An investigation into agile learning processes and knowledge sharing practices to prevent identity theft in the online retail organisations

Lack of individual awareness of knowledge sharing practices to prevent identity theft is a significant issue for online retail organisations (OROs). Agile learning processes and sharing of knowledge is essential, but the lack of relevant training inhibits these processes within the online industry. This study identifies the inhibiting factors in the agile learning and knowledge sharing process with recommendations for best practice for organisations and staff to effectively share knowledge on identity theft prevention. Three qualitative case studies were undertaken in OROs in the United Kingdom. Data was collected using semi-structured interviews, internal documents and related external material. The data were analysed using a thematic analysis method. The findings identified that individual staff members within OROs from the information security and fraud prevention departments often share their knowledge as a community. However, there is no formal knowledge sharing process or any related training facilitating this exchange. There is a need for agile learning environment in OROs of the United Kingdom. The study offers both theoretical and practical contributions to the extant literature of agile learning of knowledge sharing to prevent identity theft in OROs. Existing learning opportunities are not being used to enhance the knowledge of individuals, and OROs need to increase the skills and trust of their staff to share knowledge efficiently. This study identifies the systemic weaknesses inherent in the process of knowledge sharing and existing training provision within OROs. It provides ORO managers with practical guidelines in facilitating trust between individuals and developing appropriate training systems to educate staff on sharing organisational knowledge. This study contributes by extending the knowledge sharing framework proposed by Chong et al. (2011), for enhanced individual knowledge sharing processes to prevent identity theft within OROs. It also identifies OROs weaknesses in knowledge sharing learning processes for theft prevention and offers prevention guidelines and recommendations for developing effective agile learning environments

Adoption of social commerce: An empirical analysis in the context of Pakistan

Businesses are utilizing social media extensively to increase the opportunities for traditional and online businesses. However, the driving factors affecting both consumers’ and proprietors’ behavior are not well investigated so far in the context of developing countries like Pakistan. Persuading extended social commerce technology acceptance model (TAM) theory and theory of reasoned action (TRA), this paper investigates behavioral intention to use social commerce. The quantitative exploratory research approach was applied to accomplish the explicit research aims and objectives using the survey data from 2019 respondent consumers/firms active in six metropolitan cities of Pakistan. The study findings indicate a significant positive influence on behavioral intention to adopt social commerce is appraised with a greater level of perceived ease of use, usefulness, social influence, and risk factors. In the conclusion, this paper discusses research implications and utilization of the most recent ICT advancements helping business people and investors develop new commerce procedures and processes

A new hybrid algorithm for intelligent detection of sudden decline syndrome of date palm disease

Abstract Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy

A new hybrid algorithm for intelligent detection of sudden decline syndrome of date palm disease

Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy