Search results

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 paper aims to understand the impact of big data on the UAE audit profession. Mainly exploring whether the emergence of big data threatens the reliability of audit standards and whether audit standards need to be improved. Also, exploring the impact of big data on the collection of audit evidence.

Semistructured interviews were used to collect data, mainly targeting the audit-related workers of the Big Four and Non-Big Four audit firms in the UAE. Thematic analysis is adopted to analyze the original data, and the main factors affecting the audit standard and audit evidence collection.

This study found that the reliability of audit standards and the way audit evidence is collected can be affected by big data. It concludes that audit standards need to be improved and strengthened to include detailed essential elements associated with big data to ensure audit reliability, legitimacy and regularity. The results also identify the impact of big data on audit evidence in terms of adequacy, appropriateness, authenticity, consistency and reliability, as well as the impact on the validity and completeness of evidence collection. The research highlights the importance of big data skills and knowledge education, the contribution and challenges of big data to auditing, and the use of big data in future auditing.

This research provides specific empirical evidence from both Big Four and Non-Big Four audit firms in the UAE, which is lacking in the literature on the use of big data technology by auditors to assist audit works in UAE. It may serve as a reference for other researchers or those interested in relevant research.

Big data capabilities have the potential to completely transform conventional methods of doing business. Nevertheless, the role of big data capabilities in fostering green marketing capabilities and improving green competitive advantage is still not fully understood. To add new knowledge, this paper aims to propose a moderated mediation model to strengthen green competitive advantage in a big data environment. The model introduces both the mediating role of green marketing capabilities and the moderating role of big data capabilities. We developed and empirically tested a moderated mediation model.

In this study, we have adopted a survey-based methodology. The study collected data from 337 managers and empirically analyzed it to test the theoretical model of moderated mediation. We employed structural equation modeling for empirical analysis.

The findings revealed that organizational learning improves green marketing capabilities, whereas the relationship between organizational learning and green competitive advantage is insignificant. The mediating role of green marketing capabilities in the relationship between organizational learning and green competitive advantage was statistically significant, indicating that green marketing capabilities serve as a bridge between organizational learning and green competitive advantage. Big data capabilities moderate the relationship between organizational learning and green marketing capabilities. The moderated mediation was also significant, highlighting that big data capabilities further strengthen the indirect effects of organizational learning on green competitive advantage via green marketing capabilities.

This paper delivers theoretical and practical understandings of the importance of organizational learning and big data capabilities. Similarly, it extends current knowledge and provides key insights for managerial decision-making.

Financial firms are looking for better ways to harness the power of data analytics to improve their decision quality in the financial modeling era. This study aims to explore key factors influencing big data analytics-driven financial decision quality which has been given scant attention in the relevant literature.

The authors empirically examined the interrelations between five factors including technology capability, data capability, information quality, data-driven insights and financial decision quality drawing on quantitative data collected from Jordanian financial firms using a cross-sectional questionnaire survey.

The SmartPLS analysis outcomes revealed that both technology capability and data capability have a positive and direct influence on information quality and data-driven insights without any direct influence on financial decision quality. The findings also point to the importance and influence of information quality and data-driven insights on high-quality financial decisions.

The study for the first time enriches the knowledge and relevant literature by exploring the critical factors affecting big data-driven financial decision quality in the financial modeling context.

This study examines the relationship between servant leadership and its dimensions on firm performance, with big data playing the role of a mediator.

Survey responses used for analysis in this study have been taken from business managers associated reputed private sector organizations in India. A conceptual model is proposed grounded to the Conservation of Resource Theory (COR). Structural equation modeling has been used to test the proposed model.

Servant leadership significantly relates to firm performance, whereby Big Data is seen to play the role of a mediator. The results also indicate that none of the dimensions of servant leadership independently affect firm performance.

The study adds to extant research by examining the mediating mechanism of Big Data in servant leadership and firm performance. It also suggests that each dimension of servant leadership gets reflected in overall servant leadership. Here it is important to note that Big Data analytics partially mediate the effectiveness of servant leadership.

Financial technologies form the heart of considerable disruptive innovation. Fintech is the emerging financial infrastructure for modern business. Big data are the feedstock for artificial intelligence (AI) that drives many fintech sectors – start-up finance, commodities and investment instrumentation, payment systems, currencies, exchange markets/trading platforms, market-failure response forensics, underwriting, syndication, risk assessment, advisory services, banking, financial intermediaries, transaction settlement, corporate disclosure, and decentralized finance. This chapter demonstrates how analyzing big data, largely processed through cloud computing, drives fintech innovations, scholarship, forensics, and public policy. Despite their apparent virtues, some fintech mechanisms can externalize various social costs: flawed designs, opacity/obscurity, social media (SM) influences, cyber(in)security, and other malfunctions. Fintech suffers regulatory lag, the delay following the introduction of novel fintechs and later assessment, development, and deployment of reliable regulatory mechanisms. Big data can improve fintech practices by balancing three key influences: (1) fintech incentives, (2) market failure forensics, and (3) developing balanced public policy resolutions to fintech challenges.

The purpose of this study is to explore the relationship between big data and supply chain platform in China’s retail industry. With the emergence of big data resources and technologies, the business pattern of new retail advocates the combination of online and offline channels. Supply chain platform plays a key role in the implementation of retail activities, which has gradually become a research hotspot in the cross field of operations management and information system.

Through the method of literature review and case study, this study empirically explores how big data shapes supply chain platform to support new forms of online retail by grounded theory.

The model framework is validated by reliability test and coding method to process survey materials. The results identify the overall antecedents of supply chain platform and reveal positive effects between big data and new retail. The findings help firm managers build a big data-driven supply chain to support new retail.

There are insufficient studies on theoretical frameworks and interaction relationships among big data, supply chain platform and new retail.

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.

In this study, the moderator effect of the use of big data by Turkish banks on the innovation performance of the intellectual capital components, human capital, structural capital, and relational capital is discussed.

In the research, 618 survey data applied to bank employees and weighted according to population in seven regions were used. The data were analyzed through the structural equation model.

According to the empirical results, intellectual capital components and big data usage explain 65% of the variance in innovation performance. It has been determined that the other two components of intellectual capital, except structural capital, have a statistically significant effect on innovation performance. According to the Standardized Regression Weights, one unit change in human capital affects innovation performance by 0.162, and one unit change in relational capital affects innovation performance by 0.244. In addition, a one-unit change in big data usage affects innovation performance by 0.480. It has been understood that the use of big data significantly affects the innovation performance of banks with a rate of 0.480.

Although this study is important, it could have been done with senior managers instead of being based on a survey. Instead of a survey, it could have been done with a data set taken from banks' balance sheets and tables. Additionally, the use of big data has been considered as a moderator but can be reconsidered as a mediator or external construct. Moreover, this study was conducted on a sample of participants working in the developing Turkish commercial banking sector. Therefore, the results of the study can be done in different countries and at different development levels.

The study is one of the first studies to examine the moderating effect of intellectual capital by considering its subcomponents in a developing country. In addition, it is thought that the results will contribute to managers, policy makers and researchers who want to increase competition and market share in the sector, as well as filling the gap in the literature.

The rapid advancement of digital transformation requires a shift in firms’ focus from past met needs to both latent future and unmet past needs. However, how boundary-spanning search with future orientation and past orientation affects breakthrough innovation remains unclear. This study thus aims to investigate the relationship between boundary-spanning search and breakthrough innovation from the perspective of search orientation.

In terms of search orientation, this study divides boundary-spanning search into forward-looking search and backward-looking search. Drawing on resource-based view, this study develops a theoretical model in which big data analytics capability moderates the effects of forward-looking and backward-looking searches on breakthrough innovation. Empirical analyses were conducted on data from China’s advanced manufacturing firms. Research model and hypotheses were tested through multiple regression.

The results confirm that forward-looking search has a positive effect on breakthrough innovation, and big data analytics capability strengthens this positive effect. Furthermore, backward-looking search has an inverted U-shaped effect on breakthrough innovation. Interestingly, as big data analytics capability increases, this inverted U-shaped curve flattens and becomes almost linear.

This study uncovers the different effects of boundary-spanning search with different orientations on breakthrough innovation and extends the research on the relationship between boundary-spanning search and breakthrough innovation by incorporating search orientation. Furthermore, by demonstrating the moderating role of big data analytics capability, this study provides a crucial condition under which boundary-spanning search can enhance breakthrough innovation.