On the great feast of Pentecost, guest homilist Fr Basil Biberdorf teaches us the importance of knowing Who the Holy Spirit is, and of His role in our Christian life.

Fr. Gregory encourages us to live the joy of the resurrection all year round.

Christianity is a personal affair. It is not abstract, theoretical, a speculative philosophy. It is a personal encounter with the living God who is himself three persons sharing one divine nature.

What are we free and not free to do as believers in Christ? Dr. Rossi gives some practical examples of freedom used where freedom was not appropriate.

Dr. Albert Rossi share some of his favorite Bible quotations for our encouragement, and he asks us to share our favorites passages from the Holy Scriptures with him.

In honor of the 75th anniversary of St. Vladimir's Orthodox Theological Seminary, Bobby Maddex interviews Fr. Thomas Hopko, Dean Emeritus of St. Vlad's, about the history and early days of the school.

Fr. Pat preaches on putting to death our passions, as St. Paul admonishes us to do in Colossians 3:4-11.

See what happens when we are within the Apostolic Tradition! Aeneas raised from his bed, Tabitha from her death bed, the Paralysed man of 38 years raised to his feet. Christ is risen!

In today’s digital world, information spreads like wildfire. That includes information about you. What people read about you online—whether accurate or not—impacts your reputation and potentially all aspects of your life. You want the things people read about you to be true. But keeping track of everything that appears online […]

The post How to Automate Your Personal Reputation Management appeared first on .

Advanced marketing techniques such as Account-based Marketing (ABM) and 1-1 marketing require a more individualised approach than traditional inbound marketing tactics. No longer can we paint with a broad brush, as marketers. We must find ways to speak directly with individuals, rather than an audience.

Currently, the World Wide Web is mostly composed of isolated and loosely connected "data islands". Connecting them together and retrieving only the information that is of interest to the user is the common Web usage process. Creating infrastructure that would support automation of that process by aggregating and integrating Web data in accordance to user's personal preferences would greatly improve today's Web usage. A significant part of Web data is available only through the login and password protected applications. As that data is very important for the usefulness of described process, proposed infrastructure needs to support authorized access to user's personal data. In this paper we propose a semantically enhanced Web portal that presents unique personalized user's entry to the domain-specific Web information. We also propose an identity management system that supports authorized access to the protected Web data. To verify the proposed solution, we have built Sweb - a semantically enhanced Web portal that uses proposed identity management system.

In order to improve recommendation coverage, a personalised recommendation method for English online learning resources based on improved collaborative filtering algorithm is studied to enhance the comprehensiveness of personalised recommendation for learning resources. Use matrix decomposition to decompose the user English online learning resource rating matrix. Cluster low dimensional English online learning resources by improving the K-means clustering algorithm. Based on the clustering results, calculate the backfill value of English online learning resources and backfill the information matrix of low dimensional English online learning resources. Using an improved collaborative filtering algorithm to calculate the predicted score of learning resources, personalised recommendation of English online learning resources for users based on the predicted score. Experimental results have shown that this method can effectively backfill English online learning resources, and the resource backfilling effect is excellent, and it has a high recommendation coverage rate.

In order to enhance the accuracy of teaching resource recommendation results and optimise user experience, a personalised recommendation method for English teaching resources on the MOOC platform based on data mining is proposed. First, the learner's evaluation of resources and resource attributes are abstracted into the same space, and resource tags are established using the Knowledge graph. Then, interest preference constraints are introduced to mine sequential patterns of user historical learning behaviour in the MOOC platform. Finally, a graph neural network is used to construct a recommendation model, which adjusts users' short-term and short-term interest parameters to achieve dynamic personalised teaching recommendation resources. The experimental results show that the accuracy and recall of the resource recommendation results of the research method are always higher than 0.9, the normalised sorting gain is always higher than 0.5.

This paper describes the employment of a Personal Response System (PRS) during a Decision Analysis course for Management Information Systems (MIS) students. The description shows how the carefully designed PRS-based questions, the delivery, and the follow-up discussions; provided a context for eliciting and exercising central concepts of the course topics as well as central skills required for MIS majors. A sample of PRS-based questions is presented along with a description for each question of its purpose, the way it was delivered, the response rate, the responses and their frequencies, and the respective in-class discussion. Lessons from these findings are discussed.

