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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.

Blockchain technology (BCT) is one of the latest disruptive innovations, brought along with-it immense scope of diverse applications contributing towards sustainable development (SD). The consistent increase in the publications reveal that the application of BCT for SD has become popular among researchers and practitioners in past few years, in turn, urged for a systematic literature review (SLR) to get an insight into the research journey travelled so far and setting directions for future research in this area. Therefore, this study aims to identify, map and synthesize the available literature on application of BCT for SD.

The automatic and manual search resulted into 1,277 studies from Scopus and Web of Science database. Further applying inclusion and exclusion criterion resulted in bringing out total of 157 studies, which were termed as primary studies. Based on the results of descriptive analysis, conducted through Bibliometric and VOSviewer software, the characteristics of BCT and its key capabilities, contributing towards shaping the recent SD literature, were critically examined. Identified research themes for clustering primary studies were aligned with United Nations Sustainability Development Goals (UNSDG). A mind-map was also prepared on the basis of thematic classification of primary studies.

The research themes “business practice and economic sustainability”, “agriculture and food security” and “business practice and environment sustainability” were found to be the focal points of scholarly attention. Synthesis and analysis of primary studies resulted into classification of research gaps under four categories – theoretical foundation, methodological limitation, research themes and technology implementation challenges. The study was concluded by sensitizing and sanitizing the concrete research questions for future research.

The research findings shall be a roadmap for research scholars, academicians and practitioners to comprehend the present state of knowledge in the domain of “BCT application for SD” and decide upon adopting the future course of action to attain the UNSDGs by the year 2030.

To the best of the authors’ knowledge, the current study is the first attempt in its own sense to analyse and synthesize the available literature on “attaining SD through BCT” using SLR approach.