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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.

This paper aims to equip professionals and researchers in the fields of advertising, branding, public relations, marketing communications, social media analytics and marketing with a simple, effective and dynamic means of evaluating consumer behavioural sentiments and engagement through Arabic language and script, in vivo.

Using quantitative and qualitative situational linguistic analyses of Classical Arabic, found in Quranic and religious texts scripts; Modern Standard Arabic, which is commonly used in formal Arabic channels; and dialectical Arabic, which varies hugely from one Arabic country to another: this study analyses rich marketing and consumer messages (tweets) – as a basis for developing an Arabic language social media methodological tool.

Despite the popularity of Arabic language communication on social media platforms across geographies, currently, comprehensive language processing toolkits for analysing Arabic social media conversations have limitations and require further development. Furthermore, due to its unique morphology, developing text understanding capabilities specific to the Arabic language poses challenges.

This study demonstrates the application and effectiveness of the proposed methodology on a random sample of Twitter data from Arabic-speaking regions. Furthermore, as Arabic is the language of Islam, the study is of particular importance to Islamic and Muslim geographies, markets and marketing.

The findings suggest that the proposed methodology has a wider potential beyond the data set and health-care sector analysed, and therefore, can be applied to further markets, social media platforms and consumer segments.

To remedy these gaps, this study presents a new methodology and analytical approach to investigating Arabic language social media conversations, which brings together a multidisciplinary knowledge of technology, data science and marketing communications.

There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches.

The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class.

The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy.

The results facilitate halal restaurateurs in identifying customer review behavior.

Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews.

This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

I. Introduction It is no secret that Islamic countries have entered a new epoch in which serious questions regarding the nature of their economic institutions have taken on renewed urgency. During the past few years, elan debates have taken place, both nationally and internationally, among specialists in economic sciences, specialists in traditional Islamic disciplines, and intellectual exponents of various Islamic political movements. Inherently complex, the discourse, which appears in academic and semi‐ academic publications as well as the popular press, is made distinctly more complex as it often takes place in an extremely volatile environment, which has been shaped by severe economic crises, political turmoil, and social unrest.

This study aims to propose an Ethico-Religious green supply chain management (GSCM) view grounded in Islamic teachings design to govern human beings working in the industries.

This study adopts a qualitative approach that used the semi-structured-interview method as a research instrument. Three experts researching various aspects of Islam were consulted to identify Islamic teachings related to green supply chain practices.

This study identifies several verses of the Holy Quran and the hadiths (a collection of traditions containing the sayings of the Prophet Muhammad) related to GSCM. It proposes these teachings as pro-environmental ethical codes.

The proposed model has not been tested empirically. Future studies can consider an empirical test to find the possible effect of ethical codes on human behavior.

This study contributes to the literature in several ways. First, it presents an Ethico-Religious GSCM view that is new in the literature. Second, it extends the key premise of the natural resource-based view theory for achieving superior competitive advantage. Finally, it proposes a human governance approach useful for achieving firms’ environmental goals. This paper is helpful for managers who will find a human governance model supported by the Ethico-Religious GSCM view.

Zakat during the COVID-19 outbreak has played a vital role and has been significantly discussed in the virtual environment. Such information about zakat in the virtual world creates unstructured data, which contains important information and knowledge. This paper aims to discover knowledge related to zakat administration during the pandemic from the information in a virtual environment. Furthermore, the discussion is contextualised to the socio-economic debates.

This is a qualitative study operated via text mining to discover knowledge of zakat administration during the COVID-19 pandemic. The National Board of Zakat Republic of Indonesia (BAZNAS RI) is selected for a single case study. This paper samples BAZNAS RI’s situation report on COVID-19 from its virtual website. The data consists of 40 digital pages containing 19,812 characters, 3,004 words and 3,003 white spaces. The text mining analytical steps are performed via RStudio. The following R packages, networkD3, igraph, ggraph and ggplot2 are used to run the Latent Dirichlet Allocation (LDA) for topic modelling.

The machine learning analysis via RStudio results in the 16 topics associated with the 3 primary topics (i.e. Education, Sadaqah and Health Services). The topic modelling discovers knowledge about BAZNAS RI’s assistance for COVID-19 relief, which may help the readers understand zakat administration in times of the pandemic from BAZNAS RI’s virtual website. This finding may draw the theory of socio-economic zakat, which explains that zakat as a religious obligation plays a critical role in shaping a Muslim community's social and economic processes, notably during the unprecedented times of COVID-19.

This study uses data from a single zakat institution. Thus, the generalisation of the finding is limited to the sampled institution.

This research is both theoretically and practically important for academics and industry professionals. This paper contributes to the novelty in performing text mining via R in gaining knowledge about the recent zakat administration from a virtual website. The finding of this study (i.e. the topic modelling) is practically essential for zakat stakeholders to understand the contribution of zakat in managing the COVID-19 impacts.

This work derives a theory of “socio-economic zakat” that explains the importance of a zakat institution in activating zakat for managing socio-economic issues during the pandemic. Thus, paying zakat to an authorised institution may actualise more maslahah (public interest) compared to paying it directly to the asnaf (zakat beneficiaries) without any measurement

This study is among the pioneers in gaining knowledge from Indonesia’s zakat management during the COVID-19 outbreak via text mining. The authors’ way of analysing data from the virtual website using RStudio can advance Islamic economics literature.

