Ambiguous Task Research Articles

Driving Project Outcomes in Healthcare: How Servant Leadership and Team Dynamics Contribute to Success

The current research explores the project outcomes in healthcare sector of Pakistan by assessing servant leadership and team dynamics in the context of Situational Strength Theory. This research model examines the effect of servant leadership on project success via intervention of team effectiveness. Moreover, it also incorporates the moderating impacts of team level variables; team autonomy and task ambiguity in these relationships. The data is gathered from practicing health care project team members of Pakistan. Hence, for data analysis the Statistical technique Partial Least Square-Structural Equation Modelling (PLS-SEM) is employed. The findings indicate that project success is positively and significantly related with servant leadership. Servant leader increases the likelihood of project success and effectiveness directly at the team level. Mediation of team effectiveness between servant leadership and Project Success is also supported as the servant leadership practices nurtures the overall team process and foster team effectiveness. The moderation of task ambiguity is determined to be significant, while interestingly, that of team autonomy is found to be insignificant. The study also establishes the importance of servant leadership for evolving team effectiveness in project-based organizations for the achievement of project success. The results of this study are relevant for the advancement of project management practices, especially in the healthcare sector. This particular study offers practical recommendations to healthcare organizations with regard to successful implementations of effective leadership strategies that improve their team processes and enhance the attainment of desired outcomes for projects. The insights generated by this research add to the existing literature both in healthcare management and project management, fill a significant theoretical gap, and provide a basis for further studies on the role of leadership in complex settings where project organizations are involved.

Unpacking the learning dynamics of a military task force in Afghanistan from a project-based organising perspective

ABSTRACT From a theoretical perspective of project-based organising, this study analyses how a deployed military task force heuristically learns. Empirically, learning within Task Force Uruzgan (TFU) in southern Afghanistan, is scrutinised. This contribution of the Netherlands armed forces to NATO’s International Security Assistance Force (ISAF) lasted for four years and saw successive rotations of task forces simultaneously fighting an insurgency and trying to stabilise a part of Afghanistan through reconstruction and development projects. Above all, the study has found that task ambiguity can make it challenging for a task force deployed to deal with varying contingencies at the same time. Two effects are noteworthy. First, learning can become compartmentalised, with different organisational elements following their own learning trajectories. Second, learning can also become short-term focused, responding to the most imminent and familiar threat. Combined, both learning impulses affect organisational coherence in meeting the overarching mission end state.

“We do others’ Jobs”: a qualitative study of non-nursing task challenges and proposed solutions

IntroductionNon-nursing tasks (NNTs) have become a prevalent issue among healthcare professionals, affecting nurses globally. This study delves into the experiences of Jordanian nurses regarding NNTs, aiming to uncover challenges and propose solutions within the Jordanian healthcare context.ObjectiveExplore the impact of NNTs on Jordanian nurses’ roles, workload, and satisfaction. Additionally, the study aims to identify various types of NNTs performed by nurses, understand their impact, and propose solutions to mitigate challenges associated with these tasks.MethodsA qualitative-exploratory research design was employed for this study. Semi-structured interviews were conducted with Jordanian nurses using a purposeful sampling approach to ensure a diverse representation of experiences and perspectives. Thematic analysis was used to identify recurring themes and patterns related to NNTs, their challenges, and potential solutions. Ethical guidelines were strictly followed to maintain participant confidentiality and ensure the integrity of the data collected.ResultsAnalysis of the interviews revealed four major themes: challenges of NNTs, types of NNTs, impact of NNTs, and proposed solutions. Nurses faced significant difficulties due to task ambiguity, role confusion, and increased workload from NNTs, which included administrative duties, clerical work, and tasks typically performed by other healthcare professionals. These NNTs negatively impacted nurses’ effectiveness, productivity, and job satisfaction by diverting time and energy from primary nursing responsibilities, causing professional strain. To address these issues, participants suggested clearer job descriptions, stricter task assignment protocols, and systemic changes to tackle the root causes of NNTs.ConclusionThis study sheds light on the pervasive challenges posed by NNTs among Jordanian nurses and emphasizes the importance of addressing these issues to enhance nursing care quality and nurse well-being. By proposing actionable solutions tailored to the Jordanian context, this research contributes to the global discourse on NNTs and underscores the need for organizational support and advocacy to optimize nurses’ roles and improve patient care outcomes.

