Purpose – The purpose of this paper is to report on research that evaluates the perceived willingness of potential bidders to adopt public e-procurement for the supply of goods and services to the government of Nepal. The authors have identified anti-corruption attributes through an extensive literature review and developed a theoretical model representing the impact of four latent variables, monopoly of power, information asymmetry, trust and transparency and accountability on the dependent variable, the intent-to-adopt e-procurement (ITA). Design/methodology/approach – Data for this research were obtained by the use of a questionnaire survey of bidders who were officially registered with the Government of Nepal. As part of the fieldwork for this research, the first author collected the perceptions of 220 bidders regarding the potential of public e-procurement to reduce corruption in public procurement processes. Findings – The findings suggest that a high level of the ITA has a positive and significant relationship with the independent variables that might inform the developed and emerging countries to make a decision to adoption of e-procurement to combat corruption in public procurement. Research limitations/implications – This study has some limitations that should be taken into consideration. The evaluation of anti-corruption factors, as they affect the willingness of users to adopt e-procurement on the bidder’s perception research model is relatively new to e-procurement research. A limitation of the research was that it gathered and analyzed data from a single country with a limited number of respondents. More research is needed to identify the anti-corruption factors of e-procurement in reducing corruption, and also need strong empirical test to valid the factors that influence the adoption of e-procurement. Originality/value – This study aimed to contribute to the academic scholar, government agencies and public procurement practitioner in enhancing their understanding of the perceived anti-corruption factors of public e-procurement to reduce corruption.

The long short-term memory (LSTM) and gated recurrent unit (GRU) models are popular deep-learning architectures for stock market forecasting. Various studies have speculated that incorporating financial news sentiment in forecasting could produce a better performance than using stock features alone. This study carried a normalized comparison on the performances of LSTM and GRU for stock market forecasting under the same conditions and objectively assessed the significance of incorporating the financial news sentiments in stock market forecasting. This comparative study is conducted on the cooperative deep-learning architecture proposed by us. Our experiments show that: (1) both LSTM and GRU are circumstantial in stock forecasting if only the stock market features are used; (2) the performance of LSTM and GRU for stock price forecasting can be significantly improved by incorporating the financial news sentiments with the stock features as the input; (3) both the LSTM-News and GRU-News models are able to produce better forecasting in stock price equally; (4) the cooperative deep-learning architecture proposed in this study could be modified as an expert system incorporating both the LSTM-News and GRU-News models to recommend the best possible forecasting whichever model can produce dynamically.

One of the significant potential benefits of e-procurement technology is reducing opportunities for corruption in public procurement processes. The authors identified anticorruption capabilities of e-procurement through an extensive literature review and a theoretical model representing the impact of three latent variables: monopoly of power, information asymmetry, and transparency and accountability upon the dependent variable, the intent-to-adopt e-procurement. This research was guided by the PrincipalAgent theory and collected the perceptions of 46 government officers of the potential of public e-procurement to reduce corruption in public procurement processes. Results were analysed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings suggest that the intent-to-adopt e-procurement has a positive and significant relationship with the independent variables that might inform developing countries in strategies to combat corruption in public procurement.

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Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a large number of training parameters. There is a necessity to explore lightweight deep learning models without compromising the classification accuracy. In this paper, we propose a lightweight deep learning model using the pre-trained MobileNetV2 model and attention module. First, the convolution features are extracted to capture the high-level object-based information. Second, an attention module is used to capture the interesting semantic information. The convolution and attention modules are then combined together to fuse both the high-level object-based information and the interesting semantic information, which is followed by the fully connected layers and the softmax layer. Evaluation of our proposed method, which leverages transfer learning approach, on three public fruit-related benchmark datasets shows that our proposed method outperforms the four latest deep learning methods with a smaller number of trainable parameters and a superior classification accuracy. Our model has a great potential to be adopted by industries closely related to the fruit growing and retailing or processing chain for automatic fruit identification and classifications in the future.