Can big data and predictive analytics improve social and environmental sustainability?

Dubey, Rameshwar; Gunasekaran, Angappa; Childe, Stephen J.; Παπαδόπουλος, Θάνος; Luo, Zongwei; Wamba, Samuel Fosso; Roubaud, David

doi:10.1016/j.techfore.2017.06.020

Cited by 523 publications

(382 citation statements)

References 69 publications

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“…Therefore, the authors anticipate that applications within these categories will also constantly continue to grow. Furthermore, this research builds on existing B2B sustainability research Song et al, 2016;Song et al, 2017;Zhang et al, 2017), and answers the call of recent studies (Bughin et al, 2010;Dubey et al, 2017;Jeble et al, 2018) by offering insights into the B2B sustainability dyad through a stronger empirical and theoretical underpinning. This research has also mapped key big data analytical techniques from the extant literature and provided an conceptualization of B2B Social media data analytics process (Fig.…”

Section: Theoretical Contributionsmentioning

confidence: 59%

“…There is no denying the increasing role of big data analytics from several domains, Bughin et al (2010) highlight studies in big data and sustainability for firms in the auto industry. However, given the rise in studies which aim to provide information on the application of big data analytics to improve environmental sustainability (Song et al, 2017;Song, Fisher, Wang, & Cui, 2016;Zhang, Ren, Liu, & Si, 2017) and social sustainability Song et al, 2017), it is surprising that majority of these studies lack practical insights and merely offer conceptual and anecdotal evidences (Bughin et al, 2010;Dubey et al, 2017;Jeble et al, 2018). Additionally, while studies acknowledge big data analytics and its contributions to sustainability, there remains a lack of practical insights into the types of techniques which can be employed by organisations to leverage sustainability from their use of analytics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Role of big data and social media analytics for business to business sustainability: A participatory web context

Sivarajah

Irani

Gupta

et al. 2020

Industrial Marketing Management

215

124

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Section: Theoretical Contributionsmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Role of big data and social media analytics for business to business sustainability: A participatory web context

Sivarajah

Irani

Gupta

et al. 2020

Industrial Marketing Management

215

124

View full text Add to dashboard Cite

“…Reference [47] draws on the resource-based view of the firm, institutional theory, and organizational culture to test a model that describes the importance of resources for building capabilities, skills, and big data culture and, subsequently, improves operational performance. Reference [48] empirically investigates the effects of big data and predictive analytics on social performance and environmental performance. It finds that big data and predictive analytics (BDPA) has a significant impact on social performance or environmental performance.…”

Section: Discussionmentioning

confidence: 99%

The Spatial Spillover Effect in Hi-Tech Industries: Empirical Evidence from China

Chen

Shi

et al. 2020

Sustainability

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With ever-increasing economic globalization and rapid advancement of science and technology, developing high-tech industries have become an important way for many countries to achieve sustainable and environmentally friendly economic development. In this article, we aim to empirically test the critical factors, which can influence the spatial spillover of a country's high-tech industries. Using data from the high-tech industries in China during the years of 2007-2016, we establish a space lag model and a space error model to examine the space fixed effect, the time fixed effect, and the space-time double mixed effect in spatial spillover in high-tech industries. We compare the results of these two spatial panel models with those from a general panel model and find that the spatial spillover effect within high-tech industries is rather significant. Moreover, we find that the spatial-time double mixed of the spatial lag model is the best fitting effect. Our empirical results also show that the research and development (R&D) investment and international trade can positively promote spatial spillover of high-tech industries among different regions. In terms of policy insights, our results imply that the government can establish a technology transfer platform to promote the spillover in high-tech industries. This can help achieve a sustainable and balanced development of high-tech industries.Sustainability 2020, 12, 1551 2 of 16 agglomeration effects in the high-tech enterprises. In this paper, we focus on the following important questions: what are the critical factors which can influence the spatial spillover of high-tech industries? How do these factors influence the spatial spillover? What are the potential reasons for the unbalanced regional development of high-tech industries? The answers to these questions are of great theoretical and practical significance because they can help improve the output of the high-tech industries and promote the sustainable and balanced development of regional economies for many countries. For example, China is a vast country with regions with unbalanced economic development. The high-tech industries are highly concentrated in only several developed regions like Guangdong Province. How do you achieve the spillover of high-tech industries to other underdeveloped regions? This question is important not only for China's economic development, but also for many countries in the world with unbalanced regional economic development.In this paper, we employ spatial panel models to study spatial spillover in high-tech industries. We adopt the spatial panel models because they can deal with the spatial dependence among regions. In contrast, traditional models such as Ordinary Least Squares (OLS) models ignore the spatial effect and lead to estimation errors. To be more specific, in this research, we employ the spatial lag model (SLM) and the spatial error model (SEM), which are two of the most widely used spatial panel models. The SEM focuses on the influence of the error of random interferenc...

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“…Source: Scopus 2019 Dubey , Gunasekaran, Childe, Papadopoulos, Luo, Wamba & Roubaud (2019), as well as Akter, Bandara, Hani, Wamba, Foropon & Papadopoulos (2019) are the most published researchers in the 'business management and accounting' subject area and their views on big data and analytics will be addressed further. Dubey et al (2019) argue that big data and predictive analytics are not used by decisionmakers to improve transparency in supply chain management collaboration of supply chain partners. Big data analytics offer the business the opportunity to extract large volumes of data fast and predict possible future outcomes.…”

Section: Documents Per Subject Area On Scopus Searchmentioning

confidence: 99%

Information technology in accounting education

Stumke¹,

Swart²

2020

South African Accounting Education Stocktake

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Students' exposure to information technology (IT) is not the same at all universities, and thus not all students and accounting trainees have the same IT competency level. This chapter aims to elaborate on the gap in IT skills between accounting trainees and the expectations of the South African Institute of Chartered Accountants (SAICA) and employers. Findings from questionnaires and empirical research were compared with the competency framework of SAICA and evaluated to identify the possible gaps in IT skills and to consider possible plans to overcome them. This study made it evident that the main reason for a likely IT gap is the limited exposure that learners have to IT at school level. The employers further noted that firstyear accounting trainees had not mastered the essential IT competencies expected of them when they enter the workplace.

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Can big data and predictive analytics improve social and environmental sustainability?

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 523 publications

References 69 publications

Role of big data and social media analytics for business to business sustainability: A participatory web context

Role of big data and social media analytics for business to business sustainability: A participatory web context

The Spatial Spillover Effect in Hi-Tech Industries: Empirical Evidence from China

Information technology in accounting education

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