Behzad Foroughi, Mohammad Iranmanesh, Nick Hajli, Lee Shih Ling, Morteza Ghobakhloo, Davoud Nikbin
{"title":"Roles of big data analytics and organizational culture in developing innovation capabilities: a hybrid PLS‐fsQCA approach","authors":"Behzad Foroughi, Mohammad Iranmanesh, Nick Hajli, Lee Shih Ling, Morteza Ghobakhloo, Davoud Nikbin","doi":"10.1111/radm.12719","DOIUrl":null,"url":null,"abstract":"Big data analytics creates and consolidates competitive advantage by providing insights on data with enormous variety, velocity, and volume to firms. However, many companies' investments in big data analytics were unsuccessful, and they could not gain full advantage of these technologies. This study investigates the impacts of big data analytics capabilities on innovation quality and speed by considering organizational learning culture as a moderator. The study's data are obtained from a survey of 221 managers in the manufacturing industry. We integrate the Partial Least Squares (PLS) technique and fuzzy‐set Qualitative Comparative Analysis (fsQCA) to perform the analysis. The findings of PLS indicated that big data analytics capabilities positively influence both innovation quality and speed. However, innovation quality influences both market performance and financial performance, and innovation speed only affects market performance. Organizational learning culture negatively moderates the impacts of big data analytics on innovation speed and quality. fsQCA uncovered four solutions with varied combinations of factors that predict the high market and financial performance. The theoretical and practical implications are explained at the end of the paper.","PeriodicalId":21040,"journal":{"name":"R&D Management","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"R&D Management","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1111/radm.12719","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Big data analytics creates and consolidates competitive advantage by providing insights on data with enormous variety, velocity, and volume to firms. However, many companies' investments in big data analytics were unsuccessful, and they could not gain full advantage of these technologies. This study investigates the impacts of big data analytics capabilities on innovation quality and speed by considering organizational learning culture as a moderator. The study's data are obtained from a survey of 221 managers in the manufacturing industry. We integrate the Partial Least Squares (PLS) technique and fuzzy‐set Qualitative Comparative Analysis (fsQCA) to perform the analysis. The findings of PLS indicated that big data analytics capabilities positively influence both innovation quality and speed. However, innovation quality influences both market performance and financial performance, and innovation speed only affects market performance. Organizational learning culture negatively moderates the impacts of big data analytics on innovation speed and quality. fsQCA uncovered four solutions with varied combinations of factors that predict the high market and financial performance. The theoretical and practical implications are explained at the end of the paper.
期刊介绍:
R&D Management journal publishes articles which address the interests of both practising managers and academic researchers in research and development and innovation management. Covering the full range of topics in research, development, design and innovation, and related strategic and human resource issues - from exploratory science to commercial exploitation - articles also examine social, economic and environmental implications.