Link Consulting

Purpose Given how often leaders make decisions in controversial and conflict-laden situations, it is striking that there is a complete void in studying unpopular decision-making, particularly in business. Engaging responsible leadership and paradox theory, we explore how responsibility is enacted in unpopular decision-making situations and what strategies are pursued during these unpopular decision-making events. Design/methodology/approach This qualitative study's sample comprised 25 senior leaders from Australia and the United States. The data were collected using semi-structured in-depth interviews, which were analyzed using thematic analysis. Findings When leaders seem to have greater control over decision-making, their responsible courage played a critical role. Responsible courage was less likely to play a critical role when more external stakeholders were likely to be impacted by a decision-making outcome. Leaders employed a combination of strategies, including inclusive stakeholder management, stakeholder buy-in and creating a vision for change. The most unique leadership strategy in our study findings was inclusive stakeholder management. It was more prevalent when involving many external stakeholders. Research limitations/implications Although some scholars have proposed the usefulness of applying paradox theory in the responsible leadership context, this is, to our knowledge, the first study to empirically explore unpopular decision-making in conflict situations. Our findings show that leaders often undertake responsibly active decisions including being responsibly reactive, responsibly assertive and responsibly courageous in unpopular decision-making situations. To what extent responsible decision-making mandates a both/and approach is a fruitful avenue for further inquiry. Practical implications Responsible courage allows leaders to act decisively while remaining open to contestation, resistance and uncertainty. Leaders sustain legitimacy by clearly explaining why no option can satisfy all demands and by acknowledging that paradoxes cannot be resolved, only navigated. Leaders could be encouraged to prioritize the organization's purpose when called upon to manage competing and contradictory tensions. Social implications The study emphasizes that leaders must be mindful of the broader societal consequences of their decisions, particularly in high-stakes situations such as mergers, acquisitions and organizational restructuring. While these decisions may be necessary for business survival, they often have significant negative impacts, such as job losses, reduced social cohesion and threats to community well-being. Leaders should not make these choices in isolation but rather consider the ethical and social implications. Originality/value The study offers novel insights into responsible leadership by emphasizing paradox theory and responsible courage in the context of unpopular decision-making. It is the first empirical study to highlight leaders' responsible courage in unpopular decision-making to manage paradoxical tensions through a both/and approach.

The leather industry plays a crucial role in the production of high-quality leather products to remain competitive in the market. Due to various reasons such as material handling and turning, leather defects can appear at different stages of the production process. This paper proposed deep learning techniques, specifically transfer learning, deep convolutional neural networks (D-CNN), and ensemble learning for automating leather defect detection and classification. The dataset was taken from the open source Kaggle. The dataset was preprocessed from the beginning. However, we performed Error Level Analysis (ELA) to detect possible changes or fraud in the image. We also used data augmentation techniques (resizing, rescaling, flipping, rotation, zooming, and contrasting) on the dataset to improve the model performance. This study investigated deep learning architectures for automatic leather defect detection, comparing them with seven deep convolutional neural networks (D-CNN) models and transfer learning architectures. Furthermore, we introduce and evaluate two novel ensemble models: MER built on transfer learning architecture (MobileNetV2, EfficientNetB0, and ResNet50) and AZL built on D-CNN architecture (AlexNet, ZfNet, and LeNet). A key contribution of this study is the demonstration of significant performance improvements achieved by strategically integrating squeeze and excitation and label smoothing techniques into transfer learning, D-CNN, and ensemble frameworks. Our proposed MER ensemble method achieves a state-of-the-art of 96.39% for leather defect detection. The proposed model was evaluated using performance metrics including precision, recall, F1-score, AUC, ROC, and Matthews Correlation Coefficient (MCC). Additionally, Grad-CAM was employed for explainable AI to visualize and interpret the model’s decision-making process. Comparative analysis against separate transfer learning and D-CNN models fails to establish the superior performance of our MER ensemble, which highlights the potential to significantly advance automated quality inspection in leather production. This study provides insights into evaluating the best deep learning techniques for leather defect classification, paving the way for more accurate, efficient, and reliable industrial applications. Proposes a robust deep learning-based framework for automated leather defect detection using transfer learning, deep CNNs, and two novel ensemble models (MER and AZL), enhanced with squeeze-and-excitation and label smoothing techniques. Achieves state-of-the-art performance with the proposed MER ensemble model, achieve 96.39% accuracy, and demonstrates superior results across precision, recall, F1-score, AUC–ROC, and MCC. Integrates Error Level Analysis (ELA), extensive data augmentation, and Grad-CAM–based explainable AI to ensure data reliability, improve generalization, and provide transparent visual interpretation of defect regions for industrial quality inspection.

