Additive Manufacturing (AM) is reshaping multinational corporations' (MNCs) global production strategies through the intertwined roles of knowledge acquisition (KA) and explainable artificial intelligence (XAI). The study draws on survey data from international managers and AI/manufacturing specialists in MNCs and uses a dual empirical approach that combines (SEM) with XAI framework. The analysis shows that KA capabilities significantly enhance firms' ability to identify, absorb and integrate AM-relevant knowledge across borders, while XAI capabilities strengthen transparency and trust in data-driven decisions about AM deployment. Together, these capabilities support more adaptive “global factory” configurations, including greater localization of production and more sustainable manufacturing footprints. The findings suggest that AM's disruptive potential for global value chains depends less on the technology in isolation and more on how it is embedded in knowledge-based and AI-enabled organizational processes. The study contributes to theory by linking AM to knowledge-based and global factory perspectives on the MNC, by conceptualizing XAI as a strategic capability in international operations rather than a purely technical feature, and by demonstrating how integrating traditional modelling with XAI can illuminate complex capability interactions in international business.

Artificial intelligence (AI) has emerged as a transformative tool in various industries, offering significant opportunities for Small and Medium Enterprises (SMEs) to improve their decision-making processes. This study systematically explores AI applications in SMEs, highlighting trends and industry-specific implementations. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), the study synthesises insights from 97 sources, including academic and non-academic literature across multiple industries. The findings show a rising adoption of AI, particularly in generative AI, machine learning, and robotic process automation, with a notable increase in publications and real-world implementations in recent years. Geographically, AI adoption in SMEs is most pronounced in developed economies such as the United States, Germany, and China, where advanced AI infrastructure, government incentives, and digital ecosystems facilitate implementation. In emerging economies, particularly India, AI is gaining traction in manufacturing and supply chain management. Sector-wise, manufacturing exhibits the highest AI integration, leveraging robotics and machine learning for automation and optimisation, followed by the finance sector, which increasingly employs AI for fraud detection and risk assessment. This study provides an overview of key AI applications in SMEs and highlights future research directions to improve AI-driven decision-making in these enterprises.

Explainable artificial intelligence (XAI) and other digital technologies are altering the nature of social entrepreneurship, marketing, and other service activities. The structures and strategies of entrepreneurs undergo radical change as a result of the impact of XAI on marketing and innovation. Despite the increased interest in business to business (B2B) literature, there are limitations on how and what circumstances the activities of B2B marketing on social entrepreneurship. Therefore, this study outlines how XAI will impact B2B services by building resilience during and after crisis events such as the COVID-19 pandemic. To develop an in-depth understanding on the theories of social entrepreneurship, B2B marketing, and emerging technologies, this study set apart and conceptualize relevant factors and linkages. The result shows that based on a survey of 295 samples of B2B services entrepreneurial businesses, XAI enhances the establishment of a sustainable resilience for B2B marketing activities and contribute to building social entrepreneurial strategies for B2B marketing innovation.

Agri-food supply chains (AFSCs) are essential for addressing global food security and promoting the United Nation's Sustainable Development Goal of reducing hunger. These chains stand out because they deal with perishable goods that have limited shelf life and are subjected to erratic agricultural situations, such as fluctuating weather. To preserve sustainability and competitiveness, innovative approaches such as cross- border knowledge mobilization are essential for effective AFSC management. To identify the elements essential to effective knowledge mobilization within AFSCs, this study explores the dynamics of this process. Eleven potential critical success factors (CSFs) are identified and analyzed by using questionnaire surveys to collect extensive data from AFSC practitioners. The results of a multiple regression analysis show that eight of the identified criteria are strongly correlated with knowledge mobilization success. Notably, two parameters show negative correlations, indicating intricate interactions in the dynamics of knowledge. The study's findings highlight the value of strategic knowledge management for increasing the efficacy and efficiency of AFSCs and advancing the larger objective of sustainable food security. This research advances the goal of eliminating world hunger by exposing the complex effects of these CSFs and offering practitioners and policymakers useful insights for enhancing AFSC operations.

The purpose of this study was to explore the important factors for the adoption of Cloud ERP systems. When organisations make decision on implementation of innovative technology such as Cloud ERP, there is a range of factors to be considered. This paper aims to identify the most significant 9 TOE and DOI factors which have positive influence towards Cloud ERP adoption by conducting a systematic literature review (SLR). A conceptual framework was proposed which is useful reference for potential Cloud ERP adopters who are making decisions on Cloud ERP adoption. The conceptual framework includes the identified 9 factors as independent variables; adoption of Cloud ERP as dependent variable; firm sizes and countries as the two moderating variables.

