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big data analytics capability and strategic metrics for enhancing firm innovation: a PLS-SEM modelling approach

JOURNAL OF DECISION SYSTEMS 2025年第1期34卷

作者： Raissi, Nizar Hakeem, Anas Khallouli, Wajih Umm Al Qura Univ Coll Business & Econ Taif Rd Mecca 21955 Saudi Arabia

The research examines the mediating role of Strategic Performance Measure (SPM) in connecting big data analytics (BDA) with Innovation Performance (IP) in Saudi Arabian SMEs. While BDA is recognised as a catalyst for improved business performance, this study emphasises that its positive effects on IP occur primarily through SPM. By acting as an intermediary, SPM transforms BDA's data-driven insights into actionable strategies necessary for enhancing innovation. The conceptual model was tested using survey data from 216 SMEs, analysed with structural equation modelling (SEM) via SPSS (v.28) and SmartPLS (v.4). Results indicate that BDA positively influences IP, with SPM enhancing key metrics such as operational efficiency, customer satisfaction, and productivity, which are crucial for successful innovation. Additionally, the findings reveal that although Saudi SMEs acknowledge the potential of BDA across tangible and intangible data assets, they face challenges in integrating BDA within their strategic frameworks. This study offers practical implications for managers and policymakers by underscoring the importance of aligning BDA implementation with performance metrics. Furthermore, it contributes to the literature on data-driven innovation by providing empirical insights specific to Saudi Arabian SMEs and suggesting directions for future research to strengthen generalisability.

关键词： big data analytics innovation performance strategic performance measure structural equation modelling

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big data analytics capabilities and leadership: catalysts of firm performance in telecommunications

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BUSINESS PROCESS MANAGEMENT JOURNAL 2025年第3期31卷 928-947页

作者： Shafqat, Hira Zhang, Baojian Ahmed, Muhammad Ullah, Muhammad Rizwan Zulfiqar, Muhammad Shanxi Univ Dept Management Sci & Engn Taiyuan Peoples R China Govt Coll Univ Faisalabad Lyallpur Business Sch Faisalabad Pakistan Univ Faisalabad Natl Business Sch Faisalabad Pakistan Zhejiang Gongshang Univ Hangzhou Coll Commerce Hangzhou Peoples R China

PurposeThe proliferation of big data analytics (BDA)-enabled tools and technologies has endowed organizations with the capacity to augment decision-making processes, optimize operational endeavors and foster innovation across diverse business domains. Consequently, BDA has been posited as a catalyst for enhanced customer relationship management, improved risk mitigation strategies and heightened operational efficiencies, all of which converge to augment overall firm performance. Thus, the purpose of this research is to introduce a conceptual framework aimed at explaining the influence of BDA capabilities on the performance of telecommunications firms in Pakistan. Additionally, it examines the potential mediating effect of talent capabilities and moderating effect of top management attitude on firm ***/methodology/approachdata from a sample comprising 520 participants were collected via survey questionnaires. The study employed Partial Least Squares-Structural Equation Modeling to empirically evaluate the proposed *** reveal a positive association between BDA technology and information capabilities with both BDA talent capabilities and firm performance. Furthermore, the analysis suggests that BDA talent capabilities mediate the relationship between BDA dynamic capabilities and firm performance, while top management attitude acts as a moderator, enhancing the relationship between BDA talent capabilities and firm ***/valueThere is a scarcity of research that has examined the relationship of BDA capabilities, top management attitude and firm performance. This study attempts to examine their interrelationships. First, it enhances the extant literature by elucidating the mediating role of BDA talent capabilities in the relationship between BDA technology and information capabilities and firm performance. Second, the study introduces a novel dimension by incorporating top management attitude as a moderator variable. This

关键词： Dynamic capabilities Talent capabilities Top management attitude big data analytics Firm performance

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The Impact of big data analytics on Decision-Making Within the Government Sector

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big data 2025年第2期13卷 73-89页

作者： Faridoon, Laila Liu, Wei Spence, Crawford Kings Coll London Fac Nat Math & Engn Sci NMES Dept Engn London England Kings Coll London Kings Business Sch Dept Accounting & Financial Management London England Kings Coll London Fac Nat Math & Engn Sci NMES Dept Engn London WC2R 2LS England

The government sector has started adopting big data analytics capability (BDAC) to enhance its service delivery. This study examines the relationship between BDAC and decision-making capability (DMC) in the government sector. It investigates the mediation role of the cognitive style of decision makers and organizational culture in the relationship between BDAC and DMC utilizing the resource-based view of the firm theory. It further investigates the impact of BDAC on organizational performance (OP). This study attempts to extend existing research through significant findings and recommendations to enhance decision-making processes for a successful utilization of BDAC in the government sector. A survey method was adopted to collect data from government organizations in the United Arab Emirates, and partial least-squares structural equation modeling was deployed to analyze the collected data. The results empirically validate the proposed theoretical framework and confirm that BDAC positively impacts DMC via cognitive style and organizational culture, and in turn further positively impacting OP overall.

