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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.

This study aims to investigate the conflicting views in supply chain and strategic management literature regarding cooperative supply chain relationships (CSCR) and firm performance. Supply chain literature suggests a universally positive impact of CSCR on performance, irrespective of a firm’s strategy. In contrast, strategic management literature contends that the effectiveness of CSCR depends on their alignment with the firm’s competitive strategy. The research aims to clarify this disparity, offering insights into the strategic use of CSCR for enhancing firm performance.

This paper theorizes the integration of perspectives for the impact of CSCR on firm performance by examining the relationships considering the alignment of cost leadership and product differentiation strategies with supplier and customer relationships. Plant-level survey data is analyzed using regression techniques to test four hypotheses.

All four main relationships (cost leadership, product differentiation, supplier relationship and customer relationship) on firm performance are statistically significant. However, cost leadership firms are better aligned to their chosen strategy when they have strong relationships with suppliers, whereas similar relationships with customers create misalignment, negatively influencing firm performance. In contrast, product differentiators benefit by investing in relationships with customers rather than with suppliers.

A firm’s performance does not solely depend on its CSCR efforts but on aligning them with the firm’s overall strategy. Therefore, managers need to be cognizant of the firm’s competitive strategy when investing in CSCR. Failing to do so could negatively impact firm performance and, eventually, its ability to compete in the marketplace.

Scholars have advocated for the importance of examining competing perspectives of phenomena, both within and across various bodies of literature, as cross-disciplinary analysis often brings enhanced focus and depth, leading to improved understanding. This research is one of the initial efforts to empirically analyze the varying perspectives on CSCR in supply chain and strategic management literature. This cross-disciplinary approach can yield a more integrated perspective.

This study aims to find out whether firms in the local textiles industry are benefiting from the combined implementation of lean practices (LPs) and quick-response manufacturing (QRM) during the era of COVID-19. The study further explores the mediating role played by quick response manufacturing in the relationship between LPs, internal process performance (IPP) and customer performance.

A questionnaire is used to garner data from 123 local firms in Ghana’s textile industry. The analysis for all the hypothesized relationships is done using partial least square structural equation.

The results of the study indicate that LPs significantly strengthen the implementation of QRM. The result also suggests that LPs and QRM can be combined to influence IPP and customer performance. The results further suggest that QRM mediates the relationship between LPs, IPP and customer performance.

This study proposes and develops an integrated research model that explores the synergistic application of LPs and QRM in achieving improvements in IPP and customer performance from an emergent country perspective during the era of COVID-19. QRM serves as an important mechanism through which the relationship between LPs, IPP and customer performance can be explained.

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.

While firms in developing countries are increasingly adopting lean practices of inventory management, there is limited evidence showing the impact of lean practices on firm performance in countries such as India. Lean practices improve the financial performance of the firms through superior cost-reduction measures and operational efficiencies. This paper examines the impact of inventory leanness in Indian manufacturing firms on their financial performance.

The authors measure inventory leanness based on stochastic frontier analysis (SLA), apart from using conventional measures available in the literature. The authors analyze the impact of inventory leanness on the financial performance of firms by examining data for 12,334 unique Indian manufacturing firms for the period 2009–2018. The authors present a comparative analysis using different methods of inventory leanness and study the effects on firm performance.

First, the authors find that only 68 industries out of 411 industries follow lean practices, i.e. most industries do not follow lean practices. Second, the estimation results show that there exists a positive relationship between inventory leanness and firm performance. The results suggest that an inverted U-shaped relationship exists between inventory leanness and firm performance for the entire sample. In particular, 17% of the industries in the sample exhibit such a relationship, and it is sufficiently strong to show up in the average regression results for the entire sample.

The authors introduce a novel measure of inventory leanness named stochastic frontier leanness based on the SFA method used in production economics. It measures leanness by benchmarking the inventory levels against the industry “frontier”. Furthermore, the authors conduct an empirical study of the lean-financial performance relationship with a large panel dataset of Indian firms instead of the survey-based methods that were previously used in the literature.

The aim of this study is to offer valuable insights to businesses and facilitate better understanding on transformer-based models (TBMs), which are among the widely employed generative artificial intelligence (GAI) models, garnering substantial attention due to their ability to process and generate complex data.

Existing studies on TBMs tend to be limited in scope, either focusing on specific fields or being highly technical. To bridge this gap, this study conducts robust bibliometric analysis to explore the trends across journals, authors, affiliations, countries and research trajectories using science mapping techniques – co-citation, co-words and strategic diagram analysis.

Identified research gaps encompass the evolution of new closed and open-source TBMs; limited exploration across industries like education and disciplines like marketing; a lack of in-depth exploration on TBMs' adoption in the health sector; scarcity of research on TBMs' ethical considerations and potential TBMs' performance research in diverse applications, like image processing.

The study offers an updated TBMs landscape and proposes a theoretical framework for TBMs' adoption in organizations. Implications for managers and researchers along with suggested research questions to guide future investigations are provided.

Under the digital wave and the new industrial competition pattern, the automobile industry is facing multiple challenges such as the redefinition of new technologies and supply chain changes. The purpose of this study is to link big data analytics and artificial intelligence (BDA-AI) with digital supply chain transformation (DSCT) by taking Chinese automobile industry firms as a sample and to consider the role of supply chain internal integration (SCII), supply chain external integration (SCEI) and supply chain agility (SCA) between them.

Data were collected from 192 Chinese firms in the automotive industry and analyzed using partial least squares structural equation modeling (PLS-SEM). Importance-performance map analysis is used to extend the standard results reporting of path coefficient estimates in PLS-SEM.

The results indicate that BDA-AI, SCII, SCEI and SCA positively influence DSCT. In addition, this study found that SCII, SCEI and SCA play an intermediary role in BDA-AI and DSCT.

The paper enriches the research on the mechanism of digital resources affecting DSCT and expands the research of organizational information processing theory in the context of digital transformation. The paper explores how the resources deployed by firms change the strategic measures of firms from the perspective of responsiveness. By exploring the positive impact of SCA as a response capability on the DSCT strategy and its intermediary role between digital resources and DSCT, which is helpful to the further theoretical development of logistics and supply chain disciplines.

This paper aims to explore how lean manufacturing practices (LMPs) predict sustainable performance (SP) in the context of manufacturing small and medium-sized enterprises (SMEs) in less developed countries, like Yemen. In particular, it investigates the mediating effect of corporate social responsibility (CSR) under different levels of competitive intensity (CI).

Hierarchical regression analysis was used to analyze data gathered from a survey of 259 Yemeni manufacturing SMEs.

The findings confirm that LMPs affect CSR, which in turn affects SP. This study also confirms that LMPs have a positive indirect effect on SP through CSR, which diminished in the presence of CI.

This study provides useful insights for policymakers and firms’ managers, who are anticipated to show a higher commitment to CSR in their firms when adopting LMPs to enhance their firms’ SP, especially under a low level of CI.

This paper contributes to expanding knowledge on the effect of LMPs on SP through CSR constrained by the level of CI.