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Predictive Power of Self-Efficacy, Familiarity with Training Content on Job Performance: Moderating Effect of Training Effectiveness in Oman’s Higher Education Institutions
Get PDFThe higher education institutions (HEIs) in Oman have witnessed rapid development over the last decade; and yet, the quality of their training programs programmes has not yielded the anticipated results. The desired outcome, which failed to meet expectations, pertains to the effectiveness of these programs in enhancing employee job performance. This study aims to investigate the influence of self efficacy, trainee familiarity with training content and employees’ performance in Oman’s higher education sector. Additionally, the study explores the moderating effect of training effectiveness on employee’s performance. The researcher examined 184 academics as well as administrative employees that participated in a training program sponsored by Takatuf, a consulting firm responsible for assessing, training, and developing employees in Oman. Results indicated that self-efficacy and familiarity with training content contribute to high employee job performance in the country’s HEIs, confirming the significant moderating role of training effectiveness in this relationship. Furthermore, the study suggests two implications for human resource practices. First, it is crucial for managers to provide comprehensive training-related information aimed at increasing self efficacy before the actual training program. Second, HR managers and individuals can jointly improve the performance effectiveness by creating suitable methods for fostering self-efficacy. The findings suggest that organizations can enhance job performance and reduce turnover intention intentions by improving the effectiveness of training programs for employees. There is limited research linking self-efficacy, training effectiveness, and job performance in HEIs, particularly within member countries of the Gulf Cooperation Council (GCC). This research contributes to the expansion of theoretical and managerial doctrines regarding the relationships among the identified variables. Therefore, this research represents a unique attempt to determine the effectiveness of training programs in HEIs grappling with transitional challenge
The Narrative of Fraud: A Framing Analysis of Two Leading Malaysian Newspapers
No full textThis study explores how two leading Malaysian newspapers, a prominent English-language publication referred to as Newspaper A and a widely circulated Malay-language publication referred to as Newspaper B, frame fraud reporting through investigative, neutral, and sensationalist approaches, using framing theory as the analytical lens. Framing theory posits that the media influences public perceptions by emphasizing certain aspects of an issue while downplaying others. A qualitative content analysis was conducted on 94 newspaper headlines, categorized into three framing types: investigative, neutral, and sensationalist. The findings reveal significant differences in editorial strategies. While both newspapers prioritize neutral reporting, Newspaper A allocates 80% of its coverage to this frame, compared to 71% for Newspaper B. Conversely, Newspaper B demonstrates a stronger emphasis on investigative frames (14%) and sensationalist frames (20%) compared to Newspaper A (5% and 15%, respectively). These differences highlight Newspaper B's focus on engaging readers through systemic narratives and dramatic storytelling, whereas Newspaper A leans toward factual, procedural reporting. This analysis underscores the media's dual role as an informant and influencer, shaping public discourse on fraud and accountability. The findings contribute to a broader understanding of media framing in Malaysia, emphasizing the need for a balance between investigative depth and engaging narratives to foster informed citizenship and systemic accountabilit
A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction
Get PDFRisk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise the strategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall risk of IS/IT project portfolio, whereas neuro-fuzzy models were rarely used. This paper proposes a Sugeno Adaptive Neuro-Fuzzy Inference System (ANFIS) model based on Fuzzy Factor Analysis (FFA) named ANFIS-OPR to predict the overall risk of IS/IT project portfolio from historical IS/IT project risk data. The ANFIS-OPR inputs are the relevant factor loadings resulting from the FFA application on the IS/IT projects risks set to cope with the curse of dimensionality. Then, the Sugeno ANFIS model is adopted to give strategic interpretability to the predicted IS/IT project portfolio overall risk by implementing the IS/IT Project Management Office (PMO) expert knowledge, represented by fuzzy rules, on the relationship between IS/IT project portfolio strategic alignment and the IS/IT projects risks. The ANFISOPR outputs are the predicted Overall Portfolio Risk (OPR) and Root Mean Square Error (RMSE). The paper also presents an IS/ IT PMO case study that shows the proposed ANFIS-OPR efficacy, which predicted the OPR values closely to the OPR estimates with an accepted RMSE of 0.108. The proposed ANFIS-OPR is a novel intelligent decision-making tool that enables the IS/IT PMO to monitor the OPR, considering its linkage with strategic alignment; thus, contingency plans can be carried out appropriately while ensuring that the IS/IT project portfolio is strategically aligne