Purpose The persistence of remote and hybrid work has resulted in socially isolated supply chain workers working in functional groups to perform sometimes repetitive and unengaging tasks, under the auspices of performance dashboards. This study examines perseverance, a personality trait that may be a potential factor in stimulating helping behavior and improving workgroup performance. In addition, we also explore common logistics and supply chain management task structures, including goal-setting and task complexity as moderating influences. Design/methodology/approach This paper utilizes an online scenario-based vignette experiment designed in collaboration with a senior supply chain management executive to explore personality and situational factors that influence helping behavior among remote workers performing an unengaging supply chain task. Findings We find that, while a worker's perseverance is positively related to their likelihood of helping coworkers, this direct relationship is under competitive mediation through normative ability, in which we also find that perseverant workers tend to focus more on their own performance, even though assessing their normative ability is positively tied to helping behavior. Further, both of these dynamics are respectively amplified by participative goal-setting and, unexpectedly, task complexity. Practical implications As firms continue to struggle with return-to-office mandates, our results offer insights into how supply chain managers can improve workgroup performance among remote workers, particularly those tasked with unengaging and unrewarding tasks that remain commonplace in the logistics and supply chain management domain. Originality/value To our best knowledge, this is the first study to examine remote supply chain worker performance. Even as technology continues to advance, there are certain repetitive and unengaging tasks that cannot be simply automated. Our study offers a potential path for supply chain managers to improve remote worker engagement and workgroup performance on these tasks.

The use of the hydroxyapatite (HA) contained in animal bone residues for the synthesis of active catalysts is an innovative solution to manage large quantities of bone waste (BW). In this study, two methods of preparing HA - containing catalysts were explored: i) conventional route using bone char (BC) from BW calcined at 500 - 900 o C, and ii) microwaves route using aqueous phase extraction from BW by a microwave (MW) -assisted process at 95 o C. BC mainly consisted of B-type HA, in which carbonate groups replace phosphate in the structure; however, its low surface area limited its catalytic performance. Modification with silica, MW treatment and surfactant charge had a critical effect on the formation of the porous structure, and consequently, on increasing the surface area of the composite catalysts. Cationic surfactants promoted silica network formation and resulted in improved porous structures due to their weaker interaction with the positively charged BC. The obtained HA-containing catalysts were demonstrated to be effective platforms for immobilising metallic species. Incorporating Ti and Zn by direct synthesis generated highly dispersed active sites, as confirmed by physicochemical characterization. Notably, an increase in sea buckthorn pyrolysis temperature (from 250 °C to 600 °C) was observed after just 5 min of MW irradiation using catalysts produced via the MW-assisted method, indicating enhanced heat transfer within the reactor. Meanwhile, catalysts produced via the conventional method significantly impacted the composition of the pyrolysis gas phase by promoting the formation of CO 2 and hydrocarbons ≥C 2 , such as C 2 H 2 , C 2 H 6 , C 3 H 8 , C 4 H 10 , C 5 H 12 , and C 6 H 14 . • Hydroxyapatite-containing catalysts obtained from bone waste using conventional and microwave methods • Hydroxyapatite as a platform for immobilization of catalytic active sites • Formation of hierarchical structure of composite catalysts using surfactants • New methods for catalyst production - conventional vs. microwave

Deceptive and evolving conversations on online platforms threaten trust, security, and user safety, particularly when concept drift obscures malicious intent. Large Language Models (LLMs) offer strong natural language reasoning but remain unreliable in risk-sensitive scenarios due to contextual ambiguity and hallucinations. This article introduces a domain knowledge-enhanced Dual-LLM framework that integrates structured cues with pretrained models to improve fraud detection and drift classification. The proposed approach achieves 98% accuracy on benchmark datasets, significantly outperforming zero-shot LLMs and traditional classifiers. The results highlight how domain-grounded prompts enhance both accuracy and interpretability, offering a trustworthy path for applying LLMs in safety-critical applications. Beyond advancing the state of the art in fraud detection, this work has the potential to benefit domains such as cybersecurity, e-commerce, financial fraud prevention, and online content moderation.

This chapter examines the experiences of Canadian signed language interpreters and members of the deaf community in navigating the legal system. As a marginalized linguistic minority, deaf individuals face persistent structural barriers across political, educational, healthcare, and vocational contexts, which become even more severe when they engage with the justice system. Drawing on findings from a national study of deaf, hard of hearing, and deafblind Canadians, the chapter traces encounters from initial contact with police or social workers to court proceedings and correctional settings. The study reveals ongoing inequities in access to interpretation, communication, and culturally competent support for both victims and incarcerated persons. While recent initiatives—such as provincial interpreter training and collaborations with deaf mental health professionals—mark incremental progress, systemic problems remain. A 2024 human rights tribunal, for instance, found that deaf prisoners continued to face violations of linguistic rights, with inadequate interpreting provision and outdated communication technologies. These findings highlight that while interpreting services are essential, they alone cannot ensure full linguistic justice. Broader structural reforms are required to affirm deaf individuals’ rights and guarantee equitable participation in all stages of the legal process. The chapter concludes with recommendations for policy and institutional change.

The paper entitled, Neurofeedback and Basic Learning Theory: Implications for Research and Practice, published in 2011, provided an overview of our understanding of the underlying mechanisms of operant conditioning when administering neurofeedback. Here, we expand on the basic tenets described in that paper to include learning in biofeedback, address random schedules of reinforcement, the neurochemistry of reinforcement learning, placebo effects, new theories of psychophysiological learning, and the role of the client-clinician interaction in facilitating learning success. It is intended both as a companion to the 2011 publication and as an updated reflection of our increased understanding of the learning principles that underlie these modalities.