Supply chain data analytics and supply chain agility: a fuzzy sets (fsQCA) approach
Purpose Practitioners and researchers have reached a consensus that supply chain analytics is a strong determinant for desirable organizational outcomes such as supply chain performance and agility. The purpose of this paper is to examine a configural combination (i.e. causal recipes) subsuming supp...
Saved in:
| Published in: | International journal of organizational analysis (2005) Vol. 28; no. 5; pp. 1055 - 1067 |
|---|---|
| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Bingley
Emerald Publishing Limited
08-10-2020
Emerald Group Publishing Limited |
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Purpose
Practitioners and researchers have reached a consensus that supply chain analytics is a strong determinant for desirable organizational outcomes such as supply chain performance and agility. The purpose of this paper is to examine a configural combination (i.e. causal recipes) subsuming supply chain data analytics, firmsize, age and annual sales to predict supply chain agility based on knowledge-based theory.
Design/methodology/approach
Survey data (n = 215) were obtained from firms operating in the United Arab Emirates. Consequently, fuzzy sets qualitative comparative analysis (fsQCA) technique was applied to the data to establish causal recipes that are necessary and sufficient to achieve high scores of supply chain agility.
Findings
Results from fsQCA support the major tenets of complexity theory that several configural combinations (i.e. supply chain data analytics, firm size, firm age and annual sales) are sufficient and necessary conditions for achieving higher scores of supply chain agility.
Originality/value
This study is first of its kind in understanding the association between supply chain data analytics and agility with fsQCA technique. This research also offers a headway for supply chain managers in identifying configural combinations of antecedents manifesting high scores for supply chain agility. Implications for theory and practice are illustrated as well as future research course. |
|---|---|
| AbstractList | Purpose
Practitioners and researchers have reached a consensus that supply chain analytics is a strong determinant for desirable organizational outcomes such as supply chain performance and agility. The purpose of this paper is to examine a configural combination (i.e. causal recipes) subsuming supply chain data analytics, firmsize, age and annual sales to predict supply chain agility based on knowledge-based theory.
Design/methodology/approach
Survey data (n = 215) were obtained from firms operating in the United Arab Emirates. Consequently, fuzzy sets qualitative comparative analysis (fsQCA) technique was applied to the data to establish causal recipes that are necessary and sufficient to achieve high scores of supply chain agility.
Findings
Results from fsQCA support the major tenets of complexity theory that several configural combinations (i.e. supply chain data analytics, firm size, firm age and annual sales) are sufficient and necessary conditions for achieving higher scores of supply chain agility.
Originality/value