Factors Influencing Intention to Adopt E-Wallet During Covid-19 Pandemic
Get PDFThe e-wallet feature allows consumers to make payment by transferring digital money into their virtual wallets for in-application online payments or through Quick Response (QR) codes for physical purchases. This study examines the average usage, transaction size and factors influencing the intention to adopt e-wallet among consumers during the Covid-19 pandemic. A total of 153 responses were collected and analysed using Statistical Package for Social Sciences (SPSS) to determine descriptive statistics and conduct multiple regression analysis to verify that performance expectancy, social influence, facilitating conditions, and perceived security influence the intention to use e-wallets during the Covid-19 pandemic among consumers. Performance expectancy is the strongest factor while effort expectancy is the only factor that has no influence on the intention to use e-wallets during the Covid-19 pandemic. Additionally, this study reveals that due to the current situation, consumers are pushed to adopt e-wallets despite their security concerns, and they are willing to learn to use the platforms without considering the possibilities and risks. However, the usage of e-wallets as a medium of transaction is still low due to the lack of awareness and knowledge. The outcomes of this study to provided practical contributions for policymakers and
service providers on the current acceptance of e-wallets in Malaysia and to help the e-wallet industry develop more reasonable business strategie
The Role of Web Store Stimuli on Customers' Impulse Buying Behaviour Through Brand Perception
Get PDFIn light of prevailing technological advancements, online shopping has become increasingly prevalent. A well-designed webpage interface, particularly in terms of visual appeal, has the potential to evoke customer emotions such as pleasure and arousal, thereby stimulating impulse buying among consumers. In contrast to global brands, local web stores have faced challenges in devising strategies to cultivate an environment conducive to enhancing brand perception and encouraging impulsive buying behavior, specifically in the realm of online local brand retailing. Previous studies have primarily focused on customers’ cognitive perspectives, including privacy concerns, content sharing, and websites credibility. However, existing research has yet to provide a comprehensive framework for understanding customers’ online impulse buying behavior. Addressing these gaps, this study aims to investigate the effect of different web store stimuli on creating consumers’ brand perception of online web stores and its impact on customers’ impulsive buying behavior. Data was collected through
purposive sampling, involving 423 users of different online local web stores. The results showed interesting findings with implications for both theory and practice. Specifically, environmental stimuli, such as ambience (lighting), assortment, forum fnac and helping staff, were found to significantly enhance the brand perception of local web store outlets, consequently boosting customers’ impulse buying behavio
Indonesian Stock Price Prediction Using Neural Basis Expansion Analysis for Interpretable Time Series Method
Get PDFThe stock market is an attractive investment venue for many individuals and companies. However, unexpected share price fluctuations can cause significant financial losses. In stock investment, predicting stock price movements is the most frequently discussed topic because it allows investors to make the right investment decisions to make big profits. Therefore, a model is needed to predict future stock prices, one strategy for maximising investment profits. New state-of-the-art deep learning architectures for time series forecasting are being developed yearly, making them more accurate than ever. The most commonly used network for such a solution is Long Short-Term Memory (LSTM) architecture, but it has limitations such as long training time and interpretability. This study aims to evaluate another state-of-theart solution, Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), in comparison with LSTM by utilising historical\ data of PT Bank Central Asia Tbk (one of the banking companies in Indonesia) from 25 March 2013 to 21 March 2023. N-BEATS is a relatively new variable method that can produce accurate predictions using neural networks. This architecture has advantages such as interpretability, seamless applicability across diverse target domains without requiring modifications, and fast training. Based on tests carried out with prediction errors measured using the Mean Average Percentage Error (MAPE), it was found that the N-BEATS model outperformed the LSTM model with a MAPE value of 1.05 percent. In conclusion, this research shows the use of a new method of deep learning algorithms to predict stock prices, which contributes to facilitating stock buying and selling decisions by investor