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Zakat (Islamic almsgiving) plays a considerable role in dealing with the socioeconomic issues in times of COVID-19 pandemic, and such roles have been widely discussed in virtual events. This paper aims to discover knowledge of the current global zakat administration from virtual events of zakat (e.g. webinars) on YouTube and Zoom via text mining approach.

The authors purposefully sampled 12 experts from four different virtual zakat events on YouTube and Zoom. The automated text transcription software is used to pull the information from the sampled videos into text documents. A qualitative analysis is operated using text mining approach via machine learning tool (i.e. Orange Data Mining). Four research questions are developed under the Word Cloud visualisation, hierarchal clustering, topic modelling and graph and network theory.

The machine learning identifies the most important words, the relationship between the experts and their top words and discovers hidden themes from the sample. This finding is practically substantial for zakat stakeholders to understand the current issues of global zakat administration and to learn the applicable lessons from the current issues of zakat management worldwide.

This study does not establish a positivist generalisation from the findings because of the nature and objective of the study.

A policy implication is drawn pertaining to the legislation of zakat as an Islamic financial policy instrument for combating poverty in Muslim society.

This work supports the notion of “socioeconomic zakat”, implying that zakat as a religious obligation is important in shaping the social and economic processes of a Muslim community.

This work marks the novelty in making sense of the unstructured data from virtual events on YouTube and Zoom in the Islamic social finance research.

In Indonesia, subjective issues towards the fundamental of Islamic banks (IBs) have been arising. For example, they are claimed to be not in line with the Shari‘ah (Islamic law). Furthermore, the existing scholarly works have not much gained knowledge from the local IBs explaining their efforts in promoting maqasid al-Shariah (objectives of Islamic law). Hence, because religiosity drives the fundamental establishment of IBs, this paper aims to explore the knowledge of how IBs in Indonesia promote maqasid al-Shariah via their published reports.

This paper performs text mining from 24 official reports of 5 IBs in Indonesia published from 2015 to 2017. The sample contains 7,162 digital pages and approximately 3,021,618 words. Traditional text mining via human intelligence is first performed to analyse for the numerical data required in the maqasid al-Shariah index (MSI) analysis. Furthermore, a computer-driven text mining using the ‘Text Search’ feature of NVivo 12 Plus is conducted to perform qualitative analysis. These approaches are made to gain relevant knowledge of how the sampled IBs promote maqasid al-Shariah from their published reports.

The analysis using the MSI explains a quantified maqasid al-Shariah on the sample’s performance, which indictes the lowest and the highest performing banks. Furthermore, a qualitative analysis supports the evidence from the quantitative analysis. It explains the authors’ coding process that results in 2 parent nodes and 20 child nodes, which contain 435 references coded from the sampled unstructured and bilingual texts. These nodes explain the information that associates with maqasid al-Shariah from the IBs’ reports. These findings explain how maqasid al-Shariah is measured mathematically and represent relevant knowledge of how maqasid al-Shariah is informed practically via digital texts.

A positivist generalisation is neither intended nor established in this study.

This paper gains relevant knowledge of how the sampled IBs in Indonesia control and maintain the implementation of maqasid al-Shariah from large textual data. Such knowledge is practically important for IBs stakeholders in Indonesia; moreover to help navigate the Shari‘ah identity of Bank Syariah Indonesia (BSI), the new IB established from the merger of 3 state-owned IBs, which are among the sample of this study.

This paper provides evidence that might best challenge the subjective issue of IBs claiming that they are not in line with the Shari‘ah, particularly in Indonesia.

This paper is among the pioneers that discover knowledge of how IBs promote maqasid al-Shariah in Indonesia’s banking sector via a text mining approach.

This study analyses the supervision policy of Municipal Corporations (MCs) in the Indonesian Muslim community regarding its alignment with the United Nations Sustainable Development Goals (SDGs).

As a single case study, purposive sampling was adopted to select a municipal corporation policy issued by Ciamis Regency, West Java, Indonesia. A novel mixed methods approach, combining computer analytics and human intelligence, was introduced to perform text analytics.

Text mining identified the most frequently occurring words — e.g., ‘perumda’ (municipal corporation), ‘daerah’ (regional), ‘pengawas’ (supervisor), ‘peraturan’ (regulation) — from the sample but found no single word indicating business alignment with SDGs. Further qualitative inductive analysis was conducted, revealing the critical role of MCs’ supervisory boards in business planning, execution, and reporting to align the MC businesses with local SDG initiatives.

Aligning MC business activities with local SDG actions is entirely within the authority of supervisory boards which demands transformational leadership. This research pioneered an innovative blend of computer-assisted techniques and human reasoning to investigate the supervision policies of MCs concerning local SDG actions, with evidence from a Muslim community in Indonesia.