Aim/Purpose : Share research finding about ransomware, depict the ransomware work in a format that commonly used by researchers and practitioners and illustrate personal case experience in dealing with ransomware. Background: Author was hit with Ransomware, suffered a lot from it, and did a lot of research about this topic. Author wants to share findings in his research and his experience in dealing with the aftermath of being hit with ransomware. Methodology: Case study. Applying the literature review for a personal case study. Contribution: More knowledge and awareness about ransomware, how it attacks peoples’ computers, and how well informed users can be hit with this malware. Findings: Even advanced computer users can be hit and suffer from Ransomware attacks. Awareness is very helpful. In addition, this study drew in chart format what is termed “The Ransomware Process”, depicting in chart format the steps that ransomware hits users and collects ransom. Recommendations for Practitioners : Study reiterates other recommendations made for dealing with ransomware attacks but puts them in personal context for more effective awareness about this malware. Recommendation for Researchers: This study lays the foundation for additional research to find solutions to the ransomware problem. IT researchers are aware of chart representations to depict cycles (like SDLC). This paper puts the problem in similar representation to show the work of ransomware. Impact on Society: Society will be better informed about ransomware. Through combining research, illustrating personal experience, and graphically representing the work of ransomware, society at large will be better informed about the risk of this malware. Future Research: Research into solutions for this problem and how to apply them to personal cases.

Aim/Purpose: The proposed Personal Knowledge Management (PKM) for Empowerment (PKM4E) Framework expands on the notions of the Ignorance Map and Matrix to support the educational and informing concept of a PKM system-in-progress. Background: The accelerating information abundance is depleting the very attention our cognitive capabilities are able to master, contributing to widening individual and collective opportunity divides. Support is urgently needed to benefit Knowledge Workers irrespective of space (developed/developing countries), time (study or career phase), discipline (natural or social science), or role (student, professional, leader). Methodology: The Design Science Research (DSR) project conceptualizing the PKM System (PKMS) aims to support a scenario of a ‘Decentralizing KM Revolution’ giving more power and autonomy to individuals and self-organized groups. Contribution: The informing-science-related approach synthesizes and visualizes concepts related to ignorance and entropy, learning and innovation, chance discovery and abduction to inform diverse audiences and potential beneficiaries. Findings: see Recommendation for Researchers Recommendations for Practitioners: The PKM4E learning cycles and workflows apply ‘cumulative synthesis’, a concept which convincingly couples the activities of researchers and entrepreneurs and assists users to advance their capability endowments via applied learning. Recommendation for Researchers: In substituting document-centric with meme-based knowledge bases, the PKMS approach merges distinctive voluntarily shared knowledge objects/assets of diverse disciplines into a single unified digital knowledge repository and provides the means for advancing current metrics and reputation systems. Impact on Society: The PKMS features provide the means to tackle the widening opportunity divides by affording knowledge workers with continuous life-long support from trainee, student, novice, or mentee towards professional, expert, mentor, or leader. Future Research: After completing the test phase of the PKMS prototype, its transformation into a viable PKM system and cloud-based server based on a rapid development platform and a noSQL-database is estimated to take 12 months.

Aim/Purpose: This exploratory study investigates the influential factors of users’ decisions in the dilemma of whether to agree to online personalization or to protect their online privacy. Background: Various factors related to online privacy and anonymity were considered, such as user’s privacy concern on the Web in general and particularly on social networks, user online privacy literacy, and field of study. Methodology: To this end, 155 students from different fields of study in the Israeli academia were administered closed-ended questionnaires. Findings: The multivariate linear regression analysis showed that as the participants’ privacy concern increases, they tend to prefer privacy protection over online personalization. In addition, there were significant differences between men and women, as men tended to favor privacy protection more than women did. Impact on Society: This research has social implications for the academia and general public as they show it is possible to influence the personalization-privacy tradeoff and encourage users to prefer privacy protection by raising their concern for the preservation of their online privacy. Furthermore, the users’ preference to protect their privacy even at the expense of their online malleability may lead to the reduction of online privacy-paradox behavior. Future Research: Since our results were based on students' self-perceptions, which might be biased, future work should apply qualitative analysis to explore additional types and influencing factors of online privacy behavior.

