This study proposes a Fusion, Feature-Level, Face Recognition System (FFLFRS) that is based on the Multi-Resolution, Singular Value Decomposition (MSVD) fusion technique. Face recognition in the FFLFRS is achieved via four processes: face detection, feature extraction, feature fusion, and face classification. In this system, the most significant face features (that is, the eyes, nose, and mouth) are first detected. Then, local and global features are extracted by the Local Binary Pattern (LBP) and Principal Component Analysis (PCA) extraction approaches. Afterwards, the extracted features are fused by the MSVD method and classified by the Artificial Neural Network (ANN). The proposed FFLFRS was verified on 10,000 face images drawn from the face images database of the Olivetti Research Laboratory (ORL). Face recognition performance of this system was contrasted with levels of performance of three state of the art, fusion-level, face recognition systems (FRSs) depending on the Frequency Partition (FP), Laplacian Pyramid (LP), and Covariance Intersection (CI) fusion methods. Ten-thousand images were employed to test the proposed model and assess its performance, which was evaluated in terms of changes in pose, illumination, and expression, besides low resolution and presence of occlusion. The face recognition results of the proposed FFLFRS are encouraging. This system proved to be effective in dealing with images having challenges to face recognition and it could achieve a recognition accuracy as high as 97.78%.

Social media influencers have proved to be a major influence on customers’ purchasing decisions, especially with the increased usage of social media platforms. Social media influencers provide many opportunities for companies to increase their customer base and sales, and to enhance the attitude towards a brand. Despite the advantages of utilizing social media influencers, there are factors that social influencers need to take into consideration in order to engage customers and influence their decisions to purchase. The purpose of this study is to examine the factors that influence customers’ intention to purchase based on social media influencers. An online questionnaire was used to collect data from 439 Instagram platform users. A partial least square-SEM (PLS-SEM) approach was used to analyze and examine the proposed model. The results indicate that all constructs, namely Information Quality (IQ), Trustworthiness (TRU), Attractiveness (ATT), Meaning Transfer (TRA) and Expertise (EXP) significantly influence customers’ purchase intentions. This finding could provide insights for companies’ decision-makers when it comes to promoting their brands and increasing their sales. In addition, it could provide insights for social media influencers in terms of recognizing the important factors that encourage customers to engage and purchase.

Recently, the credibility of digital reviews has played an essential role in the shopper's buying behaviors and decisions. Since there is a dearth of experimental research about the shoppers' credibility evaluation regarding digital reviews, this study aimed to investigate the factors that affect digital review credibility and its influence on buying choices among Jordanian consumers. With the help of elaboration likelihood theory, a research model has been established that experimentally test it through structural equation modelling from the data gathered from 246 users of the digital review website Amazon. The study's findings suggest factors that consist of the argument quality, like accuracy, completeness and quantity of digital reviews, and the peripheral cues, such as reviewer expertise, rating of goods or services, and website reputation. However, both significantly influence digital review credibility. Thus, they positively impact the buying decisions of shoppers.