Explore the vast range of titles available.

Purpose This study aims to utilize the cognitive appraisal theory of stress and coping by conducting a joint investigation of the mediating role of knowledge hiding behaviors in the relationship of exploitative leadership on employee’s work related attitudes (i.e. turnover intentions) and behaviors (e.g. job performance, creativity) and fear of negative evaluation in influencing this mediation. Design/methodology/approach Using the Preacher and Hayes’ (2004) moderated-mediation approach, the authors tested the model by collecting multi-wave and two-source data from employees and fellow peers (n = 281) working in the service sector of Pakistan. Findings Results of the study demonstrate that exploitative leadership adversely influences one’s performance and turnover intentions through knowledge hiding behaviors. The fear of negative evaluation moderates the indirect effects of exploitative leadership on employee’s outcomes through knowledge hiding behaviors such that these indirect effects are stronger for individuals possessing low levels of fear of negative evaluation. Originality/value The current study contributes to knowledge management and dark leadership literature by suggesting knowledge hiding behaviors as a process through which exploitative leaders unveil their negative effects on employee’s outcomes. This study is also unique in the sense, as it posits that employees might vary because of their dispositional traits (i.e. low fear of negative evaluation) in responding to exploitative leadership with greater knowledge hiding behaviors.

In this paper, we explore methods to enhance the performance of one of the frequently used variants of Quantum Convolutional Neural Networks, known as Quanvolutional Neural Networks (QuNNs) by introducing trainable quanvolutional layers and addressing the challenges associated with training multi-layered or deep QuNNs. Traditional QuNNs mostly rely on static (non-trainable) quanvolutional layers, limiting their feature extraction capabilities. Our approach enables the training of these layers, significantly improving the scalability and learning potential of QuNNs. However, multi-layered deep QuNNs face difficulties in gradient-based optimization due to limited gradient flow across all the layers of the network. To overcome this, we propose Residual Quanvolutional Neural Networks (ResQuNNs), which utilize residual learning by adding skip connections between quanvolutional layers. These residual blocks enhance gradient flow throughout the network, facilitating effective training in deep QuNNs, thus enabling deep learning in QuNNs . Moreover, we provide empirical evidence on the optimal placement of these residual blocks, demonstrating how strategic configurations improve gradient flow and lead to more efficient training. Our findings represent a significant advancement in quantum deep learning, opening new possibilities for both theoretical exploration and practical quantum computing applications.

Stroke is the fourth leading cause of mortality and is estimated to be one of the major reasons for long-lasting disability worldwide. There are limited studies that describe the application of physical therapy interventions to prevent disabilities in stroke survivors and promote recovery after a stroke. In this review, we have described a wide range of interventions based on impairments, activity limitations, and goals in recovery during different stages of a stroke. This article mainly focuses on stroke rehabilitation tactics, including those for sensory function impairments, motor learning programs, hemianopia and unilateral neglect, flexibility and joint integrity, strength training, hypertonicity, postural control, and gait training. We conclude that, aside from medicine, stroke rehabilitation must address specific functional limitations to allow for group activities and superior use of a hemiparetic extremity. Medical doctors are often surprised by the variety of physiotherapeutic techniques available; they are unfamiliar with the approaches of researchers such as Bobath, Coulter, and Brunnstrom, among others, as well as the scientific reasoning behind these techniques.

Purpose Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area. Design/methodology/approach The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data. Findings Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior. Originality/value The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain. Objetivo La Inteligencia Artificial (IA) ofrece muchos beneficios para mejorar la práctica del marketing predictivo. Sin embargo, plantea preocupaciones éticas relacionadas con la priorización de clientes, la concentración de cuota de mercado y la manipulación del consumidor. Este artículo explora estas preocupaciones éticas desde una perspectiva contemporánea, basándose en las experiencias y perspectivas de profesionales en IA y marketing predictivo. El estudio tiene como objetivo contribuir a la literatura de este ámbito al proporcionar una perspectiva moderna sobre las preocupaciones éticas del uso de la IA en el marketing predictivo, basándose en las experiencias y perspectivas de profesionales en el área. Diseño/metodología/enfoque Para realizar el estudio se realizaron entrevistas semiestructuradas durante seis semanas con 14 participantes con experiencia en sistemas habilitados para IA en marketing, utilizando técnicas de muestreo intencional y de bola de nieve. Se utilizó un análisis temático para explorar los temas que surgieron de los datos. Resultados Los resultados revelan que el uso de la IA en marketing podría tener consecuencias no deseadas, como perpetuar sesgos existentes, violar la privacidad del cliente, limitar la competencia y manipular el comportamiento del consumidor. Originalidad El estudio identifica siete temas y los comparan con el modelo de Ashok para proporcionar una perspectiva estructurada para interpretar los resultados. El marco presentado por esta investigación es único y puede utilizarse para respaldar investigaciones éticas que abarquen aspectos sociales, tecnológicos y económicos dentro del ámbito del marketing predictivo. 人工智能（AI）为改进预测营销实践带来了诸多益处。然而, 这也引发了与客户优先级、市场份额集中和消费者操纵等伦理问题相关的观点。本文从当代角度深入探讨了这些伦理观点, 充分借鉴了人工智能和预测营销领域专业人士的经验和观点。旨在通过现代视角提供关于在预测营销中应用人工智能时所涉及的伦理观点, 为该领域做出有益贡献。 研究方法 本研究采用了目的性和雪球抽样技术, 与14位在人工智能营销系统领域具有丰富经验的参与者进行为期六周的半结构化访谈。研究采用主题分析方法, 旨在深入挖掘数据中显现的主要主题。 研究发现 研究结果表明, 在营销领域使用人工智能可能引发一系列意外后果, 包括但不限于加强现有偏见、侵犯客户隐私、限制竞争以及操纵消费者行为。 独创性 本研究通过明确定义七个独特的主题, 并采用阿肖克模型进行基准比较, 为读者提供了一个结构化的视角, 以解释研究结果。所提出的框架具有独特之处, 可有效支持在跨足社会、技术和经济领域的预测营销中展开的伦理研究。

First paper, “Exploring inside the box: a cross cultural examination of stimuli affecting fast food addiction”, authored by Hania and colleagues is a cross-cultural study and highlights the role of personal, social and demographic factors affecting food addiction. [...]paper, “Social media analysis of anti-consumption in Turkey”, authored by Khan and colleagues aims to examine the consumer perceptions of food anti-consumption in a cultural context of Turkey. [...]paper, “Factors influencing Turkish parents’ intentions towards anti-consumption of junk food”, authored by Yarimoglu and colleagues is aimed at investigating the role of attitude, norms and behavioural control to determine junk food anti-consumption among children in a Turkish context. [...]the paper, “Model construction of engagement and outcomes in consumers food life: evidence from chain stores customer”, authored by Saman and colleagues signifies the role of consumer lifestyles and personality to examine their satisfaction and loyalty.

The unprecedented success of classical neural networks and the recent advances in quantum computing have motivated the research community to explore the interplay between these two technologies, leading to the so-called quantum neural networks. In fact, universal quantum computers are anticipated to both speed up and improve the accuracy of neural networks. However, whether such quantum neural networks will result in a clear advantage on noisy intermediate-scale quantum (NISQ) devices is still not clear. In this paper, we propose a systematic methodology for designing quantum layer(s) in hybrid quantum–classical neural network (HQCNN) architectures. Following our proposed methodology, we develop different variants of hybrid neural networks and compare them with pure classical architectures of equivalent size. Finally, we empirically evaluate our proposed hybrid variants and show that the addition of quantum layers does provide a noticeable computational advantage.

Cotton is a valuable crop for the textile industry yet, its production is significantly affected by Cotton Leaf Curl Disease (CLCuD), a major cotton constraint. The present study was conducted under field trials and glasshouse conditions to analyze the effect of CLCuD in cotton. Single plant progeny rows (SPPRs) of different cotton accessions were grown in the field. In the glasshouse, two sets of cotton plants were maintained in a controlled environment. One set was kept healthy, while the other was graft-inoculated with a Cotton Leaf Curl Virus (CLCuV) infected plant. After 90 days post-inoculation, SPPRs and grafted plants were screened for symptom development using a disease rating scale from 0 to 6. Estimation of antioxidants and metabolites revealed significant differences in CLCuD-resistant and susceptible varieties. Elevated levels of total phenolic content (TPC), tannins, total oxidant status (TOS), total soluble proteins (TSP), and malondialdehyde (MDA) were observed by CLCuD-susceptible genotypes in the field and glasshouse. In contrast, increased antioxidants for example, peroxidase (POD), ascorbate peroxidase (APX), and, catalase (CAT) were observed in CLCuD- resistant varieties. Under field conditions, CLCuD-resistant varieties showed elevated antioxidant enzymes, with CAT, POD, and APX activities increasing by 32%, 3%, and 8% respectively, while superoxide dismutase (SOD) activity decreased by 25% compared to susceptible lines. Under glasshouse conditions, resistant genotypes showed stronger antioxidant responses than susceptible ones; for instance, POD and APX activities were ~ 62% and ~ 6% higher, respectively, while CAT and SOD increased by 15% and 3%. Principal component analysis (PCA) of the field experiment indicated that five key factors contributed to 80.26% of the variation observed among genotypes. Analysis of the glasshouse experiment explained 74.24% of the total cumulative variability. These factors were identified as the most influential in explaining differences in morphological and biochemical traits. In our study, genotypes Mac-07, T7-1-2, and T7-2-5, showed high chlorophyll a, lycopene, TPC, tannins, MDA, and antioxidant enzymes in the field. Under glasshouse conditions, their un-inoculated plants exhibited elevated level of chlorophyll a and b, total chlorophyll, lycopene, APX, SOD, CAT, and POD. Overall, Mac-07, T7-1-2, and T7-2-5 demonstrated superior performance against CLCuD across both conditions and can be considered strong candidates for future CLCuD-resistant cotton breeding programs.

