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1 – 10 of over 5000
Article
Publication date: 13 July 2022

Trevor 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…

1283

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.

Details

International Journal of Operations & Production Management, vol. 42 no. 9
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 5 August 2021

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…

2948

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.

Details

Benchmarking: An International Journal, vol. 29 no. 4
Type: Research Article
ISSN: 1463-5771

Keywords

Open Access
Article
Publication date: 6 December 2021

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…

4166

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.

Details

European Journal of Innovation Management, vol. 25 no. 6
Type: Research Article
ISSN: 1460-1060

Keywords

Article
Publication date: 21 May 2021

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…

928

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.

Details

The TQM Journal, vol. 35 no. 1
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 15 June 2023

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.

Article
Publication date: 30 August 2022

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.

Details

Industrial Management & Data Systems, vol. 122 no. 11
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 7 February 2024

Moh’d Anwer AL-Shboul

This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the…

Abstract

Purpose

This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the manufacturing sector in the countries located in North Africa (NA). These are considered developing countries through generating green product innovation (GPI) and using green process innovations (GPrLs) in their processes and functions as mediating factors, as well as the moderating role of data-driven competitive sustainability (DDCS).

Design/methodology/approach

To achieve the aim of this study, 346 useable surveys out of 1,601 were analyzed, and valid responses were retrieved for analysis, representing a 21.6% response rate by applying the quantitative methodology for collecting primary data. Convergent validity and discriminant validity tests were applied to structural equation modeling (SEM) in the CB-covariance-based structural equation modeling (SEM) program, and the data reliability was confirmed. Additionally, a multivariate analysis technique was used via CB-SEM, as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model. Further, a bootstrapping technique was used to analyze the data. We included GPI and GPrI as mediating factors, while using DDCS as a moderated factor.

Findings

The empirical findings indicated that the proposed moderated-mediation model was accepted due to the relationships between the constructs being statistically significant. Further, the findings showed that there is a significant positive effect in the relationship between reliable BCDA capabilities and CAs as well as a mediating effect of GPI and GPrI, which is supported by the proposed formulated hypothesis. Additionally, the findings confirmed that there is a moderating effect represented by data-driven competitive advantage suitability between GPI, GPrI and CA.

Research limitations/implications

One of the main limitations of this study is that an applied cross-sectional study provides a snapshot at a given moment in time. Furthermore, it used only one type of methodological approach (i.e. quantitative) rather than using mixed methods to reach more accurate data.

Originality/value

This study developed a theoretical model that is obtained from reliable BCDA capabilities, CA, DDCS, green innovation and GPrI. Thus, this piece of work bridges the existing research gap in the literature by testing the moderated-mediation model with a focus on the manufacturing sector that benefits from big data analytics capabilities to improve levels of GPI and competitive advantage. Finally, this study is considered a road map and gaudiness for the importance of applying these factors, which offers new valuable information and findings for managers, practitioners and decision-makers in the manufacturing sector in the NA region.

Details

International Journal of Productivity and Performance Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0401

Keywords

Open Access
Article
Publication date: 2 August 2022

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…

3273

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.

Details

European Journal of Innovation Management, vol. 25 no. 6
Type: Research Article
ISSN: 1460-1060

Keywords

Article
Publication date: 8 June 2023

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…

1536

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.

Book part
Publication date: 30 January 2023

Anne-Mari Järvenpää, Jari Jussila and Iivari Kunttu

The circular economy (CE) model is seen as an alternative model to the linear economy models, which seem to be reaching their physical limits. The CE business model aims to reuse…

Abstract

The circular economy (CE) model is seen as an alternative model to the linear economy models, which seem to be reaching their physical limits. The CE business model aims to reuse materials and decrease the need for virgin materials. This requires the implementation of a reverse supply chain, close collaboration between actors, as well as well-organized logistics. For this reason, the CE companies have typically high demand for digitalized processes and the utilization of data on both operational and business development dimensions. Also the utilization of big data collected from the companies’ business environment can provide new opportunities for business development in CE. Despite the fact that utilization of data collected from the business environment and operations enables data-driven approaches for various decision-making functions in companies, many companies still struggle to figure out how to use analytics to take advantage of their data. In the small- and medium-sized enterprises (SMEs), in particular, the managers are facing difficulties with ever-increasing amounts of data and sophisticated analytics. Indeed, prior research identified several kinds of barriers to the effective utilization of data in SMEs. Still, research on data-driven decision-making remains scarce in CE context. This chapter presents a case study consisting of seven cases, all representing SMEs operating in the field of CE in Finland. In the case study, the barriers and practical challenges for data-driven decision-making in CE SMEs are investigated. Based on the case study results, this chapter proposes that utilization of data, lack of resources, lack of capabilities, and regulation are the main barriers to data-driven decision-making in CE SMEs.

Details

Big Data and Decision-Making: Applications and Uses in the Public and Private Sector
Type: Book
ISBN: 978-1-80382-552-6

Keywords

1 – 10 of over 5000