Enhancing Transportation Efficiency with Interval-Valued Fermatean Neutrosophic Numbers: A Multi-Item Optimization Approach

In this study, we derive a simple transportation scheme by post-optimizing the costs of a modified problem. The strategy attempts to make the original (mainly feasible) option more practicable by adjusting the building components’ costs. Next, we employ the previously mentioned cell or area cost operators to gradually restore the modified costs to their initial levels, while simultaneously implementing the necessary adjustments to the “optimal” solution. This work presents a multi-goal, multi-item substantial transportation problem with interval-valued fuzzy variables, such as transportation costs, supplies, and demands, as parameters to maintain the transportation cost. This research addresses two circumstances where task ambiguity may occur: the interval solids transportation problem and the fuzzy substantial transportation issue. In the first scenario, we express data problems as intervals instead of exact values using an interval-valued fermatean neutrosophic number; in the second case, the information is not entirely obvious. We address both models when uncertainty solely affects the constraint set. For the interval scenario, we define an additional problem to solve. Our existing efficient systems have dependable transportation, so they are also capable of handling this new problem. In the fuzzy case, a parametric technique generates a fuzzy solution to the preceding problem. Since transportation costs have a direct impact on market prices, lowering them is the primary goal. Using parametric analysis, we provide optimal parameterization solutions for complementary situations. We provide a recommended algorithm for determining the stability set. In conclusion, we offer a sensitivity analysis and a numerical example of the transportation problem involving both balanced and imbalanced loads.

Task Ambiguity: The Effects of Missing Standard Operating Procedures and Inter-Leadership Harmony in Organizations

Task Ambiguity: The Effects of Missing Standard Operating Procedures and Inter-Leadership Harmony in Organizations

Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops.

Accurately predicting the pairing order of bases in RNA molecules is essential for anticipating RNA secondary structures. Consequently, this task holds significant importance in unveiling previously unknown biological processes. The urgent need to comprehend RNA structures has been accentuated by the unprecedented impact of the widespread COVID-19 pandemic. This paper presents a framework, Knotify_V2.0, which makes use of syntactic pattern recognition techniques in order to predict RNA structures, with a specific emphasis on tackling the demanding task of predicting H-type pseudoknots that encompass bulges and hairpins. By leveraging the expressive capabilities of a Context-Free Grammar (CFG), the suggested framework integrates the inherent benefits of CFG and makes use of minimum free energy and maximum base pairing criteria. This integration enables the effective management of this inherently ambiguous task. The main contribution of Knotify_V2.0 compared to earlier versions lies in its capacity to identify additional motifs like bulges and hairpins within the internal loops of the pseudoknot. Notably, the proposed methodology, Knotify_V2.0, demonstrates superior accuracy in predicting core stems compared to state-of-the-art frameworks. Knotify_V2.0 exhibited exceptional performance by accurately identifying both core base pairing that form the ground truth pseudoknot in 70% of the examined sequences. Furthermore, Knotify_V2.0 narrowed the performance gap with Knotty, which had demonstrated better performance than Knotify and even surpassed it in Recall and F1-score metrics. Knotify_V2.0 achieved a higher count of true positives (tp) and a significantly lower count of false negatives (fn) compared to Knotify, highlighting improvements in Prediction and Recall metrics, respectively. Consequently, Knotify_V2.0 achieved a higher F1-score than any other platform. The source code and comprehensive implementation details of Knotify_V2.0 are publicly available on GitHub.

Self-initiated self-repairs of connected speech and novel vocabulary learning during the first year of recovery from aphasia: four longitudinal case studies

