Search results

With a team interaction analysis model, the authors sought to identify a varying range of individual and collective intellectual behaviors in a series of communicative intents particularly expressed with multimodal interaction methods. In this paper, the authors aim to present a new construct (i.e. collective intelligence ratio (CIR)) which refers to a numeric indicator representing the degree of intelligence of a team in which each team member demonstrates an individual intelligence ratio (IR) specific to a team goal.

The authors analyzed multimodal team interaction data linked to communicative intents with a Poisson‐hierarchical generalized linear model (HGLM).

The study found evidence of a distinctive IR for each team member in selecting a communicative method for a certain task, ultimately leading to varying degrees of team CIR.

The authors limited the type and nature of human intelligence observed with a very short list of categories. Also, the data were evaluated by only one subject matter expert, leading to reliability issues. Therefore, generalization should be limited to situations in which teams, with pre‐specified team goals and tasks, are collaborating in multimodal interaction environments.

This study presents potential ways to directly or indirectly optimize team performance by identifying and incorporating IRs and CIRs in team composition strategies.

In the literature of team cognition and performance, the authors offer a new insight on team schema by suggesting a new task‐expertise‐person (TEP) unit integrating information on who uses what communicative methods to best tackle on what cognitive task (i.e. optimum cognition with least cognitive burden). Individual and collective intelligence ratios should be considered as new extensions to conventional transactive memory systems in multimodal team interaction scenarios.

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.