Evaluating the impact of the categories of the Forward-Looking information disclosure pattern

Reporting presents an overview of the status of companies to the outside world. In addition to historical information disclosure, forward looking information disclosure by companies is required by investors that leads to the improvement of reporting and decision-making process. Thus, this study investigated the effect of forward lookinginformation disclosure model on the context of structural equation modeling.

The method was conducted using structural equation modeling based on the partial least squares approach. In the quantitative method, the research hypotheses were developed and tested by using a questionnaire. Then, 368 subjects were selected from the capital market activists, including analysts, investors, financial managers, and auditors as statistical population by simple random sampling method. The hypotheses were tested by structural equation method using PLS software. Confirmatory factor analysis using the partial least squares method was used fir validating 11 main variables and items of future information disclosure.

The result of structural equations revealed that causal, underlying, and intervening conditions have a significant positive effect on the central category phenomenon. In addition, the axial category has a significant effect on strategies. Finally, strategies have a significant positive mediating effect on the relationships between the axial categories and consequences.

Finally, the forward looking information disclosure model has a high predictability and can be used as an effective factor on prospective information disclosure.

Contribution: 

The knowledge creation of this study in addition to the development of literature on forward looking information disclosure led to improved reporting and highlighted the significance of the dimensions of the model categories for beneficiaries.