Search results

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

This study aims to investigate the relationship between academic motivations and the risk of Not in Employment, Education or Training (NEET) among university undergraduates and Vocational Education and Training (VET) undergraduates.

The sample included 402 Vietnamese university undergraduates and 250 VET undergraduates in the southern region of Vietnam. Students took part in a survey, with all participants being informed about the study’s purpose and assured that their involvement was entirely voluntary. In addition to descriptive statistics, the study employed linear regression in SPSS to examine hypotheses.

The findings indicate that, for university students, intrinsic motivation and mastery approach motivation are associated with reduced NEET risk, while performance avoidance motivation is positively linked to this tendency. In contrast, for VET students, extrinsic motivation and performance approach motivation are negatively associated with NEET risk, but mastery approach motivation may exacerbate the risk.

Grounded in the principles of Self-Determination Theory (SDT) and Achievement Goal Theory (AGT), the study proposes that university students may prioritize competence improvement, knowledge acquisition and the satisfaction of their learning interests, which they believe will help them acquire valuable knowledge beneficial for their future careers. Conversely, VET students emphasize performance and external achievement, which may enhance their outcome and reduce NEET risk. These findings offer significant theoretical and practical insights into the adoption of SDT and AGT and also provide educators or policymakers with more detailed information regarding university and VET students’ learning and development.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

The accelerated digital transformation and the growing emphasis on privacy, safety and security present ongoing challenges for cybersecurity experts. Alongside these challenges, the multidisciplinary, everchanging and complex nature of the cybersecurity domain has further challenged the acquisition and retention of cybersecurity talent. Empowering reskilling and upskilling in cybersecurity necessitates efficacious educational endeavours which promote self-confidence and foster a growth mindset. The purpose of this paper is to highlight that cultivating self-efficacy in cybersecurity education can help promote competency development and effectively address the prominent skills gaps. This notion applies equally to both aspiring individuals pursuing a career in cybersecurity and professionals in the field who may wish to better articulate the skills they already possess, the skills they lack and newly surfacing skills that need to be developed.

The study discusses the imminent need for adopting a “skills-first” approach in cybersecurity and explores innovative pedagogies and professional frameworks that can inform and frame such an approach. Subsequently, a critical analysis of the importance of self-efficacy towards motivating and supporting upskilling in cybersecurity is performed. A case study is presented, expanding the authors’ previous work on cybersecurity professional development, to demonstrate the mediating role that self-efficacy can play in developing core cybersecurity competencies. The case study presents the design of a new cybersecurity curriculum in the context of postgraduate, synchronous distance cybersecurity education, and it is utilised as a basis to discuss how the proposed curriculum cultivates self-efficacy attitudes.

A skills-first approach is becoming the new norm in contemporary workplaces. This work highlights the importance of actively nurturing self-efficacy attitudes through innovative cybersecurity curricula that can be tailored to the learners’ needs, instigating a drive for learning and, ultimately, helping learners effectively upskilling by portraying a self-directed learning path and a professional growth mindset in cybersecurity.

The authors present the importance of cultivating self-efficacy in higher and lifelong education to foster reskilling and upskilling in cybersecurity. An innovative cybersecurity curriculum was constructed and delivered with a group of learners demonstrating how self-efficacy can be leveraged through interactive, reflective and self-assessment educational activities that enhanced motivation and self-awareness, curiosity, attention to detail and resilience – key skills for a successful career in cybersecurity.

The study aims to explore how young adults perceive cooking contents on social media and their interaction with it.

Qualitative study using semi structured interviews with Brazilian young adults who cook (19–24 years old, n = 31) was the method employed. Interview audio recordings were transcribed and submitted to inductive and reflexive thematic analysis.

Data analysis generated six different themes. Young adults valued the profiles’ aesthetics and quality in terms of straightforwardness, trustworthiness, novelty and authenticity of information; they also identified with the profiles’ authors. When pressed for time, aesthetics was less valued than straightforwardness. Participants felt overwhelmed by the excessive information on social media and developed coping strategies to deal with this issue.

The lack of cooking and food skills is a recognized barrier for healthy eating in the young adult population. Social media can play a prominent role in delivering cooking-related information to the younger generation, but research on people’s perceptions of the cooking content available online is scarce and does not focus on how actual engagement with the available content happens. This qualitative exploration revealed which features young adults prefer when using social media to access cooking content, which can inform future interventions to promote cooking and improve health.

Climate change poses risks to society and the demand for carbon literacy within small and medium-sized enterprises is increasing. Skills and knowledge are required for organizational greenhouse gas accounting and science-based decisions to help businesses reduce transitional risks. At the University of Copenhagen and the University of Northern British Columbia, two carbon management courses have been developed to respond to this growing need. Using an action-based co-learning model, students and business are paired to quantify and report emissions and develop climate plans and communication strategies.

This paper draws on surveys of businesses that have partnered with the co-learning model, designed to provide insight on carbon reductions and the impacts of co-learning. Data collected from 12 respondents in Denmark and 19 respondents in Canada allow for cross-institutional and international comparison in a Global North context.

Results show that while co-learning for carbon literacy is welcomed, companies identify limitations: time and resources; solution feasibility; governance and reporting structures; and communication methods. Findings reveal a need for extension, both forwards and backwards in time, indicating that the collaborations need to be lengthened and/or intensified. Balancing academic requirements detracts from usability for businesses, and while municipal and national policy and emission targets help generate a general societal understanding of the issue, there is no concrete guidance on how businesses can implement operational changes based on inventory results.

The research brings new knowledge to the field of transitional climate risks and does so with a focus on both small businesses and universities as important co-learning actors in low-carbon transitions. The comparison across geographies and institutions contributes an international solution perspective to climate change mitigation and adaptation strategies.

This study aims to use regression Least-Square Support Vector Machine (LS-SVM) as a probabilistic model to determine the factor of safety (FS) and probability of failure (PF) of anisotropic soil slopes.

This research uses machine learning (ML) techniques to predict soil slope failure. Due to the lack of analytical solutions for measuring FS and PF, it is more convenient to use surrogate models like probabilistic modeling, which is suitable for performing repetitive calculations to compute the effect of uncertainty on the anisotropic soil slope stability. The study first uses the Limit Equilibrium Method (LEM) based on a probabilistic evaluation over the Latin Hypercube Sampling (LHS) technique for two anisotropic soil slope profiles to assess FS and PF. Then, using one of the supervised methods of ML named LS-SVM, the outcomes (FS and PF) were compared to evaluate the efficiency of the LS-SVM method in predicting the stability of such complex soil slope profiles.

This method increases the computational performance of low-probability analysis significantly. The compared results by FS-PF plots show that the proposed method is valuable for analyzing complex slopes under different probabilistic distributions. Accordingly, to obtain a precise estimate of slope stability, all layers must be included in the probabilistic modeling in the LS-SVM method.

Combining LS-SVM and LEM offers a unique and innovative approach to address the anisotropic behavior of soil slope stability analysis. The initiative part of this paper is to evaluate the stability of an anisotropic soil slope based on one ML method, the Least-Square Support Vector Machine (LS-SVM). The soil slope is defined as complex because there are uncertainties in the slope profile characteristics transformed to LS-SVM. Consequently, several input parameters are effective in finding FS and PF as output parameters.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.