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Although big data analytics (BDA) have great benefits for higher education institutions (HEIs), due to lack of sufficient evidence on how BDA investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The purpose of this paper is to propose a big data academic and learning analytics enabled business value model to explain BDA potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs.

The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the BDA current and potential benefits and business value creation path for big data academic and learning analytics success in HEIs.

The pressure of compliance with all legal and regulatory requirements and competition had pushed HEIs hard to adopt BDA tools. However, the study found out that application of risk and security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, the results provide new insights to higher education administrators on ways to create BDA capabilities for HEIs transformation and suggest an empirical foundation that can lead to more thorough analysis of BDA implementation.

A distinctive theoretical contribution of this study is its conceptualization of understanding business value from BDA in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of BDA and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

This study aims to examine the key antecedents influencing the private firm’s intention to adopt big data analytics (BDA) in developing economies. To do so, the study follows the sequential explanatory approach.

To test the hypothesized model that draws on the technology–organization–environment (TOE) framework paired with the diffusion of innovation (DOI) theory, a purposive sampling technique was applied to gather data from 156 IT and management domain experts from the private firms that intend to adopt BDA and operate in Pakistan’s service industry, including telecommunication, information technology, agriculture, and e-commerce. The data were analysed using the partial least squares structural equations modelling (PLS-SEM) technique and complemented with qualitative analysis of 10 semi-structured interviews in NVIVO 12 based on grounded theory.

The empirical findings revealed that the two constructs – perceived benefits and top management support – are the powerful drivers of a firm’s intention to adopt BDA in the private sector, whereas IT infrastructure, data quality, technological complexity and financial readiness, along with the moderators, BDA adoption of competitors and government policy and regulation, do not significantly influence the intention. In addition, the qualitative analysis validates and further complements the SEM findings.

Unlike the previous studies on technology adoption, this study proposed a unique research model with contextualized indicators to measure the constructs relevant to private firms, based on the TOE framework and DOI theory, to investigate the causal relationship between drivers and intention. Furthermore, the findings of PLS-SEM were complemented by qualitative analysis to validate the causation. The findings of this study have both theoretical and practical implications.

Introduction: The Internet has tremendously transformed the computer and networking world. Information reaches our fingertips and adds data to our repository within a second. Big data was initially defined as three Vs, where data come with greater variety, increasing volumes and extra velocity. Big data is a collection of structured, unstructured and semi-structured data gathered from different sources and applications. It has become the most powerful buzzword in almost all the business sectors. The real success of any industry can be counted based on how the big data is analysed, potential knowledge is discovered and productive business decisions are made. New technologies such as artificial intelligence and machine learning have added more efficiency to storing and analysing data. This big data analytics (BDA) becomes more valuable to those companies, focusing on getting insight into customer behaviour, trends and patterns. This popularity of big data has inspired insurance companies to utilise big data at their core systems and advance the financial operations, improve customer service, construct a personalised environment and take all possible measures to increase revenue and profits.

Purpose: This study aims to recognise what big data stands for in the insurance sector and how the application of BDA has opened the door for new and innovative changes in the insurance industry.

Methodology: This study describes the field of BDA in the insurance sector, discusses the benefits, outlines tools, architectural framework, the method, describes applications in general and specific and briefly discusses the opportunities and challenges.

Findings: The study concludes that BDA in insurance is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however, there remain challenges to overcome.

The advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.

This study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.

The authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.

These results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.

Big data analytics (BDA) is recognized as a recent breakthrough technology with potential business impact, however, the roadmap for its successful implementation and the path to exploiting its essential value remains unclear. This study aims to provide a deeper understanding of the enablers facilitating BDA implementation in the banking and financial service sector from the perspective of interdependencies and interrelations.

We use an integrated approach that incorporates Delphi study, interpretive structural modelling (ISM) and fuzzy MICMAC methodology to identify the interactions among enablers that determine the success of BDA implementation. Our integrated approach utilizes experts' domain knowledge and gains a novel insight into the underlying causal relations associated with enablers, linguistic evaluation of the mutual impacts among variables and incorporating two innovative ways for visualizing the results.

Our findings highlight the key role of enabling factors, including technical and skilled workforce, financial support, infrastructure readiness and selecting appropriate big data technologies, that have significant driving impacts on other enablers in a hierarchical model. The results provide reliable, robust and easy to understand insights about the dynamics of BDA implementation in banking and financial service as a whole system while demonstrating potential influences of all interconnected influential factors.

This study explores the key enablers leading to successful BDA implementation in the banking and financial service sector. More importantly, it reveals the interrelationships of factors by calculating driving and dependence degrees. This exploration provides managers with a clear strategic path towards effective BDA implementation.

This study investigates the impact of traditional marketing analytics and big data analytics on the success of a new product. Moreover, it assesses the mediating effects of the quality of big data system.

This study is based on primary data that were collected through an online questionnaire survey from large manufacturing firms operating in UAE. Out of total distributed 421 samples, 327 samples were used for final data analysis. The survey was conducted from March–April 2020, and data analysis was done via Structural Equation Modelling (SEM-PLS).

It emerges that big data analysis (BDA), traditional marketing analysis (TMA) and big data system quality (BDSQ) are significant determinants of new product development (NPD) success. Meanwhile, the BDA and TMA significantly affect the BDSQ. Results of the mediating role of BDSQ in the relationship between the BDA and NPD, as well as TMA and NPD, are significant.

There are significant policy implications for practitioners and researchers concerning the role of analytics, particularly big data analytics and big data system quality, when attempting to achieve success in developing new products.

This is an original study based on primary data from UAE.

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.

A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.

Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.

This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.

For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

This paper aims to improve understanding of data-driven marketing by examining the experiences of managers implementing big data analytics in the marketing function. Through a series of research questions, this exploratory study seeks to define what big data analytics means in marketing practice. It also seeks to uncover the challenges and identifiable stages of big data analytics implementation.

A total of 15 open-ended in-depth interviews were conducted with marketing and analytics executives in a variety of industries in Ireland and the USA. Interview transcripts were subjected to open coding and axial coding to address the research questions.

The study reveals that managers consider marketing big data analytics to be a series of tools and capabilities used to inform product innovation and marketing strategy-making processes and to defend the brand against emerging risks. Additionally, the study reveals that big data analytics implementation is championed at different organizational levels using different types of dynamic learning capabilities, contingent on the champion’s stature within the organization.

From the qualitative analysis, it is proposed that marketing departments undergo five stages of big data analytics implementation: sprouting, recognition, commitment, culture shift and data-driven marketing. Each stage identifies the key characteristics and potential pitfalls to be avoided and provides advice to marketing managers on how to implement big data analytics.

The study aims to identify the potential drivers of big data analytics (BDA) practices in the supply chain and develop a sustainability evaluation model to evaluate drivers of big data for sustainability development.

The mixed-method approach was applied to assess sustainability dimensions and calculate the score using two phases. In Phase I, the BDA drivers in the e-commerce industry were finalised using the partial least square based structural equation modelling (PLS-SEM) method. In Phase II, a case study in the Indian fashion e-commerce industry was carried out to evaluate sustainability dimensions with respect to drivers of BDA and the sustainability score was calculated using the fuzzy analytical hierarchical process (AHP) method.

The index for economic sustainability (0.220), social sustainability (0.142) and environmental sustainability (0.182) were derived. The higher index value of economic sustainability compared to social sustainability and environmental sustainability signified those drivers of big data bring social and environmental uncertainty along with economic sustainability.

The study will help practitioners promote BDA use for developing environmental/social/economic sustainability in supply chains. Policymakers must ensure whether the integration of BDA practices brings down cost and brings strategic value for ensuring big data success. The study will help managers decide a constant trade-off between the requirement for social, environmental and economic performance.

The study corroborates and adds to the BDA literature by emphasising the positive role of BDA in sustainability development in the supply chain area and highlighting the significant role of different drivers of BDA in sustainability development.