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Article
Publication date: 21 September 2015

Anthony Marshall, Stefan Mueck and Rebecca Shockley

To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation…

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Abstract

Purpose

To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation.

Design/methodology/approach

Researchers asked about innovation goals, barriers to innovation, metrics used to measure innovation outcomes, treatment and types of innovation projects and the role of big data and analytics in innovation processes.

Findings

Three distinct groups emerged: Leaders, Strivers and Strugglers. Leaders are markedly different as a group: they innovate using big data and analytics within a structured approach, and they focus in particular on collaboration.

Research limitations/implications

Respondents were from the 2014 IBM Innovation Survey. We conducted cluster analysis with 81 variables. The three cluster solution was determined deploying latent class analysis (LCA), a family of techniques based around clustering and data reduction for segmentation projects. It uses a number of underlying statistical models to capture differences between observed data or stimuli in the form of discrete (unordered) population segments; group segments; ordered factors (segments with an underlying numeric order); continuous factors; or mixtures of the above.

Practical implications

Leaders don’t just embrace analytics and actionable insights; they take them to the next level, integrating analytics and insights with innovation. Leaders follow three basic strategies that center on data, skills and tools and culture: promote excellent data quality and accessibility; make analytics and innovation a part of every role; build a quantitative innovation culture.

Originality/value

The research found that leaders leverage big data and analytics more effectively over a wider range of organizational processes and functions. They are significantly better at leveraging big data and analytics throughout the innovation process – from conceiving new ideas to creating new business models and developing new products and services.

Details

Strategy & Leadership, vol. 43 no. 5
Type: Research Article
ISSN: 1087-8572

Keywords

Available. Content available
Article
Publication date: 21 September 2015

Robert Randall

156

Abstract

Details

Strategy & Leadership, vol. 43 no. 5
Type: Research Article
ISSN: 1087-8572

Available. Content available
Article
Publication date: 21 September 2015

Catherine Gorrell

130

Abstract

Details

Strategy & Leadership, vol. 43 no. 5
Type: Research Article
ISSN: 1087-8572

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Article
Publication date: 1 August 2020

Sanjiv Narula, Surya Prakash, Maheshwar Dwivedy, Vishal Talwar and Surendra Prasad Tiwari

This research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.

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Abstract

Purpose

This research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.

Design/methodology/approach

This article identifies the factor pool responsible for I4.0 from the extant literature. It aims to identify the set of key factors for the I4.0 application in the manufacturing industry and validate, classify factor pool using appropriate statistical tools, for example, factor analysis, principal component analysis and item analysis.

Findings

This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries from the factor pool. This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries. Strategy, leadership and culture are found key elements of transformation in the journey of I4.0. Additionally, design and development in the digital twin, virtual testing and simulations were also important factors to consider by manufacturing firms.

Research limitations/implications

The proposed I4.0 factor stratification model will act as a starting point while designing strategy, adopting readiness index for I4.0 and creating a roadmap for I4.0 application in manufacturing. The I4.0 factors identified and validated in this paper will act as a guide for policymakers, researchers, academicians and practitioners working on the implementation of Industry 4.0. This work establishes a solid groundwork for developing an I4.0 maturity model for manufacturing industries.

Originality/value

The existing I4.0 literature is critically examined for creating a factor pool that further presented to experts to ensure sufficient rigor and comprehensiveness, particularly checking the relevance of subfactors for the manufacturing sector. This work is an attempt to identify and validate major I4.0 factors that can impact its mass adoption that is further empirically tested for factor stratification.

Details

Journal of Advances in Management Research, vol. 17 no. 5
Type: Research Article
ISSN: 0972-7981

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