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The purpose of this study is to describe the development of market based accounting research (MBAR) published in Indonesia for 10 years. This study attempts to explain the topics of MBAR, research method used, the variables, between-variable relationship formed, and the units analysis used in MBAR. This research uses qualitative-descriptive method to create descriptive models of MBAR articles published in accounting journals that have been accredited with minimum grade of B. The analysis of 109 MBAR articles of five accounting journals shows that 10 MBAR themes are still potential. Among three methods in MBAR, the multivariate association study is dominant. Some papers use intervening and moderating model to explore the relationship between accounting data and capital market reaction. The results for each theme are described in a research map that shows the relationship between variables (constructs) of MBAR from three units of analysis. This paper finds some implications to MBAR research agenda in the future, especially for meta-analysis research and triangulation research, due to many inconsistencies of the MBAR findings in Indonesia. In addition, accounting standard research topic is still promising in the moment of accounting standards transition.

Discussions about digital tourism continue to increase among scholars as Information Communication and Technology (ICT) infrastructure develops. Dynamic changes due to technological aspects have given rise to various developments in the tourism industry. Therefore, this study aims to evaluate the scientific structure of the development of digital tourism topics through a bibliometric analysis approach. In total, 102 publications from research on digital tourism were taken from Scopus database between 2001 and 2021, for further bibliometric analysis using the VOSviewer application. Interesting findings describe the most cited digital tourism publications, the contribution of digital tourism by various authors, institutions, countries, co-citation analysis, bibliographic coupling, and co-occurrence for the main trends of digital tourism. This study compiles a detailed review of digital tourism research. This article adds substantial value to the digital tourism topic by analyzing bibliometric data. It provided scientific information regarding digital tourism for other researchers and future research.

This research was conducted with the aim of examining and explaining the effect of strategic leadership, corporate governance, organizational culture, business infrastructure and corporate alignment as determinants of corporate hospitality; testing and explaining the direct effects of corporate hospitality and corporate sustainability.

This research used a quantitative approach with a survey method. The research population was all subsidiaries, branches and departments in PT Pelindo 1 (Persero). The sample size in this research was n = N = 61, which covered 5 subsidiaries, 17 branches and 39 directorates at PT Pelindo 1 (Persero). Data analysis was done with inferential statistics using WarpPLS analysis using the help of WarpPLS package computer programs.

There is a significant and positive direct effect between Strategic Leadership, Corporate Culture, Corporate Governance, Business Infrastructure and Corporate Alignment on Corporate Hospitality which means higher Strategic Leadership, Corporate Culture, Corporate Governance, Business Infrastructure and Corporate Alignment will result in a higher Corporate Hospitality. The analysis also shows that there is a significant and positive indirect effect between Strategic Leadership, Corporate Culture, Corporate Governance, Business Infrastructure and Corporate Alignment on Corporate Sustainability through Corporate Hospitality which means higher Strategic Leadership, Corporate Culture, Corporate Governance, Business Infrastructure and Corporate Alignment will lead to higher Corporate Sustainability through Corporate Hospitality.

The originality of this research can be proven from the lack of research on hospitality. Some other research on hospitality, mostly doing research at airports, hospitals and hotels. However, this research was conducted at the port, where company friendliness is a discipline that includes many marketing studies.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.

This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).

The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.

This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.

This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.