This study aims to examine the influence of Big Data analytics, innovation & capabilities in the supply chain as well as to find the moderating effect of organisational flexibility on the performance of the supply chain (SCP) and find the effect of competitive intensity as a controlling variable on the performance of supply chain. This research aims to present a theoretical model based on the resource-based theory's relational view (RBV). The data was collected through a survey questionnaire and 25 manufacturer firms managers participate in this survey. According to the findings of this study BDA has a favourable and strong association with SCI and SCC, as well as SCP. The majority of the manufacturers' firms in this research employed BDA to speed up their standing algorithms quicker with massive data sets.

E-commerce is one of the industries most affected by big data, from collection to analytics in the highly competitive market. Previous research on big data analytics in E-commerce focused only on particular applications, and there is still a gap in presenting a framework to evaluate big data applications from a challenges-values point of view. This study employs a three-phase methodology to evaluate big data applications in E-commerce with respect to big data challenges and values using a hybrid multi-criteria decision-making technique that combines BWM and fuzzy TOPSIS. The results showed process challenge and the strategic value obtained the highest weight for challenges and values criteria. Financial fraud detection is relatively the most challenging, and online review analytics is the most valuable application of big data in E-commerce among identified applications. Evaluating big data applications based on cost and benefit criteria is practical for E-commerce managers and experts to make decisions on implementation priorities to overcome the challenges and make the most of values. Moreover, the proposed approach is not only limited to big data analytics in E-commerce and can also be applied in other industries to evaluate emerging technology applications.

Strategy is a way to be able to find effective and efficient ways to achieve goals. There are three important strategies carried out by companies, namely strategic planning, strategic role, and strategic maneuvering. The interesting thing is that relatively not much research has been conducted regarding the measurement of financial performance which is studied from the perspective of corporate strategy that influences financial performance. This is also supported by technological developments that affect companies, so companies must have the right strategy so that the company's financial performance can improve. This study examines the effect of strategic planning, strategic roles, and strategic maneuvers on financial performance. This study uses resource-based view theory as a study in testing the influence model of strategic planning, strategic role, and strategic maneuvering on financial performance. The study is conducted in Indonesia with a sample consisting of owners, directors and managers who use big data analytics in the companies they run. The sampling technique used is convenient sampling. The analysis technique used in this study is multiple linear regression analysis with the independent variables namely strategic planning, strategic role, and strategic maneuvering and the dependent variable namely financial performance. The results of this study indicate that strategic planning had no significant effect on financial performance, but strategic role had a significant effect on financial performance. Furthermore, strategic maneuvering has a significant effect on financial performance.

As supply networks become more complex and international, the task of controlling associated risks becomes more difficult. This article investigates the usefulness of risk alert technologies in supply chain management, with a focus on Big Data Analytics (BDA) and Artificial Intelligence (AI). The study investigates the impact of BDA capabilities, solid IT infrastructure, managerial views, and AI-apprehensions on the effectiveness of risk alert tools using a questionnaire-based survey of 420 managerial personnel and Structural Equation Modeling (SEM) via SMART PLS. The work proposes the concept of AI-Apprehensions as a moderating variable, which is a relatively unexplored field. According to the findings, while BDA capabilities and IT infrastructure considerably improve the effectiveness of risk alert tools, AI-apprehensions can negate these advantages. The study provides useful insights for policymakers and practitioners, emphasizing the importance of balancing technical and human components for effective risk management.

The study aimed at exploring the impact of three smart innovative solutions, i.e., Internet-of-Things, Big Data Analytics, and Cloud Computing on supply chain performance. Collecting data by questionnaires administered to a sample consists of supply chain managers of industrial firms. The results pointed out that these three smart solutions significantly and positively lift firms’ supply chain performance. That is, the hypotheses that Internet-of-Things, Big Data Analytics, and Cloud Computing have significant impacts on supply chain performance were supported. Therefore, the study concluded that for industrial firms to improve supply chain performance, such smart solutions should be assessed and applied. The study contributes to both academics and practitioners through providing empirical results on concurrent impacts of three advanced technologies in supply chain management context.