The recent development in the technology has increased the complexity of image contents and demand for image classification becomes more imperative. Digital images play a vital role in many applied domains such as remote sensing, scene analysis, medical care, textile industry and crime investigation. Feature extraction and image representation is considered as an important step in scene analysis as it affects the image classification performance. Automatic classification of images is an open research problem for image analysis and pattern recognition applications. The Bag-of-Features (BoF) model is commonly used to solve image classification, object recognition and other computer vision-based problems. In BoF model, the final feature vector representation of an image contains no information about the co-occurrence of features in the 2D image space. This is considered as a limitation, as the spatial arrangement among visual words in image space contains the information that is beneficial for image representation and learning of classification model. To deal with this, researchers have proposed different image representations. Among these, the division of image-space into different geometric sub-regions for the extraction of histogram for BoF model is considered as a notable contribution for the extraction of spatial clues. Keeping this in view, we aim to explore a Hybrid Geometric Spatial Image Representation (HGSIR) that is based on the combination of histograms computed over the rectangular, triangular and circular regions of the image. Five standard image datasets are used to evaluate the performance of the proposed research. The quantitative analysis demonstrates that the proposed research outperforms the state-of-art research in terms of classification accuracy.

Quantum neural networks (QNNs) are often challenged with the problem of flat cost function landscapes during training, known as barren plateaus (BP). A solution to potentially overcome the problem of the BP has recently been proposed by Cerezo et al In this solution, it is shown that, for an arbitrary deep quantum layer(s) in QNNs, a global cost function (all qubits measured in an n -qubit system) will always experience BP, whereas a local cost function (single qubit measured in an n -qubit system) can help to alleviate the problem of BP to a certain depth ( )). In this paper, we empirically analyze the locality and globality of the cost function in hybrid quantum neural networks. We consider two application scenarios namely, binary and multi-class classification, and show that for multiclass classification, the local cost function setting does not follow the claims of Cerezo et al ; that is, the local cost function does not result in an extended quantum layer’s depth. We also show that for multiclass classification, the overall performance in terms of accuracy for the global cost function setting is significantly higher than the local cost function setting. On the other hand, for binary classification, our results show that the local cost function setting follows the claims of Cerezo et al , and results in an extended depth of quantum layers. However, the global cost function setting still performs slightly better than the local cost function.

PurposeThe core aim of this study is to explore how psychological safety, psychological meaningfulness and psychological empowerment predict psychological well-being in a mediating role of promotive voice.Design/methodology/approachA cross-sectional survey is employed to collect data from 456 front-line employees (FLEs) working in the banking sector of Pakistan. The collected data were analyzed utilizing the structural equation modelling (SEM) technique.FindingsThe relationship between psychological safety and empowerment is significant. The results support the direct and mediating role of promotive voice to predict psychological well-being among frontliners. Interestingly, the mediation of promotive voice to predict the relationship between psychological meaningfulness and psychological well-being is not supported.Practical implicationsThe managers should delegate authority to FLEs working at the front end. Moreover, voicing should be a delightful experience for employees. The management should listen to them carefully and also update the staff about the outcomes of suggestions rendered by them. Finally, rewarding employees can encourage promotive voicing among FLEs.Originality/valueThe psychological safety as an antecedent to promotive voice, promotive voice as a predictor of psychological well-being and the collectivist country context of Pakistan are unique products of this study.