关键词： big data analytics cognitive style organizational culture decision-making organizational performance

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big data analytics adoption success: value chain process-level perspective

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BUSINESS PROCESS MANAGEMENT JOURNAL 2025年第2期31卷 686-707页

作者： El-Haddadeh, Ramzi Fadlalla, Adam Hindi, Nitham M. Qatar Univ Coll Business & Econ Doha Qatar Lusail Univ Lusail Qatar

PurposeDespite the considerable hype about how big data analytics (BDA) can transform businesses and advance their capabilities, recognising its strategic value through successful adoption is yet to be appreciated. The purpose of this paper is to focus on the process-level value-chain realisation of BDA adoption between SMEs and large ***/methodology/approachResource-based theory offered the lens for developing a conceptual BDA process-level value chain adoption model. A combined two-staged regression-artificial neural network approach has been utilised for 369 small, medium (SMEs) and large organisations to verify their critical value chain process-level drivers for successful organisational adoption of *** findings revealed that organisational BDA adoption success is driven predominantly by product-and service-process-level value, with distinctive discrepancies dependent on the organisation's size. Large organisations primarily embrace BDA for their external value chain dimensions, while SMEs encompass its internal value chain cues. As such, businesses will be advised to acknowledge their organisational dynamics and precise size to develop the right strategies to adopt BDA *** limitations/implicationsThe study advances the understanding of the role of internal and external value chain drivers in influencing how BDA can be successfully adopted in SMEs and large organisations. Thus, appreciating the organisation's unique attributes, including its size, will need to be carefully examined. By investigating these elements, this research has shed new light on how developing such innovative capabilities and competencies must be carefully crafted to help create a sustainable competitive *** implicationsFor an organisational positioning, acknowledging the role of internal and external value chain drivers is critical for implementing the right strategies for adopting BDA. For larger businesses, resources for inn

关键词： big data analytics Adoption Value chain Resource-based theory Artificial neural network analysis

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Identification and prioritization of critical socio-technical factors influencing big data analytics adoption in healthcare using Delphi method

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BEHAVIOUR & INFORMATION TECHNOLOGY 2025年

作者： Salahuddin, Lizawati Wolseley, Nik Nurdini Aboobaider, Burhanuddin Mohd Ikram, Raja Rina Raja Hashim, Ummi Rabaah Said, Mohd Shahrir Mohamed Hassan, Noor Hafizah Univ Teknikal Malaysia Melaka UTeM Fak Teknol Maklumat & Komunikasi Durian Tunggal Melaka Malaysia Univ Kebangsaan Malaysia Hosp Canselor Tuanku Muhriz Fac Med Med Ctr Kuala Lumpur Malaysia Univ Teknol Malaysia UTM Razak Fac Technol & Informat Kuala Lumpur Malaysia

The healthcare sector faces significant challenges in adopting big data analytics, primarily due to the complex interplay of various socio-technical factors that influence successful implementation. While prior research has identified numerous factors affecting big data analytics adoption, there is limited understanding of their relative importance in the healthcare context. This study aims to identify and prioritise the critical socio-technical factors influencing big data analytics adoption in healthcare through expert consensus. A four-round Delphi study was conducted with ten experts representing diverse stakeholders including academics, data scientists, healthcare practitioners, and top management. The study employed a comprehensive framework encompassing person, technology, organisation, environment, and task dimensions. The results revealed that analytical skills, knowledge, and experience were the most critical person-related factors. In the technology dimension, system reliability, and data quality emerged as top priorities, while organisational support, funding, and infrastructure were identified as crucial organisational factors. The study also highlighted technological advancement and government regulations as key environmental factors, with task reliability emerging as the most significant task-related factor. The study offers a comprehensive understanding of the relative importance of various social-technical factors, thereby enabling more focused and effective big data analytics adoption strategies in healthcare settings.

