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This study aims to investigate the mediating role of different types of intellectual capital (human, structural and relational) in the relationship between artificial intelligence-driven supply chain analytics capability (AI-SCAC) and various supply chain decision-making processes, specifically rational, bounded and tacit decision-making.

The study used a quantitative survey strategy to collect the data. A total of 320 valid questionnaires were received from manufacturing companies. The data were analysed using structural equation modeling with partial least squares (PLS-SEM) approach through SmartPLS software.

The results indicate that human and structural capital significantly mediate the relationship between AI-SCAC and rational and bounded decision-making processes. However, structural capital does not mediate the relationship between AI-SCAC and the tacit decision-making process. Moreover, relational capital does not show a significant mediating effect on all of the decision-making processes. Notably, structural capital has the strongest impact on rational and bounded decision-making, while human capital plays a critical role across all three decision-making processes, including tacit decision-making.

This study contributes to the literature by providing a nuanced understanding of the differentiated impact of intellectual capital components on various decision-making processes within the context of AI-SCAC. While previous studies have broadly acknowledged the role of intellectual capital in decision-making, this research provides more understanding of how specific types of intellectual capital interact with AI to influence distinct decision-making processes. Notably, the differential impact of structural capital on rational and bounded decision-making versus tacit decision-making highlights the need for organisations to adopt a more tailored approach in leveraging their intellectual capital.

Manufacturing firms must strengthen their supply chain resilience to survive in turbulent business environments. This study explores how artificial intelligence (AI) can be leveraged to enhance supply chain resilience.

Drawing on organizational information processing theory, the research investigates the impact of AI usage on proactive and reactive supply chain resilience by fostering referent power in the context of demand dynamism. The study analyzes survey data from 285 Chinese manufacturing firms using structural equation modeling and regression analysis.

The results indicate that AI usage can enhance both proactive and reactive supply chain resilience. Referent power only mediates the relationship between AI usage and reactive supply chain resilience. Furthermore, this mediating effect is stronger under high-level demand dynamism.

This study highlights the value of AI usage in strengthening supply chain resilience and uncovers its underlying mechanisms. Theoretical and practical implications are discussed.

The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM.

This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework.

This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance.

This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.

This research aims to determine if agility and internal integration constitute direct antecedents to resilience and organizational recovery in small and medium enterprises (SMEs) manufacturing. This study is based on the resource-based view in developing the framework. The literature-based review is drawn up to link internal integration and agile and resilient practices to its recovery.

The study proposes a random sampling technique to draw the samples from Malaysian SME manufacturing firms. A total of 110 samples were collected and analysed using partial least squares structural equation modelling.

The results confirmed that agility and internal integration positively affect resilient directly and indirectly. A higher level of agility, internal integration and resilience also improves organizational recovery.

The study underlines that organizational strategies should be designed with resilience as agility and internal integration alone are inadequate for organizations to attain recovery and competitive advantage. The results are practical since the proposed structural model employs empirical data.

With the current wave of modernization in the dairy industry, the global dairy market has seen significant shifts. Making the most of inventory planning, machine learning (ML) maximizes the movement of commodities from one site to another. By facilitating waste reduction and quality improvement across numerous components, it reduces operational expenses. The focus of this study was to analyze existing dairy supply chain (DSC) optimization strategies and to look for ways in which DSC could be further improved. This study tends to enhance the operational excellence and continuous improvements of optimization strategies for DSC management

Preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for systematic reviews are served as inspiration for the study's methodology. The accepted protocol for reporting evidence in systematic reviews and meta-analyses is PRISMA. Health sciences associations and publications support the standards. For this study, the authors relied on descriptive statistics.

As a result of this modernization initiative, dairy sector has been able to boost operational efficiency by using cutting-edge optimization strategies. Historically, DSC researchers have relied on mathematical modeling tools, but recently authors have started using artificial intelligence (AI) and ML-based approaches. While mathematical modeling-based methods are still most often used, AI/ML-based methods are quickly becoming the preferred method. During the transit phase, cloud computing, shared databases and software actually transmit data to distributors, logistics companies and retailers. The company has developed comprehensive deployment, distribution and storage space selection methods as well as a supply chain road map.

Many sorts of environmental degradation, including large emissions of greenhouse gases that fuel climate change, are caused by the dairy industry. The industry not only harms the environment, but it also causes a great deal of animal suffering. Smaller farms struggle to make milk at the low prices that large farms, which are frequently supported by subsidies and other financial incentives, set.

This paper addresses a need in the dairy business by giving a primer on optimization methods and outlining how farmers and distributors may increase the efficiency of dairy processing facilities. The majority of the studies just briefly mentioned supply chain optimization.

This paper aims to examine the mediating role of employees’ intrinsic motivation in the relationship between perceived ambidextrous organizational culture and innovative behaviour among information technology (IT) sector employees.

The study used a quantitative research methodology, using a questionnaire to gather data from a sample of 510 employees across a range of IT organizations and various roles in the Indian IT sector. Smart partial least squares structural equation modeling Version 3 was used for the analysis and interpretation of the study.

The findings revealed a significant positive relationship between perceived ambidextrous organizational culture and employees’ innovative behaviour in the Indian IT sector. Moreover, the study established that employees’ intrinsic motivation played a significant mediating role in this relationship.

This study stands out for its exploration into how employees’ intrinsic motivation mediates the relationship between ambidextrous organizational culture and innovative behaviour. It offers valuable insights for enhancing organizational creativity by understanding the critical role of intrinsic motivation.