Aim/Purpose: The present study investigates the relationship between the five-factor model (FFM) of personality traits and sales performance in Pakistan. Background: Personality is a well-researched area in which numerous studies have examined the correlation between personality traits and job performance. In this study, a positive effect between the various dimensions of the five-factor model (extraversion, agreeableness, conscientiousness, emotional stability, and open to experience) and sales performance in Pakistan is investigated. Methodology: Pearson’s correlation values as well as analysis methodologies were employed to gather descriptive statistics, reliability analysis, correlation analysis, and use the analytical hierarchy process (AHP). Cronbach’s alpha value helped determine the internal consistency of the group items. Questionnaires were distributed among 600 salespersons in various cities of Pakistan from April 2015 to January 2016. Subsequently, 510 questionnaires were acquired for the sample. Contribution: The current study contributes to the literature on personality traits and sales performance by applying empirical evidence from sales managers in three industries of Pakistan: pharmaceutical, insurance, and electronics. Findings: The results affirmed a positive effect of the five-factor model on sales performance among various industries in Pakistan. The effect of each sub-factor from the five-factor model was examined autonomously. There is a favorable benefit to sales managers in considering FFM when making hiring decisions. Impact on Society: FFM offers important insights into personality traits that work well within Pakistani sales industry structure. Future Research: A broader rendering of the effects of FFM on sales organizations in other geographical locations around Pakistan should be considered. Additionally, an extended study should be conducted to investigate the effects of FFM on female sales employees involving religious and cultural forces within that country.

Aim/Purpose: This study aimed to increase our understanding of how the stages of the customer purchase journey, privacy trade-offs, and information sensitivity of different business service sectors affect consumers’ privacy concerns. Background: The study investigated young consumers’ willingness to provide consent to use their personal data at different phases of the customer journey. This study also examined their readiness to provide consent if they receive personal benefits, and how information sensitivity varied between different individuals and business sectors. Methodology: Data was collected by a quantitative survey (n=309) and analyzed with R using the Bayesian linear mixed effect modeling approach. The sample consisted of university students in Finland, who represented a group of young and digitally native consumers. The questionnaire was designed for this study and included constructs with primarily Likert-scale items. Contribution: The study contributed to data privacy and consent management research in information sensitivity, privacy trade-off, and the customer journey. The study underlined the need for a stronger user experience focus and contextuality. Findings: The results showed that readiness to disclose personal data varied at different phases of the customer journey as privacy concerns did not decrease in a linear fashion throughout the purchase process. Perceived benefits affected the willingness to provide consent for data usage, but concerned consumers would be less trade-off oriented. Self-benefit was the most relevant reason for sharing, while customization was the least. There is a connection between the information sensitivity of different business sector information and privacy concerns. No support for gender differences was found, but age affected benefits and business sector variables. Recommendations for Practitioners: The study recommends approaching consumers’ data privacy concerns from a customer journey perspective while trying to motivate consumers to share their personal data with relevant perceived benefits. The self-benefit was the most relevant benefit for willingness to provide consent, while customization was the least. Recommendation for Researchers: The study shows that individual preference for privacy was a major factor directly and via interaction for all three models. This study also showed that consumers’ subjective decision-making in privacy issues is both a situational and a contextual factor. Impact on Society: This study could encourage policymakers and societies to develop guidelines on how to develop privacy practices and consent management to be more user centric as individuals are increasingly concerned about their online privacy. Future Research: This study encourages examining consumers’ motivational factors to provide digital consent for companies with experimental research settings. This study also calls to explore perceived benefits in all age groups from the perspective of different information in various business sectors. This study shows that privacy concern is a contextual and situational factor.

