Investigating Reading Challenges Faced by EFL Learners at Elementary Level

It is hard to ignore the importance of reading skills for desired proficiency in foreign languages. Reading can be beneficial for learners to immerse themselves in the target language and learn it efficiently. In EFL contexts like Saudi Arabia, learners face many challenges in reading skills. The main purpose of this research was to explore reading problems of elementary level students and causes of the readings skills inabilities. Following random and convenience sampling techniques; this mixed-method research obtained quantitative data from 290 elementary level students and qualitative data from nine teachers and supervisors. The analysis of quantitative data from the reading test and checklist and qualitative data retrieved from interviews suggests that students considerably perform relatively low in reading skills, and the main reasons are poor vocabulary, incorrect pronunciation, wrong spellings, slow reading pace, and flawed grammar. These five areas account for more than 90% of the challenges faced by learners in reading skills. Based on evidence from this research, we suggest that policymakers, teachers, and students should focus on these five areas for solving the issues related to reading skills. Although other avenues are essential, these items demand special attention to develop the reading skills of EFL learners in Saudi Arabia and elsewhere in the world.Keywords: Reading skill, Elementary level, Reading Pace, EFL  

SMEs Intention towards Use and Adoption of Digital Financial Services

The purpose of this study is to analyses the SMEs owners and managers intention towards use and adoption of digital financial services in South Punjab, Pakistsan. SMEs are considered key contributors to economic activity, as a key-source of employment, growth, and invention. SMEs perform significant role in the system of national financial regulation all over the world, Jobs creation, and contributing to modernization in the financial regulation are significant exertions to achieve ecological sustainability and more growth in access to finance. Access to finance in emerging countries is one of core issue in development of SME sector as well as obstacle in economic growth. Microfinance institutions are considered a good manifesto to enhance access to inclusive finance as well as stress-free access to finance and financial literacy for SMEs to support economic growth but in Pakistan, to obtain these finances not easy for SMEs sectors. SMEs are unfortunately still struggling due to lack of inclusive finance and financial literacy. Recently financial institution introduced digital financial services at micro level that provide access to financial services to each and every citizen in the country without any restriction. To accomplish the purpose of the research, the data was acquired from the SMEs owners and mangers with a sample of 232 chosen randomly of South Punjab, Pakistan. In this research study, merged two best theories; the theory of planned behavior and technology acceptance model was used to describe their behavior. Multiple statistical tests run to check the normality by using Shapiro-Wilk test, reliability and multicollinearity of the data to test the assumptions of regression and correlation. Finally, the results of this intended study revealed that there is a moderate significant but positive relationship between the SMEs owners and managers intention towards use and adoption of digital financial services. The Government of Pakistan should create finance related awareness program, training, technical institution and make a rules and regulation. Further this study suggests to researchers that focus on internal and as well as external factors of SMEs in Pakistan

Determinants of Adaptation Strategies to Climate Change by Farmers in District Sargodha, Pakistan

Pakistan is one of the most affected countries vulnerable to climate change. Additionally, being a predominantly agricultural economy, farming community is more at risk and climate change is predicted to decrease crop yields in Pakistan as a result of higher temperature, variability in rainfall and floods. Understanding the perception and adoption strategies to climate is important to preparing farming community for climate change impact. The present study was aimed to explore determinants of adaptation strategies to climate change by farmers. The data were collected through interview schedule. Logit regression model was used to explore the factors influencing the decision of farmer adaptation strategies to climate change. The study explored that education, farming experience, annual farm income, access to television, access to extension services, access to climate change information and membership in community based organization are main factors influencing the decision of farmers to climate change adoption. The study recommends that Government must ensure extension service, climate information and credit schemes to farmers to alter the production strategies in response to climate change

Factors Affecting Intention to Use of Small-scale Renewable Energy Technologies in District Sialkot, Pakistan

Due to existing energy shortage; there is need to adopt renewable energy technologies. Low awareness and acceptance are major barriers to development of renewable energy technologies. The present study was conducted to explore factors affecting intention to use of small-scale renewable energy technologies in Pakistan at household level. This present study was conducted in Sialkot District. and non-probability sampling technique was used to select the respondents. There were 160 respondents participated in this study. The theoretical model in this research was developed based on Technology Acceptance Model, Theory of Reasoned Action, and Innovation Diffusion Theory by Rogers and Theory of Planned Behavior. Major findings of this study highlighted that socio-economic factors, facilitating conditions perceived ease of use, perceived usefulness, knowledge, persuasion, and subjective norms are significant determinants of intention to use small-scale renewable energy technologies. The study recommended that government and other stakeholders should use strategies to create awareness and sensitized the people about benefits of adopting small-scale renewable energy technologies and setting up awareness centers at local and national level for encouraging people to adopt small scale renewable energy technologies

Factors Affecting Intention to Use of Small-scale Renewable Energy Technologies in District Sialkot, Pakistan

Due to existing energy shortage; there is need to adopt renewable energy technologies. Low awareness and acceptance are major barriers to development of renewable energy technologies. The present study was conducted to explore factors affecting intention to use of small-scale renewable energy technologies in Pakistan at household level. This present study was conducted in Sialkot District. and non-probability sampling technique was used to select the respondents. There were 160 respondents participated in this study. The theoretical model in this research was developed based on Technology Acceptance Model, Theory of Reasoned Action, and Innovation Diffusion Theory by Rogers and Theory of Planned Behavior. Major findings of this study highlighted that socio-economic factors, facilitating conditions perceived ease of use, perceived usefulness, knowledge, persuasion, and subjective norms are significant determinants of intention to use small-scale renewable energy technologies. The study recommended that government and other stakeholders should use strategies to create awareness and sensitized the people about benefits of adopting small-scale renewable energy technologies and setting up awareness centers at local and national level for encouraging people to adopt small scale renewable energy technologies

