experiences for this demographic.

This paper aims to provide insights into the potential of digital technologies-based innovations for more inclusive healthcare by alleviating the affordability, accessibility and availability barriers to utilization of healthcare services. Also, it aims to provide insights into the potential of digital technologies-based innovations for more inclusive services, broadly.

A conceptual framework is inductively developed by analyzing real-world examples of digital technologies-based innovations for more inclusive healthcare through the lenses of economics of information in digital form and certain characteristics of services.

Concurrent implementation of digital technologies-based healthcare innovations with innovations and/or modifications in service processes can enable greater inclusivity by alleviating the affordability, accessibility and availability barriers to utilization of healthcare services.

Issues relating to inequities in healthcare, as a social problem, are the focus of research at multiple levels (e.g. global, national, regional and local) in several academic disciplines. In relation to the scope of the problems and challenges pertaining to providing quality healthcare to the unserved and underserved segments of society, worldwide, the contribution of the proposed framework to practice is modest. However, by highlighting the promise and potential of digital technologies-based innovations as solutions for alleviating barriers to affordability, accessibility and availability of healthcare services during various stages (prevention, detection, diagnosis, treatment and post-treatment follow-up) with illustrative vignettes and developing a framework, the article offers insights for future research. For instance, in reference to mission-driven social enterprises that operate in the product-market space for inclusive innovations under resource constraints, a resourcefulness-based view of the social enterprise constitutes a potential avenue for theory development and research.

Given the conceptual nature of the article, the implications for practice are limited to cognitive implications. Action implications (instrumental implications or implications for practice) are outside of the scope of the article.

Innovations that are economically viable, environmentally sustainable and socially impactful is one of the important issues of our times.

The proposed framework provides insights into the potential of digital technologies-based innovations for more inclusive healthcare by alleviating the affordability, accessibility and availability barriers in the context of emerging and less developed country markets and base of the pyramid segments of society in these markets.

This study aims to evaluate the enhancement in prosthetic supply chain capabilities resulting from the implementation of additive manufacturing (AM) technologies. The study presents an emerging model outlining the key areas that undergo changes when integrating 3D printing technologies into the prosthetic supply chain.

Employing a qualitative approach, data were collected through field observations and 31 in-depth interviews conducted within various Jordanian organizations associated with the prosthetic industry and 3D printing technologies.

The findings suggest that the adoption of 3D printing technologies improves the prosthetic supply chain’s capabilities in terms of customization, responsiveness, innovation, environmental sustainability, cost minimization and patient empowerment. The study sheds light on the specific areas affected in the prosthetic supply chain following the adoption of 3D printing technologies, emphasizing the overall improvement in supply chain capabilities within the prosthetic industry.

This study provides recommendations for governmental bodies and prosthetic organizations to maximize the benefits derived from the use of 3D printing technologies.

This study contributes as the first of its kind in exploring the impact of 3D printing technology adoption in the Jordanian prosthetic industry, elucidating the effects on the supply chain and identifying challenges for decision-makers in an emerging market context.

The objective is to identify the effects of suspicion as well as knowledge gaps, especially in noninterpersonal contexts. This study aims to propose a robust framework for future research. The overarching goal is to foster a comprehensive understanding of consumer suspicion, its implications and its potential avenues in the ever-evolving field of consumer behavior.

Based on a focused review of the literature, this study synthesizes the effects of suspicion in interpersonal and noninterpersonal contexts to unveil its importance for consumer behavior.

The cognitive, affective and behavioral effects of suspicion are identified. Furthermore, a discernible imbalance is observed, as the predominant focus on interpersonal consumer contexts leaves a significant gap in the comprehension of how consumers navigate and perceive suspicion in noninterpersonal interactions. This topic is important especially in an era dominated by complex brand interrelationships and digital touchpoints. Also, the operationalization of the suspicion construct in a plethora of studies seems to be suboptimal, suggesting a need for improvements with respect to its dynamic nature. In this regard, this review provides insightful directions to advance research in the abovementioned domains.

The synthesis of the findings of the empirical articles did not focus on variations in consumer suspicion across different cultures or regions. In addition, the dynamic nature of suspicion and the evolving landscape of consumer behavior mean that findings and implications may require periodic reassessment to maintain relevance. Also, this review did not delve into the methodological diversities across the studies examined.

This review offers marketers and businesses critical insights into the consumer suspicion dynamics. By understanding these nuances, companies can tailor strategies to mitigate suspicion and optimize consumer relationships.

Through synthesizing the effects of suspicion and providing avenues for future research, this study significantly contributes to consumer behavior literature.