This study is first of its kind in understanding the association between supply chain data analytics and agility with fsQCA technique. This research also offers a headway for supply chain managers in identifying configural combinations of antecedents manifesting high scores for supply chain agility. Implications for theory and practice are illustrated as well as future research course. Purpose Practitioners and researchers have reached a consensus that supply chain analytics is a strong determinant for desirable organizational outcomes such as supply chain performance and agility. The purpose of this paper is to examine a configural combination (i.e. causal recipes) subsuming supply chain data analytics, firmsize, age and annual sales to predict supply chain agility based on knowledge-based theory. Design/methodology/approach Survey data ( n = 215) were obtained from firms operating in the United Arab Emirates. Consequently, fuzzy sets qualitative comparative analysis (fsQCA) technique was applied to the data to establish causal recipes that are necessary and sufficient to achieve high scores of supply chain agility. Findings Results from fsQCA support the major tenets of complexity theory that several configural combinations (i.e. supply chain data analytics, firm size, firm age and annual sales) are sufficient and necessary conditions for achieving higher scores of supply chain agility. Originality/value This study is first of its kind in understanding the association between supply chain data analytics and agility with fsQCA technique. This research also offers a headway for supply chain managers in identifying configural combinations of antecedents manifesting high scores for supply chain agility. Implications for theory and practice are illustrated as well as future research course. PurposePractitioners and researchers have reached a consensus that supply chain analytics is a strong determinant for desirable organizational outcomes such as supply chain performance and agility. The purpose of this paper is to examine a configural combination (i.e. causal recipes) subsuming supply chain data analytics, firmsize, age and annual sales to predict supply chain agility based on knowledge-based theory.Design/methodology/approachSurvey data (n = 215) were obtained from firms operating in the United Arab Emirates. Consequently, fuzzy sets qualitative comparative analysis (fsQCA) technique was applied to the data to establish causal recipes that are necessary and sufficient to achieve high scores of supply chain agility.FindingsResults from fsQCA support the major tenets of complexity theory that several configural combinations (i.e. supply chain data analytics, firm size, firm age and annual sales) are sufficient and necessary conditions for achieving higher scores of supply chain agility.Originality/valueThis study is first of its kind in understanding the association between supply chain data analytics and agility with fsQCA technique. This research also offers a headway for supply chain managers in identifying configural combinations of antecedents manifesting high scores for supply chain agility. Implications for theory and practice are illustrated as well as future research course. |
| Author | Shamout, Mohamed Dawood |
| Author_xml | – sequence: 1 givenname: Mohamed Dawood surname: Shamout fullname: Shamout, Mohamed Dawood email: shamoutmd@yandex.com |
| BookMark | eNptkM1OwzAQhC1UJFrgAbhZ4gIHgx3bscOtqvgpqlShwtnaxDZNlSYhTg7p05OoHEDiNCPtzO7qm6FJWZUOoStG7xij-n75up4TKklEWUKYkskJmg6iiZYxmww-4YJozeUZmoWwo1QqpdUUbTZdXRc9zraQl9hCCxhKKPo2z8LgLA6_5_CZF3nbP2DAvjscehxcG_CND2-L-S2Gum4qyLYX6NRDEdzlj56jj6fH98ULWa2fl4v5imQi0i3hjHqpZBZ5bWNrOajUpi4VaSoiq5hSFuLIp5nnPJGWS-aZpEyKxMtEgo_5Obo-7h3OfnUutGZXdc3wfTCREDqmMRVqSLFjKmuqEBrnTd3ke2h6w6gZ2ZmRnaHSjOzMyG7o0GPH7V0Dhf238gc3_wYHOHGZ |