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.

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.

Despite the COVID-19 recession, the collection of zakat (almsgiving) managed by the National Board of Zakat Republic of Indonesia (BAZNAS RI) has increased, especially during Ramaḍān 1441 Hijra. Previous works show a positive relationship between digital zakat campaign and zakat collection. This paper aims to study the means of digital zakat campaign during COVID-19 outbreak. This topic is theoretically and practically important in the emerging debate of Islamic marketing, notably in Islamic social finance field.

This paper uses a qualitative research approach. A case study is engaged in the selection of BAZNAS RI for a detailed discussion of a zakat organisation. Meanwhile, a netnographic approach is used to analyse the number of 549 posts from BAZNAS RI’s social media, which are Facebook, Instagram, Twitter and YouTube. Furthermore, a qualitative software analysis of NVivo 12 Plus is used in performing the analytical procedures.

This work explains the means of digital zakat campaign during COVID-19 outbreak with a case of BAZNAS RI. It is identified the number of 6 parent nodes and 64 child nodes from the analysis using NVivo 12 Plus. The authors’ parent nodes are “donation”, “infaq” (Islamic spending for charities), “Ramaḍān matters”, “ṣadaqah” (voluntary charity), “virtual events” and “zakat”. These nodes detail digital campaign of BAZNAS RI posted in its social media during COVID-19 period in Ramaḍān. A theoretical implication of inclusive marketing is derived from the analysis. It explains that the inclusiveness of digital contents is practically significant in campaigning zakat as a religious obligation that contributes to social and financial benefits.

This paper does not claim a positivist perspective on the relationship between digital zakat campaign and zakat collection. Instead, this paper explores in-depth the practice of digital zakat campaign, which the previous study confirms its association with a muzakki’s (Muslims who are obliged to pay zakat) decision to pay zakat.

This paper establishes the Islamic marketing theory that is derived from industrial practices. The inclusiveness of digital contents in zakat campaign is critical in activating zakat as a religious obligation that authentically shapes the social and economic processes of a Muslim community. This theory is practically important for 'amils (employees) of zakat institution who work in the marketing division, chiefly to create such contents to post in social media.

The authors’ node of zakat distribution for COVID-19 relief indicates the importance of a formalised zakat institution to actualise zakat’s role in handling socioeconomic problems. Thus, paying zakat formally in an authorised organisation may contribute to a greater social contribution and maṣlaḥah (public interest) than paying it informally without any effective measurement.

This study contributes to the novelty in the Islamic marketing debate within two folds. First, this paper is among the pioneers in studying digital zakat campaign during COVID-19 outbreak by using a netnographic approach. Therefore, a theoretical implication derived from industrial practices is contributed. Second, this paper details the steps in using NVivo 12 Plus to analyse the unstructured data sampled from the internet. The future studies may thus refer to this work to understand the application of netnography and the procedures in analysing data from social media using this software.

This paper aims to develop a performance measure for Islamic banks (IBs) by harmonizing related studies. Furthermore, this work uses the developed yardstick to analyze the performance of a sample of 11 IBs from across different countries.

This paper uses the mix-mode method. The qualitative approach is engaged first to construct the IBs performance yardstick. Following this, the quantitative approach is applied through the use of the performance yardstick to measure the sample’s performance.

This study develops a maqāṣid-based performance yardstick adapted from previous works. The developed model in this study is called an integrated maqāṣid al-Sharīʿah--based performance measure (IMSPM). By using this performance measure, the present paper finds that the sample performed highest on the objective of nafs (self) over the three-year period. In addition, this study identifies the information which best indicates the sample’s performance during the analysis.

This paper uses the sample’s annual reports. The analysis is thus limited to informational disclosure.

Islamic banking and financial institutions may use the IMSPM to communicate a measurable report on their promotion of the maqāṣid al-Sharīʿah (objectives of Islamic law).

The evidence from 11 IBs is indicative of their efforts to realize maqāṣid al-Sharīʿah in the banking industry. This point may best challenge the practice of stigmatizing IBs for not being in line with the Sharīʿah (Islamic law) or of imitating conventional banks.

The novelty of this study lies in two points. First, this study harmonizes previous works to integrate financial and religious measures in a single yardstick. Second, by using the developed standard, this study offers a fresh insight into the global IBs’ performance, represented by 11 IBs worldwide.

This paper aims to explore netizen’s opinions on cryptocurrency under the lens of emotion theory and lexicon sentiments analysis via machine learning.

An automated Web-scrapping via RStudio is performed to collect the data of 15,000 tweets on cryptocurrency. Sentiment lexicon analysis is done via machine learning to evaluate the emotion score of the sample. The types of emotion tested are anger, anticipation, disgust, fear, joy, sadness, surprise, trust and the two primary sentiments, i.e. negative and positive.

