Big data and the transformation of operations models: a framework and a new research agenda

Roden, Sinéad; Nucciarelli, Alberto; Li, F.; Graham, Gary

doi:10.1080/09537287.2017.1336792

Cited by 73 publications

(53 citation statements)

References 36 publications

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“…This performance perspective includes papers that show how BD can help to improve the understanding of purchasing decisions (Tian 2017). It can also be seen how BD affects operation models (Roden et al 2017), and whether BD might affect R&D Management (Blackburn et al 2017), as well as "how the use of cloud-based accounting/finance infrastructure affects the business performance of small and medium-sized enterprises" (Cleary and Quinn 2016, p. 225). Braganza et al (2017) propose to utilize theories drawn from strategy and leadership fields.…”

Section: Knowledge Managementmentioning

confidence: 99%

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

et al. 2019

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Digital transformation (DT) has become a buzzword, triggering different disciplines in research and influencing practice, which leads to independent research streams. Scholars investigate the antecedents, contingencies, and consequences of these disruptive technologies by examining the use of single technologies or of digitization, in general. Approaches are often very specialized and restricted to their domains. Thus, the immense breadth of technologies and their possible applications conditions a fragmentation of research, impeding a holistic view. With this systematic literature review, we aim to fill this gap in providing an overview of the different disciplines of DT research from a holistic business perspective. We identified the major research streams and clustered them with co-citation network analysis in nine main areas. Our research shows the main fields of interest in digital transformation research, overlaps of the research areas and fields that are still underrepresented. Within the business research areas, we identified three dominant areas in literature: finance, marketing, and innovation management. However, research streams also arise in terms of single branches like manufacturing or tourism. This study highlights these diverse research streams with the aim of deepening the understanding of digital transformation in research. Yet, research on DT still lacks in the areas of accounting, human resource management, and sustainability. The findings were distilled into a framework of the nine main areas for assisting the implications on potential research gaps on DT from a business perspective.

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Section: Knowledge Managementmentioning

confidence: 99%

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

et al. 2019

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“…According to Fatorachian and Kazemi (2018), Lanza et al, (2015) and Roden et al, (2017) the people and cultural aspects should also be considered when assessing Industry 4.0, as it will impact and be influenced by values such as openness in the way data are managed, the ability of the workforce to adopt new technology, continuous improvement, innovation and communication.…”

Section: The Categorisation Of a Newmentioning

confidence: 99%

“…This type of data does not currently add value to manufacturing organisations without the capability. However, Roden et al, (2017) have illustrated the potential of Big Data in four cases: Ebay, Volkswagen, Philips and Walmart comparing its role and impact on the different components of a firm operational model.…”

Section: Big Datamentioning

confidence: 99%

“…The "People and Culture" dimension is a fundamental part of implementing Industry 4.0 due to the value and power of the people within organisations (Schwab, 2017). A culture of innovation and continues improvement is one which embraces change and thrives at new opportunities (Fatorachian & Kazemi, 2018;Lanza et al, 2015;Roden et al, 2017;Stock & Seliger, 2016).…”

Section: Industry 40 Assessment and Maturity Modelsmentioning

confidence: 99%

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Defining and assessing industry 4.0 maturity levels – case of the defence sector

Bibby

Dehe

2018

Production Planning & Control

295

189

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The development of more digitalised environment is transforming the future of manufacturing. This is known as Industry 4.0. Firms do not currently fully appreciate the complex characteristics of Industry 4.0 and as a result are uncertain about what it represents for them and indeterminate as which initiatives could be beneficial to their business. In this study, an assessment model is developed to measure the level of implementation of Industry 4.0 technologies, around three dimensions "Factory of the Future", "People and Culture", "Strategy". The "Factory of the Future" is the main dimension and is composed of eight attributes: Additive Manufacturing, Cloud, Manufacturing Execution System, Internet of Things and Cyber Physical Systems, Big Data, Sensors, e-Value Chains, and Autonomous Robots. The assessment tool measures firms" maturity in these three key dimensions, which add up to produce an overall Industry 4.0 score. The study uses a defence manufacturing firm to develop, test and validate the model and report on 12 partners. We concluded that the focal firm has an Industry 4.0 maturity level of 59.35, above the sector average of 55.58. This research contributes to the field by empirically developing and testing a model and providing a clear analysis of major firms in the Defence supply network.

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“…AlmadaLobo 2015; Majeed and Rupasinghe 2017). In many cases, such radical transformations of business are based on the implementation of big data approaches 'in order to generate value for the organisation by optimising, adapting or radically transforming the operations model' (Roden et al 2017). Therefore, nearly all industries expectations of established as well as potentially new customers are rising according to one main subject: the digitisation of digital products or (connected) services, or, at least, increasing convenience and productivity because of the intelligent interpretation of gathered data connected with autonomous process flows (Manyika 2017).…”

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confidence: 99%

Operations management and digital technologies

Schiavone

Sprenger

2017

Production Planning & Control

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Big data and the transformation of operations models: a framework and a new research agenda

Abstract: This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link

Cited by 73 publications

References 36 publications

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

Defining and assessing industry 4.0 maturity levels – case of the defence sector

Operations management and digital technologies

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