| CitedBy_id | crossref_primary_10_1108_JM2_01_2023_0002 crossref_primary_10_1080_09537287_2022_2032450 crossref_primary_10_1108_JBIM_12_2020_0564 crossref_primary_10_1108_EJIM_10_2023_0888 crossref_primary_10_1080_16258312_2022_2064721 crossref_primary_10_1108_K_09_2021_0853 crossref_primary_10_3846_tede_2021_14433 crossref_primary_10_1108_JEIM_03_2022_0077 crossref_primary_10_1177_02666669221108438 crossref_primary_10_3390_su152015088 crossref_primary_10_1002_bsd2_374 crossref_primary_10_1080_10941665_2021_1998162 crossref_primary_10_3390_jtaer16070183 crossref_primary_10_1108_K_10_2020_0684 crossref_primary_10_3390_su15065185 crossref_primary_10_1002_mde_4012 crossref_primary_10_1155_2021_3811417 crossref_primary_10_1002_bsd2_228 crossref_primary_10_1016_j_jcom_2023_100203 |
| Cites_doi | 10.1108/JMD-06-2017-0207 10.1016/j.ijinfomgt.2018.12.011 10.1007/s11365-017-0461-8 10.1007/s10796-016-9720-4 10.5465/amj.2011.60263120 10.1016/j.jbusres.2016.08.004 10.1111/poms.12746 10.1016/j.jbusres.2019.01.044 10.1016/j.im.2017.03.004 10.1016/j.ijpe.2015.10.005 10.1108/IJOPM-03-2016-0129 10.1108/VJIKMS-08-2017-0054 10.1016/j.ijinfomgt.2019.02.006 10.1108/IJOA-12-2017-1302 10.1057/ejis.2013.4 10.1016/j.ijpe.2008.09.002 10.1108/MD-01-2018-0119 10.1016/j.jbusres.2015.11.015 10.1016/j.jom.2010.12.008 10.1111/poms.12838 10.1016/j.ijpe.2016.05.008 10.1016/j.tele.2016.08.021 10.1016/j.jom.2009.03.001 10.1108/JKM-08-2015-0301 10.1016/j.jom.2008.09.006 10.1016/j.dss.2012.12.016 10.1016/j.jom.2005.11.011 10.1016/j.jbusres.2011.02.009 10.7146/jod.9799 10.1016/j.techfore.2017.09.010 10.1016/j.dss.2017.06.004 |
| ContentType | Journal Article |
| Copyright | Emerald Publishing Limited Emerald Publishing Limited 2020 |
| Copyright_xml | – notice: Emerald Publishing Limited – notice: Emerald Publishing Limited 2020 |
| DBID | AAYXX CITATION 0U~ 1-H 7WY 7WZ 7XB 8AO 8FI AFKRA AZQEC BENPR BEZIV CCPQU DWQXO FYUFA F~G GNUQQ K6~ K8~ L.- L.0 M0C M0T M2M PQBIZ PQEST PQQKQ PQUKI PRINS PSYQQ Q9U S0X |
| DOI | 10.1108/IJOA-05-2019-1759 |
| DatabaseName | CrossRef Global News & ABI/Inform Professional Trade PRO ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ProQuest Pharma Collection Hospital Premium Collection ProQuest Central ProQuest Central Essentials ProQuest Central Business Premium Collection ProQuest One Community College ProQuest Central Korea Health Research Premium Collection ABI/INFORM Global (Corporate) ProQuest Central Student ProQuest Business Collection DELNET Management Collection ABI/INFORM Professional Advanced ABI/INFORM Professional Standard ABI/INFORM Global Healthcare Administration Database Psychology Database One Business ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology ProQuest Central Basic SIRS Editorial |
| DatabaseTitle | CrossRef ABI/INFORM Global (Corporate) ProQuest One Business ProQuest One Psychology ProQuest Central Student ProQuest Central Essentials SIRS Editorial ProQuest One Community College Trade PRO ProQuest Pharma Collection ProQuest Central China ABI/INFORM Complete ProQuest Central Global News & ABI/Inform Professional ABI/INFORM Professional Advanced Health Research Premium Collection ABI/INFORM Professional Standard ProQuest Central Korea Business Premium Collection ABI/INFORM Global ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Health Management ProQuest Hospital Collection ProQuest Business Collection ProQuest Psychology Journals ProQuest One Academic UKI Edition ProQuest DELNET Management Collection ProQuest One Academic |
| DatabaseTitleList | CrossRef ABI/INFORM Global (Corporate) |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Business |
| EISSN | 1758-8561 |
| EndPage | 1067 |
| ExternalDocumentID | 10_1108_IJOA_05_2019_1759 10.1108/IJOA-05-2019-1759 |