ABSTRACT Background Self-initiated self-repair of problems in speech production has been suggested to be associated with aphasia treatment gains and recovery, but the topic has been little studied. Similarly, the ability to learn novel words has been associated with anomia treatment benefits, although longitudinal data on such learning is lacking. Aims The present study’s aim was to examine whether and how self-initiated self-repairs in connected speech and the ability to learn novel vocabulary reflected recovery from aphasia as measured with cognitive-linguistic tests. Additionally, our aim was to explore whether self-initiated repair and novel word learning changed in parallel during recovery. Methods & Procedures Four people with aphasia (PWA) (2 anomic, 1 Wernicke, and 1 global aphasia) were tested in the subacute (11 to 68 days post stroke) and chronic (12 months post stroke) phases of recovery with cognitive-linguistic tests, connected speech tasks, and a computerized learning task. In the learning task, PWA associated 6 pseudowords with 6 unfamiliar referents. The associations were learned from an ambiguous setup with feedback. At both time points, learning was measured as correct immediate recognition of the items and recognition maintenance one week post-training. The proportion of self-initiated self-repairs and the proportion of unrepaired problems in connected speech were used as measures of self-repair behavior. Outcomes & Results All participants initiated self-repairs both in the subacute and chronic phases, and their self-repairs were most often related to aphasic difficulties in speech production. Between the subacute and chronic phases, self-repairs changed both in quality and quantity. The proportion of self-repair elements decreased in 4/4 participants and the proportion of unrepaired problem segments decreased in 3/4 participants. Of the participants, 3/4 showed significant novel word learning and/or maintenance already in the subacute phase and 4 /4 in the chronic phase. Conclusion Reduction in self-initiated self-repairs of speech reflected recovery from aphasia. Novel word learning improved during aphasia recovery from the subacute to the chronic phase. Novel word learning was possible even in subacute, severe aphasia and even in an ambiguous task set-up with no explicitly provided word-referent connections. Novel word learning and self-initiated self-repairs did not change in a clearly parallel manner; however, these findings need to be tested with a larger sample.

Sequential Label Enhancement.

Label distribution learning (LDL) is a novel machine learning paradigm for solving ambiguous tasks, where the degree to which each label describing the instance is ambiguous. However, obtaining the label distribution is high cost and the description degree is difficult to quantify. Most existing research works focus on designing an objective function to obtain the whole description degrees at once but seldom care about the sequentiality in the process of recovering the label distribution. In this article, we formulate the label distribution recovering task as a sequential decision process called sequential label enhancement (Seq_LE), which is more consistent with the process of annotating the label distribution in human brains. Specifically, the discrete label and its description degree are serially mapped by the reinforcement learning (RL) agent. Besides, we carefully design a joint reward function to drive the agent to fully learn the optimal decision policy. Extensive experiments on 16 LDL datasets are conducted under various evaluation metrics. The experimental results demonstrate convincingly that the proposed sequential label enhancement (LE) leads to better performance over the state-of-the-art methods.

Like clockwork: A within‐person analysis of time theft in the workplace

AbstractEmployee time theft is a silent and costly behavior that is difficult to manage in organizations. Time theft is less understood than other forms of deviance as it is engaged in more frequently and considered to be less harmful by those who engage in it. This study tests a within‐person model of time theft in which negative emotion mediates the positive relationship between job stressors (task ambiguity, task conflict) and time theft. Results show support for the mediating role of emotions in the positive stressor−time theft relationship at the within‐person level. Results also show that high levels of between‐person emotional stability weakened the impact of negative emotions on time theft. These results indicate that job related stressors create an environment in which time theft may become a frequent behavior. In discussing these results, we provide recommendations for employers to combat this phenomenon.

Me or we? Action-outcome learning in synchronous joint action

Goal-directed behaviour requires mental representations that encode instrumental relationships between actions and their outcomes. The present study investigated how people acquire representations of joint actions where co-actors perform synchronized action contributions to produce joint outcomes in the environment. Adapting an experimental procedure to assess individual action-outcome learning, we tested whether co-acting individuals link jointly produced action outcomes to individual-level features of their own action contributions or to group-level features of their joint action instead. In a learning phase, pairs of participants produced musical chords by synchronizing individual key press responses. In a subsequent test phase, the previously produced chords were presented as imperative stimuli requiring forced-choice responses by both pair members. Stimulus-response mappings were systematically manipulated to be either compatible or incompatible with the individual and joint action-outcome mappings of the preceding learning phase. Only joint but not individual compatibility was found to modulate participants' performance in the test phase. Yet, opposite to predictions of associative accounts of action-outcome learning, jointly incompatible mappings between learning and test phase resulted in better performance. We discuss a possible explanation of this finding, proposing that pairs' group-level learning experience modulated how participants encoded ambiguous task instructions in the test phase. Our findings inform current debates about mechanistic explanations of action-outcome learning effects and provide novel evidence that joint action is supported by dedicated mental representations encoding own and others' actions on a group level.

