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Eyewitness testimony can determine the outcome of criminal investigations. The cognitive interview (CI) has been widely used to collect informative and accurate accounts. However, face-to-face interviews have been restricted during the current pandemic, raising the need for using video-conferencing. The authors tested whether virtual interviews could produce elaborate accounts from eyewitnesses and if the CI superiority effect against a structured interview (SI) could be fully replicated online.

The authors used a 2 × 2 factorial design with interview condition (CI vs SI) and environment (face-to-face vs virtual) manipulated between-subjects. A total of 88 participants were randomly assigned to one of the four conditions. Participants watched a mock robbery and were interviewed 48 h later using either the SI or the CI. Both interviews contained the same structure and interview phases but only the CI included its key cognitive mnemonics/ instructions. Both sessions were either face-to-face or online.

Participants interviewed with the CI recalled more information than participants interviewed with the SI, regardless of the interview environment. Both environments produced a comparable amount of recall. Report accuracy was high for all groups.

This can be crucial to inform police practices and research in this field by suggesting investigative interviews can be conducted virtually in situations such as the current pandemic or when time and resources do not allow for face-to-face interviewing.

To the best of the authors’ knowledge, this is the first study showing that the CI superiority effect can be replicated online and that a fully remote CI can produce elaborate accounts.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.