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Linking data-driven innovation to firm performance: a theoretical framework and case analysis.

ABSTRACT: This paper examines the impact of data-driven innovation (DDI) on firm performance, based on an exploratory case study of a manufacturing firm in China's textile and apparel industry. It explores the influence of various contextual variables on the firm's DDI and suggests ways to enhance DDI and thereby firm performance. Extending the literature on DDI, the paper proposes and validates a theoretical framework that incorporates the influence of various contextual factors on firms' DDI. The findings show that (1) individual context is associated with DDI; (2) organizational context is associated with DDI; and (3) DDI is associated with firm performance. This paper extends our understanding of how firm performance can be improved through DDI and shows that DDI should match a firm's contextual environment.

Supplementary information

The online version contains supplementary material available at 10.1007/s10479-022-05038-y.

SUBMITTER: Wong DTW

PROVIDER: S-EPMC9640841 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Linking data-driven innovation to firm performance: a theoretical framework and case analysis.

Wong David T W DTW Ngai Eric W T EWT

Annals of operations research 20221107

This paper examines the impact of data-driven innovation (DDI) on firm performance, based on an exploratory case study of a manufacturing firm in China's textile and apparel industry. It explores the influence of various contextual variables on the firm's DDI and suggests ways to enhance DDI and thereby firm performance. Extending the literature on DDI, the paper proposes and validates a theoretical framework that incorporates the influence of various contextual factors on firms' DDI. The findin ...[more]

PMID: 36407941

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Project description:BackgroundScientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation.ResultsSidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick.ConclusionsSidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to approach genomic analysis that traditional single gene lists do not, particularly in areas such as interaction discovery.

Dataset Information

Linking data-driven innovation to firm performance: a theoretical framework and case analysis.

Supplementary information

Publications

Linking data-driven innovation to firm performance: a theoretical framework and case analysis.

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