Search results

Although E-learning has been in use for over two decades, running parallel to traditional learning systems, it has gained increased attention due to its vital role in universities in the wake of the COVID-19 pandemic. The primary challenge within E-learning pertains to the maintenance of sustainable effectiveness and the assurance of stakeholders' satisfaction. This research focuses on an intelligence-driven solution to recommend the most effective approach to education policymakers by considering the unique characteristics of all components within the educational system (course type, student and teacher characteristics, and technological features) to achieve a sustainable E-learning system.

Through a systematic literature review and qualitative content analysis, a conceptual model of the critical components influencing E-learning quality and satisfaction has been developed. The proposed model comprises six main dimensions: usage, service quality, learning system quality, content quality, perceived usefulness, and individual characteristics. These dimensions are further divided into 15 components and 114 sub-components. A data mining process encompassing two scenarios has been designed to prioritize the components impacting E-learning success.

In the first scenario, data mining techniques identified the most influential components based on the features outlined in the conceptual model. According to the results, the components affecting E-learning quality enhancement in the studied case are “usage purpose, system loyalty, technical and supportive system quality, and student characteristics.” The second scenario examines the impact of individuals' personality types and learning styles on E-learning satisfaction across various aspects (Average System Satisfaction, Overall System Satisfaction, Efficiency and Effectiveness, Skill Enhancement, and Personal Enhancement). The findings reveal that, with an accuracy of over 70%, E-learning satisfaction for diplomat and guard learners is influenced by the alignment between “course learning style” and “student-suggested course learning style.” Conversely, for analyzer and researcher types, satisfaction levels are impacted by the “learning style compatible with their personality type.”

Implementing a dynamic model founded on data mining enables educational institutions to personalize the E-learning experience for each individual as much as possible. The study’s findings indicate that “achieving higher satisfaction levels in the E-learning process is not necessarily contingent upon providing a learning style congruent with learners' personality types.” Rather, perceived and suggested learning styles exert a more profound influence. Consequently, providing prescriptive principles for higher education institutions seeking to enhance E-learning quality is inadvisable. Instead, adopting a dynamic, knowledge-based process that furnishes recommendations to policymakers for each course—tailored to the specific course type, teaching records, current processes and technology, and student type—is highly recommended.

This study investigates the level and correlation between talent management and teacher personal qualities among MICSS (Malaysian Independent Chinese Secondary School) teachers by integrating the talent management model proposed by Davies and Davies (2011) and the Big Five Structure established by Lewis R. Goldberg (1992).

The researcher conducted quantitative research methods in this study, collecting numerical data through the use of questionnaires and utilizing the stratified random sampling technique. The sample consisted of 357 Malaysian teachers who are employed by MICSS throughout the whole Malaysian context.

In essence, the survey revealed a significant degree of proficiency in talent management and teacher personal qualities among teachers. In addition, this study also revealed a moderate correlation between talent management and the personal traits of teachers across MICSS teachers in Malaysia.

Limiting the investigation solely to MICSS is a noteworthy limitation. The examination also utilizes AMOS structural equation modeling (SEM) analysis, and it may be considered a restriction of this research that quantitative survey research is employed.

Academic establishments and universities that nurture prospective educators should incorporate talent management strategies and the personal qualities of teachers into the modules of teacher training to ensure that these individuals are not only adequately prepared but also assured of their ability to execute all duties assigned to them in a professional manner.

This research is notably innovative in the context of the Malaysian MICSS, where little evidence exists regarding talent management and teacher personality traits in education. This study, to the best of our knowledge, is the first attempt to investigate the practices and correlation between talent management and teacher personality traits in the entire Malaysian context. The research focuses on the practices of talent management towards MICSS teachers in Malaysia as well as the personal qualities of MICSS teachers.

Supply chain (SC) professionals are increasingly working alongside business partners of diverse backgrounds, which has been argued to engender both innovation and creativity but also found as potentially detrimental to SC relationships and performance. To reconcile these views, this study explores two mechanisms – supplementary (similarity) and complementary fits – at the surface (observable traits) and deep (unobservable characteristics) levels and their impact on a focal firm representative’s perception of a SC partner’s trustworthiness.

Model was tested using survey data from 285 managers involved in interorganizational SC relationships.

Results indicate that a focal firm representative’s perception of supplementary and complementary fits with a SC partner positively impacts their perception of the partner’s trustworthiness. However, the effects of similarity at both surface and deep levels and complementarity weaken each other.

Understanding the mechanisms of diversity in SC relationships is crucial for fostering trustworthiness and achieving organizational objectives. Firms should evaluate both supplementary and complementary fits when hiring or assigning roles. Embracing a complementary fit not only promotes diversity but also mitigates the negative impact of similarity bias, ultimately strengthening trustworthiness within the organization's SC ecosystem.

