Search results

This paper aims to use Q methodology to investigate Millennial perceptions toward private label or national brand apparel.

Q methodology was chosen to identify factors, which correspond to patterns of perceptions prevalent among Millennials. Participants were supplied with 14 statements that they sorted into two Q sorts – One representing perceptions of private label and the other representing perceptions of national brands. The Q sorts were completed through Qualtrics and participants answered open-ended questions on the placement of each statement within each Q sort.

Two factors emerged on private labels, highlighting patterns in price consciousness and uniqueness (acknowledged as patterns surrounding the desire for particular apparel characteristics). Three factors arose for national brand apparel, emphasizing the need for national brands to provide consumers with product security, quality and uniqueness (as identified through the unpreferred qualities national brands typically exhibit).

This study illustrates the various viewpoints retailers must consider when marketing apparel to a specific target demographic. In addition, a single perception (uniqueness) was found to connect motivations, which led to the development of a model for future inquiry.

Despite complete Q sorts and qualitative statements, participants' unfamiliarity with Q methodology and the sorting action of statements could be considered a limitation. The use of MTurk is also considered a limitation owing to the anonymity and possible deception of the workforce.

Private label brand personality growth has many retailers expanding their brand portfolios. Based on the findings of this study, specific opportunities are highlighted for the expansion and marketing of private labels and brand labels based on specific perceptions of a broad Millennial cohort.

Grounded in strategic fit theory, this study aims to identify external and internal factors that influence retailers’ strategic choices regarding their own product brands. Furthermore, it seeks to explore the variations between different own product brand strategies in achieving both external and internal strategic fit.

The systematic review method, incorporating a thematic analysis, was adopted, and 318 articles were included for review.

The factors that influence retailers’ strategic choices regarding their own product brands encompass a range of external macro and industrial environmental factors, along with various internal resource and capability factors. Moreover, the effects of these factors vary across different own product brand strategies.

To our knowledge, this is the first systematic review of research on retailers’ own product brands from a strategic management perspective, offering systematic and structured guidance for retailers.

The purpose of this paper is to study haemodynamic flow characteristics and entropy analysis in a bifurcated artery system subjected to stenosis, magnetohydrodynamic (MHD) flow and aneurysm conditions. The findings of this study offer significant insights into the intricate interplay encompassing electro-osmosis, MHD flow, microorganisms, Joule heating and the ternary hybrid nanofluid.

The governing equations are first non-dimensionalised, and subsequently, a coordinate transformation is used to regularise the irregular boundaries. The discretisation of the governing equations is accomplished by using the Crank–Nicolson scheme. Furthermore, the tri-diagonal matrix algorithm is applied to solve the resulting matrix arising from the discretisation.

The investigation reveals that the velocity profile experiences enhancement with an increase in the Debye–Hückel parameter, whereas the magnetic field parameter exhibits the opposite effect, reducing the velocity profile. A comparative study demonstrates the velocity distribution in Au-CuO hybrid nanofluid and Au-CuO-GO ternary hybrid nanofluid. The results indicate a notable enhancement in velocity for the ternary hybrid nanofluid compared to the hybrid nanofluids. Moreover, an increase in the Brinkmann number results in an augmentation in entropy generation.

This study investigates the flow characteristics and entropy analysis in a bifurcated artery system subjected to stenosis, MHD flow and aneurysm conditions. The governing equations are non-dimensionalised, and a coordinate transformation is applied to regularise the irregular boundaries. The Crank–Nicolson scheme is used to model blood flow in the presence of a ternary hybrid nanofluid (Au-CuO-GO/blood) within the arterial domain. The findings shed light on the complex interactions involving stenosis, MHD flow, aneurysms, Joule heating and the ternary hybrid nanofluid. The results indicate a decrease in the wall shear stress (WSS) profile with increasing stenosis size. The MHD effects are observed to influence the velocity distribution, as the velocity profile exhibits a declining nature with an increase in the Hartmann number. In addition, entropy generation increases with an enhancement in the Brinkmann number. This research contributes to understanding fluid dynamics and heat transfer mechanisms in bifurcated arteries, providing valuable insights for diagnosing and treating cardiovascular diseases.

Work overload has become a relevant issue in the Information Technology (IT) industry, with negative effects for individuals and organizations alike. This study aims to analyse the role of personal resources in a broad model regarding the effects of work overload on performance and well-being for the particular case of IT professionals. Considering the specificities of the IT industry, three personal resources were included in this study: one stable personality variable (conscientiousness) and two more malleable variables (work-life balance and psychological detachment).