关键词： big data analytics healthcare technology adoption socio-technical Delphi method

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Blockchain-Enabled Secure Collaborative Model Learning Using Differential Privacy for IoT-Based big data analytics

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IEEE TRANSACTIONS ON big data 2025年第1期11卷 141-156页

作者： Tekchandani, Prakash Bisht, Abhishek Das, Ashok Kumar Kumar, Neeraj Karuppiah, Marimuthu Vijayakumar, Pandi Park, Youngho Int Inst Informat Technol Ctr Secur Theory & Algorithm Res Hyderabad 500032 India Thapar Univ Dept Comp Sci & Engn Patiala 147004 India Presidency Univ Sch Comp Sci & Engn & Informat Sci Bengaluru 560064 India Univ Coll Engn Tindivanam Dept Comp Sci & Engn Villupuram 604001 Tamil Nadu India Kyungpook Natl Univ Sch Elect Engn Daegu 41566 South Korea

With the rise of big data generated by Internet of Things (IoT) smart devices, there is an increasing need to leverage its potential while protecting privacy and maintaining confidentiality. Privacy and confidentiality in big data aims to enable data analysis and machine learning on large-scale datasets without compromising the dataset sensitive information. Usually current big data analytics models either efficiently achieves privacy or confidentiality. In this article, we aim to design a novel blockchain-enabled secured collaborative machine learning approach that provides privacy and confidentially on large scale datasets generated by IoT devices. Blockchain is used as secured platform to store and access data as well as to provide immutability and traceability. We also propose an efficient approach to obtain robust machine learning model through use of cryptographic techniques and differential privacy in which the data among involved parties is shared in a secured way while maintaining privacy and confidentiality of the data. The experimental evaluation along with security and performance analysis show that the proposed approach provides accuracy and scalability without compromising the privacy and security.

关键词： data models Blockchains big data Security Privacy Differential privacy Machine learning Internet of things (IoT) differential privacy collaborative model learning blockchain big data analytics security

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Designing Secure Location-Based Authenticated Key Agreement Mechanism in Maritime Internet of Vessels for big data analytics

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 4641-4656页

作者： Agrawal, Saurabh Vangala, Anusha Das, Ashok Kumar Kumar, Neeraj Alenazi, Mohammed J. F. Shetty, Sachin Int Inst Informat Technol Ctr Secur Theory & Algorithm Res Hyderabad 500032 India Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA Korea Univ Coll Informat Dept Comp Sci & Engn Seoul 02841 South Korea Thapar Univ Dept Comp Sci & Engn Patiala 147004 India UEHS Warsaw Poland King Saud Univ Coll Comp & Informat Sci Dept Comp Engn Riyadh 11451 Saudi Arabia Old Dominion Univ Virginia Modeling Anal & Simulat Ctr Dept Computat Modeling & Simulat Engn Suffolk VA 23435 USA

Maritime communication, critical for global oceanic trade, faces challenges and opportunities with advancements in Information and Communication Technology (ICT). Traditional methods are susceptible to interception due to open channels, limited authentication, jamming, and other security risks. Securing vessel movements and locations in Internet of Vessels (IoV) is essential to prevent unauthorized data interception and tampering during transmission. We propose a ship authentication method using location-based secure keys to ensure the confidentiality of a vessel's whereabouts. The proposed scheme's robustness is validated through formal and informal security analyses, and formal verification using the Scyther automated verification tool, demonstrating its effectiveness against potential attacks in maritime networks. Comparative studies indicate that utilizing location-based keys maintains anonymity and untraceability without imposing significant computational or communication burdens. Experimental findings from comprehensive big data analytics and simulations using NS3 validate the scheme's feasibility and performance.

关键词： Security Authentication Artificial intelligence Marine vehicles Internet of Things Trajectory Protocols Authentication and key establishment big data analytics Internet of Vessels (IoV) location maritime communication security

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The Role of big data analytics in Revolutionizing Diabetes Management and Healthcare Decision-Making

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IEEE ACCESS 2025年 13卷 10767-10785页

作者： Nauman, Muhammad Almadhor, Ahmad S. Albekairi, Mohammed Ansari, Ali R. Fayyaz, Muhammad A. B. Nawaz, Raheel Islamia Univ Bahawalpur Fac Comp Dept Software Engn Bahawalpur 63100 Punjab Pakistan Jouf Univ Coll Comp & Informat Sci Dept Comp Engn & Networks Sakaka 72388 Saudi Arabia Jouf Univ Coll Engn Dept Elect Engn Sakaka 72388 Saudi Arabia Gulf Univ Sci & Technol Dept Math & Nat Sci Mishref 32093 Kuwait Manchester Metropolitan Univ Dept OTEHM Manchester M15 6BX England Staffordshire Univ Stoke On Trent ST4 2DE England

The evolving healthcare domain necessitates an upgrade through digitization, integrating patient data, and advanced medical results. In the last couple of decades, advances in information and storage technologies in healthcare have produced vast amounts of data. The remarkable increases in data volumes, along with the enticing prospects and potential inherent in data analysis, have contributed to the concept of big data. There is a pressing need within the research community to analyze these large volumes of big data. To address this challenge, big data analytics (BDA), the systematic process of examining large and complex datasets to uncover hidden patterns, correlations, and insights for informed decision-making, has emerged. It employs various methodologies and techniques to enable informed decision-making. This study delves into using Machine Learning (ML) in big data environments, explicitly utilizing the MLib library in Apache Spark to derive meaningful insights from diabetic healthcare dataset. The CDC's Behavioral Risk Factor Surveillance System (BRFSS) was used to empirically demonstrate the advantages of integrating BDA with ML for medical decision-making in big data environments. The research finding highlighted the superior performance of Logistic Regression (LR) models compared to other models like Naive Bayes (NB), providing valuable insights for healthcare applications.

