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Unpacking Developer Intentions: Assessing the Behavioural Factors Influencing ChatGPT Adoption in Software Development
No full textEmerging AI tools such as ChatGPT have the potential to revolutionize software development. To provide a comprehensive study, our research combined two influential frameworks: the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Task-Technology Fit (TTF). This convergence allowed for a comprehensive evaluation that encompassed individual behavioral motivations as well as the compatibility between the technology's capabilities and the particulars of development tasks. Our research methodology was multifaceted. Beginning with a Measurement Model Assessment to confirm our constructs, we moved on to a Structural Model Assessment to uncover underlying relationships. Using the capabilities of Artificial Neural Networks (ANN), we implemented additional Root Mean Squared Error (RMSE) and Sensitivity Analysis evaluations to enhance the accuracy of our insights. Ten-fold cross-validation was rigorously applied to a dataset containing 461 observations. Our comprehensive study sought to understand the factors influencing the adoption of ChatGPT in the software development domain. Central to our findings was the pivotal role of Performance Expectancy (PE), indicating developers' inclination towards tools that enhance their efficiency and streamline processes. Similarly, the importance of Social Influence (SI) underscored the collective nature of the developer community, where endorsements from peers or influential figures can significantly bolster adoption. Habit Behavior (HB) emerged as a defining factor, emphasizing the relevance of ingrained routines in the adoption of new technologies. Moreover, Task-Technology Fit (TTF) and its interplay with PE highlighted the importance of aligning AI tools with specific developer tasks to amplify expected outcomes. Conversely, factors traditionally deemed significant in technology adoption, such as Effort Expectancy (EE), Facilitating Conditions (FC), and Hedonic Motivation (HM), did not exert a considerable influence in the context of ChatGPT
Comparison between Nonlinear Analysis and ACI Simplified Effective Stiffness Analysis Methods for Reinforced Concrete Frames Under Lateral Load
No full textWhen reinforced concrete frames subjected to lateral load manifesting in inelastic damage in the form of cracks that diminish the structural element's stiffness. Evaluating this damage often employs non-linear analysis, yet simplified methods prescribed by international codes and numerous studies estimate the effective stiffness of these elements are used for elastic linear analysis. The accurate estimation of effective stiffness is crucial as it profoundly impacts the overall structural performance. This research endeavors to compare the non-linear performance of the structure with the linear performance using the assumptions proposed by ACI-318-19, permitting the use of 0.7EIg and 0.35EIg as effective stiffness values for columns and beams, respectively. To achieve this, 66 one-bay one-story frames are modelled and subjected to non-linear static analysis using ETABS software. The model parameters encompass compressive strength, reinforcement ratios, column height and axial load. The analysis presents load-deflection characteristics for the entire frame which used to calculate the effective stiffness. Results subjected to statistical analysis which indicate that increasing compressive strength augments structural stiffness, whereas heightened axial load and frame height notably diminish stiffness. Conversely, employing the non-linear analysis method result in higher effective stiffness than simplified method by mean value of 1.36, 1.44 and 1.57 for frames with compressive strength 40,80 and 120mpa respectively.
Keywords: effective stiffness, static nonlinear analysis, simplified stiffness metho
The impact of teachers’ leadership styles on the students’ academic achievement and behaviour in a cycle 1 government school in Dubai
No full textEducation professionals must be leaders in order to succeed. Leadership approaches were evaluated in Dubai's government cycle 1 schools. A study was conducted to examine the impact of teachers' leadership on student academic success and conduct. To increase the validity and dependability of the study, the researcher used both quantitative and qualitative methodologies. In order to understand successful leadership styles that will help improve academic performance and student behavior, the researcher communicated with instructors throughout the study by implementing observations, surveys, and interviews. The study results indicate that real leaders can positively influence students' academic achievement and behavior by having a thorough understanding of the curriculum, being able to use a variety of teaching methods, establishing positive relationships with students, providing constructive feedback, and letting students share their ideas and innovate
Factors Affecting Cybersecurity Behaviour in the Metaverse: A Hybrid SEM-ANN Approach Based on Deep Learning
No full textCybersecurity procedures and policies are prevalent countermeasures for protecting organizations from cybercrimes and security incidents. However, without considering human behaviours, implementing these countermeasures will remain useless. Cybersecurity behaviour has gained much attention in recent years. However, little is known concerning the factors that influence the cybersecurity behaviour of Metaverse users. Consequently, this research has three key objectives. A comprehensive systematic review is steered to address research gaps in the current literature on cybersecurity behaviour via the lens of information system models and theories to identify the most prevalent factors, theoretical models, technologies and services, and participants. The systematic review identified 2,210 empirical studies published on cybersecurity behaviour between 2012 and 2021. In line with the existing gaps found in the literature, this research, therefore, develops an integrated model based on extracting constructs from the Protection Motivation Theory (PMT), Health Belief Model (HBM), and Theory of Interpersonal Behaviour (TIB). An external factor, “trust”, is also incorporated in the model to understand better the factors affecting the cybersecurity behaviour in the Metaverse. The developed model was then evaluated based on survey responses from 531 Metaverse users in the United Arab Emirates who used the Metaverse for personal or professional purposes. The empirical data were analysed using a deep learning-based hybrid Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) Approach. The integrated model explained 66.1% of the total variance in cybersecurity behaviour. The hypotheses testing results reinforced most of the suggested hypotheses in the developed model. The sensitivity analysis results for the ANN model revealed that “cues to action” have the most considerable importance in understanding cybersecurity behaviour in the Metaverse, with 97.8% normalized importance, followed by habit (69.7%), perceived vulnerability (69.6%), self-efficacy (40.9%), and trust (27.2%). Theoretically, integrating the PMT, HBM, and TIB along with the external factor, “trust”, is believed to add a significant value to validating the three theories in general and the cybersecurity behaviour in specific. Practically, understanding the impact of security factors would assist in understanding the effect of security incidents on cybersecurity behaviour in the Metaverse. Furthermore, policymakers and regulators should pay attention to and analyse the present data privacy policies and legislation to create specific policies and regulations for using the Metaverse. Moreover, cybersecurity companies, system analysts, and developers can use the insights from the essential factors as a form of a lesson to the refinement of presently implemented solutions as well as the anticipation of new future technological advancements
The Impact of Differentiated Instruction on the Acquisition of Short Stories by High School ESL Learners in an American School in the UAE
No full textAbstract
This study was conducted to examine the impact of Differentiated Instruction on the acquisition of short stories by high ESL learners in an American school in the UAE. To achieve the objective of this study, a quasi-experimental design was used, in which pre- and post-tests were administered to two groups of participants, a control group and an experimental group. A traditional instructional approach was used in the control group, whereas differentiated instruction was used in the experimental group. Using the t-test, a statistical analysis was carried out on the results of the study. In light of the higher scores achieved by the experimental group as compared to the control group, it can be concluded that differentiated instruction is a worthwhile approach. Through the use of Differentiated Instruction, this study will contribute to the existing body of literature concerning the teaching of short stories and will thereby enhance the literary skills of students. As a final point, additional research should be conducted to examine the impact of differentiated instruction on the acquisition of different literary genres
Governmental Data Analytics: An Agile Framework Development and Real-World Data Analytics Case Studies
No full textTeacher's Perspectives and Professional Development Needs to Integrate AI Tools in K-12 Sector in the UAE
No full textArtificial intelligence (AI) has enhanced teaching and learning in all sectors of education. Learning about AI at a very young age can foster the 21st century skills of learners. There is an emerging trend to integrate AI across the K–12 curriculum. Teachers play an important role in bringing any kind of innovation to the classroom and in integrating new technologies. To address the growing trend of integrating AI in K–12, the aim of this study was to investigate teachers’ perspectives, beliefs, attitudes, and concerns for the adoption of AI tools in K–12 curriculum and their professional development training needs for the successful integration of these technologies in the K -12 classrooms. This study was theoretically anchored on Technology acceptance model (TAM), Theory of planned behavior (TPB) and Technological pedagogical content knowledge (TPACK). A mixed methods research approach was used to carry out this study. An anonymous questionnaire was used to collect quantitative data from (n= 66) K-12 teachers in the UAE, and qualitative data was collected using focus group interviews (n = 12) with high school teachers. Ten constructs, namely perceived use (PU), Perceived Ease of Use(PEOU) , Self-efficacy(SE), Anxiety, Technological pedagogical knowledge (TPK), Technological knowledge(TK), Technological content knowledge(TCK), Technological pedagogical content knowledge (TPACK), Behavior intention (BI) and ethics were measure through 7-point Likert scale in the survey. Analysis of the data revealed PU as the most significant factor predicting BI and TPACK and SE significantly predicted PU. In addition to this, TK significantly predicted Ethics. Results also revealed educators are positive and motivated to adopt AI tools, although they acknowledge the ethical implications due to the use of AI tools and demand for policies and regulations. Although teachers expressed discomfort and concerns about the use of AI, anxiety didn’t negatively predict their BI. These insights are very useful for policymakers, curriculum developers, and school leaders for planning effective teacher development programs and preservice teacher education
Analyzing and Simulating Traffic Collision Data to Recommend Policy for Autonomous Taxi Deployment in Dubai
No full textThe adoption of appropriate and suitable Autonomous vehicles regulations and guidelines is one of the main challenges that governments and transportation decision makers are facing nowadays. Appropriate guidelines to operate autonomous vehicles successfully and to maximize their benefits is crucial for safe and seamless deployment. This research focuses on analyzing the key safety benefits of deploying autonomous taxis for traffic collision avoidance. The research also suggests and recommends policies and guidance to accelerate appropriate and safe autonomous vehicles deployment. The research aims at supporting policy makers and governments with the recommendations for safer deployment of autonomous taxi.