| GroupedDBID | 0R 29J 3FY 4.4 5GY 5VS 70U 7WY 8AO 8FI 8R4 8R5 9E0 9F- AADTA AADXL AAGBP AAMCF AAPBV AAUDR ABIJV ABIVO ABSDC ACGFS ACMTK ADBBV ADOMW AEBZA AEUCW AFKRA AIUCU AJEBP ALMA_UNASSIGNED_HOLDINGS AMPDF AQUVI ASMFL ATGMP AUCOK AVELQ AZQEC BBAFP BENPR BEZIV BLEHN BPHCQ BUONS BVXVI CAG CS3 DWQXO EBS FNNZZ FYUFA GEI GMX GNUQQ GQ. HZ IJT IPNFZ J1Y JL0 K6 K8 M0C M0T M2M O9- PQBIZ PQEST PQQKQ PQUKI PRINS PROAC Q2X Q3A S0X TFF TGG TMD TMF V1G VP9 WT Z11 Z12 ZYZAG .WT 0R~ AAKOT AAPSD AAYXX ABYQI ACDYV ACTSA ADQHX AEZCW AFNZV AGTVX ALIPV AODMV CCPQU CITATION H13 HZ~ K6~ K8~ PSYQQ RIG UKHRP 0U~ 1-H 7XB AAXBI L.- L.0 Q9U |
| ID | FETCH-LOGICAL-c428t-310f575c2f8d6dd3a7bdbeb4bb42d7177da62fbcf3395d351f1501549f595af63 |
| IEDL.DBID | GQ. |
| ISSN | 1934-8835 |
| IngestDate | Mon Nov 04 11:04:57 EST 2024 Thu Sep 12 18:24:35 EDT 2024 Tue Oct 26 17:04:58 EDT 2021 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | Supply chain Fuzzy sets Innovation Analytics Agility |
| Language | English |
| License | Licensed re-use rights only |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c428t-310f575c2f8d6dd3a7bdbeb4bb42d7177da62fbcf3395d351f1501549f595af63 |
| PQID | 2448606047 |
| PQPubID | 29366 |
| PageCount | 13 |
| ParticipantIDs | proquest_journals_2448606047 crossref_primary_10_1108_IJOA_05_2019_1759 emerald_primary_10_1108_IJOA-05-2019-1759 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-10-08 |
| PublicationDateYYYYMMDD | 2020-10-08 |
| PublicationDate_xml | – month: 10 year: 2020 text: 2020-10-08 day: 08 |
| PublicationDecade | 2020 |
| PublicationPlace | Bingley |
| PublicationPlace_xml | – name: Bingley |
| PublicationTitle | International journal of organizational analysis (2005) |
| PublicationYear | 2020 |
| Publisher | Emerald Publishing Limited Emerald Group Publishing Limited |
| Publisher_xml | – name: Emerald Publishing Limited – name: Emerald Group Publishing Limited |
| References | (key2020100612144793900_ref044) 2008; 116 (key2020100612144793900_ref018) 2016; 179 (key2020100612144793900_ref040) 2009; 27 (key2020100612144793900_ref037) 2008 (key2020100612144793900_ref020) 2017; 70 (key2020100612144793900_ref001) 2018; 37 (key2020100612144793900_ref017) 2015; 33 (key2020100612144793900_ref045) 2017; 21 (key2020100612144793900_ref023) 2018; 48 (key2020100612144793900_ref039) 2012; 65 (key2020100612144793900_ref004) 2018 (key2020100612144793900_ref015) 2018; 50 (key2020100612144793900_ref021) 2018; 56 (key2020100612144793900_ref031) 2018; 270 (key2020100612144793900_ref028) 2013; 54 (key2020100612144793900_ref002) 2019; 27 (key2020100612144793900_ref014) 2011; 54 (key2020100612144793900_ref006) 2009; 27 (key2020100612144793900_ref030) 2019; 98 (key2020100612144793900_ref027) 2018 (key2020100612144793900_ref010) 2014; 23 (key2020100612144793900_ref041) 2019; 52 (key2020100612144793900_ref013) 2018; 38 (key2020100612144793900_ref038) 2017; 37 (key2020100612144793900_ref009) 2011; 29 (key2020100612144793900_ref036) 2018; 20 (key2020100612144793900_ref005) 2019 (key2020100612144793900_ref032) 2006; 24 (key2020100612144793900_ref019) 2014; 3 (key2020100612144793900_ref022) 2017; 144 (key2020100612144793900_ref016) 2017; 101 (key2020100612144793900_ref026) 2017; 28 key2020100612144793900_ref029 (key2020100612144793900_ref043) 2018; 27 (key2020100612144793900_ref007) 2016; 171 (key2020100612144793900_ref024) 2017; 54 (key2020100612144793900_ref033) 2016; 69 (key2020100612144793900_ref003) 2019; 49 (key2020100612144793900_ref008) 2011 (key2020100612144793900_ref011) 2018; 27 key2020100612144793900_ref046 (key2020100612144793900_ref012) 2018; 57 (key2020100612144793900_ref025) 2018; 14 (key2020100612144793900_ref034) 2017; 28 (key2020100612144793900_ref035) 2017; 34 key2020100612144793900_ref042 |
