Purpose
The purpose of this study is to examine the role played by coercive, normative and mimetic pressures in stimulating timeliness of corporate internet reporting (TCIR).
Design/methodology/approach
This study uses content analysis technique to track the TCIR practices of top 100 non-financial companies listed on the Dhaka Stock Exchange. A disclosure index of 14 items is developed to capture the extent of TCIR. The authors collected the relevant data from multiple sources, such as corporate websites, monthly review reports and corporate annual reports for the year-end 2019. This study uses Poisson regression models to explore the association between institutional pressures and TCIR.
Findings
Consistent with the predictions of institutional isomorphism theory, the authors find that coercive isomorphic pressures through ownership by foreign investors, government, general public and connection with parent multinational corporations have positive associations with TCIR. The authors also find that normative pressures resulting from cross-directorships have positive influence on TCIR. The authors provide evidence of mimetic pressures through industry memberships (i.e. companies operating in technology-based industry) positively impacting TCIR. The additional analysis suggests that institutional pressures are rather associated with the extent of voluntary TICR and to a lesser extent to regulatory TICR.
Originality/value
To the best of the authors’ knowledge, this study is the first to show the positive impacts of coercive, normative and mimetic isomorphic pressures on TCIR in an emerging economy characterized by weak institutional environment and mixed prospects for TCIR.
The discipline of forecasting and prediction is witnessing a surge in the application of these techniques as a direct result of the strong empirical performance that approaches based on machine learning (ML) have shown over the past few years. Especially to predict wind direction, air and water quality, and flooding. In the context of doing this research, an MLP-LSTM Hybrid Model was developed to be able to generate predictions of this nature. An investigation into the Beijing Multi-Site Air-Quality Data Set was carried out in the context of an experiment. In this particular scenario, the model generated MSE values that came in at 0.00016, MAE values that came in at 0.00746, RMSE values that came in at 13.45, MAPE values that came in at 0.42, and R2 values that came in at 0.95. This is an indication that the model is functioning effectively. The conventional modeling techniques for forecasting, do not give the level of performance that is required. On the other hand, the results of this study will be useful for any type of time-specific forecasting prediction that requires a high level of accuracy.
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