This research focuses on people’s activities in the Liuhua Clothing Wholesale District in Guangzhou, China. The increasing use of social media in business, especially during the COVID-19 pandemic, has created inevitable changes to the way space is utilised. Lockdowns and transport restrictions pushed the clothing wholesale traders to engage in livestreaming to maintain their business. This research aims to understand how spaces have been mediatised with the use of social media.

To investigate changes in the use of spaces, this research draws on actor-network theory and regards spaces as actors, adopting qualitative research methods, including observation, semi-structured interviews and mapping.

The research finds that spaces are mediatised for presentation on social media. During the COVID-19 pandemic, when in-person activities were suspended, the virtual space, constituted by elements that exist in physical and virtual spaces, became more valued. Physical space is no longer perceived as a whole but as elements, such as background, sound and light, all of which are involved in the construction of virtual space on social media. The perception of physical space has become less important than the images presented on social media.

Social media now exists in many people’s everyday lives, but its influence on architecture and space has received insufficient attention. This research interrogates this phenomenon in a clothing wholesale district in China to reflect on the influence. Its significance lies in documenting the spatial implications of dependence on social media and the changes to spatial use in the age of social media.

The logistics industry has undergone a tremendous transformation. This transformation is necessary to cope with the fundamental changes in customer expectations and the need for digitalization imposed by the pandemic, changes in the socioeconomic world, and innovative technology solutions. This paper aims to present digital transformation as an integrated framework for transforming the operating model and applying advanced solutions to the ecosystem of a quintile logistics (5PL) company. 5PL operators are typically an ecosystem. Loosely coupled or self-organized entities that collaborate in a symbiotic relationship represent this ecosystem. They aim to jointly develop capabilities, create innovative services or solutions, share knowledge, facilitate transactions, and leverage network synergies in a logistics environment to provide optimized or novel customer- or partner-centric solutions (Lamberjohann and Otto, 2020).

Currently, there is no single definition of an integrated logistics operations model in 5PL practice, so the qualitative method used in this paper allows for investigation from an exploratory perspective. The paper follows a qualitative research methodology, collecting and analyzing data/facts through interviews and visits to subject matter experts, industry practitioners, and academic researchers, combined with an extensive review of academic publications, industry reports, and written and media content from established organizations in the marketplace. This paper follows a qualitative research methodology, as it is an inquiry rather than a statistical study. The qualitative method allows the study of the concepts of phenomena and definitions, their characteristics, and the defining features that serve as the basis (Berg, 2007). It emphasizes generalized interpretation and deeper understanding of concepts, which would be more difficult in quantitative, statistically based research. Fact-finding was conducted in two ways: in-depth interviews with experts from academia, information and communication technology organizations, and key players in the logistics industry; and academic publications, industry reports, and written and media content from established national and international organizations in the market.

The operations model introduced considers six aspects: persons, processes, platforms, partners, protection and preservation. A virtual team approach can support the personal side of the 5PL ecosystem’s digital transformation. Managing a 5PL ecosystem should be based on collaborative planning, forecasting, and replenishment methods (Parsa et al., 2020). A digital platform can support trust among the stakeholders in the ecosystem. A blockchain solution can powerfully support the 5PL ecosystem from partner relationships’ points of view. The implementation of a cybersecurity reference model is important for protection (Bandari, 2023). Reverse logistics and an integrated approach support the preservation of the ecosystem.

While the author has experience applying the different components of the operations model presented, it would be interesting to find a 5PL that would use all the components presented in an integrated way. The operations model presented applies to any similar ecosystem with minor adaptations.

This paper addresses operations models and digital transformation challenges for optimizing 5PL operators. It provides several opportunities and considerations for 5PL operators interested in improving their management and operations to cope with the growing challenges of today’s world.

The competitiveness and long-term performance of 5PL operators depend on selecting and carefully implementing their operations models. This paper emphasizes the importance of using advanced operations models.

The operations model derives from the author’s personal experiences in research and the innovative application of these models to logistics operators (DHL, UPS, Poste Italiane and others). This paper brings together academic and industry perspectives and operations models in an integrated business digital transformation. This paper defines an original optimal operations model for a 5PL operator and can add sustainable value to organizations and society. In doing so, it outlines different solution requirements, the critical success factors and the challenges for solutions and brings logistical performance objectives when implementing a digital business transformation.

This research aims to investigate the interplay between flexibilities and strategic orientations in the context of supply chain agility (SCA), particularly in the medical equipment supply chain. The study seeks to identify key internal and external flexibility factors, along with the firm’s strategic orientations, and understand why and how these factors are interrelated and contribute to the development or enhancement of SCA.

The study adopts an inductive exploratory multiple case study design to empirically identify and examine the underlying flexibility and strategic orientation factors and their link to SCA. Data collection tools included semi-structured interviews and access to key company documentation and archives from six major medical equipment suppliers in Jordan.

The research findings lead to the proposal of an emerging theoretical model describing the nature of relationships among internal/external flexibility factors, strategic orientations and SCA, with underlying research propositions that can later be subjected to deductive testing and empirical quantitative validation.

This research advances the theoretical understanding of SCA by investigating its strategic antecedents, including various orientations and their direct and indirect effects. Second, it provides a comprehensive insight into the combined impact of internal and external flexibilities on SCA, an aspect relatively underexplored in previous literature.