Shafi Mohamad and Fatimah Bujang
This study was conducted as a result of the declining numbers of Diploma in Accountancy (DIA) students obtaining a Cumulative Grade Point Average (CGPA) of 2.75 and above in the…
Abstract
This study was conducted as a result of the declining numbers of Diploma in Accountancy (DIA) students obtaining a Cumulative Grade Point Average (CGPA) of 2.75 and above in the Sarawak Campus of UiTM. The reason why CGPA 2.75 was identified as the cut‐off point was because this is the minimum entry requirement for students to enter the degree program in Accountancy. A questionnaire survey was carried out at UiTM Sarawak Campus with the hope of finding the root cause(s) of this problem by focusing on DIA students who were in their final semesters. A total of 65 questionnaires were distributed to selected respondents in Parts 6, 7 and 8 of the DIA program. In addition to that, these students were observed without much interference to their ordinary situation, so that a more reliable outcome could be obtained. From the survey, the researchers found that the factors that caused the decline in the numbers of students achieving a CGPA of 2.75 and above can be categorized into avoidable and unavoidable. The avoidable factors include students’ attitude, study skills and peer influence. These factors are considered avoidable because they are within the students’ control. Final exam paper difficulty is an unavoidable factor, because it is not under the students’ control. The findings show that the university, lecturers and students all have significant roles to play in helping these students obtain a CGPA of 2.75 and above. They should complement one another so that their joint effort can be optimized. Based on the above findings, the researchers conclude that the university, lecturers and students should work together to produce better results not only in terms of the CGPA outcomes but also to improve the students’ attitude.
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Muhammad Umair Ashraf, Nusrat Ali, Muhammad Rashid Hafeez, Siraj Hussain and Muhammad Imran
Deviance includes behaviors that deviate from societal norms. Scholars belonging to various disciplines have extensively studied this phenomenon. This study aims to understand the…
Abstract
Purpose
Deviance includes behaviors that deviate from societal norms. Scholars belonging to various disciplines have extensively studied this phenomenon. This study aims to understand the length and breadth of the deviance landscape.
Design/methodology/approach
Through a systematic analysis of publications, authors, journals and countries involved in research, this investigation unveils the inherently interdisciplinary nature of the subject. It unveils the prominent journals and influential authors who have made significant contributions to the field, shedding light on the evolving trends and shifting emphases over time.
Findings
The findings underscore the ever-growing relevance and importance of deviance research in contemporary society. They emphasize the pressing need for ongoing exploration to grapple with the intricate challenges posed by deviant behaviors.
Originality/value
This comprehensive bibliometric analysis serves as an invaluable resource, catering to the needs of researchers and practitioners with a vested interest in comprehending and advancing the study of deviance in its myriad manifestations.
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Bingzi Jin and Xiaojie Xu
Developing price forecasts for various agricultural commodities has long been a significant undertaking for a variety of agricultural market players. The weekly wholesale price of…
Abstract
Purpose
Developing price forecasts for various agricultural commodities has long been a significant undertaking for a variety of agricultural market players. The weekly wholesale price of edible oil in the Chinese market over a ten-year period, from January 1, 2010 to January 3, 2020, is the forecasting issue we explore.
Design/methodology/approach
Using Bayesian optimisations and cross-validation, we study Gaussian process (GP) regressions for our forecasting needs.
Findings
The produced models delivered precise price predictions for the one-year period between January 4, 2019 and January 3, 2020, with an out-of-sample relative root mean square error of 5.0812%, a root mean square error (RMSEA) of 4.7324 and a mean absolute error (MAE) of 2.9382.
Originality/value
The projection’s output may be utilised as stand-alone technical predictions or in combination with other projections for policy research that involves making assessment.