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1 – 10 of over 4000Trevor Cadden, Ronan McIvor, Guangming Cao, Raymond Treacy, Ying Yang, Manjul Gupta and George Onofrei
Increasingly, studies are reporting supply chain analytical capabilities as a key enabler of supply chain agility (SCAG) and supply chain performance (SCP). This study…
Abstract
Purpose
Increasingly, studies are reporting supply chain analytical capabilities as a key enabler of supply chain agility (SCAG) and supply chain performance (SCP). This study investigates the impact of environmental dynamism and competitive pressures in a supply chain analytics setting, and how intangible supply chain analytical capabilities (ISCAC) moderate the relationship between big data characteristics (BDC's) and SCAG in support of enhanced SCP.
Design/methodology/approach
The study draws on the literature on big data, supply chain analytical capabilities, and dynamic capability theory to empirically develop and test a supply chain analytical capabilities model in support of SCAG and SCP. ISCAC was the moderated construct and was tested using two sub-dimensions, supply chain organisational learning and supply chain data driven culture.
Findings
The results show that whilst environmental dynamism has a significant relationship on the three key BDC's, only the volume and velocity dimensions are significant in relation to competitive pressures. Furthermore, only the velocity element of BDC's has a significant positive impact on SCAG. In terms of moderation, the supply chain organisational learning dimension of ISCAC was shown to only moderate the velocity aspect of BDC's on SCAG, whereas for the supply chain data driven culture dimension of ISCAC, only the variety aspect was shown to moderate of BDC on SCAG. SCAG had a significant impact on SCP.
Originality/value
This study adds to the existing knowledge in the supply chain analytical capabilities domain by presenting a nuanced moderation model that includes external factors (environmental dynamism and competitive pressures), their relationships with BDC's and how ISCAC (namely, supply chain organisational learning and supply chain data driven culture) moderates and strengthens aspects of BDC's in support of SCAG and enhanced SCP.
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Najah Almazmomi, Aboobucker Ilmudeen and Alaa A. Qaffas
In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's competitive…
Abstract
Purpose
In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's competitive advantage. Though widespread attention has been given to the above concepts, there hasn't been much research done on how it could support achieving competitive advantage.
Design/methodology/approach
This research strongly lies on the theoretical background and empirically tests the hypothesized relationships. The primary survey of 272 responses was analysed by using the partial least squares structural equation modelling (PLS-SEM).
Findings
The findings of this study show a significant relationship for the constructs in the research model except for the third hypothesis. Accordingly, the firm's data-driven culture does not have a significant impact on new product newness.
Originality/value
This study empirically tests the business analytics capability, data-driven culture, and new product development features in the context of a firm's competitive advantage. The findings of this study contribute to the theoretical, practical and managerial aspects of this field.
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Anna Visvizi, Orlando Troisi, Mara Grimaldi and Francesca Loia
The study queries the drivers of innovation management in contemporary data-driven organizations/companies. It is argued that data-driven organizations that integrate a strategic…
Abstract
Purpose
The study queries the drivers of innovation management in contemporary data-driven organizations/companies. It is argued that data-driven organizations that integrate a strategic orientation grounded in data, human abilities and proactive management are more effective in triggering innovation.
Design/methodology/approach
Research reported in this paper employs constructivist grounded theory, Gioia methodology, and the abductive approach. The data collected through semi-structured interviews administered to 20 Italian start-up founders are then examined.
Findings
The paper identifies the key enablers of innovation development in data-driven companies and reveals that data-driven companies may generate different innovation patterns depending on the kind of capabilities activated.
Originality/value
The study provides evidence of how the combination of data-driven culture, skills' enhancement and the promotion of human resources may boost the emergence of innovation.
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Rohit Agrawal, Vishal Ashok Wankhede, Anil Kumar and Sunil Luthra
This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated…
Abstract
Purpose
This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.
Design/methodology/approach
A systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.
Findings
The findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.
Originality/value
The paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.
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Denise Chenger and Rachael N. Pettigrew
Companies are turning to big data (BD) programs to help mitigate supply chain (SC) disruptions and risks that are increasing in frequency and severity. The purpose of this paper…
Abstract
Purpose
Companies are turning to big data (BD) programs to help mitigate supply chain (SC) disruptions and risks that are increasing in frequency and severity. The purpose of this paper is to explore exactly how companies translate data into meaningful information used to manage SC risk and create economic value; an area not well researched. As companies are turning to big-data programs to help mitigate supply chain (SC) disruptions and risks that are increasing in frequency and severity, having the capability to internally integrate SC information is cited as the most critical risk to manage.
Design/methodology/approach
Information processing theory and resource-based view are applied to support capability development used to make value-based BD decisions. Semi-structured interviews were conducted with leaders in both the oil and gas industry and logistics SC partners to explore each companies’ BD transformation.
Findings
Findings illuminate how companies can build internal capability to more effectively manage SC risk, optimize operating assets and drive employee engagement.
Research limitations/implications
The oil and gas industry were early adopters of gathering BD; more studies addressing how companies translate data to create value and manage SC risk would be beneficial.
Practical implications
Guidance for senior leaders to proactively introduce BD to their company through a practical framework. Further, this study provides insight into where the maximum benefit may reside, as data intersects with other company resources to build an internal capability.
Originality/value
This study presents a framework highlighting best practices for introducing BD plus creating a culture capable of using that data to reduce risk during design, implementation and ongoing operations. The steps for producing the maximum benefit are laid out in this study.
