Intelligent Personal Assistant (IPA) adoption has ushered in a technological revolution in today's society. There are several well-known IPA agents, like Google Assistant and Cortana, which were both created by Microsoft. The benefits of IPAs' speech comprehension capabilities have resulted in an increase in IPA users year after year. The goal of the study is to look at how an IPA's speech comprehension capability could be improved in the future, making it easier for IPA users to handle their everyday routines. This study will employ a survey and an unstructured interview to learn about users' expectations for an IPA's speech comprehension capabilities and how IPA adaptation affects their daily routine management. To fulfill the research's goal, an IPA with a daily routine management system will be suggested using the methodology and project design that have been determined.

As a result of the advanced technological development, the businesses operations have been involved within modern marketing activities to promote their products and services. This study highlights the role of key various digital marketing channels. The study addressed the role of digital marketing channels by using some applications (such as online advertising, social media, emails marketing, and websites search engine) to be examined on the consumer buying decisions with a mediating effect of the eWOM. A quantitative research approach was used to achieve study objectives and examine the hypothesized research framework by using a customized survey questionnaire in the retailing sector. A total of 255 valid responses were considered for further analysis by using SmartPLS3 software to conduct the key analyses. The results revealed the significant effect and role of all digital marketing channels on the consumers buying decisions, with the moderated role of the eWOM on the effect of digital marketing channels on consumer buying decisions. The study offers additional contributions to the existing literature and supports the assumptions to provide a better grasp about the study topic.

The determinants of bring-your-own-device (BYOD) use protection intentions affecting BYOD usage protection behaviors were examined in this study. The determinants of employees’ behavioral intention to use and their actual protection behavior in protecting their devices BYOD environment were identified. Jordanian residents aged 18 and above, with mobile learning behavioral usage awareness, made up the study population. A survey questionnaire was used to obtain the data, while the proposed research model was tested using structural equation modeling (SEM). The results show positive impact of BYOD usage protection intention on mobile learning behavioral usage, while attitude showed insignificant impact on BODY usage protection intention. Subjective norms significantly affected BYOD usage protection intention, while information security awareness showed insignificant impact on BYOD usage protection intention.

Recent research on Social Media Marketing Activities (SMMA) in the airline industry emphasizes on areas of brand equity, but some research has looked into the function of brand equity in determining the impact of SMMAs on customers. This study aims to find out how service users and followers of Airline's Instagram social media profiles respond to SMMA, brand awareness, brand image, e-WOM and commitment. The data collection method in this study was done by distributing questionnaires to respondents as many as 386 service users and followers of the airline on Instagram social media. This study used Partial Least Square Structural Equation Model (PLS-SEM) to analyze data. Based on hypothesis testing, this study shows that SMMA has a significant positive effect on brand awareness and brand image. Furthermore, this study also reveals that brand awareness and brand image have significant positive effects on e-WOM and Batik Air airline commitment.

This study explores and analyzes students’ difficulties in learning an ERP system to help design more appropriate teaching methods and materials. Global enterprises have widely used ERP systems to manage their operations effectively and efficiently. Hence, many business schools have offered courses on ERP systems to sharpen ERP skills for their students. To help design more appropriate teaching methods and materials for ERP learning, one must know students’ difficulties in understanding. This study analyzes students’ difficulties in learning the Oracle E-Business Suite ERP system through interviews and qualitative analysis. As a result, this study identifies five categories of problems in the various areas of the Revised Bloom’s Taxonomy. Their relevant educational objectives can guide the redesign of ERP teaching methods and materials. One of the difficulties belongs to the area of Remember Factual Knowledge. The rest of them are in Understand, Remember, Apply, and Analysis of Procedural Knowledge. Lastly, this study provides some implications for teaching ERP.