关键词： Medical services Diabetes big data Decision making Medical diagnostic imaging Bioinformatics Diseases data analysis STEM Market research big data analytics healthcare decision-making diabetes management data analytics machine learning apache spark

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Quantum-inspired framework for big data analytics: evaluating the impact of movie trailers and its financial returns

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JOURNAL OF big data 2025年第1期12卷 1-30页

作者： Singh, Jaiteg Bhangu, Kamalpreet Singh Ali, Farman Alzubi, Ahmad Ali Shah, Babar Chitkara Univ Chitkara Univ Inst Engn & Technol Patiala 140401 Punjab India Sungkyunkwan Univ Coll Comp & Informat Sch Convergence Dept Appl AI Seoul 03063 South Korea King Saud Univ Community Coll Dept Comp Sci Riyadh Saudi Arabia Zayed Univ Coll Technol Innovat Dubai U Arab Emirates

In the context of the growing influence of businesses and marketers on social media platforms, understanding the impact of emotionally charged content on consumer behavior has become increasingly crucial. This study proposes a novel framework, leveraging quantum computing principles, to assess the emotional impact of movie trailers. The framework incorporates big data analytics and utilizes Quantum Walk andQuantum Time Series models to investigate the relationship between a movie trailer's emotional intensity and its financial performance. Unlike sequential problem-solving approach of traditional computing models, Quantum superposition allows exploring multiple options at once. An analysis of 141 movie trailers released after January 1, 2022, revealed a positive correlation between a trailer's emotive score and its financial success. These findings suggest that trailers evoking a stronger emotional response tend to achieve greater box office returns compared to those with a lower emotional impact. This research underscores the pivotal role of emotionally resonant content in shaping consumer behavior and cinematic outcomes. It would offer valuable insights for filmmakers and marketers to optimize audience engagement and financial returns.

关键词： Quantum computing big data analytics Fama-French model Movie trailer reviews Financial returns

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Cloud storage-based secure big data analytics mechanism for drone-assisted healthcare 5.0 data fusion system

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INFORMATION FUSION 2025年 121卷

作者： Wazid, system Mohammad Singh, Jaskaran Das, Ashok Kumar Garg, Sahil Susilo, Willy Hassan, Mohammad Mehedi Graph Era Deemed Univ Dept Comp Sci & Engn Dehra Dun 248002 India Univ Nottingham Sch Comp Sci Nottingham NG7 2QL England Int Inst Informat Technol Ctr Secur Theory & Algorithm Res Hyderabad 500032 India Korea Univ Coll Informat Dept Comp Sci & Engn Seoul 02841 South Korea Ecole Technol Super Elect Engn Dept Montreal PQ H3C 1K3 Canada Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Univ Wollongong Inst Cybersecur & Cryptol Wollongong NSW 2522 Australia King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11543 Saudi Arabia

Drone-assisted healthcare 5.0 data fusion system is the amalgamation of some cutting-edge technologies, like the Internet of Things (IoT), blockchain, robotics, drones or Internet of Drones (IoD), big data, artificial intelligence (AI), and cloud computing. It offers a range of various medical services, like healthcare decision making, patient remote monitoring and tracking, operating virtual clinics, patient shifting through smart ambulances, ambient supported living, self-illness management, reminders of treatment, compliance, and adherence. Since the devices communicate through the Internet, the potential attackers have some chances to launch different attacks on the drone-assisted healthcare 5.0 data fusion system. Under these circumstances, the healthcare data of the system may be revealed to unauthorized parties, or it may be updated in an unauthorized way. Therefore, to mitigate these issues, a cloud storage-based secure big data analytics mechanism for drone-assisted healthcare 5.0 data fusion system is proposed in the paper (in short, CSDM-DHF). Due to the deployed security mechanism, the proposed CSDM-DHF is capable enough to provide security to multi-sensor data fusion process. The provided security analysis proves the robustness of CSDM-DHF against various potential attacks. Moreover, the formal security verification of CSDM-DHF is also provided through the well-known scyther software validation tool. A comparative analysis of the existing schemes shows that the proposed CSDMDHF outperforms other schemes. Finally, a practical demonstration of CSDM-DHF is provided, and essential performance parameters are computed and analyzed.

关键词： Internet of Drones (ioD) data fusion big data analytics Healthcare 5.0 Multi-sensor fusion Security

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