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This paper aims to examine the nature and extent of disclosure on the use of big data by online platform companies and how these disclosures address and discharge stakeholder accountability.

Content analysis of annual reports and data policy documents of 100 online platform companies were used for this study. More specifically, the study develops a comprehensive big data disclosure framework to assess the nature and extent of disclosures provided in corporate reports. This framework also assists in evaluating the effect of the size of the company, industry and country in which they operate on disclosures.

The analysis reveals that most companies made limited disclosure on how they manage big data. Only two of the 100 online platform companies have provided moderate disclosures on big data related issues. The focus of disclosure by the online platform companies is more on data regulation compliance and privacy protection, but significantly less on the accountability and ethical issues of big data use. More specifically, critical issues, such as stakeholder engagement, breaches of customer information and data reporting and controlling mechanisms are largely overlooked in current disclosures. The analysis confirms that current attention has been predominantly given to powerful stakeholders such as regulators as a result of compliance pressure while the accountability pressure has yet to keep up the pace.

The study findings may be limited by the use of a new accountability disclosure index and the specific focus on online platform companies.

Although big data permeates, the number of users and uses grow and big data use has become more ingrained into society, this study provides evidence that ethical and accountability issues persist, even among the largest online companies. The findings of this study improve the understanding of the current state of online companies’ reporting practices on big data use, particularly the issues and gaps in the reporting process, which will help policymakers and standard setters develop future data disclosure policies.

From these findings, the study improves the understanding of the current state of online companies’ reporting practices on big data use, particularly the issues and gaps in the reporting process – which are helpful for policymakers and standard setters to develop data disclosure policies.

This study provides an analysis of ethical and social issues surrounding big data accountability, an emerging but increasingly important area that needs urgent attention and more research. It also adds a new disclosure dimension to the existing accountability literature and provides practical suggestions to balance the interaction between online platform companies and their stakeholders to promote the responsible use of big data.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.