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This study examines the impact of inter-organisational justice (i.e. distributive, procedural and interactional) in the buyer–supplier relationship on supply risk and, in turn, on a firm’s marketing and financial performance.

A structured survey was administered both online and in-person to Jordan-based manufacturing companies. The 137 responses received were analysed using partial least structural equation modelling.

The study found that while establishing both procedural and interactional justice in the relationship has a negative impact on supply risk, promoting distributive justice, surprisingly, has no impact. Moreover, supply risk was found to be detrimental to the firm’s marketing and financial performance.

This study considers only the direct role of inter-organisational justice in reducing supply risk. Future research could enhance our understanding of this role by exploring the underlying mechanisms and conditions that could govern it.

Managers can alleviate supply risk by ensuring procedural and interactional justice in the relationship through involving suppliers in the decision-making processes, consistently adhering to established procedures and communicating transparent and ample information.

Addressing supply risk can help in maintaining community resilience and economic stability.

The study highlights inter-organisational justice as a new approach to mitigating supply risk. Moreover, by examining how supply risk can affect a firm’s marketing performance, it also highlights a new implication of supply risk. Furthermore, by exclusively examining the impact of supply risk on a firm’s financial performance, the study provides a more nuanced interpretation of the effect of supply risk and how it can be reduced.

We demonstrate the practical application of machine learning (ML) techniques in document processing, addressing the increasing need for digitalization in the real estate industry and beyond. Our focus lies on identifying efficient algorithms for extracting individual documents from multi-page PDF files. Through the implementation of these algorithms, organizations can accelerate the digitization of paper-based files on a large scale, eliminating the laborious process of one-by- one scanning. Additionally, we showcase ML-powered methods for automating the classification of both digital and digitized documents, thereby simplifying the categorization process.

We compare two segmentation models that are presented in this paper to analyze the individual pages within a bulk scan, identifying the starting and ending points of each document contained in the PDF. This process involves extracting relevant features from both the textual content and page design elements, such as fonts, layouts and existing page numbers. By leveraging these features, the algorithm accurately splits multi-document PDFs into their respective components. An outlook is provided with a classification code that effectively categorizes the segmented documents into different real estate document classes.

The case study provides an overview of different ML methods employed in the development of these models while also evaluating their performance across various conditions. As a result, it offers insight into solutions and lessons learned for processing documents in real estate on a case-by-case basis. The findings presented in this study lay the groundwork for addressing this prevalent problem. The methods, for which we provide the code as open source, establish a solid foundation for expediting real estate document processing, enabling a seamless transition from scanning or inbox management to digital storage, ultimately facilitating machine-based information extraction.

The process of digitally managing documents in the real estate industry can be a daunting task, particularly due to the substantial volume of documents involved, whether they are paper-based, digitized or in digital formats. Our approach aims to streamline this often tedious and time-consuming process by offering two models as simplified solutions that encourage companies to embrace much-needed digitization. The methods we present in this context are crucial for digitizing all facets of real estate management, offering significant potential in advancing PropTech business cases. The open-source codes can be trained further by researchers and practitioners with access to large volumes of documents.

This study illustrates effective methods for processing paper-based, digitized and digital files, along with tailored ML models designed to enhance these methods, particularly within the real estate sector. The methods are showcased on two datasets, and lessons learned are discussed.