The Al Muraqabat area was selected for the simulation model as it had a high frequency of collisions. Statistical analysis of taxi traffic collision data was conducted to identify the human factors that contribute to traffic collisions. The statistical model was developed using the SPSS software. Ten reasons for traffic collisions were studied and 76% of the traffic collisions were due to three reasons that were associated with human factors. Experience, Age and fatigue were identified as main causes of traffic collision which are associated with human drivers.
The outcome of the statistical model was used to build a traffic simulation model using the Vissim traffic simulation Software. Seven scenarios were simulated which included a baseline model and three autonomous driving behavior scenarios that were simulated on both 100% and 50% penetration levels. When autonomous scenarios were simulated, the lowest number of traffic collisions occurred for the case of 100% normal driving behavior (367) which was 21% lower than baseline scenario.
Policy analysis was used to identify gaps in the current legislations and exploring best practice globally. The interview questions were formulated based on the outcome of statistical analysis, simulation and legislation gaps. The thematic analysis was used to identify the experts’ ideas and thoughts using Nvivo software. Experts suggested focused on engaging the government and providing incentives to the private sector. Restudying urban planning, dedicated autonomous vehicles centers and developing smart infrastructure were the main recommendations. Revising the traffic and developing autonomous vehicles standard are critical for safe deployment
Adoption of Metaverse in Aerospace Industry: Organizational Perspective
Get PDFThis study aims to identify the factors that influence the adoption of metaverse in the Aerospace Industry. Utilizing the Technology-Organization-Environment (TOE) Framework to develop a deductive questionnaire-based approach. The framework consisted of thirteen independent variables and one dependent variable (Metaverse Use Intention). 13 hypotheses are developed to statistically test the relationships between the 14 variables of the research model. The study adopted a convenience sampling approach, the population focused on the aerospace industry worldwide and on a general organizational type. The results from the data analysis from linear regression analysis suggest that the there is no statistical significance on the adoption of metaverse. The descriptive statistics collected offer an understanding of the perceptions and attitudes towards metaverse in the Aerospace Industry. The results show that this study needs to be more focused on specific sectors of the Aerospace Industry and to evaluate the individual perception as well as the organizational perception, utilizing the TOE Framework and TAM Model. The study contributes to innovation in the Aerospace Industry and provide a steppingstone to taking next steps in adopting Metaverse
The Impact of Macroeconomic Factors on the Nifty Auto Index
No full textThis open access book presents contributions on a wide range of scientific areas originating from the BUiD Doctoral Research Conference (BDRC 2022)The aim of the paper is to investigate the association between selected macroeconomic variables like crude price, exchange rate, index of industrial production, inflation, interest rate, repo rate, gold price and the auto index of the National Stock Exchange (NSE) of India during a time when the automotive sector in India witnessed the sharpest dip in sales. The study adopts Autoregressive Distributed Lag (ARDL) co-integration approach and performs suitable diagnostic tests. Results indicate that, exchange rate has a significant negative relationship with Nifty auto index in the long run. Additionally, crude price, index of industrial production and repo rates are statistically significant determinants of Nifty auto index. On the contrary, first lag of crude price is found to be a possible predictor of the index in the short run. The study provides important implications for researchers, corporations, portfolio managers, investors, and government. Despite the availability of a large body of literature exploring the association between macro-economic factors and stock market in India, research exploring the association between the former and Indian auto indices has been sparse. Hence, this study is intended to fill this gap in the literature