Job pressures affecting the quality of teachers’ performance at the College of Physical Education and Sports Sciences/University of Baghdad

The research aimed to prepare a measure of job stressors affecting the quality of teaching performance in the College of Physical Education and Sports Sciences at the University of Baghdad, to identify their levels, if any, and the most influential dimensions on the quality of teaching performance within the college’s work environment, as well as the deanship’s ability to help prevent and Alleviating its severity through logical administrative methods, on a scale that includes four dimensions that include (the administrative and financial environment, the organizational environment, the social and humanitarian environment, and the scientific and research environment). The descriptive approach was adopted using the case study method for a sample of (166) teachers from the original community of (182) teachers, with a percentage of (91.209%). The research results were processed using the statistical package system (SPSS), version (V28), (statistical package for social sciences). The results showed that teachers were exposed to pressures from the social and humanitarian environment in the first place, followed by pressures from the scientific and research environment, then the organizational environment, and pressures from the administrative and financial environment, respectively, at varying levels. Which enabled us to conclude that there is ambiguity in tasks, roles, and overlapping work, in addition to the need to assign higher administrative levels and their moral and material support, and the college deanship can help in ensuring prevention and alleviating the severity of the pressures that teachers are exposed to in the dimensions and All functional environment. The most important recommendations that came out of the study emphasize giving priority and attention to teachers as a supreme value in the College of Physical Education and Sports Sciences, in addition to consolidating human relations and social connections and solving problems between colleagues in general, and teachers and students in particular, and ensuring the requirements for the success of quality Moral and material performance for teachers, in addition to all entitlements.

Performance management and policy evaluation of information and communication technology graduate program for developing countries

As an educational aid, the Korean government has developed and funded a graduate-level global information telecommunications technology program (ITTP) for ICT officers in developing countries. Questions have arisen about whether the program can achieve its goals and obtain mutual benefits between Korea and participating nations. Using a total quality management (TQM) framework as a unique case analysis, we evaluated ITTP’s resources and performance from 2006 to 2014. Our results show that ITTP has focused on the quantitative promotion of students and alums from various countries and their learning of Korea-centric ICT, policies, and culture. In particular, the Korean government’s lax evaluation of ITTP performance has led to weak responsibility, ambiguous task identity, missed opportunities for self-improvement, and low participation among students and stakeholders. These findings highlight the need for graduate programs and their stakeholders to reinforce internal and external evaluations of the program’s performance and provide feedback for reciprocal development.

Language-controllable programmable metasurface empowered by large language models.

Programmable metasurface has become a prominent tool in various areas including control, communication, computing, and so on, due to its unique capability in the electromagnetic (EM) manipulation. However, it is lack of the intelligence in the sense that it usually requires the manual intervention, and thus makes it hard to behavior as the human process. To endow the programmable metasurface with the intelligence, we here proposed the concept of the language-controllable programmable metasurface for autonomous EM manipulations by exploring the notable capability of large language models (LLMs) in attaining the human-like intelligence. We have established a proof-of-principle system of language-controllable programmable metasurface, where, for illustration, the programmable metasurface is designed to have 32 × 24 binary electronically controllable meta-atoms and work at around 5.5 GHz. In addition, we have constructed a visual-semantic map to facilitate the language-controllable EM manipulation in three-dimensional (3D) physical environments. We have experimentally demonstrated that our language-controllable programmable metasurface is capable of decomposing autonomously an ambiguous task of EM manipulation into a sequence of executable ones and implementing them individually in real-world indoor settings. We expect that the presented strategy could hold promising potential in pushing programmable metasurfaces towards human-level autonomous agents, which are capable of accomplishing the smart EM-involved multi-modality manipulations through self-directed planning and actions.

A FORMAÇÃO CONTINUADA PEDAGÓGICA NA DOCÊNCIA DO ENSINO SUPERIOR: REFLEXÕES SOBRE O LUGAR DOS FACILITADORES NA EDUCAÇÃO A DISTÂNCIA

ABSTRACT: In recent decades, there has been an expansion in access to higher education in Brazil. The high demand for distance learning undergraduate courses highlights new ways of accessing higher education and new institutional arrangements in teaching and learning processes. In the context of distance education, the pedagogical work carried out by facilitators becomes important in the students' learning process. This study aims to analyze the perceptions of facilitators about the pedagogical experiences lived in the continuing education of higher education teachers at Virtual University of the State of São Paulo (UNIVESP). The research is qualitative and used a virtual questionnaire for data collection during the first semester of 2023. The research participants recognize the experience of being a facilitator as important in their professional formation, but perceive the practice as an ambiguous task due to the combination of teaching and non-teaching functions. Regarding the training course undertaken by the facilitators, some see it as an initial approach to pedagogical work. For others, the training is merely a source of income. Other facilitators view the training as an important pedagogical experience in their career but point out limitations. Thus, there is a complexity in the facilitator's role, and the existing difficulties in pedagogical practice demand greater recognition of these facilitators' pedagogical work.

