Developing a digital transformation model to enhance the strategy development process for leadership in the South African manufacturing sector

被引:13
|
作者
Gaffley, Garth [1 ]
Pelser, Theuns G. [1 ]
机构
[1] Univ KwaZulu Natal, Coll Law & Management Studies, Grad Sch Business & Leadership, Durban, South Africa
关键词
digital transformation; technology; business leadership; data management; digital migration; digital capability; strategy development; CIO; South Africa; CHALLENGES; CLOUD;
D O I
10.4102/sajbm.v52i1.2357
中图分类号
F [经济];
学科分类号
02 ;
摘要
Purpose: This study's aim was to gain insight into the transformative skills of business leaders in the South African manufacturing sector to drive their business' digital transformation process. Technology recources lead digital transformation requires skills not understood by leadership. Cloud computing has facilitated machine learning and artificial intelligence where human comprehension is limited, using algorithms for analytics requiring size and scale to provide data for decision-making and enabled disruptive technologies that have changed the face of industry sectors. Design/methodology/approach: A pragmatic postmodern paradigm supports the theoretical framing of this study, conducted using descriptive research by e-questionnaire using quantitative analysis for deductive statistical evaluation. Findings/results: The findings formed the basis of a model developed to assist chief executive officers (CEOs) to implement digital transformation successfully. Practical implications: The CEO is responsible for the digital transformation of the business and must understand that data management is the most important asset in the digital era. The collection, storage, analysis, reporting and usage of data are key to competing in the digital economy, which requires the appointment of the chief information officer (CIO) to manage data and who should report directly to the CEO. Originality/value: Reporting to the CIO would be data scientists and analysts who work with data; their roles focus on building algorithms from machine learning and developing predictive models from data and simulation models to test if technologies used to drive digital migration are optimal.
引用
收藏
页数:12
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