Assessing the Impact of Conflicting Talent Philosophies within the Organisation
: An Investigation into Talent Development within Tees, Esk and Wear Valley NHS Foundation Trust

Student thesis: Doctoral Thesis


Talent has many subtle conflicting components that most hold a subconscious bias toward. Is talent the individual or their skill? Is it natural or nurtured? This thesis investigated the consequences to employees holding differing definitions and philosophies of talent to the organisation by identifying and comparing their preferences in philosophy and definition against their experiences within the organisation through the objective measurement of language. This was achieved through the development of a pragmatic methodology that encompassed Mixed Content Analysis Methods (MCAM), evaluating interview transcripts and organisation talent management documents in line with Actor-Network Theory (ANT), to hermeneutically identify expressions aligning to philosophical preferences and definitions of talent drawn from theory and the experiences of the Tees Esk and Wear Valley’s NHS Trust. Following exploration of thirteen trust actant artefacts – non-human actors within ANT – and twenty-seven employees in interviews, results of this investigation suggest that there is evidence that levels of exposure to actant artefacts may alter an individual’s philosophy and values to align to that of the organisation more closely when talent interventions are implemented; or alternatively drive them to opposing characteristics when denied. However, no specific statistically relevant relationship between could be clearly identified between the variables. The development of the MCAM also produced an innovative means to generate objective, numerical data-outputs, with potential value for HR Analytic applications. These findings strive towards furthering understanding of the significance of differing perceptions of talent as a phenomenon between individuals and the organisation, as wider academic understanding of the talent phenomenon matures towards the ultimate objective of facilitating predictive talent management decision making.
Date of Award1 Dec 2022
Original languageEnglish
Awarding Institution
  • Teesside University
SupervisorXiaoxian Zhu (Supervisor) & Georgios Antonopoulos (Supervisor)

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