{"title":"解读转型：关于制造业中小企业大数据分析、绿色创新及其对经济和环境绩效影响的三波时滞研究","authors":"Khalid Mehmood, Fauzia Jabeen, Md Rashid, Safiya Mukhtar Alshibani, Alessandro Lanteri, Gabriele Santoro","doi":"10.1108/ejim-10-2023-0903","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>The firms’ adoption and improvement of big data analytics capabilities to improve economic and environmental performance have recently increased. This makes it important to discover the underlying mechanism influencing the association between big data analytics (BDA) and economic and environmental performance, which is missing in the existing literature. The present study discovers the indirect effect of green innovation (GI) and the moderating role of corporate green image (CgI) on the impact of BDA capabilities, including big data management capability (MC) and big data talent capability (TC), on economic and environmental performance.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>A time-lagged design was employed to collect data from 417 manufacturing firms, and study hypotheses were evaluated using Mplus.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The empirical outcomes indicate that both BDA capabilities of firms significantly influence green innovation (GI), which significantly mediates the relationship between BDA and economic and environmental performance. Our findings also revealed that CgI strengthened the effect of GI on economic and environmental performance. The empirical evidence provides important theoretical and practical repercussions for manufacturing SMEs and policymakers.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>This study contributes to the literature on BDA by empirically exploring the effects of MC and TC on improving the EcP and EnP of manufacturing firms. It does so through the indirect impact of GIs and the moderating effect of CgI, thereby extending the Dynamic capabilities view (DCV) paradigm.</p><!--/ Abstract__block -->","PeriodicalId":51462,"journal":{"name":"European Journal of Innovation Management","volume":"17 1","pages":""},"PeriodicalIF":5.0000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unraveling the transformation: the three-wave time-lagged study on big data analytics, green innovation and their impact on economic and environmental performance in manufacturing SMEs\",\"authors\":\"Khalid Mehmood, Fauzia Jabeen, Md Rashid, Safiya Mukhtar Alshibani, Alessandro Lanteri, Gabriele Santoro\",\"doi\":\"10.1108/ejim-10-2023-0903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>The firms’ adoption and improvement of big data analytics capabilities to improve economic and environmental performance have recently increased. This makes it important to discover the underlying mechanism influencing the association between big data analytics (BDA) and economic and environmental performance, which is missing in the existing literature. The present study discovers the indirect effect of green innovation (GI) and the moderating role of corporate green image (CgI) on the impact of BDA capabilities, including big data management capability (MC) and big data talent capability (TC), on economic and environmental performance.</p><!--/ Abstract__block -->\\n<h3>Design/methodology/approach</h3>\\n<p>A time-lagged design was employed to collect data from 417 manufacturing firms, and study hypotheses were evaluated using Mplus.</p><!--/ Abstract__block -->\\n<h3>Findings</h3>\\n<p>The empirical outcomes indicate that both BDA capabilities of firms significantly influence green innovation (GI), which significantly mediates the relationship between BDA and economic and environmental performance. Our findings also revealed that CgI strengthened the effect of GI on economic and environmental performance. The empirical evidence provides important theoretical and practical repercussions for manufacturing SMEs and policymakers.</p><!--/ Abstract__block -->\\n<h3>Originality/value</h3>\\n<p>This study contributes to the literature on BDA by empirically exploring the effects of MC and TC on improving the EcP and EnP of manufacturing firms. It does so through the indirect impact of GIs and the moderating effect of CgI, thereby extending the Dynamic capabilities view (DCV) paradigm.</p><!--/ Abstract__block -->\",\"PeriodicalId\":51462,\"journal\":{\"name\":\"European Journal of Innovation Management\",\"volume\":\"17 1\",\"pages\":\"\"},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Innovation Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1108/ejim-10-2023-0903\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Innovation Management","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/ejim-10-2023-0903","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
Unraveling the transformation: the three-wave time-lagged study on big data analytics, green innovation and their impact on economic and environmental performance in manufacturing SMEs
Purpose
The firms’ adoption and improvement of big data analytics capabilities to improve economic and environmental performance have recently increased. This makes it important to discover the underlying mechanism influencing the association between big data analytics (BDA) and economic and environmental performance, which is missing in the existing literature. The present study discovers the indirect effect of green innovation (GI) and the moderating role of corporate green image (CgI) on the impact of BDA capabilities, including big data management capability (MC) and big data talent capability (TC), on economic and environmental performance.
Design/methodology/approach
A time-lagged design was employed to collect data from 417 manufacturing firms, and study hypotheses were evaluated using Mplus.
Findings
The empirical outcomes indicate that both BDA capabilities of firms significantly influence green innovation (GI), which significantly mediates the relationship between BDA and economic and environmental performance. Our findings also revealed that CgI strengthened the effect of GI on economic and environmental performance. The empirical evidence provides important theoretical and practical repercussions for manufacturing SMEs and policymakers.
Originality/value
This study contributes to the literature on BDA by empirically exploring the effects of MC and TC on improving the EcP and EnP of manufacturing firms. It does so through the indirect impact of GIs and the moderating effect of CgI, thereby extending the Dynamic capabilities view (DCV) paradigm.
期刊介绍：
The subject of innovation is receiving increased interest both from companies because of their increased awareness of the impact of innovation in determining market success and also from the research community. Academics are increasingly beginning to place innovation as a priority area in their research agenda. This impetus has been partly fuelled by the Economic & Social Research Council (ESRC) who have designated innovation as one of nine research areas in their research initiative schemes.
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