Aim/Purpose: This study aims to develop a solution for personalized tourism recommendations that addresses information overload, data sparsity, and the cold-start problem. It focuses on enabling tourists to choose the most suitable tourism-related facilities, such as restaurants and hotels, that match their individual needs and preferences. Background: The tourism industry is experiencing a significant shift towards digitalization due to the increasing use of online platforms and the abundance of user data. Travelers now heavily rely on online resources to explore destinations and associated options like hotels, restaurants, attractions, transportation, and events. In this dynamic landscape, personalized recommendation systems play a crucial role in enhancing user experience and ensuring customer satisfaction. However, existing recommendation systems encounter major challenges in precisely understanding the complexities of user preferences within the tourism domain. Traditional approaches often rely solely on user ratings, neglecting the complex nature of travel choices. Data sparsity further complicates the issue, as users might have limited interactions with the system or incomplete preference profiles. This sparsity can hinder the effectiveness of these systems, leading to inaccurate or irrelevant recommendations. The cold-start problem presents another challenge, particularly with new users who lack a substantial interaction history within the system, thereby complicating the task of recommending relevant options. These limitations can greatly hinder the performance of recommendation systems and ultimately reduce user satisfaction with the overall experience. Methodology: The proposed User-based Multi-Criteria Trust-aware Collaborative Filtering (UMCTCF) approach exploits two key aspects to enhance both the accuracy and coverage of recommendations within tourism recommender systems: multi-criteria user preferences and implicit trust networks. Multi-criteria ratings capture the various factors that influence user preferences for specific tourism items, such as restaurants or hotels. These factors surpass a simple one-star rating and take into account the complex nature of travel choices. Implicit trust relationships refer to connections between users that are established through shared interests and past interactions without the need for explicit trust declarations. By integrating these elements, UMCTCF aims to provide more accurate and reliable recommendations, especially when data sparsity limits the ability to accurately predict user preferences, particularly for new users. Furthermore, the approach employs a switch hybridization scheme, which combines predictions from different components within UMCTCF. This scheme leads to a more robust recommendation strategy by leveraging diverse sources of information. Extensive experiments were conducted using real-world tourism datasets encompassing restaurants and hotels to evaluate the effectiveness of UMCTCF. The performance of UMCTCF was then compared against baseline methods to assess its prediction accuracy and coverage. Contribution: This study introduces a novel and effective recommendation approach, UMCTCF, which addresses the limitations of existing methods in personalized tourism recommendations by offering several key contributions. First, it transcends simple item preferences by incorporating multi-criteria user preferences. This allows UMCTCF to consider the various factors that users prioritize when making tourism decisions, leading to a more comprehensive understanding of user choices and, ultimately, more accurate recommendations. Second, UMCTCF leverages the collective wisdom of users by incorporating an implicit trust network into the recommendation process. By incorporating these trust relationships into the recommendation process, UMCTCF enhances its effectiveness, particularly in scenarios with data sparsity or new users with limited interaction history. Finally, UMCTCF demonstrates robustness towards data sparsity and the cold-start problem. This resilience in situations with limited data or incomplete user profiles makes UMCTCF particularly suitable for real-world applications in the tourism domain. Findings: The results consistently demonstrated UMCTCF’s superiority in key metrics, effectively addressing the challenges of data sparsity and new users while enhancing both prediction accuracy and coverage. In terms of prediction accuracy, UMCTCF yielded significantly more accurate predictions of user preferences for tourism items compared to baseline methods. Furthermore, UMCTCF achieved superior coverage compared to baseline methods, signifying its ability to recommend a wider range of tourism items, particularly for new users who might have limited interaction history within the system. This increased coverage has the potential to enhance user satisfaction by offering a more diverse and enriching set of recommendations. These findings collectively highlight the effectiveness of UMCTCF in addressing the challenges of personalized tourism recommendations, paving the way for improved user satisfaction and decision-making within the tourism domain. Recommendations for Practitioners: The proposed UMCTCF approach offers a potential opportunity for tourism recommendation systems, enabling practitioners to create solutions that prioritize the needs and preferences of users. By incorporating UMCTCF into online tourism platforms, tourists can utilize its capabilities to make well-informed decisions when selecting tourism-related facilities. Furthermore, UMCTCF’s robust design allows it to function effectively even in scenarios with data sparsity or new users with limited interaction history. This characteristic makes UMCTCF particularly valuable for real-world applications, especially in scenarios where these limitations are common obstacles. Recommendation for Researchers: The success of UMCTCF can open up new avenues in personalized recommendation research. One promising direction lies in exploring the integration of additional contextual information, such as temporal (time-based) or location-based information. By incorporating these elements, the model could be further improved, allowing for even more personalized recommendations. Furthermore, exploring the potential of UMCTCF in domains other than tourism has considerable significance. By exploring its effectiveness in other e-commerce domains, researchers can broaden the impact of UMCTCF and contribute to the advancement of personalized recommendation systems across various industries. Impact on Society: UMCTCF has the potential to make a positive impact on society in various ways. By delivering accurate and diverse recommendations that are tailored to individual user preferences, UMCTCF fosters a more positive and rewarding user experience with tourism recommendation systems. This can lead to increased user engagement with tourism platforms, ultimately enhancing overall satisfaction with travel planning. Furthermore, UMCTCF enables users to make more informed decisions through broader and more accurate recommendations, potentially reducing planning stress and leading to more fulfilling travel experiences. Future Research: Expanding upon the success of UMCTCF, future research activities can explore several promising paths. Enriching UMCTCF with various contextual data, such as spatial or location-based data, to enhance recommendation accuracy and relevance. Leveraging user-generated content, like reviews and social media posts, could provide deeper insights into user preferences and sentiments, improving personalization. Additionally, applying UMCTCF in various e-commerce domains beyond tourism, such as online shopping, entertainment, and healthcare, could yield valuable insights and enhance recommendation systems. Finally, exploring the integration of optimization algorithms could improve both recommendation accuracy and efficiency.

