Examining the adoption of Big data analytics in supply chain management under competitive pressure: evidence from Saudi Arabia

被引:32
作者
Alaskar, Thamir Hamad [1 ]
Mezghani, Karim [1 ]
Alsadi, Amin Khalil [1 ]
机构
[1] Imam Mohammad Ibn Saud Islamic Univ IMSIU, Dept Business Adm, Riyadh, Saudi Arabia
关键词
Adoption intention; big data analytics; supply chain management; competitive pressure; toe framework; pls SEM; Saudi context;
D O I
10.1080/12460125.2020.1859714
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
Grounded in the Technology-Organisation-Environment (TOE) framework, this study identifies the main factors affecting the intention to adopt Big Data Analytics (BDA) in supply chain management (SCM) for firms based in Saudi Arabia. This study focuses on identifying and analysing the role of competitive pressure as a contextual variable that can moderate the effects of these factors on the adoption intention. A survey of 220 IT managers revealed that compatibility, relative advantage, and top management support are positively perceived factors as they foster the firms' intentions to adopt BDA in SCM. Their effects on intentions were positively moderated by competitive pressure as a contextual variable. However, BDA complexity and organisational readiness were not supported as influencing firms' intentions to adopt BDA. The statistical analyses also indicated that the effects of complexity and organisational readiness on intentions are not significantly moderated by competitive pressure. This study contributes to the literature by emphasising the interaction between TOE factors, instead of considering them separately. It also offers guidance to managers aiming to adopt and use BDA in SCM.
引用
收藏
页码:300 / 320
页数:21
相关论文
共 67 条
[1]  
Agrawal KP, 2015, AMCIS 2015 PROCEEDINGS
[2]   Analysis of barriers in implementation of digital transformation of supply chain using interpretive structural modelling approach [J].
Agrawal, Prakash ;
Narain, Rakesh ;
Ullah, Inayat .
JOURNAL OF MODELLING IN MANAGEMENT, 2020, 15 (01) :297-317
[3]  
Akash S.M., 2017, IJRDO J BUSINESS MAN, V3, P196
[4]  
Al-Qirim N., 2017, P INT C ALG COMP SYS, P88, DOI DOI 10.1145/3127942.3127961
[5]  
Aloulou, 2019, BUSINESS TRANSFORMAT, P106, DOI [10.4018/978-1-5225-7262-6.ch007, DOI 10.4018/978-1-5225-7262-6.CH007]
[6]   The contingent role of dependency in predicting the intention to adopt B2B e-commerce [J].
Alsaad, Abdallah ;
Mohamad, Rosli ;
Ismail, Noor Azizi .
INFORMATION TECHNOLOGY FOR DEVELOPMENT, 2019, 25 (04) :686-714
[7]   Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice [J].
Arunachalam, Deepak ;
Kumar, Niraj ;
Kawalek, John Paul .
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2018, 114 :416-436
[8]   A model of adoption determinants of ERP within T-O-E framework [J].
Awa, Hart O. ;
Ojiabo, Ojiabo Ukoha .
INFORMATION TECHNOLOGY & PEOPLE, 2016, 29 (04) :901-930
[9]  
Brinch M., 2019, CONCEPTUALIZATION VA
[10]   How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management [J].
Chen, Daniel Q. ;
Preston, David S. ;
Swink, Morgan .
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS, 2015, 32 (04) :4-39