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The complexity of citizenship behavior in organizations has long been a focus of research. Traditional methodologies have been predominantly used to address this complexity. This paper aims to tackle the problem using a cutting-edge technological tool: business process mining. The objective is to enhance citizenship behaviors by leveraging primary data collected from 326 white-collar employees in the Indian service industry.

The study focuses on two main processes: training and creativity, with the ultimate goal of fostering organizational citizenship behavior (OCB), both in its overall manifestation (OCB-O) and its individual components (OCB-I). Seven different machine learning algorithms were used: artificial neural, behavior, prediction network, linear discriminant classifier, K-nearest neighbor, support vector machine, extreme gradient boosting (XGBoost), random forest and naive Bayes. The approach involved mining the most effective path for predicting the outcome and automating the entire process to enhance efficiency and sustainability.

The study successfully predicted the OCB-O construct, demonstrating the effectiveness of the approach. An optimized path for prediction was identified, highlighting the potential for automation to streamline the process and improve accuracy. These findings suggest that leveraging automation can facilitate the prediction of behavioral constructs, enabling the customization of policies for future employees.

The findings have significant implications for organizations aiming to enhance citizenship behaviors among their employees. By leveraging advanced technological tools such as business process mining and machine learning algorithms, companies can develop more effective strategies for fostering desirable behaviors. Furthermore, the automation of these processes offers the potential to streamline operations, reduce manual effort and improve predictive accuracy.

This study contributes to the existing literature by offering a novel approach to addressing the complexity of citizenship behavior in organizations. By combining business process mining with machine learning techniques, a unique perspective is provided on how technological advancements can be leveraged to enhance organizational outcomes. Moreover, the findings underscore the value of automation in refining existing processes and developing models applicable to future employees, thus improving overall organizational efficiency and effectiveness.

This study aims to examine the role of Bilateral Investment Treaties (BITs) in attracting higher foreign direct investment (FDI) inflows into the major emerging economies namely Brazil, Russia, India, China and South Africa (BRICS) from the source developed, developing and other emerging economies over a period of 18 years from 2001 to 2018.

To estimate the results, panel data regression on a gravity-knowledge capital model has been used. To account for the problem of endogeneity we have used the two-step difference Generalised Method of Moments estimator proposed by Arellano and Bond (1991).

We find that contradictory to theory and expectations, BITs result in a fall in FDI inflows in BRICS economies. BITs ratified by BRICS economies are not able to provide a sound and secure investment environment to foreign investors, thereby discouraging FDI in these economies.

To the best of the authors’ knowledge, this study is the first to examine the impact of BITs on FDI inflows into the emerging BRICS economies. Further, the impact of BITs on FDI flows among developed nations, i.e. north-north FDI and from developed to developing countries, i.e. north-south FDI has already been studied by many researchers. But so far, no study has examined this impact on FDI among developing and emerging economies (south-south FDI), despite an increase in FDI flows among these economies. Therefore, this study seeks to overcome the limitations of previous studies and tries to find out the impact of BITs on FDI inflows in BRICS economies not only from source developed but also from source developing and other emerging economies.