| References_xml | – volume: 28 start-page: 873 issue: 11/12 year: 2017 ident: key2020100612144793900_ref034 article-title: Big data and analytics in operations and supply chain management: managerial aspects and practical challenges publication-title: Production Planning and Control – volume: 37 start-page: 226 issue: 3 year: 2018 ident: key2020100612144793900_ref001 article-title: Linking work-family interference, workplace incivility, gender and psychological distress publication-title: Journal of Management Development doi: 10.1108/JMD-06-2017-0207 – volume: 50 start-page: 395 issue: 1 year: 2018 ident: key2020100612144793900_ref015 article-title: The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage publication-title: International Journal of Information Management doi: 10.1016/j.ijinfomgt.2018.12.011 – volume-title: Redesigning Social Inquiry: Fuzzy Sets and Beyond year: 2008 ident: key2020100612144793900_ref037 – volume: 14 start-page: 15 issue: 1 year: 2018 ident: key2020100612144793900_ref025 article-title: Fuzzy-set qualitative comparative analysis (fsQCA) in entrepreneurship and innovation research – the rise of a method publication-title: International Entrepreneurship and Management Journal doi: 10.1007/s11365-017-0461-8 – volume: 20 start-page: 209 issue: 2 year: 2018 ident: key2020100612144793900_ref036 article-title: The impact of big data analytics on firms’ high value business performance publication-title: Information Systems Frontiers doi: 10.1007/s10796-016-9720-4 – volume: 28 start-page: 877 issue: 11/12 year: 2017 ident: key2020100612144793900_ref026 article-title: Big data in operations and supply chain management: current trends and future perspectives publication-title: Production Planning and Control – volume: 270 start-page: 313 issue: 1/2 year: 2018 ident: key2020100612144793900_ref031 article-title: Big data and supply chain management: a review and bibliometric analysis publication-title: Annals of Operations Research – volume: 54 start-page: 393 issue: 2 year: 2011 ident: key2020100612144793900_ref014 article-title: Building better causal theories: a fuzzy set approach to typologies in organization research publication-title: Academy of Management Journal doi: 10.5465/amj.2011.60263120 – volume: 70 start-page: 308 year: 2017 ident: key2020100612144793900_ref020 article-title: Big data and predictive analytics for supply chain and organizational performance publication-title: Journal of Business Research doi: 10.1016/j.jbusres.2016.08.004 – volume: 27 start-page: 1849 issue: 10 year: 2018 ident: key2020100612144793900_ref043 article-title: An investigation of visibility and flexibility as complements to supply chain analytics: an organizational information processing theory perspective publication-title: Production and Operations Management doi: 10.1111/poms.12746 – volume: 98 start-page: 261 year: 2019 ident: key2020100612144793900_ref030 article-title: Big data analytics and firm performance: findings from a mixed-method approach publication-title: Journal of Business Research doi: 10.1016/j.jbusres.2019.01.044 – volume: 54 start-page: 814 issue: 6 year: 2017 ident: key2020100612144793900_ref024 article-title: Explaining traveler’s online information satisfaction: a complexity theory approach on information needs, barriers, sources and personal characteristics publication-title: Information and Management doi: 10.1016/j.im.2017.03.004 – start-page: 1 year: 2018 ident: key2020100612144793900_ref004 article-title: Workplace injuries safety climate and behaviors: application of an artificial neural network publication-title: International Journal of Occupational Safety and Ergonomics – volume: 171 start-page: 46 year: 2016 ident: key2020100612144793900_ref007 article-title: Does supply chain visibility enhance agility? publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2015.10.005 – volume: 37 start-page: 557 issue: 5 year: 2017 ident: key2020100612144793900_ref038 article-title: The impact of risk management on the frequency of supply chain disruptions: a configurational approach publication-title: International Journal of Operations and Production Management doi: 10.1108/IJOPM-03-2016-0129 – volume: 33 start-page: 71 year: 2015 ident: key2020100612144793900_ref017 article-title: Performance outcomes of supply chain agility: when should you be agile? publication-title: Journal of Operations Management – volume: 48 start-page: 470 issue: 4 year: 2018 ident: key2020100612144793900_ref023 article-title: Knowledge content quality, perceived usefulness, KMS use for sharing and retrieval: a flock leadership application publication-title: VINE Journal of Information and Knowledge Management Systems doi: 10.1108/VJIKMS-08-2017-0054 – volume: 49 start-page: 45 year: 2019 ident: key2020100612144793900_ref003 article-title: Applying artificial intelligence technique to predict knowledge hiding behavior publication-title: International Journal of Information Management doi: 10.1016/j.ijinfomgt.2019.02.006 – volume: 52 issue: 2 year: 2019 ident: key2020100612144793900_ref041 article-title: Does supply chain analytics enhance supply chain innovation and robustness capability? publication-title: Organizacija – volume: 56 start-page: 385 issue: 1/2 year: 2018 ident: key2020100612144793900_ref021 article-title: Agile manufacturing practices: the role of big data and business analytics with multiple case studies publication-title: International Journal of Production Research – volume: 27 start-page: 1093 issue: 4 year: 2019 ident: key2020100612144793900_ref002 article-title: An integrative review: high performance work systems, psychological capital and future time perspective publication-title: International Journal of Organizational Analysis doi: 10.1108/IJOA-12-2017-1302 – year: 2011 ident: key2020100612144793900_ref008 article-title: Strength in numbers: how does data-driven decision-making affect firm performance? – volume: 23 start-page: 326 issue: 3 year: 2014 ident: key2020100612144793900_ref010 article-title: IT capability and organizational performance: the roles of business process agility and environmental factors publication-title: European Journal of Information Systems doi: 10.1057/ejis.2013.4 – volume: 116 start-page: 288 issue: 2 year: 2008 ident: key2020100612144793900_ref044 article-title: Achieving supply chain agility through IT integration and flexibility publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2008.09.002 – ident: key2020100612144793900_ref046 – volume: 57 start-page: 2092 issue: 8 year: 2018 ident: key2020100612144793900_ref012 article-title: Big data analytics capability in supply chain agility: the moderating effect of organizational flexibility publication-title: Management Decision doi: 10.1108/MD-01-2018-0119 – volume: 69 start-page: 2791 issue: 8 year: 2016 ident: key2020100612144793900_ref033 article-title: Asymmetric modeling of intention to purchase tourism weather insurance and loyalty publication-title: Journal of Business Research doi: 10.1016/j.jbusres.2015.11.015 – start-page: 1 year: 2018 ident: key2020100612144793900_ref027 article-title: Market-sensing capability, innovativeness, brand