Aim/Purpose: This study aims to evaluate the success of deep learning in building user profiles for personalized information access. Background: To better express document content and information during the matching phase of the information retrieval (IR) process, deep learning architectures could potentially offer a feasible and optimal alternative to user profile building for personalized information access. Methodology: This study uses deep learning-based models to deduce the domain of the document deemed implicitly relevant by a user that corresponds to their center of interest, and then used predicted domain by the best given architecture with user’s characteristics to predict other centers of interest. Contribution: This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to their context and their preferences meeting their precise needs. To better express document content and information during this phase, deep learning models are employed to learn complex representations of documents and queries. These models can capture hierarchical, sequential, or attention-based patterns in textual data. Findings: The results show that deep learning models were highly effective for building user profiles for personalized information access since they leveraged the power of neural networks in analyzing and understanding complex patterns in user behavior, preferences, and user interactions. Recommendations for Practitioners: Building effective user profiles for personalized information access is an ongoing process that requires a combination of technology, user engagement, and a commitment to privacy and security. Recommendation for Researchers: Researchers involved in building user profiles for personalized information access play a crucial role in advancing the field and developing more innovative deep-based networks solutions by exploring novel data sources, such as biometric data, sentiment analysis, or physiological signals, to enhance user profiles. They can investigate the integration of multimodal data for a more comprehensive understanding of user preferences. Impact on Society: The proposed models can provide companies with an alternative and sophisticated recommendation system to foster progress in building user profiles by analyzing complex user behavior, preferences, and interactions, leading to more effective and dynamic content suggestions. Future Research: The development of user profile evolution models and their integration into a personalized information search system may be confronted with other problems such as the interpretability and transparency of the learning-based models. Developing interpretable machine learning techniques and visualization tools to explain how user profiles are constructed and used for personalized information access seems necessary to us as a future extension of our work.