This study aims to determine and analyze the influence of private university digital leadership and information system success models (system quality, information quality, service quality) on the satisfaction of e-learning users of agricultural students. This research method is a quantitative survey, and the number of research samples is 323 agricultural students who were selected by a random sampling system. The sampling technique used is simple random sampling. The analytical method used is SEM with the help of SmartPLS 3.0 software. The results show that the role of digital leadership had a significant positive effect on the three variables of the information system success model. Likewise, system quality, information quality, and service quality have a significant positive and significant effect on user satisfaction of e-learning systems. This finding can increase the exploration ability of agricultural students as users to obtain various agricultural information. This finding confirms previous studies which state that usage has a significant effect on user satisfaction. Suggestions for further research, in this study only involves a single student perspective. Future research is recommended to use the perspective of the organization/institution (e-learning system management unit), lecturers, and university employees and agricultural stakeholders.

In online shopping enterprises, AI technology has been widely used to provide accurate and fast personalized consumer services. This research demonstrates the use of AI technology in the e-commerce business, specifically online enterprises, to determine different effects. The study was conducted in Jordan and involved about 230 participants. The study evaluated different impacts of AI, such as e-payment and stimulating consumers' sentiments. The study used the Stimulus–Organism–Response model (SOR) empirical model, which states that the examination of human processes differs from that of the machine assessment. The model classified the AI technology experienced by the customers' when they visit online to do purchasing. Online purchasing behaviors can be influenced by insight, accuracy, and interaction experience. Also, the perceived value was used as a mediating variable from the prospects of perceived hedonic and utility value. The research integrated empirical research models such as SEM and SPSS to analyze the data on the effects of three-dimension. The results indicated that the AI technology accuracy, interactive experience, and insight significantly affected customers' perceived hedonic and utilitarian values.

Recent studies on e-feedback have answered many questions concerning the effectiveness of e-feedback in educational and non-educational sectors. They stated clearly that e-feedback is efficient and practical. From both teachers’ and students’ perspectives, e-feedback has influenced their learning and teaching environment effectively. It is a good technique to personalize the learning strategies. Based on the previous assumption, this study aims at exploring the effectiveness of e-feedback in an educational environment taking into consideration the TAM model and the external factors of trustworthiness and enjoyment. The data is collected by an online questionnaire that was distributed among a group of students. Facilitating communication among teachers and students. It helps in replacing the traditional feedback and assess the learning environment during the pandemic periods. The two constructs of perceived ease of use and perceived usefulness affect positively the intention to use the e-feedback and initiates this type of feedback as a prominent procedure to be used frequently in the learning environment. In addition, the perceived enjoyment and perceived trustworthiness increase the chance of using e-feedback. Recently, e-feedback is highly dominant among online platform users.

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%.

This analysis integrates the “technology acceptance model (TAM)” with the “use of gratifications theory (U&G)” to develop an embedded model that predicts the use and satisfaction of emotional icons called stickers through WhatsApp. The explanation for combining these two theories is that U&G offers accurate information and a thorough knowledge of use, while TAM theory has been firmly established in several technical implementations. A newly developed hybrid analysis procedure has been applied within this research. Using an artificial neural network (ANN), and the structural equation model (SEM) have been combined. The research also uses the importance-performance map analysis (IPMA) to present each factor’s performance as well as importance. The ANN and IPMA research have both indicated that for sticker use intention, a highly essential predictor is Socialization. An online questionnaire survey was developed to assess the recommended model. The intention to use stickers was significantly affected by “Socialization, Self Presentation, Enjoyment, Novelty, Unique Function, Perceived Ease of Use, and Perceived Usefulness”. The research's main achievement is the convergence of two separate theories into a single conceptualization to accurately calculate the TAM components when it comes to the usage of stickers in WhatsApp. Theoretically, the recommended model provides enough insight for aspects which affect the intention to use stickers with relevance to the socialization’s factors considering interpersonal aspects. Practically, the higher education decision-makers along with professionals would extract variables that are important as compared to others and policies would be developed accordingly. The deep ANN model competence has been analyzed within the research to decide upon the non-linear associations between variables of the theoretical model, methodologically.