By simultaneously examining individual and combined effects of two unique mechanisms of supplementarity and complementarity at the surface and deep levels, this study sheds light on inconsistent findings of the effects of diversity in the SCM literature.

This paper aims to contribute to the existing literature by providing a unique exploration of the challenges in the adoption and usage of corporate websites, job aggregators, digital job boards, professional social media and artificial intelligence (AI)-enabled tools for recruitment.

In this exploratory study, interviews were conducted with 15 experts in digital companies with AI, recruitment or human eesources expertise.

The findings suggest three major themes, including technological, human–technology interaction and peripheral challenges. Moreover, seven sub-themes of challenges emerged from this study, namely, inefficient facilities and resources, inefficient data accumulation, resistance to change, distrust in technology, restricting regulations, toxic work culture and economic and social obstacles. Finally, this study proposes important implications and practical solutions to help professionals, companies and employers overcome challenges associated with adopting and using online recruitment tools.

Electronic human resource management has not studied the challenges associated with online recruitment tools in the context of Iranian digital organizations. This paper provides a unique exploration of the challenges in the adoption of AI in recruitment.

Provisions for the minimisation of human error are essential through governance structures such as recruitment, human resource allocation and education/training. As predictors of safety attitudes/behaviours, employees’ personality traits (e.g. conscientiousness, sensation-seeking, agreeableness, etc.) have been examined in relation to human error and safety education.

This review aimed to explore research activity on the safety attitudes of healthcare staff and their relationship with the different types of personalities, compared to other complex and highly regulated industries. A scoping review was conducted on five electronic databases on all industrial/work areas from 2001 to July 2023. A total of 60 studies were included in this review.

Studies were categorised as driving/traffic and industrial to draw useful comparisons between healthcare. Certain employees’ personality traits were matched to positive and negative relationships with safety attitudes/behaviours. Results are proposed to be used as a baseline when conducting further relevant research in healthcare.

Only two studies were identified in the healthcare sector.

The necessity for additional research in healthcare and for comparisons to other complex and highly regulated industries has been established. Safety will be enhanced through healthcare governance through personality-based recruitment, human resource allocation and education/training.

The personalities of leaders have been shown to impact the culture of their organizations and are also expected to have a more distal impact on the firm’s financial performance. However, the authors also expect that leader gender is an important intervening variable such that exhibiting various personality dimensions may result in unique cultural and performance-based outcomes for women and men leaders. Thus, the authors seek to examine first the impact of leader personality on organizational performance, as driven through organizational culture as a mediating mechanism. In doing so, the authors propose the expected impact of specific personality dimensions on certain types of organizational cultures, and those cultures’ subsequent impact on the organization’s performance. The authors then extend to consider the moderating effects of leader gender on the relationship between leader personality and organization. To support their propositions, the authors draw from upper echelons and implicit leadership theories. The authors encourage researchers to consider the proposition within a sample of the largest publicly traded US companies (i.e., Fortune 500) at an important era in history such that for the first time, 10% of these companies are led by women. In doing so, the authors hope to understand the leadership dynamics at the highest echelons of corporate governance and provide actionable insights for companies aiming to optimize their leadership composition and drive sustainable performance.

This paper aims to explore advances in indirect personality assessment, with emphasis on the psychology of digital behavior based on the analysis of new technological devices and platforms for interpersonal relationships, identifying – along the way – those findings that may be useful to carry out a reconstructive psychological assessment (RPA) of applicability in the legal context.

Different fields of knowledge are explored, transferring the findings to the field of psychology of digital behavior, analyzing the publications that report findings on the analysis of new technological devices and platforms for interpersonal relationships and identifying – along the way – those findings that may result useful to carry out an RPA of applicability in the legal context.

The application of RPA represents a significant advance in the integration of criminal psychology and forensic technology in legal contexts, opening new fields of action for forensic psychology.

The article has transferred advances in computer science to the field of forensic psychology, with emphasis on the relevance of RPA (from the analysis of digital behavioral residues) in the interpretation of behavioral evidence for the indirect evaluation of the personality and within the judicial context (when the victim and/or accused are not included).

To support digital humanities research more effectively and efficiently, this study develops a novel Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) for the Digital Humanities Research Platform for Biographies of Chinese Malaysian Personalities (DHRP-BCMP) based on artificial intelligence (AI) technology that would not only allow humanities scholars to look at the relationships between people but also has the potential for aiding digital humanities research by identifying latent relationships between people via relationships between people and organizations.

To verify the effectiveness of KGAT-PO, a counterbalanced design was applied to compare research participants in two groups using DHRP-BCMP with and without KGAT-PO, respectively, to perform people relationship inquiry and to see if there were significant differences in the effectiveness and efficiency of exploring relationships between people, and the use of technology acceptance between the two groups. Interviews and Lag Sequential Analysis were also used to observe research participants’ perceptions and behaviors.