The paper introduces a novel Personal Knowledge Management (PKM) concept and prototype system. The system’s objective is to aid life-long-learning, resourcefulness, creativity, and teamwork of individuals throughout their academic and professional life and as contributors and beneficiaries of organizational and societal performance. Such a scope offers appealing and viable opportunities for stakeholders in the educational, professional, and developmental context. To further validate the underlying PKM application design, the systems thinking techniques of the transdiscipline of Informing Science (IS) are employed. By applying Cohen’s IS-Framework, Leavitt’s Diamond Model, the IS-Meta Approach, and Gill’s and Murphy’s Three Dimensions of Design Task Complexity, the more specific KM models and methodologies central to the PKMS concept are aligned, introduced, and visualized. The extent of this introduction offers an essential overview, which can be deepened and broadened by using the cited URL and DOI links pointing to the available resources of the author’s prior publications. The paper emphasizes the differences of the proposed meme-based PKM System compared to its traditional organizational document-centric counterparts as well as its inherent complementing synergies. As a result, it shows how the system is closing in on Vannevar Bush’s still unfulfilled vison of the ‘Memex’, an as-close-as-it-gets imaginary ancestor celebrating its 70th anniversary as an inspiring idea never realized. It also addresses the scenario recently put forward by Levy which foresees a decentralizing revolution of knowledge management that gives more power and autonomy to individuals and self-organized groups. Accordingly, it also touches on the PKM potential in terms of Kuhn’s Scientific Revolutions and Disruptive Innovations.

The article presents Personal Knowledge Management (PKM) as an overdue individualized as well as a collaborative approach for knowledge workers. Designing a PKM-supporting system, however, resembles a so-called “wicked” problem (ill-defined; incomplete, contradictory, changing requirements, complex interdependencies) where the information needed to understand the challenges depends on upon one’s idea for solving them. Accordingly, three main areas are attended to. Firstly, in dealing with a range of growing complexities, the notion of Popper’s Worlds is applied as three distinct spheres of reality and further expanded into six digital ecosystems (technologies, extelligence, society, knowledge worker, institutions, and ideosphere) that not only form the basis for the PKM System Concept named ‘Knowcations’ but also form a closely related Personal Knowledge Management for Development (PKM4D) framework detailed in a separate dedicated paper. Reflecting back on a United Nations scenario of knowledge mass production (KMP) over time, the complexities closely related to the digital ecosystems and the inherent risks of today’s accelerating attention-consuming over-abundance of redundant information are scrutinized, concluding in a chain of meta-arguments favoring the idea of the PKM concept and system put forward. Secondly, in light of the digital ecosystems and complexities introduced, the findings of a prior article are further refined in order to assess the PKM concept and system as a potential General-Purpose-Technology. Thirdly, the development process and resulting prototype are verified against accepted general design science research (DSR) guidelines. DSR aims at creating innovative IT artifacts (that extend human and social capabilities and meet desired outcomes) and at validating design processes (as evidence of their relevance, utility, rigor, resonance, and publishability). Together with the incorporated references to around thirty prior publications covering technical and methodological details, a kind of ‘Long Discussion Case’ emerges aiming to potentially assist IT researchers and entrepreneurs engaged in similar projects.

Aim/Purpose: Personal Knowledge Management (PKM) has been envisaged as a crucial tool for the growing creative class of knowledge workers, but adequate technological solutions have not been forthcoming. Background: Based on former affordance-related publications (primarily concerned with communication, community-building, collaboration, and social knowledge sharing), the common and differing narratives in relation to PKM are investigated in order to suggest further PKM capabilities and affordances in need to be conferred. Methodology: The paper follows up on a series of the author’s PKM-related publications, firmly rooted in design science research (DSR) methods and aimed at creating an innovative PKM concept and prototype system. Contribution: The affordances presented offer PKM system users the means to retain and build upon knowledge acquired in order to sustain personal growth and facilitate productive collaborations between fellow learners and/or professional acquaintances. Findings: The results call for an extension of Nonaka’s SECI model and ‘ba’ concept and provide arguments for and evidence supporting the claims that the PKM concept and system is able to facilitate better knowledge traceability and KM practices. Recommendations and Impact on Society: Together with the prior publications, the paper points to current KM shortcomings and presents a novel trans-disciplinary approach offering appealing opportunities for stakeholders engaged in the context of curation, education, research, development, business, and entrepreneurship. Its potential to tackle opportunity divides has been addressed via a PKM for Development (PKM4D) Framework. Future DSR Activities: After completing the test phase of the prototype, its transformation into a viable PKM system and cloud-based server based on a rapid development platform and a noSQL-database is estimated to take 12 months.

