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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.

During the late 1900s, there was a notable trend of rapid urbanization worldwide, coinciding with a rise in atmospheric carbon dioxide levels. Agricultural production driven by expanded trade is recognized as a major contributor to global pollution and biodiversity depletion. Additionally, tropical deforestation resulting from agricultural activities significantly impacts global greenhouse gas emissions. This chapter aims to examine the interrelationships between crop production, livestock production, transport services, and CO₂ emissions from 1998 to 2019. To achieve this goal, the study begins by conducting stationary tests to determine the order of integration for the variables under consideration. Following this, panel unit root tests are employed, and subsequently, panel cointegration tests are conducted to identify any long-term relationships among the selected variables. The findings reveal a significant long-term relationship among crop production, livestock production, transport services, and CO₂ emissions.

This study aims to explore an interpersonal predictor of coworkers-directed knowledge hiding behavior – the leader–member exchange social comparison (LMXSC). This study integrates leader–member exchange literature with social comparison theory to hypothesize that an individual’s upward LMXSC is positively correlated with coworkers-directed knowledge hiding and that an individual’s feelings of envy are mediated by the relationship between upward LMXSC and coworkers-directed knowledge hiding behavior. Also, this study proposes two-way and three-way interaction patterns of goal interdependence, which can influence LMXSC–envy relationships.

Two independent studies are conducted to test the hypothesized relationships. In Study 1, the authors collected multi-wave data from a large public sector university in China (N = 1,131). The authors then replicated the Study 1 findings by collecting multi-source and multi-wave data from a telecom company based in China (n = 379).

The authors found support across both studies for the idea that upward LMXSC is a possible interpersonal predictor of coworkers-directed knowledge hiding behavior. More specifically, it was found that feelings of envy ensue from upward LMXSC, resulting in further coworkers-directed knowledge hiding behavior. Further, this study shows that the influence of upward LMXSC on knowledge hiding behavior via feelings of envy was weaker (stronger) when employees have high (low) cooperative goal interdependence with coworkers, respectively, and when employees have low (high) competitive goal interdependence with the coworkers, respectively.

This study extends current knowledge management literature by introducing LMXSC as an interpersonal predictor of coworkers-directed knowledge hiding behavior. This will help practitioners to curb such counterproductive behavior.

Although voice endorsement is essential for individuals, teams and organizational performance, leaders who consider followers' voice to be threatening are reluctant to implement followers' ideas. The authors, taking note of this phenomenon, investigate why leaders who feel a threat from followers' voice exhibit voice rejection at the workplace and when this detrimental tendency can be diminished. Thus, based on the self-defense tendency as per self-affirmation theory, the authors argue that those leaders who experience threat triggered by followers' voice, justify voice rejection through the self-defense tactics: message derogation and source derogation. In addition, the authors also propose that a leader's positive (negative) affect experienced before voice exposure may decrease (increase) self-defense and voice rejection.

To test the authors’ moderated mediation model, they conducted two independent vignette studies (N = 269; N = 208). The purpose of the first vignette study was to test the simple mediation (i.e. the direct and indirect effects), whereas the second study aimed to test the moderated mediation model.

In Study 1, the authors found that the leader's perceived threat to competence provoked by followers' voice was positively related to voice rejection, and the relationship was partially mediated by message derogation and source derogation. In line with this, in Study 2, the authors tested the moderated mediation model and replicated the findings of Study 1. They found that the effects of leader's perceived threat to competence on voice rejection through self-defense tactics are weaker (stronger) at the high (low) values of a leader's positive affect. In contrast, the effects of a leader's perceived threat to competence on voice rejection through self-defense tactics are stronger (weaker) at the high (low) values of a leader's negative affect.

This study suggests that leaders who experience a threat to competence instigated by employee voice are more likely to think that ideas proposed by employees are non-constructive and employees who suggest those ideas are not credible, and these appraisals have a direct influence on voice rejection. However, if leaders are in a good mood vs. bad mood, they will be less likely to think negatively about employees and their ideas even when they experience psychological threats. The findings highlight several avenues for future researchers to extend the literature on employee voice management and leadership coaching by providing theoretical and managerial implications.

The current study examines the impact of big data analytics capabilities (BDAC) on supply chain performances of the manufacturing firms. Based on the underpinning of resource-based view (RBV) theory, the current study will highlight the significance of BDAC on green dynamic capabilities (GDC), supply chain agility (SCA) and green competitive advantage (GCA). Furthermore, the study examines the moderating effect of supply chain innovativeness (SCI) on the relationship between GCA and firm performance (FP).

Online survey method was employed for the data collection from the 331 managers employed in Pakistan Stock Exchange (PSX)-listed manufacturing firms. The hypothesized model was tested using partial least squares structural equation modeling (PLS-SEM) technique.

The study results indicate that BDAC has a positive influence on both GDC and SCA, leading to enhanced GCA. Furthermore, the results demonstrate that GCA significantly and positively impacts FP, and the relationship between them is positively moderated by SCI.

