DECAS: a modern data-driven decision theory for big data and analytics

Elgendy, Nada; Elragal, Ahmed; Päivärinta, Tero

doi:10.1080/12460125.2021.1894674

Cited by 48 publications

(24 citation statements)

References 63 publications

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“…Because of the importance of BDA in enhancing OP in the supply chain (Awan et al. , 2022; Elgendy et al. , 2022; Rana et al.…”

Section: Introductionmentioning

confidence: 99%

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Internet of things, big data analytics and operational performance: the mediating effect of supply chain visibility

Al-Khatib¹

2022

JMTM

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PurposeThis paper aims to investigate hypothesized relationships between the Internet of things (IoT) and big data analytics (BDA) with supply chain visibility (SCV) and operational performance (OP) in the pharmaceutical manufacturing sector in Jordan. The paper also aims to test the conceptual model related to the indirect effects of SCV.Design/methodology/approachTo achieve the objectives of this paper, a conceptual model was developed through a review of the current literature. Data analysis was performed by covariance-based structural equation modelling using Amos 25 software, and the convergent validity, discriminant validity, reliability and confirmatory factor analysis were verified. Then the hypotheses were tested.FindingsThe results of the study indicate that there is a positive and statistically significant relationship between the IoT and BDA on SCV and OP. The relationship was positive and statistically significant between SCV and OP. In addition, support for the mediation hypothesis that SCV mediates the relationship among IoT, BDA and OP was confirmed.Originality/valueThis paper provides new theoretical and managerial contributions that have not been covered in previous studies, and it is considered the first study that uses this conceptual model with this mechanism in terms of the theoretical lens and empirical application. This paper contributes to understanding the dynamic mechanisms of the IoT and BDA in enhancing OP, which contributes to creating a supply chain capable of facing various environmental fluctuations and pressures. This study presents new implications that can be used in the supply chain literature.

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“…Because of the importance of BDA in enhancing OP in the supply chain (Awan et al. , 2022; Elgendy et al. , 2022; Rana et al.…”

Section: Introductionmentioning

confidence: 99%

“…Because of the importance of BDA in enhancing OP in the supply chain (Awan et al, 2022;Elgendy et al, 2022;Rana et al, 2022) and creating competitive advantages (Al-Khatib, 2022a, b;Dong et al, 2022;Shah, 2022), several recent studies (e.g. Leveling et al, 2014) have shown an important and positive role for BDA in enhancing SCV.…”

Section: Introductionmentioning

confidence: 99%

Internet of things, big data analytics and operational performance: the mediating effect of supply chain visibility

Al-Khatib¹

2022

JMTM

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“…The stream of theoretical developments in data economy research embraces theories from different disciplines (Table 5). The integrated theories are trust, security, privacy (Meijer et al , 2014; Kobayashi et al , 2018) and dynamic capability theory (Lee and Yoo, 2019; Shan et al , 2019), knowledge management (Sumbal et al , 2017) and decision theory (Elgendy et al , 2021), graph theory (Yıldırım et al , 2021) and attitudes (Tenopir et al , 2020; Baždarić et al , 2021). Figure 2 displays the prominent theories used in data economy research.…”

Section: Resultsmentioning

confidence: 99%

The composition of data economy: a bibliometric approach and TCCM framework of conceptual, intellectual and social structure

Olaleye

Mogaji

Agbo

et al. 2022

IDD

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Purpose The data economy mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human experiences into behavioral data that can be harnessed in the marketing sphere. This study aims to focus on investigating the domain of data economy with the methodological lens of quantitative bibliometric analysis of published literature. Design/methodology/approach The bibliometric analysis seeks to unravel trends and timelines for the emergence of the data economy, its conceptualization, scientific progression and thematic synergy that could predict the future of the field. A total of 591 data between 2008 and June 2021 were used in the analysis with the Biblioshiny app on the web interfaced and VOSviewer version 1.6.16 to analyze data from Web of Science and Scopus. Findings This study combined findable, accessible, interoperable and reusable (FAIR) data and data economy and contributed to the literature on big data, information discovery and delivery by shedding light on the conceptual, intellectual and social structure of data economy and demonstrating data relevance as a key strategic asset for companies and academia now and in the future. Research limitations/implications Findings from this study provide a steppingstone for researchers who may engage in further empirical and longitudinal studies by employing, for example, a quantitative and systematic review approach. In addition, future research could expand the scope of this study beyond FAIR data and data economy to examine aspects such as theories and show a plausible explanation of several phenomena in the emerging field. Practical implications The researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies. Originality/value This study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.

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“…Scientific research in the form of scholar-practitioner collaboration is, therefore, valuable and necessary for practitioners to stay abreast of the challenges, complexities and the changing contexts under which the logistics industry is evolving. Elgendy et al (2021), for example, state that despite data-driven decision-making being increasingly important, it still requires further research for it to mature in order to realize its benefits and overcome its associated challenges and implications. They suggest that there is a lack of coherent theories to cohesively support all the data-driven decision-making elements.…”

Section: Discussionmentioning

confidence: 99%

Illustrating scholar–practitioner collaboration for data-driven decision-making in the optimization of logistics facility location and implications for increasing the adoption of AR and VR practices

Rohani

Peerally

Moghavvemi

et al. 2021

TQM

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PurposeThis study illustrates the experience of scholar–practitioner collaboration for data-driven decision-making through the problematic of optimizing facility locations and minimizing logistics costs for La Palette Rouge (LPR) of Portugal.Design/methodology/approachThe authors used a mixed mixed-method approach involving (1) a quantitative exploratory analysis of big data, which applied analytics and mathematical modeling to optimize LPR's logistics network, and (2) an illustrative case of scholar–practitioner collaboration for data-driven decision-making.FindingsThe quantitative analysis compared more than 20 million possible configurations and proposed the optimal logistics structures. The proposed optimization model minimizes the logistics costs by 22%. Another optimal configuration revealed that LPR can minimize logistics costs by 12% through closing one of its facilities. The illustrative description demonstrates that well-established resource-rich multinational enterprises do not necessarily have the in-house capabilities and competencies to handle and analyze big data.Practical implicationsThe mathematical modeling for optimizing logistics networks demonstrates that outcomes are readily actionable for practitioners and can be extended to other country and industry contexts with logistics operations. The case illustrates that synergistic relationships can be created, and the opportunities exist between scholars and practitioners in the field of Logistics 4.0 and that scientific researcher is necessary for solving problems and issues that arise in practice while advancing knowledge.Originality/valueThe study illustrates that several Logistics 4.0 challenges highlighted in the literature can be collectively addressed through scholar–practitioner collaborations. The authors discuss the implications of such collaborations for adopting virtual and augmented reality (AR) technologies and to develop the capabilities for maximizing their benefits in mature low-medium technology industries, such as the food logistics industry.

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DECAS: a modern data-driven decision theory for big data and analytics

Cited by 48 publications

References 63 publications

Internet of things, big data analytics and operational performance: the mediating effect of supply chain visibility

Internet of things, big data analytics and operational performance: the mediating effect of supply chain visibility

The composition of data economy: a bibliometric approach and TCCM framework of conceptual, intellectual and social structure

Illustrating scholar–practitioner collaboration for data-driven decision-making in the optimization of logistics facility location and implications for increasing the adoption of AR and VR practices

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