Aim/Purpose: This study builds upon existing research by investigating the elements contributing to or buffering the onset of burnout symptoms. We examine the relationship between empowering leadership and burnout, considering the concurrent mediation effects of interpersonal workplace conflict, work-home conflict, and support from coworkers. Background: Burnout is a phenomenon that has been widely considered in the scientific literature due to its negative effect on individual and organizational well-being, as well as implications for leadership, coworker support, and conflict resolution. A deeper understanding of burnout prevention strategies across various professional contexts is paramount for enhancing productivity and job satisfaction. Methodology: Using a survey-based cross-sectional design, we employed a combination of Structural Equation Modelling (SEM) and Artificial Neural Network (ANN) to investigate the direct and indirect influences of empowering leadership on four dimensions of employee burnout, mediated by coworker support, interpersonal conflict at work, and work-home conflict. Contribution: This study provides initial insights into the direct and indirect influences of empowering leadership on various dimensions of burnout, highlighting the complex interplay with coworker support, work-home conflict, and workplace interpersonal conflicts. Ultimately, the study provides a comprehensive approach to understanding and mitigating burnout. Findings: Empowering leadership and coworker support can significantly reduce burnout symptoms, while high levels of work-home conflict and interpersonal conflict at work can exacerbate them. Our findings underscore the paramount role of interpersonal conflict in predicting burnout, urging organizations to prioritize resolving such issues for burnout prevention. Recommendation for Researchers: Following our findings, organizations should (a) promote empowering leadership styles, (b) foster coworker support and work-life balance, and (c) address interpersonal conflicts to reduce the likelihood of employee burnout while ensuring that these strategies are tailored to the specific context and culture of the workplace. Future Research: Future research should broaden the exploration of leadership styles’ effects on burnout, identify additional mediators and moderators, expand studies across sectors and cultures, examine differential impacts on burnout dimensions, leverage advanced analytical models, and investigate the nuanced relationship between work contract types and burnout.

This paper proposes an efficient solution for personalised web directories recommendation using fast FCM+DQN. At first, web directory usage file obtained from given dataset is fed into the accretion matrix computation module, where visitor chain matrix, visitor chain binary matrix, directory chain matrix and directory chain binary matrix are formulated. In this, directory grouping is accomplished based on fast FCM and matching among query and group is conducted based on Kumar Hassebrook and Kulczynski similarity. The user preferred directory is restored at this stage and at last, personalised web directories are recommended to the visitors by means of DQN. The proposed approach has received superior results with respect to maximum accuracy of 0.910, minimum mean squared error (MSE) of 0.0206 and root mean squared error (RMSE) of 0.144. Although the system offered magnificent outcomes, it failed to order web directories in the form of highly, medium and low interested directories.

We consider how social and psychological connections among CEOs explain the propensity for corporate leaders to distance themselves socially from journalists who engage in negative reporting about firm leadership at other companies, and we examine the consequences for the valence of journalists' subsequent coverage. Our theoretical framework suggests that journalists who have engaged in negative coverage of a firm's leadership and strategy are especially likely to experience distancing from other leaders who (i) have friendship ties to the firm's CEO, (ii) are demographically similar to the CEO on salient dimensions, or (iii) are socially identified with the CEO as a fellow member of the corporate elite. Our theory and findings ultimately suggest that, due to the multiple sources of social identification between CEOs, journalists who engage in negative coverage of firm leadership tend to experience social distancing from multiple CEOs, and such distancing has a powerful influence on the valence of journalists' subsequent reporting about firm leadership and strategy across all the firms that they cover. We also extend our theoretical framework to suggest how the effect of social distancing on the valence of journalists' coverage is moderated by the early and late stages of a journalist's career.