The purpose of this research study is to extract and identify named entities from Hadith literature. Named entity recognition (NER) refers to the identification of the named entities in a computer readable text having an annotation of categorization tags for information extraction. NER is an active research area in information management and information retrieval systems. NER serves as a baseline for machines to understand the context of a given content and helps in knowledge extraction. Although NER is considered as a solved task in major languages such as English, in languages such as Urdu, NER is still a challenging task. Moreover, NER depends on the language and domain of study; thus, it is gaining the attention of researchers in different domains.

This paper proposes a knowledge extraction framework using finite-state transducers (FSTs) – KEFST – to extract the named entities. KEFST consists of five steps: content extraction, tokenization, part of speech tagging, multi-word detection and NER. An extensive empirical analysis using the data corpus of Urdu translation of Sahih Al-Bukhari, a widely known hadith book, reveals that the proposed method effectively recognizes the entities to obtain better results.

The significant performance in terms of f-measure, precision and recall validates that the proposed model outperforms the existing methods for NER in the relevant literature.

This research is novel in this regard that no previous work is proposed in the Urdu language to extract named entities using FSTs and no previous work is proposed for Urdu hadith data NER.

This study aims to examine the factors that influence the increase in purchase intention toward green batik products with religiosity as an intervening variable.

The study used a survey method that involved a sample size of 185 participants selected through purposive and accidental sampling techniques. The analysis was conducted by using IBM SPSS AMOS 21 software. The collected data were subjected to path analysis using multiple linear regression models.

The result indicated that religiosity plays a mediating role in the association between factors and the intention to purchase green product (GPd) of the Indonesian natural dye batik product. This finding is in accordance with the construction of theory of planned behavior in understanding consumer purchase intentions. GPd, green brand and green price exhibited a positive correlation with green purchase intention (GPI). Interestingly, the price was found to no longer serve as the primary factor in GPI.

The analysis would have been more compelling if it had used a mixed-method approach by introducing the variables of customer satisfaction and promotion.

This research postulates that increased prices are no longer a deterrent to the purchase of GPd. Instead, consumer consciousness regarding GPd plays a pivotal role in driving GPI. GPd have revolutionized individuals’ consumption patterns to contribute to environmental preservation. The use of green batik products is seen as advantageous in helping mitigate environmental degradation.

The present research assesses the impact of religiosity, as an intervening variable, on the augmentation of GPI by gauging its significance in enhancing ecological consciousness and moral values.

This study aims at two objectives, i.e. first, to identify the core elements of the Islamic fintech ecosystem, and second, to use the identified core elements to analyse the development of such an ecosystem in Indonesia.

This work combines data analytics of text mining with qualitative analysis of human intelligence in two steps. First, knowledge discovery of the Islamic fintech ecosystem’s core elements using a sample of eight academic articles totalling 102 pages and 75,082 words. Second, using the identified core elements from step one to explore such ecosystem development in Indonesia. This stage employs a sample of 11 documents totalling 371 pages and 143,032 words from cyberspace.

The core elements of the Islamic fintech ecosystem identified are financial customers, fintech startups, government, technology developers, traditional financial institutions and fatwa (Islamic legal opinion). Furthermore, the development of the Islamic fintech ecosystem in Indonesia is examined under these identified core elements, providing critical insights into the Islamic fintech ecosystem currently established in the country's industry.

This study primarily used semi-structured data from cyberspace. Traditional approaches to qualitative data collection, e.g. focused group discussions and interviews, may be beneficial for future studies in addressing the Islamic fintech ecosystem issues.

Academia worldwide may benefit from this work in incorporating knowledge of Islamic fintech ecosystem’s core elements into Islamic finance literature. Specifically, fintech stakeholders in Indonesia may be advantaged to understand how far the Islamic fintech ecosystem has grown in the country.

The rise of unethical fintech peer-to-peer lending shows social problems in Indonesia’s fintech industry. The finding derives social implications that elucidate the current state of the country’s Islamic fintech ecosystem.

Using a kind of big data (i.e. semi-structured text data) from cyberspace and applying steps of text mining combined with qualitative analysis, may contribute to the creation of novelties for qualitative research on financial issues.

Ijara is one of the exchange contracts that is based on selling benefits and services. There are several forms of Ijara: Ijara specified objects, specified work and hiring a private or joint employee. The target of all these is obtaining a benefit. For instance, in the specified Ijara, the lessee desires to obtain a benefit from a specified person in particular, and in the forward Ijara, the purpose is obtaining the benefit in accordance with specified specifications. Indeed, Islamic banks can obtain these benefits through Ijara them from their providers and re-Ijara them to customers upon request. Hence, the purpose of this paper is to determine the Shariah ruling of the Islamic law of Ijara persons' benefits. Moreover, it aims to determine to what extent is it possible to consider Ijara and selling the benefits prescribed in disclosure and as a financing instrument in Islamic banks aiming at providing citizens with services, mainly in the virtual era.

The current research is framed within the descriptive and analytical research through illustrating the nature of Ijara, its conditions, pillars, evidence of its legitimacy and its kinds as well. The methodology used attributes the Quranic verses to their contexts in the Holy Quran and authenticates the noble hadiths of the Prophet.

The current research concludes that it is legally permissible to lease these benefits and re-lease them. However, in the specified Ijara, the hired person must perform the work himself and it is not permissible to be replaced by a third party, whereas in the forward Ijara, the contracted work is permitted to be carried out by a third party as long as it complies with the specifications required and contracted.

This study contributes to the current literature by providing a guide to Islamic banking regulators to promote Ijara in Islamic banking.