Search results

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

From 1979 to 2023, the takaful structure has been adopted in many jurisdictions, making the documenting of its early days of establishment relatively difficult and somewhat unreliable. This is unlike conventional insurance, where the history and legislation are well documented and archived in various research (Hellwege, 2016; Marano and Siri, 2017). The purpose of this paper is to provide a chronology for the establishment and development of takaful via the takaful establishment in each jurisdiction, documenting its first takaful operator and first takaful regulation.

This paper has used a qualitative method in the form of reviewing literature and available data such as journals, books and official resources. The data is thoroughly analysed in order to build the chronology for takaful. It adopted an exploratory research design, which is deemed suitable in situations where few works of literature have examined the subject (Neuman, 2014). The paper explores the establishment and non-establishment of takaful in 57 countries. The paper categorises the countries into seven regions starting with the GCC, Levant, Asia, Central Asia, Africa, Europe and Others.

The takaful chronology presented in this paper shows that takaful operations exist in 47 jurisdictions, starting from Sudan and the UAE in 1979, with the most recent adopters being Morocco and Iran in December 2021. It is found that 22 jurisdictions do not have takaful regulations, and the Takaful Act 1984, issued in Malaysia, is considered the first takaful regulation that sets the basis for other regulations that follow.

The paper contributes to the literature by providing a comprehensive chronology of takaful, especially as the few existing timelines have been found to be incomplete and consist of contradictory information.

The quest for improved facilities management (FM) delivery is receiving immense focus through the incorporation of innovative technologies such as cyber-physical systems (CPS). The system’s high computational capabilities can aid in the abatement of some of the challenges plaguing FM functions. However, the requisite ingredients for the uptake of the system for FM have still not gained scholarly attention. Because performance measurement is a vital index in determining the outcome of FM methods, this study aims to investigate the influence of performance measurement indicators that are influential to the uptake of CPS for delivering FM functions.

A qualitative technique was adopted using the Delphi technique. The panel of experts for the study was selected through a well-defined process based on stipulated criteria. The experts gave their opinions in two rounds before consensus was attained on the identified performance measurement indicators, whereas methods of data analysis were measures of central tendency, inter-quartile deviation and Mann–Whitney U test.

Results from this study showed that 11 of the performance indicators were of very high significance in the determination of the uptake of CPS for FM functions, whereas 5 of the indicators were proven to be of high significance. Furthermore, there was no statistical difference in the opinions of the experts based on their affiliation with academic institutions and professional practice.

The findings of this study contribute practically by aiding policymakers, facility managers and relevant stakeholders with the vital knowledge of delivery mandates for efficient FM services that can spur the uptake of digital technologies such as CPS.

This study contributes to the body of knowledge as it unveils a roadmap of the expected performance output and its accompanying evaluation that would drive the adoption of a promising technology such as CPS in the delivery of FM tasks.

The purpose of this study is to examine the influence of service quality on student’s satisfaction.

Using empirical research, the study identified previously validated scales of service quality and student satisfaction. Using the SERVPERF scale, data were collected from 279 students studying in public and private universities across Saudi Arabia. The model fit of the scale was assessed to ensure that the data produced accurate outcomes. Structural equation modelling was used to test the effects of independent variables on dependent variables.

The results suggest that four of the five dimensions of service quality, namely, tangibility, reliability, responsiveness and assurance had a significant effect on students’ satisfaction. Empathy was not found to contribute to student satisfaction. The findings broaden and deepen our understanding of how the dimensions of service quality reinforce students’ satisfaction.

Future research can also incorporate in the model other variables, academic and non-academic, related to student satisfaction.

The results have useful implications for decision-makers in higher education institutions who strive to enhance students’ satisfaction and increase the quality of higher education programmes, particularly in Saudi Arabia and the Gulf region in general.

This study uses the SERVPERF scale, which is empirically superior to the SERVQUAL scale for measuring student satisfaction.

Under the unique context of COVID-19, this paper aims to analyze how the Government of Pakistan (GoP) provides financial and non-financial support to women entrepreneurs in Pakistan. Drawing on the resource-based theory of entrepreneurship (RBTE), the study advances the understanding of resources and grants offered by GoP to women entrepreneurs to help them in business survival.