Management educators often assume that research-based arguments ought to be convincing to students. However, college students do not always accept even well-documented research findings. Among the reasons this might happen, we focus on the potential role of psychological mechanisms triggered by scholarly arguments that affect students' self-concepts, leading them to engage in self-enhancing or self-protective responses. We investigated such processes by examining students' reactions to a research argument emphasizing the importance of intelligence to job performance, in comparison to their reactions to research arguments emphasizing the importance of emotional intelligence and/or fit. Consistent with our predictions, students were less likely to accept the argument for the importance of intelligence compared to the alternative, less threatening, arguments (i.e., the importance of emotional intelligence or fit). Further, acceptance of the argument about the importance of intelligence was affected by students' grade point average (GPA) and moderated by their emotional stability. Specifically, consistent with self-enhancement theory, students with lower GPAs were more likely to reject the argument for intelligence and give self-protective reasons for their responses, whereas students with higher GPAs were more likely to accept the argument and give self-enhancing reasons. Implications for future research and for management teaching are discussed.

Accumulated knowledge on organizational justice leaves little reason to doubt the notion that organizational members benefit when leaders adhere to interpersonal justice rules. However, upon considering how justice behaviors influence subordinates' cognitive processes, we predict that interpersonal justice has a surprising, unintended negative consequence. Supervisors who violate interpersonal justice rules trigger subordinates to search for reasons why their supervisors are threatening them, causing subordinates to be more attuned to supervisors' individual characteristics and therefore unlikely to use stereotypes when evaluating them. In contrast, supervisors who adhere to interpersonal justice rules allow subordinates to divert attention away from them, leading subordinates' judgments of their supervisors to be influenced by stereotypes. Consistent with these predictions, in a survey we found that minority supervisors faced bias relative to Caucasian supervisors when supervisors adhered to—but not when they violated—interpersonal justice rules. We replicated this effect in an experiment and established that it is explained by an alternating pattern of stereotype activation and inhibition: participants viewed minority supervisors to be more deceitful than Caucasians when supervisors adhered to—but not when they violated—interpersonal justice rules. We then conducted exploratory analyses and identified one factor (unit size) that mitigates this troubling pattern.

Despite recognizing the importance of personal identification in organizations, the literature has rarely explored its dynamics. We define personal identification as perceived oneness with another individual, where one defines oneself in terms of the other. While many scholars have found that personal identification is associated with helpful effects, others have found it harmful. To resolve this contradiction, we distinguish between three paths to personal identification -threat-focused, opportunity-focused, and closeness-focused - and articulate a model that includes each. We examine the contextual features, how individuals' identities are constructed, and the likely outcomes that follow in the three paths. We conclude with a discussion of how the threat-, opportunity-, and closeness-focused personal identification processes potentially blend, as well as implications for future research and practice.

Identification is integral to mentoring relationships, yet we know relatively little about the process through which mentors and protégés identify with each other, how this mutual identification shifts through the phases of the mentoring relationship, and how identification impacts the quality of the relationship over time. In this paper, we integrate theories of the self, relationships, and relational mentoring to consider the role of identification in informal mentoring. Specifically, we theorize how the process of personal identification occurs in mentoring from the perspective of both the mentor and protégé and offer a model that demonstrates how shifts in identification relate to the quality of the relationship that develops over time. We conclude with a discussion of implications for research and theory in mentoring.

Discover the Personal Defense Weapon (PDW) - the perfect balance of firepower and portability for those in need of a defensive weapon.

Suspect, identified as Atif alias Vicky Babu, had previously gone viral on social media for his videos.

As Techdirt stories attest, Wikipedia has been attacked in the past for publishing true information that somebody doesn’t like. As well as wanting articles to be censored, those behind such attacks often also demand the names of those who worked on the article. Something similar is now happening in India, where the Indian news agency […]

In this Quality for life video, Kevin Holston, a certified Black Belt, shares how he uses Six Sigma tools in his everyday life, including providing humorous examples of how keeps his life in order and on track.

Facebook and Instagram users in Europe will get the option to see less personalized ads if they don't want to pay for an ad-free subscription, social media company Meta said Tuesday, bowing to pressure from Brussels over digital competition concerns.

“Go out there, represent the Red Sox, and become a challenge to the Yankees.”

The post What a Yankees fan recently told Pedro Martinez that became ‘personal’ appeared first on Boston.com.