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Yang Liu, Wei Fang, Taiwen Feng and Na Gao
Based on organizational information processing theory, this research explores how big data analytics capability (BDAC) contributes to green supply chain integration (GSCI) and the…
Abstract
Purpose
Based on organizational information processing theory, this research explores how big data analytics capability (BDAC) contributes to green supply chain integration (GSCI) and the contingency role that data-driven decision culture plays.
Design/methodology/approach
Using the two-wave survey data collected from 317 Chinese manufacturing firms, the authors validate the hypotheses.
Findings
The results show that big data managerial capability has positive impacts on three dimensions of GSCI, while big data technical capability has positive impacts on green internal and customer integration. Moreover, green internal integration mediates the impacts of big data technical capability and managerial capability on green supplier and customer integration. Finally, data-driven decision culture alleviates the positive impacts of big data technical and managerial capability on green internal integration.
Practical implications
The findings suggest that firms can leverage big data technical and managerial capability to enhance information processing capability for achieving a higher degree of GSCI. Further, the critical role of data-driven decision culture in affecting the link between BDAC and GSCI should not be overlooked.
Originality/value
This research contributes to literature on green supply chain management by revealing the role of BDAC in improving GSCI.
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Keywords
Maria Cristina Pietronudo, Fuli Zhou, Andrea Caporuscio, Giuseppe La Ragione and Marcello Risitano
This article aims to understand the role of intermediaries that manage innovation challenges in the healthcare scenario. More specifically, it explores the role of digital…
Abstract
Purpose
This article aims to understand the role of intermediaries that manage innovation challenges in the healthcare scenario. More specifically, it explores the role of digital platforms in addressing data challenges and fostering data-driven innovation in the health sector.
Design/methodology/approach
For exploring the role of platforms, the authors propose a theoretical model based on the platform’s dynamic capabilities, assuming that, because of their set of capabilities, platforms may trigger innovation practices in actor interactions. To corroborate the theoretical framework, the authors present a detailed in-depth case study analysis of Apheris, an innovative data-driven digital platform operating in the healthcare scenario.
Findings
The paper finds that the innovative data-driven digital platform can be used to revolutionize established practices in the health sector (a) accelerating research and innovation; (b) overcoming challenges related to healthcare data. The case study demonstrates how data and intellectual property sharing can be privacy-compliant and enable new capabilities.
Originality/value
The paper attempts to fill the gap between the use of the data-driven digital platform and the critical innovation practices in the healthcare industry.
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Gioconda Mele, Guido Capaldo, Giustina Secundo and Vincenzo Corvello
In the landscape created by digital transformation, developing the ability to adapt and innovate by absorbing and generating new knowledge has become a strategic priority for…
Abstract
Purpose
In the landscape created by digital transformation, developing the ability to adapt and innovate by absorbing and generating new knowledge has become a strategic priority for organizations. The theory of dynamic capabilities, especially from a knowledge-based perspective, has proven particularly useful in studying the phenomena of transformation and change. Moving from this premise, this paper aims to map the state of research and to define guidelines for the actualization of dynamic capabilities theory in the digital transformation era.
Design/methodology/approach
A structured literature review of 75 papers, using descriptive, bibliographic and content analysis, was performed to analyze the evolution of dynamic capabilities in the context of digital transformation.
Findings
Studies concerning knowledge-based dynamic capabilities for digital transformation have been clustered into five main research areas: the micro-foundation of dynamic capabilities for digital transformation; dynamic capabilities for value creation in digital transformation; dynamic capabilities for digital transition in specific industries; dynamic capabilities for “data-driven organizations”; and dynamic capabilities for digital transformation in SMEs and family firms. A future research agenda for scholars in strategic management is presented.
Practical implications
A conceptual framework and a future research agenda are presented to highlight directions for this promising research field concerning the renewal of dynamic capabilities in the context of digital transformation.
Originality/value
The originality of the paper lies in the conceptual framework aiming to systematize current research on knowledge-based dynamic capabilities for digital transformation and to provide a new conceptualization of digital dynamic capabilities, clarifying how organizations create and share knowledge in the era of digitalization.
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Muhammad Irfan and Mingzheng Wang
The purpose of this paper is to analyze the effects of data-driven capabilities on supply chain integration (SCI) and competitive performance of firms in the food and beverages (F…
Abstract
Purpose
The purpose of this paper is to analyze the effects of data-driven capabilities on supply chain integration (SCI) and competitive performance of firms in the food and beverages (F & B) industry in Pakistan.
Design/methodology/approach
The authors adopt the structural equation modeling approach to test the proposed hypotheses using AMOS 23. Survey data were collected from 240 firms in the F & B industry in Pakistan.
Findings
The results revealed that SCI (i.e. internal integration (II) and external integration (EI)) significantly mediates the effect of data-driven capabilities (i.e. flexible information technology resources and data assimilation) on a firm’s competitive performance. In addition to the direct effects, II also has an indirect effect on competitive performance through EI.
Practical implications
The study has several implications for managers in the context of big data application in food supply chain management (FSCM) in a developing country context. The study posits that firms can achieve excellence in performance by governing data-driven supply chain operations. The study also has implications for distributors and importers in the F & B industry. The cloud-based sharing of data can improve the operational performance of channel members while reducing their overall cost of operations. In practice, food franchises largely get the advantage of shared resources of their suppliers in managing orders, payments, inventory and after-sales services.
Originality/value
The study is novel and deepens the understanding about the use of big data in FSCM keeping in view the industry trends and stakeholder’s priorities in a developing country context.
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Yinhua Liu, Rui Sun and Sun Jin
Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control…
Abstract
Purpose
Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.
Design/methodology/approach
This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.
Findings
A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.
Originality/value
This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.
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