The study adopts qualitative research method to address the questions: how does the Pakistani government respond to issues faced by women entrepreneurs during the COVID-19 pandemic, and what assistance and initiatives were implemented by GoP? Semi-structured interviews were conducted with twenty on-job government officials related to the entrepreneurial sector in Pakistan.

The paper reveals that during the pandemic, GoP keenly communicated with women entrepreneurial representatives to comprehend their business challenges. In addition, waivers and incentives were provided to support their business activities. GoP further invited women entrepreneurs to contribute their knowledge and give suggestions in policy making.

Lots of research have been conducted to identify the issues faced by women entrepreneurs during the pandemic. However, the specific strategies, policies, and support provided by the governments to address these issues have often been overlooked. This paper fills such gap with focus on the governing bodies and policymakers in Pakistan towards women entrepreneurship during the COVID-19 crisis.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

This study aims to investigate risks associated with climate change vulnerability and in response the adaptation methods used by farming communities to reduce its negative impacts on agriculture in Pakistan.

The study used household survey method of data collection in Charsadda district of Khyber Pakhtunkhwa province, involving 116 randomly selected respondents.

Prevalent crops diseases, water scarcity, soil fertility loss and poor socio-economic conditions were main contributing factors of climate change vulnerability. The results further showed that changing crops type and cultivation pattern, improved seed varieties, planting shaded trees and the provision of excessive fertilizers are the measures adapted to improve agricultural productivity, which may reduce the climate change vulnerability at a household level.

The major limitation of this study was the exclusion of women from the survey due to religious and cultural barriers of in Pashtun society, wherein women and men do not mingle.

Reducing climate change vulnerability and developing more effective adaptation techniques require assistance from the government. This help can be in the form of providing basic resources, such as access to good quality agricultural inputs, access to information and extension services on climate change adaptation and modern technologies. Consultation with other key stakeholder is also required to create awareness and to build the capacity of the locals toward reducing climate change vulnerability and facilitating timely and effective adaptation.

This original research work provides evidence about farm-level vulnerability, adaptation strategies and risk perceptions on dealing with climate-change-induced natural disasters in Pakistan. This paper enriches existing knowledge of climate change vulnerability and adaptation in this resource-limited country so that effective measures can be taken to reduce vulnerability of farming communities, and enhance their adaptive capability.

The paper aims to understand the young residents’ household waste intentions through place attachment (PA) approach where place dependency (PD) and place identity (PLI) influence recycling intentions (RIs). Furthermore, the effect of norms (both subjective and moral) on residents’ association with PLI was also analyzed.

The conceptual model, including the hypothesized relationship between variables, was established through relevant literature. The study extends the theory of planned behavior (TPB) through a place-based approach in young residents’ household waste RIs. The proposed conceptual model also replaced the position of norms (subjective and moral) as antecedents to PLI in the proposed extended and modified TPB model. Partial least square structural equation modeling (PLS-SEM) has been used for the statistical analysis of the data. The questionnaires were distributed digitally. The convenience sampling approach was adopted for collecting data.

The results tenably billed the inclusion of placed-based approach in the TPB and norms (subjective and moral) in predicting PLI of young residents. All the alternative hypotheses in the proposed model were accepted. The predictive power of RIs was 41.4%.

The research only considered the educated and financially opulent residents, among whom the waste disposal system was well established and may have led to favorable results. The study only limits to measuring intentions, and its organic nature opens vistas for future research studies where more variables could be agglutinated to achieve pronounced prediction power and also further measure actual recycling behavior and practice.

The study adds to pragmatic implications for local governments and municipalities where the waste collection apparatuses could capitalize on the findings to achieve efficiency in household waste collection and recycling.

With young generation of residents at the helm for forging a cleaner environment, the study motivates environmental enthusiasts and social scientists to better understand household waste RIs. The study will help young generation to become more sensitized towards the environment by making green changes in daily disposal habits.

The study explored two prospects. First, PA (place dependence and place identity) was added as an external variable and precedent to RIs, and second, the norms (both subjective and moral) were taken as antecedents to place identity.

Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.