Roling-window bounds testing approach to analyze the relationship between oil prices and metal prices

[ArticleInPress]This paper is to find how the existence of a long-run relationship between oil prices and metals prices evolved for the time from January 1979 to December 2017. The rolling-window autoregressive lag mod- eling (RARDL) testing approach of cointegration has been introduced and applied to assess the long-run relationship considering four rolling windows of 5, 10, 15, and 20 years. The empirical evidence concludes that for a small rolling window of 5 years, there is no evidence of the long-run relationship between oil prices and metals prices, i.e. gold, platinum, and silver. However, there is a long-run relationship between oil prices and steel prices from December 2003 to December 2014. At larger rolling windows of 10, 15 and 20 years, oil prices and gold prices are not cointegrated; however, steel, silver, and platinum have a long-run relationship with oil prices in different periods

MaPLe: Multi-modal Prompt Learning

Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. However, they are sensitive to the choice of input text prompts and require careful selection of prompt templates to perform well. Inspired by the Natural Language Processing (NLP) literature, recent CLIP adaptation approaches learn prompts as the textual inputs to fine-tune CLIP for downstream tasks. We note that using prompting to adapt representations in a single branch of CLIP (language or vision) is sub-optimal since it does not allow the flexibility to dynamically adjust both representation spaces on a downstream task. In this work, we propose Multi-modal Prompt Learning (MaPLe) for both vision and language branches to improve alignment between the vision and language representations. Our design promotes strong coupling between the vision-language prompts to ensure mutual synergy and discourages learning independent uni-modal solutions. Further, we learn separate prompts across different early stages to progressively model the stage-wise feature relationships to allow rich context learning. We evaluate the effectiveness of our approach on three representative tasks of generalization to novel classes, new target datasets and unseen domain shifts. Compared with the state-of-the-art method Co-CoOp, MaPLe exhibits favorable performance and achieves an absolute gain of 3.45% on novel classes and 2.72% on overall harmonic-mean, averaged over 11 diverse image recognition datasets. Our code and pre-trained models are available at https://github.com/muzairkhattak/multimodal-prompt-learning.Comment: Accepted at CVPR202

Fine-tuned CLIP Models are Efficient Video Learners

Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP model. Since training on a similar scale for videos is infeasible, recent approaches focus on the effective transfer of image-based CLIP to the video domain. In this pursuit, new parametric modules are added to learn temporal information and inter-frame relationships which require meticulous design efforts. Furthermore, when the resulting models are learned on videos, they tend to overfit on the given task distribution and lack in generalization aspect. This begs the following question: How to effectively transfer image-level CLIP representations to videos? In this work, we show that a simple Video Fine-tuned CLIP (ViFi-CLIP) baseline is generally sufficient to bridge the domain gap from images to videos. Our qualitative analysis illustrates that the frame-level processing from CLIP image-encoder followed by feature pooling and similarity matching with corresponding text embeddings helps in implicitly modeling the temporal cues within ViFi-CLIP. Such fine-tuning helps the model to focus on scene dynamics, moving objects and inter-object relationships. For low-data regimes where full fine-tuning is not viable, we propose a `bridge and prompt' approach that first uses fine-tuning to bridge the domain gap and then learns prompts on language and vision side to adapt CLIP representations. We extensively evaluate this simple yet strong baseline on zero-shot, base-to-novel generalization, few-shot and fully supervised settings across five video benchmarks. Our code is available at https://github.com/muzairkhattak/ViFi-CLIP.Comment: Accepted at CVPR 202

Stock Market Dynamics in Pakistan: What Do Political Events and Budget Announcements Disclose?

The purpose of this study is to capture Pakistani stock market dynamics in response to political -and budget events. The study of political events and budget announcements together is quite reasonable in that: swift transformations of governments are likely to change annual budget policies, so both political -and budget events go side by side and influence the stock market performance. Moreover, we want to check: whether budget is one possible channel to off- set or somehow reduce the damage occurred due to political instability? We employ event study methodology over the sample period 1998-2016 of KSE-100 index daily returns to test the investor’s reaction to political -and budget news and, investigated whether stock market behaviour is consistent with efficient market hypothesis or over-and underreaction hypothesis or uncertain information hypothesis. We find evidence that Pakistani stock market exhibits weak form of the EMH for expected political -and budget events. The results of unexpected political events report that investors overreact to good political news while underreact to bad political news, indicate that results are consistent to UIH. The study may suggest that budget policies may not be very useful to offset the negative impact of political instability for a longer time period. So, the developing nations, especially, with more exposure towards political risk may use this suggestion as a strategic tool and, focus on some other channels to compensate the harmful effects of political instability on stock market performance. Keywords: Pakistan, KSE-100 index, Political events, Budget events, Event Stud

IMPACT OF R&D INVESTMENT ON FIRM PERFORMANCE AND FIRM VALUE: EVIDENCE FROM DEVELOPED NATIONS (G-7)

The purpose of the study is to evaluate the impact of R&D investment on firm performance and firm value among G-7 countries. To testify such relationship we use firm level and country level data collected from firms’ financial statement, countries stock exchanges and World Bank databases. Based on data structure (country level, industry level and firm level) we use HLM regression analysis technique along with robust standard error. By using diverse range of control variables (firm size, age, leverage, GDP growth, interest rate and financial crises dummy), results suggest that same year R&D investment has negative impact on firm performance and positive impact on firm value. However one year lagged period R&D investment has positive relationship with both firm performance and firm value. Nevertheless two year lagged period R&D investment has not effect on both firm performance and value