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|Title:||The dynamics of knowledge retention and aging workforce in the oil and gas industry||Authors:||Sumbal, Muhammad Saleem Ullah Khan||Degree:||Ph.D.||Issue Date:||2018||Abstract:||In the current age of the knowledge-based economy, the brains rather than the brawn of the workforce contribute towards success and learning of the organizations. For organizations, knowledge is the key component for maintaining competitive advantage and is the principal asset of astute workers in the organizations. When these workers leave, they leave with their valuable and much-needed skills and experience that have been accumulated over the years. Of course, not all knowledge possessed by workers is critical, but retention of the knowledge that is rare, non-substitutable and relevant is crucial. The literature reveals scanty research work conducted on knowledge retention (KR). Further, organizations, in general, are not taking any measures to retain knowledge of leaving employees. In the oil and gas industry, the majority of the workforce will be approaching retirement age in the next 5-10 years. This mass exodus of the aging workforce will inevitably cause massive loss of valuable knowledge. This research focuses on this highly important issue of knowledge retention and the aging workforce in the oil and gas industry. The main objectives of the research are i) To investigate how companies are handling the task of knowledge retention (challenges and strategies) from pending retirees in the oil and gas sector. ii) To investigate the dominant likelihood factors and types of knowledge lost when employee depart in the oil and gas sector, and iii) To investigate the relationship of big data and knowledge management regarding knowledge retention and retiring workforce issue. Semi-structured interviews were carried out and the grounded theory approach of systematic data inquiry were utilized for data analysis. Grounded theory is suitable when the topic is underexplored, and the aim is to produce some fresh knowledge on the topic.
The results reveal that current oil slump has made a profound effect on KR. KR activities tend to be inconsistent in the majority of the oil and gas companies and not much work being done regarding knowledge loss from old employees because of the fall in oil prices and layoffs. Further, the issue of an aging workforce is acuter in the upstream sector and more prevalent in developed countries. Dominant factors of knowledge loss in companies are retirements, layoffs, and contract workforce. The different types of knowledge possessed by departing employees include specialized technical knowledge, contextual knowledge, knowledge of train wrecks and history of the company, knowledge of relationships, knowledge of management and knowledge of business systems and processes. The departing employees should be assessed against these knowledge types, and the relevance of each knowledge should also be checked. Useful predictive knowledge can be generated through big data that can help companies improve their knowledge management capability. Further, a combination of the tacit knowledge of experienced staff with predictive knowledge obtained from big data improves decision-making ability, thus, signifying the importance of knowledge retention from experts. Technologies like big data can be useful in the future to replace the expertise of people, but at the moment, the expertise of the employees are vital in running the businesses smoothly. This research has provided useful insights to managers and executives regarding the workforce crisis, strategies and challenges related to knowledge retention, and what to look for when the employees leave the organization. Further, this research also helps to raise the attention of executives on the use of tacit knowledge possessed by experienced employees in conjunction with predictive knowledge derived from the big data to make decisions for enhancing the organizational performance.
|Subjects:||Hong Kong Polytechnic University -- Dissertations
Petroleum industry and trade -- Management
|Pages:||xiii, 237 pages : color illustrations|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/9349
Citations as of May 28, 2023
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