The enablers of Big Data Analytics (BDA) on the BDA adoption intention of consumer goods’ retailing firms were measured in this study along with innovation culture as a moderator. Based on a literature review, six BDA adoption intention enablers: financial readiness, perceived advantages, top management support, IT infrastructure, technology sophistication, and data quality were identified. The study collected data from different levels of managers in the consumer goods’ retailing sector in Jordan to test the proposed study framework. To obtain primary data, a quantitative method was used, and a survey (structured questionnaire) was conducted. SmartPLS version 3.3 was used to analyze and test the proposed study model, which included 211 respondents. Three BDA enablers, including perceived advantages, top management support, and IT infrastructure, were found to have a statistically significant effect on BDA adoption intention in their supply chain operations. Furthermore, the relationship between financial readiness and BDA adoption intention was significantly moderated by innovation culture. This research model can be used to determine the challenges and enablers to BDA adoption in supply chain operations for both developed and developing countries. Future research may replicate the model in various sectors or the same sector in different countries.

Academics and practitioners have become more interested in big data analytics (BDA) in recent years. There have been few empirical studies on the relationship between BDA and green supply chain management (GSCM), as well as the importance of environmental orientation (EO). A total of 128 responses from Jordanian industrial businesses were evaluated using the structural equation modeling method. The BDA, EO has a favorable and significant relationship with external and internal GSCM, according to the findings of this study. Furthermore, EO serves as a mediator between BDA and the external, and internal GSCM. The findings provide managerial insight into how to use BDA to establish a proactive environmental policy that covers all GSCM activities.

The objective of this paper will be to determine how electronic human resource (E-HR) practices affect risk management performance mediated by operations strategy in the Saudi Stock Exchange (Tadawul). Under the quantitative research design, 370 managerial and human resource (HR) employees in the financial and investment institutions listed under Tadawul will be used to collect data. The research paper examines how E-HR systems, digital recruitment, performance analytics, and electronic training programs are integrated to lower institutional risk responsiveness, efficiency in operations, and strategic performance. To test the hypothesized relationships and to verify the mediating variable of operations strategy, Structural Equation Modeling (SEM) is used. The results have indicated that E-HR practices are effective in increasing the level of risk management by fostering operational agility, data-driven decision-making, and proactive financial and operational risks identification. Moreover, the relationship between operations strategy plays the mediating role by converting digital HR initiatives into operational improvements which are well-organized and provide organizational resiliency and control mechanisms. The research finds that E-HR practices are an effective enabler of proper risk management practice in the digital age that support the correspondence of technological change, human potential and operational effectiveness. In order to improve the resilience of the institutions within the Tadawul, it is advised that the listed institutions invest more in E-HR infrastructure, digital workforce development, and operational optimization systems that will maintain the sustainability of financial performance.

Digital transformation has encouraged companies to optimize their digital platform capabilities and big data analytics as strategic resources in creating innovation excellence. This study aims to examine the influence of Digital Platform Capability (DPC) and Big Data Analytics Capability (BDAC) on Innovation Performance (IP) in the telecommunications industry in Indonesia. Data was collected from 331 managerial respondents through a survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results show that both DPC (β = 0.316; T = 5.282; P < 0.001) and BDAC (β = 0.484; T = 8.033; P < 0.001) have a significant positive effect on IP. These findings emphasize the importance of companies' ability to manage digital platform integration and utilize big data analytics to strengthen innovation performance. Theoretically, this study expands on the Resource-Based View (RBV) and Dynamic Capability View (DCV) by emphasizing the role of DPC and BDAC as dynamic resources that support innovation. The practical implications suggest that telecommunications companies need to develop integrated digital strategies, strengthen their analytical infrastructure, and foster a data-driven culture to enhance their competitiveness.

The purpose of the current study is to examine the potentials provided by big data analytics and cloud computing in supporting entrepreneurial ventures. Quantitative approach was adopted through utilizing a questionnaire that was self-administered by (333) operational managers within entrepreneurial ventures operating in Saudi Arabia. SPSS was employed to screen and analyze gathered primary data. Results of study indicated acceptance of study hypotheses and confirmed that big data analytics and cloud computing are able to open many potentials for entrepreneurial ventures through big data analytics and its high potentials of informed decision making, and cloud computing along with its scalability and flexibility. The study suggested that entrepreneurs and their teams must have the necessary skills and knowledge. Investing in education and training programs can help entrepreneurs and their teams to stay up-to-date with the latest technologies and best practices in these areas. Further recommendations were presented in the study. Examining the potentials provided by big data analytics and cloud computing in supporting entrepreneurial ventures is significant because it can help entrepreneurs to make informed decisions, enhance the customer experience, increase efficiency, gain a competitive advantage, and drive innovation.