Search results

With the widespread of e‐services, provided by different organizations at the internal intranet level, the business extranet level, and the public internet level, compliance with international information security management standards is becoming of increasing importance for establishing a common and safe environment for such services. The purpose of this paper is to examine the development of a mathematical model that enables the investigation of compliance of organizations with the widely acknowledged international information security management standard ISO 17799‐2005.

The model is based on the strategy, technology, organization, people and environment – STOPE – framework that provides an integrated well‐structured view of the various factors involved. The paper addresses the use of the model for practical investigations; it describes a practical example illustrating possible practical results.

The results show the strengths and the weaknesses of compliance, with the standard, at different levels: from the level of the measures associated with each of the “131” standard protection controls, up to the level of the STOPE domains.

The paper addresses the use of a mathematical model for practical investigations of compliance with the international information security management standard.

This study aims to reveal the knowledge structure of social media influencer marketing literature by performing science mapping analysis through a state-of-the-art bibliometric approach to determine the current and future trends. Social media influencer marketing is one of the most effective approaches to presenting a brand and offering value to consumers via social media.

This study evaluates the knowledge structure to uncover the emerging trends and future predictions in social media influencer marketing through bibliographic coupling and co-word analysis. In total, 917 journal publications were retrieved from the Web of Science database and analyzed using VOSviewer software.

The central theme in social media influencer marketing reflects digital engagement between influencers and followers and communication between influencers and followers. The theoretical and managerial implications are discussed.

This study unleashes the knowledge structure according to the fundamental literature of social media influencer marketing and the underlying themes related to the phenomenon.

This paper aims to investigate seven prominent translations of the Qur'anic verb-noun collocations into English (Pickthall, 1930; AL-Hilali and Khan, 1977; Ali, 1934; Arberry, 1955; Shakir, 1999; Sarwar, 1981; Saheeh International, 1997) to unfold their renditions of the style and meaning of such Qur'anic verb-noun collocation into English.

The study follows a corpus-based research in a sense that the study is conducted on seven translations of the Noble Qur'an that have been taken form The Qur'anic Arabic Corpus, using linguistic and exegetical analyses. Based on Reiss’ model of text analysis (2000), the author analyses the intralinguistic and extralinguistic features of the Qur'anic verb-noun collocations.

Findings reveal that linguistic and exegetical analyses are perquisites for adequate rendition, which prevent deviation in meaning and translation loss. It is also found that Qur'anic collocations use unique literary techniques and devices, which hinder their natural and adequate renditions into English.

The novelty of this study lies in studying the architectural design of the Qur'anic verb-noun collocations in terms of the unique selection of words and style. Such unique architectural design of such collocations creates monumental hindrances in their rendition into other languages, which have not been given due attention in translation studies.

In the context of information retrieval, text genre is as important as its content, and knowledge of the text genre enhances the search engine features by providing customized retrieval. The purpose of this study is to explore and evaluate the use of stylometric analysis, a quantitative analysis for the linguistics features of text, to support the task of automated text genre detection for Classical Arabic text.

Unsupervised clustering and supervised classification were applied on the King Saud University Corpus of Classical Arabic texts (KSUCCA) using the most frequent words in the corpus (MFWs) as stylometric features. Four popular distance measures established in stylometric research are evaluated for the genre detection task.

The results of the experiments show that stylometry-based genre clustering and classification align well with human-defined genre. The evidence suggests that genre style signals exist for Classical Arabic and can be used to support the task of automated genre detection.

This work targets the task of genre detection in Classical Arabic text using stylometric features, an approach that has only been previously applied to Arabic authorship attribution. The study also provides a comparison of four distance measures used in stylomtreic analysis on the KSUCCA, a corpus with over 50 million words of Classical Arabic using clustering and classification.

This paper revolves around the usage of data analytics in the Qur’an and Hadith through a new text mining technique to answer the main research question of whether the activities and the data flows of the Murabaha financing contract is compatible with Sharia law. The purpose of this paper is to provide a thorough and comprehensive database that will be used to examine existing practices in Islamic banks’ and improve compliancy with Islamic financial law (Sharia).

To design a Sharia-compliant Murabaha business process originated on text mining, the authors start by identifying the factors deemed necessary in their text mining techniques of both texts; using a four-step strategy to analyze those text mining analytics; then, they list the three basic approaches in text mining used for new knowledge discovery in databases: the co-occurrence approach based on the recursive co-occurrence algorithm; the machine learning or statistical-based; and the knowledge-based. They identify any variation and association between the Murabaha business processes produced using text mining against the one developed through data collection.

The main finding attained in this paper is to confirm the compatibility of all activities and the data flows in the Murabaha financing contract produced using data analytics of the Quran and Hadith texts against the Murabaha business process that was developed based on data collection. Another key finding is revealing some shortcomings regarding Islamic banks business process compliance with Sharia law.

Given Murabaha as the most popular mode of Islamic financing with more than 75% in total transactions, this research has managed to touch-base on an area that is interesting to the vast majority of those dealing with Islamic finance instruments. By reaching findings that could improve the existing Islamic Murabaha business process and concluding on Sharia compliance of the existing Murabaha business process, this research is quite relevant and could be used in practice as well as in influencing public policy. In fact, Islamic Sharia law experts, Islamic finance professionals and Islamic banks may find the results of this study very useful in improving at least one aspect of the Islamic finance transactions.

By using a novel, fresh text mining methods built on recursive occurrence of synonym words from the Qur’an and Hadith to enrich Islamic finance, this research study can claim to have been the first of its kind in using machine learning to mine the Quran, Hadith and in extracting valuable knowledge to support and consolidate the Islamic financial business processes and make them more compliant with the i.