Strategic role of information, knowledge and technology in manufacturing industry performance

• Published in: Industrial management + data systems 2016-07, Vol.116 (6), p.1259-1278
• Main Authors: Mandal, Purnendu, Bagchi, Kallol
• Format: Article
• Language: English
• Subjects: Competition; Competitive advantage; Corporate growth; Data management systems; Decision analysis; Decision making; Empirical analysis; Environment models; Impact analysis; Influence; Information & knowledge management; Information management; Information systems; Knowledge management; Manufacturing; Mathematical models; Mediation; Modelling; Nonlinearity; Profitability; Research methodology; Roles; Strategy; Studies; Support systems; Sustainability; Sustainable development
• ISSN: 0263-5577; 1758-5783
• DOI: 10.1108/IMDS-07-2015-0297

Purpose – The purpose of this paper is to study how knowledge management strategy (KMS), technology management strategy (TMS), and information management strategy (IMS) influence an organization’s performance. Design/methodology/approach – The authors use manufacturing data and employ causal modeling, with structural equation modeling, which will enable managers to understand the interrelationships among the use of information, knowledge management, and technology strategy in manufacturing enterprises. Findings – There are two major findings of the research: first, an organization’s financial performance (FINP) is directly dependent on its information management and TMSs, and second, KMSs indirectly influence FINP via mediation. Practical implications – Managers should be aware of various roles that TMS, IMS, and KMS play on an organization’s performance. The model analysis provides a direction to decision makers in understanding various intricate relationships through which information, technology, and KMSs could impact corporate sustainability. Originality/value – First, the authors present a specific approach to capture and model information-related strategies in a powerful decisions support system framework. Second, the modeling environment provides examination of non-linear relationships among latent variables, as well as examining additive, moderating, and mediating relations so that other modelers could replicate the experiment in other sectors or enterprises. Last, this work addresses one of the major problems in manufacturing research: the lack of empirical research in sustainability development. The paper shows empirically that both TMS and IMS impact organization’s performance directly and KMS does so indirectly.