scholarly journals Big data analytics: a link between knowledge management capabilities and superior cyber protection

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Peter Oluseyi Obitade
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Knowledge Management Capabilities ◽  
Management Capabilities

Big data analytics capabilities and knowledge management: impact on firm performance

2019 ◽  
Vol 57 (8) ◽  
pp. 1923-1936 ◽  
Author(s):  
Alberto Ferraris ◽  
Alberto Mazzoleni ◽  
Alain Devalle ◽  
Jerome Couturier
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Structural Equation ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Integrated Approach ◽  
Content Type ◽  
Decision Making Processes ◽  
External Sources

Purpose Big data analytics (BDA) guarantees that data may be analysed and categorised into useful information for businesses and transformed into big data related-knowledge and efficient decision-making processes, thereby improving performance. However, the management of the knowledge generated from the BDA as well as its integration and combination with firm knowledge have scarcely been investigated, despite an emergent need of a structured and integrated approach. The paper aims to discuss these issues. Design/methodology/approach Through an empirical analysis based on structural equation modelling with data collected from 88 Italian SMEs, the authors tested if BDA capabilities have a positive impact on firm performances, as well as the mediator effect of knowledge management (KM) on this relationship. Findings The findings of this paper show that firms that developed more BDA capabilities than others, both technological and managerial, increased their performances and that KM orientation plays a significant role in amplifying the effect of BDA capabilities. Originality/value BDA has the potential to change the way firms compete through better understanding, processing, and exploiting of huge amounts of data coming from different internal and external sources and processes. Some managerial and theoretical implications are proposed and discussed in light of the emergence of this new phenomenon.

The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility

2019 ◽  
Vol 32 (2) ◽  
pp. 297-318 ◽  
Author(s):  
Santanu Mandal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Investment Decision ◽  
Big Data Analytics ◽  
Content Type ◽  
Investment Decision Making ◽  
Management Capabilities ◽  
The Impact

Purpose The importance of big data analytics (BDA) on the development of supply chain (SC) resilience is not clearly understood. To address this, the purpose of this paper is to explore the impact of BDA management capabilities, namely, BDA planning, BDA investment decision making, BDA coordination and BDA control on SC resilience dimensions, namely, SC preparedness, SC alertness and SC agility. Design/methodology/approach The study relied on perceptual measures to test the proposed associations. Using extant measures, the scales for all the constructs were contextualized based on expert feedback. Using online survey, 249 complete responses were collected and were analyzed using partial least squares in SmartPLS 2.0.M3. The study targeted professionals with sufficient experience in analytics in different industry sectors for survey participation. Findings Results indicate BDA planning, BDA coordination and BDA control are critical enablers of SC preparedness, SC alertness and SC agility. BDA investment decision making did not have any prominent influence on any of the SC resilience dimensions. Originality/value The study is important as it addresses the contribution of BDA capabilities on the development of SC resilience, an important gap in the extant literature.

scholarly journals Achieving strategic flexibility in the era of big data

2020 ◽  
Vol 58 (8) ◽  
pp. 1585-1600 ◽  
Author(s):  
Riccardo Rialti ◽  
Giacomo Marzi ◽  
Andrea Caputo ◽  
Kayode Abraham Mayah
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Structural Equation ◽  
Dynamic Capabilities ◽  
Structural Model ◽  
Methodological Approach ◽  
Strategic Flexibility ◽  
Content Type ◽  
Knowledge Management Capabilities ◽  
Management Capabilities

PurposeThis research unpacks the micro-mechanisms that exist between an organisation's ability to conduct big data analytics (BDA) and its achievement of strategic flexibility. Knowledge management capabilities and organisational ambidexterity have long been considered factors influencing the aforementioned relationship. In order to assess this, the authors build on dynamic capabilities as the main theoretical lens through which to examine.Design/methodology/approachStructural equation modelling (SEM) is the main methodological approach used in this research. A structural model was developed and tested based on 215 survey responses collected from managers of organisations in continental Europe.FindingsThe results indicate that BDA capabilities are a significant antecedent of an organisation's strategic flexibility. This relationship, however, is influenced by knowledge management capabilities and ambidexterity.Practical implicationsManagers wishing to properly exploit the potential of big data should invest in the elaboration of knowledge management processes across their organisation. This strategy can foster strategic flexibility.Originality/valuePrevious research has explored the theoretical links between big data, knowledge management and strategic flexibility. However, little attention has been paid to the quantitative investigation of the phenomenon.