The supervised machine learning discovers a total score of 53,077 sentiments from the sampled 15,000 tweets. This score is from the artificial intelligence evaluation of eight emotions, i.e. anger (2%), anticipation (18%), disgust (1%), fear (3%), joy (15%), sadness (3%), surprise (7%), trust (15%) and the two sentiments, i.e. negative (4%) and positive (33%). The result indicates that the sample primarily contains positive sentiments. This finding is theoretically significant to measure the emotion theory on the sampled tweets that can best explain the social implications of the cryptocurrency phenomenon.

This work is limited to evaluate the sampled tweets’ sentiment scores to explain the social implication of cryptocurrency.

The finding is necessary to explain the recent phenomenon of cryptocurrency. The positive sentiment may describe the increase in investment in the decentralised finance market. Meanwhile, the anticipation emotion may illustrate the public’s reaction to the bubble prices of cryptocurrencies.

Previous studies find that the social signals, e.g. word-of-mouth, netizens’ opinions, among others, affect the cryptocurrencies’ movement prices. This paper helps explain the social implications of such dynamic of pricing via sentiment analysis.

This study contributes to theoretically explain the implications of the cryptocurrency phenomenon under the emotion theory. Specifically, this study shows how supervised machine learning can measure the emotion theory from data tweets to explain the implications of cryptocurrencies.

Despite the practice of credit card services by Islamic financial institutions (IFIs) is debatable, Islamic banks (IBs) have been offering this product. Both Muslim and non-Muslim customers have subscribed to the products. Thus, it is critical to analyse the strategy of IBs’ moral messages in reminding their Muslim and non-Muslim customers to repay their credit card debts. This paper aims to investigate this issue in Indonesia using data mining via machine learning.

This study examines the IBs’ customers across the 32 provinces of Indonesia regarding their moral status in credit card debt repayment. This work considers 6,979 observations of the variables that affect the moral status of the IBs’ customers in repaying their debt. The five types of data mining via machine learning (i.e. Boruta, logistic regression, Bayesian regression, random forest, XGBoost and spatial cluster) are used. Boruta, random forest and XGBoost are used to select the important features to investigate the moral aspects. Bayesian regression is used to get the odds and opportunity for the transition of each variable and spatially formed based on the information from the logistical intercepts. The best method is selected based on the highest accuracy value to deliver the information on the relationship between moral status categories in the selected 32 provinces in Indonesia.

A different variable on moral status in each province is found. The XGBoost finds an accuracy value of 93.42%, which the three provincial groups have the same information based on the importance of the variables. The strategy of IBs’ moral messages by sending the verse of al-Qur’an and al-Hadith (traditions or sayings of the Prophet Muhammad PBUH) and simple messages reminders do not impact the customers’ repaying their debts. Both Muslim and non-Muslim groups are primarily found in the non-moral group.

This study does not consider socio-economic demographics and culture. This limitation calls future works to consider such factors when conducting a similar topic.

The industry professionals can take benefit from this study to understand the Indonesian customers’ moral status in repaying credit card debt. In addition, future works may advance the recent findings by considering socio-cultural factors to investigate the moral status approach to Islamic credit warnings that is not covered by this study.

This work finds that religious text of credit card repayment reminders sent to Muslims in several provinces of Indonesia does not affect their decision to repay their debts. To some extent, this finding draws a social issue that the local IBs need to consider when implementing the strategy of credit card repayment reminders.

This study credits a novelty in the discourse of data science for Islamic finance practices. Specifically, this study pioneers an example of using data mining to investigate Islamic-moral incentives in credit card debt repayment.

This study aims to explore the recent state of zakat metaverse innovation from unstructured data available in cyberspace, i.e. YouTube, Instagram, X (Twitter), Facebook, LinkedIn and Google.

This study used “zakat metaverse” keywords to harvest unstructured data and analysed using a mixed-method approach. First step of the analysis applied quantitative text analytics via machine learning tool, followed by the final step of qualitative inductive analysis.

Quantitative text analytics identified keywords related to zakat metaverse innovation, whereas qualitative analysis explored the critical insights behind those keywords, presented in thematic interpretation.

This study only used unstructured internet data, in which other relevant information may not be covered.

Shariah evaluation of zakat obligations from virtual assets requires the relevantisation of fiqh (Islamic jurisprudence) zakat, which opens future debates.

Many zakat institutions operate in emerging economies where digital poverty occurs, and such zakat metaverse innovation would potentially contribute to this digital divide. The relevance of such innovation becomes a major question regarding its inclusivity.

This study combines machine learning analytics and qualitative analysis to explore the recent state of metaverse innovation in zakat administration.