management systems, market dynamism, competitive intensity, and performance: an integrative review publication-title: Journal of the Knowledge Economy – volume: 29 start-page: 163 issue: 3 year: 2011 ident: key2020100612144793900_ref009 article-title: Supply chain collaboration: impact on collaborative advantage and firm performance publication-title: Journal of Operations Management doi: 10.1016/j.jom.2010.12.008 – volume: 27 start-page: 1868 issue: 10 year: 2018 ident: key2020100612144793900_ref011 article-title: Big data analytics in operations management publication-title: Production and Operations Management doi: 10.1111/poms.12838 – volume: 179 start-page: 24 year: 2016 ident: key2020100612144793900_ref018 article-title: An exploration of the strategic antecedents of firm supply chain agility: the role of a firm’s orientations publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2016.05.008 – volume: 34 start-page: 730 issue: 5 year: 2017 ident: key2020100612144793900_ref035 article-title: The interplay of online shopping motivations and experiential factors on personalized e-commerce: a complexity theory approach publication-title: Telematics and Informatics doi: 10.1016/j.tele.2016.08.021 – volume: 27 start-page: 462 issue: 6 year: 2009 ident: key2020100612144793900_ref040 article-title: A contingent view of e-collaboration and performance in manufacturing publication-title: Journal of Operations Management doi: 10.1016/j.jom.2009.03.001 – volume: 21 start-page: 517 issue: 3 year: 2017 ident: key2020100612144793900_ref045 article-title: Business analytics-enabled decision-making effectiveness through knowledge absorptive capacity in health care publication-title: Journal of Knowledge Management doi: 10.1108/JKM-08-2015-0301 – volume: 27 start-page: 119 issue: 2 year: 2009 ident: key2020100612144793900_ref006 article-title: The organizational antecedents of a firm’s supply chain agility for risk mitigation and response publication-title: Journal of Operations Management doi: 10.1016/j.jom.2008.09.006 – volume: 54 start-page: 1452 issue: 3 year: 2013 ident: key2020100612144793900_ref028 article-title: The impact of IT capabilities on firm performance: the mediating roles of absorptive capacity and supply chain agility publication-title: Decision Support Systems doi: 10.1016/j.dss.2012.12.016 – volume: 24 start-page: 440 issue: 5 year: 2006 ident: key2020100612144793900_ref032 article-title: Disentangling leanness and agility: an empirical investigation publication-title: Journal of Operations Management doi: 10.1016/j.jom.2005.11.011 – volume: 65 start-page: 579 issue: 5 year: 2012 ident: key2020100612144793900_ref039 article-title: Investigating firm’s customer agility and firm performance: the importance of aligning sense and respond capabilities publication-title: Journal of Business Research doi: 10.1016/j.jbusres.2011.02.009 – start-page: 1 year: 2019 ident: key2020100612144793900_ref005 article-title: Do high-performance work systems always help to retain employees or is there a dark side? publication-title: The Service Industries Journal – volume: 3 start-page: 20 issue: 1 year: 2014 ident: key2020100612144793900_ref019 article-title: Organizational models for big data and analytics publication-title: Journal of Organization Design doi: 10.7146/jod.9799 – volume: 38 start-page: 129 issue: 1 year: 2018 ident: key2020100612144793900_ref013 article-title: Supply chain agility, adaptability and alignment: empirical evidence from the Indian auto components industry publication-title: IEEE Transactions on Engineering Management – volume: 