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The paper aims to describe dynamic presentation generator that presents the same content in different ways through different media object and presentation layouts. The different media presentation will then display to student via Learning Management Systems (LMS). The paper also presents SCORM Compliant Learning Objects and Conceptual Model Instance (CMI). The prototype is then evaluated to demonstrate the performance of the students where the CMI data are collected for each student. The data collected include media preference, test score and time spent to study the Computer Programming subjects. The results show that with more media object used, students spent more time on the Webpages. However, the results also showed that using more media object may not produce better results in the assessment.

This paper describes the architecture of using the Mobile Agent and ACID (Atomicity, Consistency, Isolation and Durability) in Mobile e-Learning (mLearning). In the coming years, there will be an incremental amount of mobile learning experiments for the purpose of implementing mobile ICTs into mainstream education. In this article, the Replication Agent and Snapshot Agent architecture is proposed as an effective way to overcome the problem of heavy loading on the limited bandwidth used in wireless transmission for mobile learning environment merge replication process. The implementation of mobile agents as middleware for mLearning environment is to provide database replication between learners and off-site database e.g., Knowledge Management Centre's (KMC) databases using handheld devices. The approach using the combination of both these agents plays an important role in wireless transmission. It provides an intelligent solution to the limitation of the wireless bandwidth by lowering the bandwidth taken up during the bursts of mobile transactions. In this proposed technique, a mLearning database is determined as a Snapshot publisher where a compact edition of files and information is generated at the snapshot location which resides on the central database server. Learners may retrieve quick bursts of information through mobile applications wirelessly. The Replication Agent offers the flexibility to move from one site (user) to another (central database) for essential data synchronization. In order to provide learners with appropriate courses, a prototype on mLearning platform supporting three-layered structure and device adapting was put forward. The architecture of the platform discussed in this study as the mobile agent approach would facilitate more widespread use of mLearning, including in courses discussions between learners and academicians.

This paper focuses on the integration of the learning objects and knowledge map as the learning sequence suggestion in the mobile learning environment and explains the technologies involved, the applications and the issues of usability, accessibility, evaluation and effectiveness. Mobile learning has open up new path for learning support and opportunities to reach wider audience (learner) for education. This research focuses on using the knowledge map to store the characteristics of each learning object via concept schemas and represent the corresponding learning accessibility in the mobile learning environment. The proposed architecture provides a medium for the learning accessibility of learners through mobile applications and wireless portable devices such as smart phones, PDAs and tablet PCs. The approach using the combination of "touch" and "observe" spatial learning objects provides an intelligent solution to creating, sharing and improving the efficiency of mobile learning. The proposed mobile learning environment architecture consists of knowledge map components mainly, navigation, concept schemas and learning object path. By using these knowledge structures, this study may enhance and enrich the concept and activity of adaptive learning in different individuals and communities. The spatial knowledge map constructed was useful in identifying the characteristics of the learning objects (e.g., learning object 1: lesson with navigating sentences, learning object 2: lesson with navigating sentence and code explanation, etc) and automatically matches the most appropriate learning contentand path suitable for learners. The architecture of the platform discussed in this study using the learning objects approach and knowledge map would facilitate a more widespread use of mobile learning, including courses or modules delivery of individualised learning path and learning style analysis.

Customer lifetime value (CLV) has received increasing attention in database marketing. Enterprises can retain valuable customers by the correct prediction of valuable customers. In the literature, many data mining and machine learning techniques have been applied to develop CLV models. Specifically, hybrid techniques have shown their superiorities over single techniques. However, it is unknown which hybrid model can perform the best in customer value prediction. Therefore, the purpose of this paper is to compares two types of commonly‐used hybrid models by classification+classification and clustering+classification hybrid approaches, respectively, in terms of customer value prediction.

To construct a hybrid model, multiple techniques are usually combined in a two‐stage manner, in which the first stage is based on either clustering or classification techniques, which can be used to pre‐process the data. Then, the output of the first stage (i.e. the processed data) is used to construct the second stage classifier as the prediction model. Specifically, decision trees, logistic regression, and neural networks are used as the classification techniques and k‐means and self‐organizing maps for the clustering techniques to construct six different hybrid models.

The experimental results over a real case dataset show that the classification+classification hybrid approach performs the best. In particular, combining two‐stage of decision trees provides the highest rate of accuracy (99.73 percent) and lowest rate of Type I/II errors (0.22 percent/0.43 percent).

The contribution of this paper is to demonstrate that hybrid machine learning techniques perform better than single ones. In addition, this paper allows us to find out which hybrid technique performs best in terms of CLV prediction.

Despite the availability of several published reviews on the adoption of blockchain (BC) in supply chain (SC), at present, the literature lacks a comprehensive review incorporating the antecedents and consequences of BC adoption. Moreover, the complex adoption of BC in SC, explained with the mediating and moderating relationships, is not fully consolidated. Thus, the aim of this study was to conduct a systematic literature review (SLR) on BC technology adoption (BCTA) in SC by integrating its antecedents and consequences.

Keyword searches were performed in multiple databases resulting 382 articles for evaluation and verification. After careful screening with respect to the purpose of the study and systematic processing of the retrieved articles, a total of 211 peer-reviewed articles were included in this study for review.

Various technological, organisational, individual, social, environmental, operational and economic factors were found as the antecedents of BCTA in SC. In addition, numerous applications of BC Technology (BCT) were identified, including asset management, identity management, transaction management, data management and operations management. Finally, the consequences of BCTA were categorised as operational, risk management, economic and sustainability outcomes.

This study can assist relevant decision-makers in managing the factors influencing BCTA and the potential uses of the technology to enhance SC performance.

By integrating the antecedents, applications and consequences of BCTA in SC, including the mediators and moderators, an integrated framework was developed that can potentially assist researchers to develop theoretical models. Further, the results of this SLR provide future directions for studying BCTA in supply chain management (SCM).