The results show that the DHRP-BCMP with KGAT-PO could help research participants improve the effectiveness of exploring relationships between people, and the research participants showed high technology acceptance towards using DHRP-BCMP with KGAT-PO. Moreover, the research participants who used DHRP-BCMP with KGAT-PO could identify helpful textual patterns to explore people’s relationships more quickly than DHRP-BCMP without KGAT-PO. The interviews revealed that most research participants agreed that the KGAT-PO is a good starting point for exploring relationships between people and improves the effectiveness and efficiency of exploring people’s relationship networks.

The research’s limitations encompass challenges related to data quality, complex people relationships, and privacy and ethics concerns. Currently, the KGAT-PO is limited to recognizing eight types of person-to-person relationships, including couple, sibling, parent-child, friend, teacher-student, relative, work, and others. These factors should be carefully considered to ensure the tool’s accuracy, usability, and ethical application in enhancing digital humanities research.

The study’s practical implications encompass enhanced research efficiency, aiding humanities scholars in uncovering latent interpersonal relationships within historical texts with high technology acceptance. Additionally, the tool’s applications can extend to social sciences, business and marketing, educational settings, and innovative research directions, ultimately contributing to data-driven insights in the field of digital humanities.

The research’s originality lies in creating a Knowledge Graph Analysis Tool of People and Organizations (KGAT-PO) using AI, bridging the gap between digital humanities research and AI technology. Its value is evident in its potential to efficiently uncover hidden people relationships, aiding digital humanities scholars in gaining new insights and perspectives, ultimately enhancing the depth and effectiveness of their research.

This article presents two Brazilian Portuguese corpora collected from different media concerning public security issues in a specific location. The primary motivation is supporting analyses, so security authorities can make appropriate decisions about their actions.

The corpora were obtained through web scraping from a newspaper's website and tweets from a Brazilian metropolitan region. Natural language processing was applied considering: text cleaning, lemmatization, summarization, part-of-speech and dependencies parsing, named entities recognition, and topic modeling.

Several results were obtained based on the methodology used, highlighting some: an example of a summarization using an automated process; dependency parsing; the most common topics in each corpus; the forty named entities and the most common slogans were extracted, highlighting those linked to public security.

Some critical tasks were identified for the research perspective, related to the applied methodology: the treatment of noise from obtaining news on their source websites, passing through textual elements quite present in social network posts such as abbreviations, emojis/emoticons, and even writing errors; the treatment of subjectivity, to eliminate noise from irony and sarcasm; the search for authentic news of issues within the target domain. All these tasks aim to improve the process to enable interested authorities to perform accurate analyses.

The corpora dedicated to the public security domain enable several analyses, such as mining public opinion on security actions in a given location; understanding criminals' behaviors reported in the news or even on social networks and drawing their attitudes timeline; detecting movements that may cause damage to public property and people welfare through texts from social networks; extracting the history and repercussions of police actions, crossing news with records on social networks; among many other possibilities.

The work on behalf of the corpora reported in this text represents one of the first initiatives to create textual bases in Portuguese, dedicated to Brazil's specific public security domain.

This paper aims to present a dynamic model for strategic and personalized decision-making in human resources (HR), using data mining techniques to enhance corporate social sustainability (CSS). The focus is on the interconnectedness of employee engagement (EE), enablement and the quality of work life.

The proposed model integrates various HR data, including demographic information, job specifications, payment and rewards, attendance and absence, alongside employees’ perceptions of their work-life quality, engagement and enablement. Data mining processes are applied to generate meaningful insights for senior and middle managers.

The study implemented the model within a production organization, revealing that factors influencing EE and enablement differ based on gender, marital status and occupational group. Performance-based rewards play a significant role in enhancing engagement, regardless of the reward amount. Factors such as “being recognized for competency” influence engagement for women, while payment has a greater impact on men. Engagement does not directly influence the quality of work life, but subcomponents like perceived transparency and the organization’s processes, particularly the “employee performance evaluation system,” improve work-life quality.

The findings are specific to the studied organization, limiting generalizability. Future research should explore the model’s effectiveness in different cultural and organizational settings.

The proposed model provides practical implications for organizations that enhance CSS. Organizations can gain insights into factors influencing EE and enablement by using data mining techniques, enabling informed decision-making and tailored human resource management practices.

This research addresses the societal concern regarding the impact of business activities on sustainability. Organizations can contribute to a more socially responsible and sustainable business environment by focusing on work-life quality and EE.

This paper offers a dynamic model using data mining and machine learning techniques for sustainable human resource management. It emphasizes the importance of customization to align practices with the unique needs of the workforce.