To test the model, the authors use a sample of 144 IT Portuguese professionals. The authors collected data through an online questionnaire shared in social networks and IT social network communities. The authors use partial least squares (PLS) for data analysis.

The results show that work overload negatively impacts on employees’ life satisfaction, psychological detachment, work-life balance and task performance. Conscientiousness is positively related with two positive outcomes: task performance and life satisfaction. Work-life balance has a mediating effect in the relationship between work overload and life satisfaction.

These findings emphasize the need to promote conscientiousness in IT professionals, as well as reduce workload and promote family-friendly working environments to foster work-life balance and life satisfaction.

By testing this model, the authors aim to contribute to the current knowledge on the role of personal resources in the Job Demands-Resources model, which is still unclear and under-researched.

This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.

School climate strikes are opening spaces of appearance, becoming differently active forms of public pedagogy where new and previously unthought collective climate action is possible. This inquiry contributes to understanding school climate strikes as important forms of climate justice activism by exploring how they work as public pedagogy.

The inquiry process involved poetic inquiry to produce an affective poetic witness statement to an event of school climate strikes, and then a performative enactment of diffractive reading using the poem created. The diffractive reading is used to conceptualise school climate strikes as public pedagogy and move towards an understanding of how school climate strikes work as public pedagogy. Diffused throughout is the question of where the more-than-human fits in public pedagogy and youth climate justice activism.

School climate strikes are dynamic and differently acting (diffracting) public pedagogies that work by open spaces of appearance that enable capacities for collective action in heterogeneous political spaces. Consideration of entanglements and intra-actions between learner, place, knowledge and climate change are productive in understanding how phenomena work as public pedagogy.

This inquiry extends on important considerations in both climate change education and public pedagogy scholarship. It diffuses consideration of the more-than-human throughout the inquiry and enacts a move beyond the humanist limits of existing public pedagogy scholarship by introducing climate intra-action, heterogeneous political spaces and non-conforming learning to an understanding of activist public pedagogies and the educative agent.

The purpose of this paper is to examine the mechanical characteristics and optimization of wear parameters of hybrid (TiO₂ + Y₂O₃) nanoparticles with Al matrix using squeeze casting technique.

The hybrid aluminium matrix nanocomposites (HAMNCs) were fabricated with varying concentrations of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃), from 2.5 to 10 Wt.% in 2.5 Wt.% increments. Dry sliding wear test variables were optimized using the Taguchi method.

The introduction of hybrid nanoparticles in the aluminium (Al) matrix was evenly distributed in contrast to the base matrix. HAMNC6 (5 Wt.% TiO₂ + 5 Wt.% Y₂O₃) reported the maximum enhancement in mechanical properties (tensile strength, flexural strength, impact strength and density) and decrease in porosity% and elongation% among other HAMNCs. The results showed that the optimal combination of parameters to achieve the lowest wear rate was A3B3C1, or 15 N load, 1.5 m/s sliding velocity and 200 m sliding distance. The sliding distance showed the greatest effect on the dry sliding wear rate of HAMNC6 followed by applied load and sliding velocity. The fractured surfaces of the tensile sample showed traces of cracking as well as substantial craters with fine dimples and the wear worn surfaces were caused by abrasion, cracks and delamination of HAMNC6.

Squeeze-cast Al-reinforced hybrid (TiO₂+Y₂O₃) nanoparticles have been investigated for their impact on mechanical properties and optimization of wear parameters.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

This paper aims to employ the use of focus groups composed of enforcement officers to explore and identify the financial methods used by terrorism-related organisations in Malaysia.

The study used an open-ended question and focus group methods to gather information from 20 Malaysian enforcement officers with extensive experience in dealing with terrorism-related activities, as they strive to prevent and counter terrorism incidents. In addition, experienced practitioners and field experts also contributed to the study.

The study reveals various innovative financial methods used by terrorist-linked organisations to evade detection by local enforcement agencies. These findings are consistent with previous research, which highlights the intelligence of these organisations in avoiding detection by financial regulators.

The findings are based on the perspectives of enforcement officers involved in preventing and countering terrorism activities. Further research could be conducted to gather insights from other government agencies, such as the judiciary or local agencies.

The study offers practical suggestions for organisations and institutions on effectively monitoring and taking appropriate actions in financial activities related to terrorism.

This study provides unique insights into the financial methods of terrorism-related organisations in an emerging country in Southeast Asia. Its findings can be applied throughout the region, given the country’s global connectivity. Furthermore, the study is distinctive in that it provides information from enforcement officers within terrorism-related government organisations, an area where resources are limited. The study also considers the impact of the pandemic on the development of these financial innovations by terrorist organisations.