Grounded in the Technology-Organisation-Environment (TOE) framework, this study identifies the main factors affecting the intention to adopt Big Data Analytics (BDA) in supply chain management (SCM) for firms based in Saudi Arabia. This study focuses on identifying and analysing the role of competitive pressure as a contextual variable that can moderate the effects of these factors on the adoption intention. A survey of 220 IT managers revealed that compatibility, relative advantage, and top management support are positively perceived factors as they foster the firms’ intentions to adopt BDA in SCM. Their effects on intentions were positively moderated by competitive pressure as a contextual variable. However, BDA complexity and organisational readiness were not supported as influencing firms’ intentions to adopt BDA. The statistical analyses also indicated that the effects of complexity and organisational readiness on intentions are not significantly moderated by competitive pressure. This study contributes to the literature by emphasising the interaction between TOE factors, instead of considering them separately. It also offers guidance to managers aiming to adopt and use BDA in SCM.

Although business analytics (BA) play an important role in improving firm performance, various firms struggle to deliver their full benefits. Many researchers have investigated the capabilities required to achieve better value through BA, but none have addressed the impact of innovation capabilities as a contextual variable mediating the effects on firm performance. By adopting the Technology-Organization-Environment (TOE) framework, this study suggests a model to evaluate the impact of BA capabilities on firm performance and addresses the mediating role of innovation capabilities. A quantitative approach was adopted for data collection and analysis. Based on 386 surveys of BA experts at Saudi Arabian firms and the use of PLS-SEM to test and validate the model. The results show that organizational factors have a highly significant impact on firm performance. While IT infrastructure and information quality as technological factors showed no significant and positive effect. Furthermore, the findings revealed that innovation capabilities positively mediate the link between IT infrastructure and information quality and firm performance as it affects directly and indirectly firm performance. The findings of this study contribute to the literature by addressing the research gap in BA in the Saudi Arabia context. Moreover, the study result stressing about the role of innovation capabilities on the BA capabilities and the importance of considering the interaction between TOE factors. However, research was carried out within one developing country (Saudi Arabia), which might restrict the findings’ generalizability of the study, and the results must be generalized with care to avoid issues such as structural and cultural variances between developed and developing countries.

Our study explores the critical role played by organizational ambidexterity capabilities in the link between big data analytics, strategic innovation capabilities, and innovation performance. We developed a conceptual framework based on resource-based and dynamic capability views to examine the direct and indirect relationships among main variables. We used a quantitative approach to collect data from 172 Saudi IT and Telecom firms. We then employed structural equation modeling through Smart-PLS to test the study hypotheses. Our findings revealed that big data analytics and strategic innovation capabilities have a significant impact on organizational ambidexterity and then on innovation performance. Ambidexterity capability mediates between big data analytics capabilities and innovation performance and between strategic innovation capabilities and innovation performance. Our study contributes to the literature on big data and innovation. It offers valuable insights into the potential impacts of big data analytics, strategic innovation, and ambidexterity capabilities on innovation performance. It demonstrates how significantly boosting a firm’s capabilities for improved firms’ innovation performance can potentially enhance performance outcomes (e.g., competitiveness and sustainability). These findings provide managers with meaningful implications regarding the innovation performance that can be achieved by leveraging these important resources and capabilities.

This study explores the intricate relationships between entrepreneurial orientation (EO), digital orientation (DO), strategic agility (SA), and competitive advantage (CA) in Saudi firms. We investigate whether strategic agility serves as the missing link in these relationships. We examine direct and indirect effects among these constructs using structural equation modeling on data collected from 307 Saudi firms. The results confirm the direct positive links between EO and SA with CA. However, the findings also reveal a surprising result—there is no direct link between DO and CA. At the same time, both DO and EO exerted significant positive influences on SA. This study emphasizes the mediating role of SA. We find that SA partially mediates the relationship between EO and CA, underlining the importance of EO while pointing out the additional value given to this relationship by SA. Additionally, SA fully mediates the relationship between DO and CA, suggesting that firms must develop SA to translate their digital capabilities into CA. These results contribute to the strategic management literature by explaining the interplay between strategic orientations, agility, and competitive advantage in the Saudi context. They also provide practical implications for managers seeking to enhance the competitiveness of their firms during this digital age by showing that investing in SA in conjunction with entrepreneurial and digital resources is of paramount importance.

Grounded in the technology-organization-environment framework, this paper investigates critical drivers of mobile commerce (m-commerce) adoption intention by SMEs in Saudi Arabia, a developing country in transition, during the COVID-19 pandemic. A quantitative approach was adopted in this study for data collection and analysis. A proposed research model was tested and validated using PLS-SEM on data collected using a structured questionnaire from a sample of 171 Saudi SMEs located in the capital city of Riyadh. The findings reveal that top management support, environmental uncertainty and perceived benefits are the critical drivers of m-commerce adoption intention. However, perceived costs do not influence firms’ intentions to adopt m-commerce. This study contributes to a better understanding of m-commerce adoption intention in developing countries, particularly in Saudi Arabia. Both theoretical and practical implications of interest can be derived from this study.

While integrated Artificial Intelligence and Business Analytics (AI-BA) represents a significant advancement in marketing analytics and greatly influences firms’ innovations, there is a considerable gap in current research regarding its impact on technological innovation. This study addresses this gap by exploring how AI-BA affects data-driven and technological innovation, considering the mediating roles of integration capabilities and digital platforms. A theoretical model has been developed based on the dynamic capability view (DCV) and organizational information processing theory (OIPT). The model has been validated using data from enterprises in Saudi Arabia, and Partial Least Squares Structural Equation Modeling (PLS-SEM) has been employed for analysis. The findings demonstrate that AI-BA directly enhances both technological and data-driven innovation. Additionally, it was discovered that data-driven innovation, integration capabilities, and digital platforms mediate these effects, thereby enhancing technological innovation within the respective industries. These findings provide both theoretical and practical insights into the relationship between AI-BA, data-driven innovation, and technological innovation. They enrich the existing literature and provide actionable guidance for practitioners aiming to align their AI-BA with improved technological innovation outcomes.