This study developed a novel theoretical perspective based on RBV theory and provided empirical evidence that manufacturing firms' performances are significantly influenced by BDAC, GDC and SCA. The study results provide valuable practical implications top management regarding the effectiveness of BDAC and SCA in the supply chain. The findings further highlight the significance of SCI strengthening relationship between GCA and FP.

With the current pandemic situation, the world is shifting to online buying and therefore the purpose of this study is to understand how the industry can improve sales based on the product recommendations shown on their online platforms.

This paper has studied content-based filtering using decision trees algorithm and collaborative filtering using K-nearest neighbour algorithm and measured their impact on sales of product of different genres on e-commerce websites and if their recommendation causes a difference in sales.This paper has conducted a field experiment to analyse the customer frequency, change in sales caused by different algorithms and also tried analysing the change in buying preferences of customers in post-pandemic situation and how this paper can improve on the search results by incorporating them in the already used algorithms.

This study indicates that different algorithms cause differences in sales and score over each other depending upon the category of the product sold. It also suggests that post-Covid, the buying frequency and the preferences of consumers have changed significantly.

The study is limited to existing users of these sites, it also requires the sites to have a huge database of active users and products. Also, the preferences and likings of Indian subcontinent might not generally apply everywhere else.

This study enables better insight into consumer behaviour, thus enabling the data scientists to design better algorithms and help the companies improve their product sales.

In the presence of informal sector in the country, designing an energy policy and the pursuit of higher economic growth become challenging for emerging economies. These economies are usually resource starved, and the presence of underground economy leads to faulty estimates of energy demand. The authors explore the energy–growth nexus in the presence of underground economy for Pakistan, an emerging economy host to large informal sector and facing recurring energy crises.

The authors evaluate the impact of underground economy on energy demand in the presence of explanatory variables, including official gross domestic product (GDP), foreign direct investment and financial development. The authors first assess the influence of official economy on the consumption of energy. The authors investigate how energy consumption is influenced solely by underground economy. Finally, the authors evaluate the impact of true GDP on the energy consumption. The authors employ combined cointegration method of Bayer and Hanck (2013) and then apply vector error correction model.

The results reveal that official GDP, underground economy and true GDP positively and significantly affect energy consumption in both short and long run. Similarly, financial development as well as foreign direct investment enhance energy consumption. The authors find unidirectional causality between energy consumption and official GDP variables (OGDP → EC), underground economy (UE → EC) and true GDP variables (TGDP → EC) in the long run. The authors observe bidirectional causality in the short run between energy consumption and official GDP (OGDP ↔ EC) and true GDP (TGDP ↔ EC).

To the best of the authors' knowledge, no study examines the causal relationship of energy consumption and underground economy. Overall, the findings assist policymakers to consider and implement different energy-related policies considering the significant role of underground economy for energy consumption in Pakistan.

This study aims to gain a new perspective on auditing by measuring investors’ fraud perception and to reveal the necessity of increasing individuals’ fraud perception by determining the effect of fraud perception on the intention to invest in crypto assets from the investor’s perspective.

As part of this quantitative research, a survey was conducted on individuals residing in Türkiye and aged 18 years and above through a convenience sampling method. A total of 446 participants were included in the study. The data collected was analyzed using the partial least squares-variance based structural equation modeling (PLS-SEM) method using the SmartPLS program.

Fraud perception causes individuals to be more risk-averse and reduces their intention to invest in crypto assets. At the same time, it has been observed that risk-averse individuals have lower intention to invest in crypto assets. According to the results of the mediating effect analysis, risk aversion behavior partially mediates between the fraud perception and the intention to invest in crypto assets. Among the emotions, only fear increases risk aversion behavior. Among the personality traits, extroversion and openness to experience personality traits reduce risk aversion behavior, whereas neuroticism personality traits increase the intention to invest in crypto assets.

In an environment where traditional auditing activities are insufficient, increasing investors’ perceptions of fraud can reduce fraud-related losses. In this context, to the best of the authors’ knowledge, the present study might be among the first to investigate the impact of individuals’ perceptions of fraud on their investment intentions in crypto assets.

The paper aims to explore “content” factors influencing consumptive and contributive use of enterprise social networking within UK higher education during the COVID-19 pandemic.

The methodology uses genre analysis and grounded theory to analyse empirical data from posts obtained through Microsoft Yammer and a focus group.

The findings reveal the motivators-outcomes-strategies and the barriers-outcomes-strategies of users. Motivators (M) include feature value, Information value, organizational requirement and adequate organizational and technical support. Barriers (B) include six factors, including resisting engagement on the online platform, emotional anxiety, loss of knowledge, the lack of organizational pressure, lack of content quality and lack of time. An Outcomes (O) framework reveals benefits and dis-benefits and strategies (S) relating to improving user engagement.

The research method and resultant model may serve as guidelines to higher educational establishments interested in motivating their staff and scholars around the use of enterprise social network (ESN) systems, especially during face-to-face restrictions.

This research study was conducted during the COVID-19 pandemic which provides a unique setting to examine consumptive and contributive user behaviour of ESN’s. Furthermore, the study develops a greater understanding of “content” factors leading to the benefits or dis-benefits of ESN use, drawing on user motivators, barriers and strategies during the COVID-19 pandemic in UK education.