The research into the critical success factors (CSFs) of ERP systems is mainly focussed on the private sector and is carried out from the clients' perspectives; this in spite of the fact that, owing to the greater outsourcing of IT systems of recent years. This study objective is to identify and classify, based on the taxonomy of key players and activities, a set of CSFs for ERP systems from the perspective of national ERP vendors. A multiple-case study of ten ERP vendors was conducted in Saudi Arabia to collect the required data. The thematic analysis identified a list of key factors. These are grouped into three main categories: key players, key activities and IT capabilities. The conclusions may provide researchers and practitioners, particularly those in developing nations, with a greater understanding of a set of CSFs for informed decision-making and develop suitable strategies for the implementation of process interventions.

Organizations are integrating big data technologies with Enterprise Resource Planning (ERP) systems with an aim to enhance ERP responsiveness (i.e., the ability of the ERP systems to react towards the large volumes of data). Yet, organizations are struggling to manage the integration between the ERP systems and big data technologies, leading to lack of ERP responsiveness. For example, it is difficult to manage large volumes of data collected through big data technologies and to identify and transform the collected data by filtering, aggregating and inferencing through the ERP systems. Building on this motivation, this research examined the factors leading to ERP responsiveness with a focus on big data technologies. The conceptual model which was developed through a systematic literature review was tested using Structural equation modelling (SEM) performed on the survey data collected from 110 industry experts. Our results suggested 12 factors (e.g., big data management and data contextualization) and their relationships which impact on ERP responsiveness. An understanding of the factors which impact on ERP responsiveness contributes to the literature on ERP and big data management as well as offers significant practical implications for ERP and big data management practice.

Supply chain finance (SCF) is receiving increasing awareness in research as a result of uncertainties in the global financing for supply chain (SC). There are limited and fragmented studies in the implementations of financial services in SC management. This article builds on recovery from the financial crisis of 2008 and posts COVID-19 pandemic, where uncertainties crippled SCF providers and brokers services. At the same time, cutting-edge technological advancements such as Artificial Intelligence (AI) are revolutionizing the processes of business ecosystem in which SCF is entrenched. This article thus adopts a fuzzy set theoretical approach to unpack the entities relationship validity for sustainable SCF mate-framework, and the originality of AI concepts to sustainable SCF to identify the issues and inefficiencies. The results indicate that AI contributes significant economic opportunities and deliver the most effective utilization of the supply networks. In addition, the article provides a theoretical contribution to financing in SC and broadens the managerial implications in improving performance.

Ransomware attack effectiveness has increased causing far reaching consequences that are not fully understood. The ability to disrupt core services, the global reach, extended duration, and the repetition has increased their ability to harm organizations. One aspect that needs to be understood better is the effect on the user. The user in the current environment is exposed to new technologies that might be adopted, but there are also habits of using existing systems. The habits have developed over time with trust increasing in the organization in contact directly and the institutions supporting it. This research explores whether the global, extended, and repeated RW attacks reduce the trust and inertia sufficiently to change long-held habits in using information systems. The model tested measures the effect of the RW attack on the e-commerce status quo to evaluate if it is significant enough to overcome the users resistance to change.

The evolution of organizational processes and performance over the past decade has been largely enabled by cutting-edge technologies such as data analytics, artificial intelligence (AI), and business intelligence applications. The increasing use of cutting-edge technologies has boosted effectiveness, efficiency and productivity, as existing and new knowledge within an organization continues to improve AI abilities. Consequently, AI can identify redundancies within business processes and offer optimal resource utilization for improved performance. However, the lack of integration of existing and new knowledge makes it problematic to ascertain the required nature of knowledge needed for AI’s ability to optimally improve organizational performance. Hence, organizations continue to face reoccurring challenges in their business processes, competition, technological advancement and finding new solutions in a fast-changing society. To address this knowledge gap, this study applies a fuzzy set-theoretic approach underpinned by the conceptualization of AI, knowledge sharing (KS) and organizational performance (OP). Our result suggests that the implementation of AI technologies alone is not sufficient in improving organizational performance. Rather, a complementary system that combines AI and KS provides a more sustainable organizational performance strategy for business operations in a constantly changing digitized society.

