Survey response rates are a critical metric in organizational research, serving as a key indicator of data quality and representativeness ([7]; [3]; [10]). High response rates ensure that survey findings accurately reflect the views and behaviours of the target population, thereby enhancing the validity of conclusions drawn from the data ([2]; [1]). However, achieving optimal response rates remains a significant challenge for many organizations, particularly in the context of employee engagement surveys, customer satisfaction assessments, and other organizational studies ([6]; [5]).

Traditional methods for predicting survey response rates often rely on statistical techniques that may not fully capture the complex, non-linear relationships inherent in the data ([6]; [5]). These methods can struggle to account for the multitude of factors influencing response behaviour, such as demographic characteristics, timing of survey invitations, and the number of reminders sent ([9]; [12]; [10]; [15]). As a result, there is a growing interest in exploring more advanced analytical approaches that can provide more accurate and nuanced predictions.

Machine learning (ML) techniques, particularly ensemble methods like Extreme Gradient Boosting (XGBoost), have emerged as powerful tools for predictive modelling in various domains, including survey research ([4]; [2]; [16]). XGBoost is renowned for its high performance and efficiency in handling large datasets with complex interactions among variables ([4]; [6]). Its ability to model non-linear relationships and interactions makes it particularly suited for predicting survey response rates, where multiple factors interplay to influence participant behaviour ([9]; [12]).

In this study, we apply XGBoost to predict survey response rates using a comprehensive dataset that includes organizational and demographic features. The dataset encompasses variables such as age, gender, job level, timing of survey invitations, allowed response window, number of reminders sent, and total number of forms sent ([7]; [1]). By leveraging these features, we aim to develop a predictive model that can accurately forecast response rates, thereby enabling organizations to optimize their survey strategies ([6]; [2]).

Our findings indicate that the XGBoost model achieves a high level of accuracy, with an R² score of 0.85 and a Mean Squared Error (MSE) of 0.02. Feature importance analysis reveals that the number of forms sent and the number of reminders are the most influential factors in determining response rates. These insights suggest that strategic adjustments in survey design, such as increasing the number of reminders or forms sent, can significantly enhance participation rates ([9]; [7]; [2]).

This research contributes to the field by demonstrating the efficacy of machine learning techniques in predicting survey response rates within organizational contexts. The application of XGBoost provides a robust framework for understanding and improving survey participation, offering practical implications for researchers and practitioners aiming to enhance the quality and reliability of survey-based data ([9]; [12]; [17]).

The results of the current study highlight the predominance of organizational factors in determining survey participation. In particular, the feature importance analysis indicates that sent_forms (Importance = 0.521) and Reminder (Importance = 0.332) were the most influential predictors of response rates, suggesting that structural and procedural elements play a greater role than demographic factors in influencing participation. Other variables, including weekday, job_level, allowed_response_window_hours, and send_hour, had smaller contributions, while demographic variables such as Age and Gender were the least influential (Importance < 0.01). This implies that organizations seeking to improve survey engagement should focus primarily on outreach strategies, follow-up reminders, and survey timing rather than demographic targeting ([8]; [5]; [10]; [13]).

From a predictive standpoint, the model performed well in capturing actual response rates, with predicted values closely aligning with observed rates across multiple survey batches. This demonstrates the utility of the XGBoost model for forecasting participation and understanding key organizational determinants ([11]; [8]).

Future research could focus on retraining the model with organization-specific data to better capture unique patterns in survey participation ([8]; [5]). Validation of predictions should accompany any retraining process to ensure the forecasted participation rates are realistic and actionable ([11]). Additionally, efforts could be made to improve prediction accuracy and reduce errors by considering the distinct characteristics and dynamics of each organization that may influence employee response behaviour ([10]; [13]).

From a practical perspective, the model enables organizations to input their own survey data and forecast expected participation rates for future surveys ([8]; [10]). Scenario simulations can also be conducted, allowing organizations to evaluate the potential impact of changes, such as adjusting the number of reminders or modifying the allowed response window, on participation rates ([5]). Implementing a continuous cycle of assessment and optimization will ensure that the model provides reliable and actionable insights, supporting informed decision-making in survey design, administration, and follow-up strategies ([8]; [5]; [10]).

Overall, these findings underscore the critical role of structural and procedural organizational factors in survey participation, highlighting actionable levers that organizations can adjust to improve employee engagement with surveys ([8]; [5]; [10]).

This study has several limitations that should be considered when interpreting the findings. First, the dataset was derived from a single consulting firm working with multiple client organizations, which may limit the generalizability of the results across different industries and organizational contexts ([4]; [9]). Second, the XGBoost model was trained using default hyperparameters; although this approach preserved interpretability and focused on feature relationships, future research could explore parameter tuning to further enhance predictive accuracy ([12]; [7]). Finally, additional contextual factors, such as organizational culture, departmental workload, Survey fatigue, or incentive, were not included in the current model. Incorporating these variables could provide a more nuanced understanding of participation behavior and improve the model’s predictive capability ([17]; [1]).

This study highlights the effectiveness of machine learning techniques, specifically the XGBoost algorithm, in predicting survey response rates within organizational contexts ([4]; [7]; [17]). By applying this model, we were able to identify key factors influencing survey participation, including organizational practices, temporal attributes, and individual demographic characteristics ([9]; [6]; [2]).

The findings demonstrate that variables such as the number of sent forms, the number of reminders, and the allowed response window play a central role in shaping response behaviour ([12]; [7]; [1]). Leveraging these insights allows organizations to optimize survey design, target the most relevant respondent segments, and implement evidence-based strategies to improve participation rates ([17]; [2]). Furthermore, the integration of predictive analytics contributes to the overall quality, reliability, and representativeness of survey data, supporting more informed and data-driven decision-making processes ([9]; [2]; [3]).

Overall, the results underscore the potential of machine learning not only as a predictive tool but also as a strategic enabler for enhancing organizational research and management practices ([4]; [7]). Future studies could further extend this work by incorporating additional variables, exploring alternative modelling approaches, or developing user-friendly interfaces for broader practical deployment ([8]; [5]; [10]).

The authors acknowledge Cultural Infusion Pty Ltd and the Diversity Atlas research team for their support, datasets, and collaborative insights. Moreover, the authors would like to thank Peter Mousaferiadis, Michael Walmsley, Nicole Lee, and Mary Legrand for their valuable assistance and contributions to this research. While their insights and support were greatly appreciated, the ideas and interpretations presented in this study remain those of the authors and may not fully reflect the perspectives of the acknowledged individuals.