Mediating effect of big data analytics on project performance of small and medium enterprises

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sachin K. Mangla ◽  
Rakesh Raut ◽  
Vaibhav S. Narwane ◽  
Zuopeng (Justin) Zhang ◽  
Pragati priyadarshinee
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Project Performance ◽  
Mediating Effect ◽  
Equation Modeling ◽  
Content Type ◽  
Operational Capabilities ◽  
Green Purchasing

PurposeThis study aims to investigate the mediating role of “Big Data Analytics” played between “Project Performance” and nine factors including top management, project knowledge management focus on sustainability, green purchasing, environmental technologies, social responsibility, project operational capabilities, project complexity, collaboration and explorative learning, and project success.Design/methodology/approachA sample of 321 responses from 106 Indian manufacturing small and medium-scaled enterprises (SMEs) was collected. Data were analyzed using empirical analysis through structural equation modeling.FindingsThe result shows that project knowledge management, green purchasing and project operational capabilities require the mediating support of big data analytics. The adoption of big data analytics has a positive influence on project performance in the manufacturing sector.Practical implicationsThis study is useful to SMEs managers, practitioners and government policymakers to develop an understanding of big data analytics, eliminate challenges in the adoption of big data, and formulate strategies to handle projects efficiently in SMEs in the context of Indian manufacturing.Originality/valueFor the first time, big data for manufacturing firms handing innovative projects was discussed in the Indian SME context.

How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Surajit Bag ◽  
Pavitra Dhamija ◽  
Sunil Luthra ◽  
Donald Huisingh
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chains ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Hypothetical Model ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Management Capabilities

PurposeIn this paper, the authors emphasize that COVID-19 pandemic is a serious pandemic as it continues to cause deaths and long-term health effects, followed by the most prolonged crisis in the 21st century and has disrupted supply chains globally. This study questions “can technological inputs such as big data analytics help to restore strength and resilience to supply chains post COVID-19 pandemic?”; toward which authors identified risks associated with purchasing and supply chain management by using a hypothetical model to achieve supply chain resilience through big data analytics.Design/methodology/approachThe hypothetical model is tested by using the partial least squares structural equation modeling (PLS-SEM) technique on the primary data collected from the manufacturing industries.FindingsIt is found that big data analytics tools can be used to help to restore and to increase resilience to supply chains. Internal risk management capabilities were developed during the COVID-19 pandemic that increased the company's external risk management capabilities.Practical implicationsThe findings provide valuable insights in ways to achieve improved competitive advantage and to build internal and external capabilities and competencies for developing more resilient and viable supply chains.Originality/valueTo the best of authors' knowledge, the model is unique and this work advances literature on supply chain resilience.

Davenport and Prusak on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 7-11 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Early Years ◽  
Content Type ◽  
Potential Risks ◽  
The Relationship

Purpose Larry Prusak and Tom Davenport have long been leading voices in the knowledge management (KM) field. This interview aims to explore their views on the relationship between KM and big data/analytics. Design/methodology/approach An interview was conducted by email with Larry Prusak and Tom Davenport in 2015 and updated in 2016. Findings Prusak and Davenport hold differing views on the role of KM today. They also see the relationship between KM and big data/analytics somewhat differently. Davenport, in particular, has much to say on how big data/analytics can be best utilized by business as well as its potential risks. Originality/value It is important to understand how two of the most serious KM thinkers since the early years of KM understand the relationship between big data/analytics, KM and organizations. Their views can help shape thinking in these fields.

Using a Revised Knowledge Pyramid to Redefine Knowledge Management Strategy

2019 ◽  
pp. 1-18
Author(s):  
Murray E. Jennex
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Organizational Learning ◽  
Internet Of Things ◽  
Data Analytics ◽  
Management Strategy ◽  
Big Data Analytics ◽  
Knowledge Management Strategy ◽  
Km Strategy ◽  
Customer Intelligence

The knowledge pyramid has been used for several years to illustrate the hierarchical relationships between data, information, knowledge, and wisdom. This chapter posits that the knowledge pyramid is too basic and fails to represent reality and presents a revised knowledge-KM pyramid. One key difference is that the revised knowledge-KM pyramid includes knowledge management as an extraction of reality with a focus on organizational learning. The revised pyramid includes newer initiatives such as business and/or customer intelligence, big data, analytics, internet of things. Finally, this chapter discusses how KM strategy can be generated using the final revised pyramid.

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