Fake news (FN) on social media (SM) rose to prominence in 2016 during the United States of America presidential election, leading people to question science, true news (TN), and societal norms. FN is increasingly affecting societal values, changing opinions on critical issues and topics as well as redefining facts, truths, and beliefs. To understand the degree to which FN has changed society and the meaning of FN, this study proposes a novel conceptual framework derived from the literature on FN, SM, and societal acceptance theory. The conceptual framework is developed into a meta-framework that analyzes survey data from 356 respondents. This study explored fuzzy set-theoretic comparative analysis; the outcomes of this research suggest that societies are split on differentiating TN from FN. The results also show splits in societal values. Overall, this study provides a new perspective on how FN on SM is disintegrating societies and replacing TN with FN.

The rapid growth and usefulness of Internet of Things (IoT) has seen it being deployed in critical and strategic infrastructure sectors like healthcare, transport, agriculture, home automation, and smart industries among many others.The benefits of comfort and reliability of IoT technologies to human beings have brought with them security concerns.This is due to its large-scale connectivity and over reliance on the internet for communication making it susceptible to cyberattacks.Digital forensics experts face a daunting task of handling these cyberattacks because of the unique and complex challenges posed by IoT.Recently, researchers have been drawn to finding solutions to these challenges, however, this is still in its infancy.This paper carries out a Systematic Literature Review (SLR) of the current research advancements in IoT forensics.We define key IoT fundamentals, IoT applications, the need for IoT forensics, identify the key factors affecting IoT forensics, and review the practicality of the available IoT forensics frameworks, models, and methodologies.The SLR reveals research gaps indicating that most of the current research is more theoretical than practical.There is a need for more practical approaches to tackle the unique IoT forensics challenges.Finally, for future research directions from the SLR, we have highlighted and discussed the open challenges and requirements for IoT forensics.

Interactions of enterprise resource planning systems and big data are crucial for the automotive industry in the process of quick and reliable decision-making with the use of large chunks of data collected by each department of the organization. Similarly, unstructured data collected by sensor systems need proper control of data to put out the best results combined with automation. This study adopts a systematic literature review conducted mainly under three phases in order to give a robust combination between the three areas, i.e. ERP systems, big data and automotive industry. The three phases are determining the combination between the enterprise resource planning systems and big data and individually explaining their interaction with the automotive industry. This study has been able to identify the strict influence of large chunks of data on the automotive industry such as data management issues, trust issues and complexity in the responsiveness of enterprise resource planning systems. It is recognized that the main reasons for the emergence of complexity in the responsiveness of enterprise resource planning systems are the unstructured data collected by sensors of emerging concepts such as connected cars and the eventual automation of automobile functions. The study depicts the major influence of an enterprise resource planning system in order to centralize the entire organization whilst a large amount of structured and unstructured data collected.

Technology has enabled consumers to gain product information from different online platforms such as social networks, online product reviews and other digital media. Large manufacturers and retailers can make use of this network information to forecast accurately, to manage the demand and thereby to improve profit margin, efficiency, etc. This paper proposes a novel framework to model and analyse consumers’ purchase decision for product choices based on information obtained from two different information networks. The model has also taken into account variables such as socio-economic and demographic characteristics. We develop a utility-based discrete choice model (DCM) to quantify the effect of consumers’ attitudinal factors from two different information networks, namely, social network and product information network. The network information modelling and analysis are discussed in detail taking into account the model complexity, heterogeneity and asymmetry due to the dimension, layer and scale of information in each type of network. The likelihood function, parameter estimation and inference procedures of the full model are also derived for the model. Finally, extensive numeric investigations were carried out to establish the model framework.

Semantic and cloud computing technologies have become vital elements for developing and deploying solutions across diverse fields in computing. While they are independent of each other, they can be integrated in diverse ways for developing solutions and this has been significantly explored in recent times. With the migration of web-based data and applications to cloud platforms and the evolution of the web itself from a social, web 2.0 to a semantic, web 3.0 comes as the convergence of both technologies. While several concepts and implementations have been provided regarding interactions between the two technologies from existing research, without an explicit classification of the modes of interaction, it can be quite challenging to articulate the interaction modes; hence, building upon them can be a very daunting task. Hence, this research identifies and describes the modes of interaction between them. Furthermore, a "cloud-driven" interaction mode which focuses on fully maximising cloud computing characteristics and benefits for driving the semantic web is described, providing an approach for evolving the semantic web and delivering automated semantic annotation on a large scale to web applications.

The ICDSST 2020 proceedings deal with decision support system technology, focusing on state-of-the-art DSS research and developments and discussing current challenges that surround decision-making processes and realistic and innovative solutions to co-develop potential business opportunities.