144 start-page: 546 issue: 1 year: 2017 ident: key2020100612144793900_ref022 article-title: Unlocking the circular economy through new business models based on large-scale data: an integrative framework and research agenda publication-title: Technological Forecasting and Social Change doi: 10.1016/j.techfore.2017.09.010 – ident: key2020100612144793900_ref029 – ident: key2020100612144793900_ref042 – volume: 101 start-page: 95 year: 2017 ident: key2020100612144793900_ref016 article-title: Increasing firm agility through the use of data analytics: the role of fit publication-title: Decision Support Systems doi: 10.1016/j.dss.2017.06.004 |
| SSID | ssj0057787 |
| Score | 2.3571293 |
| Snippet | Purpose
Practitioners and researchers have reached a consensus that supply chain analytics is a strong determinant for desirable organizational outcomes such... PurposePractitioners and researchers have reached a consensus that supply chain analytics is a strong determinant for desirable organizational outcomes such as... |
| SourceID | proquest crossref emerald |
| SourceType | Aggregation Database Publisher |
| StartPage | 1055 |
| SubjectTerms | Age Big Data Competitive advantage Data analysis Decision making Fuzzy sets Innovations Operations management Product life cycle Recipes Supply chains Technological change |
| Title | Supply chain data analytics and supply chain agility: a fuzzy sets (fsQCA) approach |
| URI | https://www.emerald.com/insight/content/doi/10.1108/IJOA-05-2019-1759/full/html https://www.proquest.com/docview/2448606047 |
| Volume | 28 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LSwMxEA62gnjxLVar5ODBCmvXfSXrrdTW6sFSVPC2JJvEFnQVd3tof70z-1ArXgRvgV0CO5Od-b5k8g0hx7YBx3pcWo6Lx4waRty4nmUMJJcg9BjL2wEN7tjtI7_soUzOsLoLk5dVFtsxeZyeJCmS1DYWbkMU_hQcwO411zfDDh7kQg4LLUiDYRu3rNvj7OW5RpYBa9vIxq5GZ1Vo9hnLO-YBaPEsDtijPOb8dbKFRPXjtu5XxM7TUH_93z9gg6yViJR2iiW0SZZ0skVWqoL4bVI0_pzReCwmCcWKUipQyQT1nWGkaPr9uXjCYtvZBRXUTOfzGU11ltITk466nRatFMx3yEO_d98dWGUrBisGfpJBpAanMj92DFeBUq5gUkktPSk9RwEjZEoEjpGxAY_7yvXPDQBNVH8zfugLE7i7pJ68JnqPUCB4AtKlUgLRBNc8EDoOQ89WKjRShg1yWtk9eisUN6Kcqdg8QoNFth-hwSI0WIO0SmP_-u6CcRukWfkuKn_UNAJ0wwMUEGL7f5jqgKw6yLvzWsAmqWfvU31IaqmaHuUL7gPQDdhr |
| link.rule.ids | 315,783,787,21707,27936,27937,53257 |
| linkProvider | Emerald |
| linkToHtml | http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8NAEB60gnrxLVar7sGDCrE1r914Kz5otSqigrdlN5u1BY1i2kP7651JE194EbwtJCxkZjKPnW-_AdhpWFSsL7TjetRmTHAlrOc71mJwCSOf83wcUOuWXz2Ik1Oiybku78LksMrxcUzup3tpRkVqnYDb6IU_CAdoek37_LpJjVyMYZGDYTCq05F1vdt_fpqEqcBDa6TbvzcHpWsOOM8n5mHS4jsCc4-izfnrZt8C1Y_bup8eOw9DZ_P__gELMFdkpKw5NqFFmEjSJZguAfHLMB78OWRxV_VSRohSpojJhPidcWVY9vW5eiSw7fCIKWYHo9GQZUk_Y7s2uzlu7rGSwXwF7s9O745bTjGKwYmxPumjp0al8iB2rTChMZ7i2uhE-1r7rsGKkBsVulbHFjUeGC84tJhoEvubDaJA2dBbhUr6kiZrwLDAUxgujVGUTYhEhCqJo8hvGBNZraMq7Jdyl69jxg2ZVyoNIUlgshFIEpgkgVVhrxD2r-9-E24VaqXuZPGjZhKzGxESgRBf_8NW2zDTurvsyE776mIDZl2qwXNcYA0q_bdBsgmTmRls5cb3DiRJ21o |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Supply+chain+data+analytics+and+supply+chain+agility%3A+a+fuzzy+sets+%28fsQCA%29+approach&rft.jtitle=International+journal+of+organizational+analysis+%282005%29&rft.au=Shamout%2C+Mohamed+Dawood&rft.date=2020-10-08&rft.pub=Emerald+Publishing+Limited&rft.issn=1934-8835&rft.eissn=1758-8561&rft.volume=28&rft.issue=5&rft.spage=1055&rft.epage=1067&rft_id=info:doi/10.1108%2FIJOA-05-2019-1759&rft.externalDocID=10.1108%2FIJOA-05-2019-1759 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1934-8835&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1934-8835&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1934-8835&client=summon |