The Internet of Things, dynamic data and information processing capabilities, and operational agility

被引:115
作者
Akhtar, Pervaiz [1 ]
Khan, Zaheer [2 ]
Tarba, Shlomo [3 ]
Jayawickrama, Uchitha [4 ]
机构
[1] Univ Hull, Management Syst, Logist Inst, Fac Business Law & Polit, Kingston Upon Hull, N Humberside, England
[2] Univ Kent, Kent Business Sch, Canterbury, Kent, England
[3] Univ Birmingham, Birmingham Business Sch, Birmingham, W Midlands, England
[4] Staffordshire Univ, Sch Comp & Digital Technol, Stoke On Trent, Staffs, England
关键词
Internet of Things; Dynamic capabilities; Dynamic data and information processing; Operational agility; Data and knowledge intensive services; COMMON METHOD VARIANCE; SUPPLY CHAIN; BIG DATA; PREDICTIVE ANALYTICS; ABSORPTIVE-CAPACITY; MEDIATING ROLE; DATA SCIENCE; PERFORMANCE; TECHNOLOGY; CHALLENGES;
D O I
10.1016/j.techfore.2017.04.023
中图分类号
F [经济];
学科分类号
02 ;
摘要
Whilst there are promising links between the Internet of Things (IoTs), dynamic data and information processing capabilities (DDIPCs), and operational agility, scholars have not conducted enough empirical studies that offer convincing evidence for the use of the IoTs and relevant linkages. This study therefore examines the links between such constructs and provides managerial implications for contemporary data and information driven managers who adopt evidence-based decision making for better operational outcomes. The results obtained from structural equation modelling indicate that the use of the IoTs is the key determinant for operational agility and also plays a vital role in establishing DDIPCs that further reinforce it. Additionally, DDIPCs mediate the relationship between the use of the IoTs and operational agility. By persuasively building these links based on theoretical arguments and testing them by using a unique dataset, this study contributes to the deeper understanding of the mechanisms by which the use of the IoTs and DDIPCs strengthen operational agility.
引用
收藏
页码:307 / 316
页数:10
相关论文
共 92 条
[1]   Endogeneity: How Failure to Correct for it can Cause Wrong Inferences and Some Remedies [J].
Abdallah, Wissam ;
Goergen, Marc ;
O'Sullivan, Noel .
BRITISH JOURNAL OF MANAGEMENT, 2015, 26 (04) :791-804
[2]   Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach [J].
Agarwal, Ashish ;
Shankar, Ravi ;
Tiwari, M. K. .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2006, 173 (01) :211-225
[3]   Data-driven and adaptive leadership contributing to sustainability: global agri-food supply chains connected with emerging markets [J].
Akhtar, Pervaiz ;
Tse, Ying Kei ;
Khan, Zaheer ;
Rao-Nicholson, Rekha .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2016, 181 :392-401
[4]  
[Anonymous], 2009, TECH REP
[5]  
[Anonymous], 2000, The Psychology of Survey Response, DOI DOI 10.1017/CBO9780511819322
[6]  
[Anonymous], 2016, FORBES
[7]  
Antonakis J., 2014, OXFORD HDB LEADERSHI, V1, P93, DOI DOI 10.1093/OXFORDHB/9780199755615.013.007
[8]   On making causal claims: A review and recommendations [J].
Antonakis, John ;
Bendahan, Samuel ;
Jacquart, Philippe ;
Lalive, Rafael .
LEADERSHIP QUARTERLY, 2010, 21 (06) :1086-1120
[9]   More on testing for validity instead of looking for it [J].
Antonakis, John ;
Dietz, Joerg .
PERSONALITY AND INDIVIDUAL DIFFERENCES, 2011, 50 (03) :418-421
[10]   From Data to Actionable Knowledge: Big Data Challenges in the Web of Things [J].
Barnaghi, Payam ;
Sheth, Amit ;
Henson, Cory .
IEEE INTELLIGENT SYSTEMS, 2013, 28 (06) :6-11