The Latent Doctor Model for Modeling Inter-Observer Variability.

Many inherently ambiguous tasks in medical imaging suffer from inter-observer variability, resulting in a reference standard defined by a distribution of labels with high variance. Training only on a consensus or majority vote label, as is common in medical imaging, discards valuable information on uncertainty amongst a panel of experts. In this work, we propose to train on the full label distribution to predict the uncertainty within a panel of experts and the most likely ground-truth label. To do so, we propose a new stochastic classification framework based on the conditional variational auto-encoder, which we refer to as the Latent Doctor Model (LDM). In an extensive comparative analysis, we compare the LDM with a model trained on the majority vote label and other methods capable of learning a distribution of labels. We show that the LDM is able to reproduce the reference-standard distribution significantly better than the majority vote baseline. Compared to the other baseline methods, we demonstrate that the LDM performs best at modeling the label distribution and its corresponding uncertainty in two prostate tumor grading tasks. Furthermore, we show competitive performance of the LDM with the more computationally demanding deep ensembles on a tumor budding classification task.

MetaGeo: A General Framework for Social User Geolocation Identification With Few-Shot Learning.

Identifying the geolocation of social media users is an important problem in a wide range of applications, spanning from disease outbreaks, emergency detection, local event recommendation, to fake news localization, online marketing planning, and even crime control and prevention. Researchers have attempted to propose various models by combining different sources of information, including text, social relation, and contextual data, which indeed has achieved promising results. However, existing approaches still suffer from certain constraints, such as: 1) a very few samples are available and 2) prediction models are not easy to be generalized for users from new regions-which are challenges that motivate our study. In this article, we propose a general framework for identifying user geolocation-MetaGeo, which is a meta-learning-based approach, learning the prior distribution of the geolocation task in order to quickly adapt the prediction toward users from new locations. Different from typical meta-learning settings that only learn a new concept from few-shot samples, MetaGeo improves the geolocation prediction with conventional settings by ensembling numerous mini-tasks. In addition, MetaGeo incorporates probabilistic inference to alleviate two issues inherent in training with few samples: location uncertainty and task ambiguity. To demonstrate the effectiveness of MetaGeo, we conduct extensive experimental evaluations on three real-world datasets and compare the performance with several state-of-the-art benchmark models. The results demonstrate the superiority of MetaGeo in both the settings where the predicted locations/regions are known or have not been seen during training.

Curriculum label distribution learning for imbalanced medical image segmentation.

Label distribution learning (LDL) has the potential to resolve boundary ambiguity in semantic segmentation tasks. However, existing LDL-based segmentation methods suffer from severe label distribution imbalance: the ambiguous label distributions contain a small fraction of the data, while the unambiguous label distributions occupy the majority of the data. The imbalanced label distributions induce model-biased distribution learning and make it challenging to accurately predict ambiguous pixels. In this paper, we propose a curriculum label distribution learning (CLDL) framework to address the above data imbalance problem by performing a novel task-oriented curriculum learning strategy. Firstly, the region label distribution learning (R-LDL) is proposed to construct more balanced label distributions and improves the imbalanced model learning. Secondly, a novel learning curriculum (TCL) is proposed to enable easy-to-hard learning in LDL-based segmentation by decomposing the segmentation task into multiple label distribution estimation tasks. Thirdly, the prior perceiving module (PPM) is proposed to effectively connect easy and hard learning stages based on the priors generated from easier stages. Benefiting from the balanced label distribution construction and prior perception, the proposed CLDL effectively conducts a curriculum learning-based LDL and significantly improves the imbalanced learning. We evaluated the proposed CLDL using the publicly available BRATS2018 and MM-WHS2017 datasets. The experimental results demonstrate that our method significantly improves different segmentation metrics compared to many state-of-the-art methods. The code will be available.1.

