Abstract:PurposeThis paper aims to investigate the use of big data (BDU) in predicting technological innovation, supply chain and SMEs' performance and whether technological innovation mediates the association between BDU and firm performance. Additionally, this research also seeks to explore the moderating effect of information sharing in the association between BDU and technological innovation.Design/methodology/approachUsing survey methods and structural associations in AMOS 24.0., the proposed model was tested on S… Show more
“…From an overall organisational perspective, the application of Big Data enables the assessment of key supply chain performance indicators (KPIs), aids management decision-making through visualisation of customers' behaviour, reduces the ripple effect, and helps in revamping information processing capacity (Gunasekaran et al 2017;Matthias et al 2017;Mishra et al 2017;Dev et al 2019;Dubey et al 2019d). Additionally, the application of Big Data helps in minimising externalities, promoting a culture of data-driven predictive performance management, enhancing operational efficiency, improving the buyer-supplier relationship, and supporting technological innovation at both product and process levels (Hazen et al 2016;Bag 2017;Mehmood et al 2017;Dubey, Gunasekaran, and Childe 2019b;Saleem et al 2020). Furthermore, Big Data management capabilities, such as planning, coordination, investment, control, and talent, prominently improve the supply chain performance and support employee development (Mandal 2019(Mandal , 2018bBag et al 2020b).…”
In the era of digitalisation, the role of Big Data is proliferating, receiving considerable attention in all sectors and domains. The domain of operations and supply chain management (OSCM) is no different since it offers multiple opportunities to generate a large magnitude of data in real-time. Such extensive opportunities for data generation have attracted academics and practitioners alike who are eager to tap different elements of Big Data application in OSCM. Despite the richness of prior studies, there is limited research that extensively reviews the extant findings to present an overview of the different facets of this area. The current study addresses this gap by conducting a systematic literature review (SLR) to uncover the existing research trends, distil key themes, and identify areas for future research. For this purpose, 116 studies were identified through a stringent search protocol and critically analysed. The key outcome of this SLR is the development of a conceptual framework titled the Dimensions-Avenues-Benefits (DAB) model for BDA adoption as well as potential research questions to support novel investigations in the area, offering actionable implications for managers working in different verticals and sectors.
“…From an overall organisational perspective, the application of Big Data enables the assessment of key supply chain performance indicators (KPIs), aids management decision-making through visualisation of customers' behaviour, reduces the ripple effect, and helps in revamping information processing capacity (Gunasekaran et al 2017;Matthias et al 2017;Mishra et al 2017;Dev et al 2019;Dubey et al 2019d). Additionally, the application of Big Data helps in minimising externalities, promoting a culture of data-driven predictive performance management, enhancing operational efficiency, improving the buyer-supplier relationship, and supporting technological innovation at both product and process levels (Hazen et al 2016;Bag 2017;Mehmood et al 2017;Dubey, Gunasekaran, and Childe 2019b;Saleem et al 2020). Furthermore, Big Data management capabilities, such as planning, coordination, investment, control, and talent, prominently improve the supply chain performance and support employee development (Mandal 2019(Mandal , 2018bBag et al 2020b).…”
In the era of digitalisation, the role of Big Data is proliferating, receiving considerable attention in all sectors and domains. The domain of operations and supply chain management (OSCM) is no different since it offers multiple opportunities to generate a large magnitude of data in real-time. Such extensive opportunities for data generation have attracted academics and practitioners alike who are eager to tap different elements of Big Data application in OSCM. Despite the richness of prior studies, there is limited research that extensively reviews the extant findings to present an overview of the different facets of this area. The current study addresses this gap by conducting a systematic literature review (SLR) to uncover the existing research trends, distil key themes, and identify areas for future research. For this purpose, 116 studies were identified through a stringent search protocol and critically analysed. The key outcome of this SLR is the development of a conceptual framework titled the Dimensions-Avenues-Benefits (DAB) model for BDA adoption as well as potential research questions to support novel investigations in the area, offering actionable implications for managers working in different verticals and sectors.
“…Most researchers explored the association between efficient supply chain management and enterprises' innovation by empirical inquiry or survey methods [1][2][3][4][5][6][7]. An increasing number of scholars recently realized the importance of data analysis and text mining for supply chain management.…”
Section: Supply Chain Management and Enterprises' Technological Innovationmentioning
confidence: 99%
“…, others, (6). The matrix W is obtained by fusing the matrices M and Q through the formula (7). The element W im in the matrix W represents the semantic similarity and relatedness of the keyword t i to the category C m the matrix W generated by the keyword t i and the category C m in the text d j can be denoted as shown in Table 2.…”
An improved text classification method based on domain ontology is proposed in this paper to organize the mass information that records node enterprises’ innovation activities under the supply chain environment. This method can classify the documents of node enterprises under the supply chain without a training set. It achieves a precision of 80% for documents’ classification, which outperforms the baseline method. Besides, the paper constructs a domain ontology of enterprises’ technological innovation under the supply chain that effectively enhances the semantic relationship between words. Therefore, it can summarize and classify the textual information generated by node enterprises in product design, production, storage, logistics, and sales.
“…Many firms increasingly use information technology (IT) to integrate their business processes into the processes of their suppliers, customers and other parties involved in the supply chain (Büyükö). By utilizing supply chain data and process knowledge, organizations can improve their visibility and streamline their operations to gain efficiency, which greatly contributes to the overall sustainability of the supply chain (Ganbold et al , 2021; Saleem et al , 2020). This operational efficiency has been amplified by the advent of digital services and digital platforms providing some well-defined reusable functionality through standardized protocols (Tiwana et al , 2010; Williams et al , 2008).…”
PurposeProcess models specific to the supply chain domain are an important tool for the analysis of interorganizational interfaces and requirements of information technology (IT) systems supporting supply chain decision-making. The purpose of this study is to examine the effectiveness of supply chain process models for novice analysts in conveying domain semantics compared to alternative textual representations.Design/methodology/approachA laboratory experiment with graduate students as proxies for novice analysts was conducted. Participants were randomly assigned to either the diagram group, which worked with “thread diagrams” created from the modeling grammar “Supply Chain Operation Reference (SCOR) model”, or the text group, which worked with semantically equivalent textual representations. Domain understanding was measured using cognitively demanding information acquisition for two different domains.FindingsDiagram users were more accurate in identifying product-related information and organizing this information in a graph compared to those using the textual representation. The authors found considerable improvements in domain understanding, and using the diagrams was perceived as easy as using the texts.Originality/valueThe study's findings are unique in providing empirical evidence for supply chain process models being an effective representation for novice analysts. Such evidence is lacking in prior research because of the evaluation methods used, which are limited to scenario, case study and informed argument. This study adds the diagram user's perspective to that literature and provides a rigorous empirical evaluation by contrasting diagrammatic and textual representations.
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