Feeling uncertain despite knowing the risk: Patients with OCD (but not controls) experience known and unknown probabilistic decisions as similarly distressing and uncertain

Patients with obsessive compulsive disorder (OCD) present as risk-averse and avoidant of feared stimuli, yet the literature examining risk aversion in OCD is conflicting. One possible explanation is that patients may exhibit aversion only on ambiguous tasks where the likelihood of possible outcomes is unknown. To test this idea, the current study assigned 30 patients with OCD versus 30 non-psychiatric controls (NPC) to conditions of known versus unknown risk (i.e., probabilities) on the Beads Task. Importantly, the task involved real financial stakes. We also examined self-reported intolerance of uncertainty (IU) as a mechanism. Results revealed a significant risk information × group interaction for certainty about the decision. Specifically, while NPCs felt significantly less certain on the unknown risk (versus known risk) task, the OCD group felt uncertain regardless of risk information. Results also revealed a significant main effect of group for distress after deciding, such that the OCD group was more distressed across all task versions compared to NPCs. Elevated trait IU was associated with higher task-related distress. Results indicate that even when patients with OCD are given information about likelihoods, they still feel uncertain and experience distress. Findings have clinical implications for addressing risk aversion and ambiguity/uncertainty in treatment.

Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detection

Liquid-based cytology (LBC) plays a crucial role in the effective early detection of cervical cancer, contributing to substantially decreasing mortality rates. However, the visual examination of microscopic slides is a challenging, time-consuming, and ambiguous task. Shortages of specialized staff and equipment are increasing the interest in developing artificial intelligence (AI)-powered portable solutions to support screening programs. This paper presents a novel approach based on a RetinaNet model with a ResNet50 backbone to detect the nuclei of cervical lesions on mobile-acquired microscopic images of cytology samples, stratifying the lesions according to The Bethesda System (TBS) guidelines. This work was supported by a new dataset of images from LBC samples digitalized with a portable smartphone-based microscope, encompassing nucleus annotations of 31,698 normal squamous cells and 1395 lesions. Several experiments were conducted to optimize the model’s detection performance, namely hyperparameter tuning, transfer learning, detected class adjustments, and per-class score threshold optimization. The proposed nucleus-based methodology improved the best baseline reported in the literature for detecting cervical lesions on microscopic images exclusively acquired with mobile devices coupled to the µSmartScope prototype, with per-class average precision, recall, and F1 scores up to 17.6%, 22.9%, and 16.0%, respectively. Performance improvements were obtained by transferring knowledge from networks pre-trained on a smaller dataset closer to the target application domain, as well as including normal squamous nuclei as a class detected by the model. Per-class tuning of the score threshold also allowed us to obtain a model more suitable to support screening procedures, achieving F1 score improvements in most TBS classes. While further improvements are still required to use the proposed approach in a clinical context, this work reinforces the potential of using AI-powered mobile-based solutions to support cervical cancer screening. Such solutions can significantly impact screening programs worldwide, particularly in areas with limited access and restricted healthcare resources.

A Moderated Model: Task Ambiguity and Task Complexity Moderating Servant Leadership and Employees Turnover Intention

Apart from contributing towards GDP and employment generation, SMEs face the challenges of retaining talented employees. Besides other factors, the type of leadership, task ambiguity, and complexity affect employees’ attitudes toward work and organizations. Given its importance, the study examines the effect of “servant leadership, task ambiguity, and complexity” on turnover intention. It also examines the moderating roles of “ambiguity and complexity on turnover intentions” in SMEs in Karachi. We collected a sample of 252 from local SMEs in Karachi. The study found servant leadership negatively affects turnover intention. Task ambiguity and task complexity positively affect turnover intention. Task ambiguity insignificantly moderates “servant leadership and turnover intention. Task complexity significantly moderate servant leadership and turnover intention. Due to a lack of job description and direction, employees often get confused, adversely affecting their motivation, well-being, and productivity. We suggest leaders delegate jobs by aligning employees’ characteristics, skills, and job requirements. They must be clear about what they expect from the employees by communicating verbally and in writing. Firms must arrange seminars, workshops, and counseling sessions on leadership styles. Such sessions may improve leaders’ attitudes and behaviors toward employees.