Evidence suggests that UK sheep farmers experience lower productivity and profit margins than other livestock sectors and that they do not necessarily know where they gain or lose income from their flocks. More efficient use of precision technology has been identified as a potential way of addressing this problem. The mandatory requirement for Electronic Identification (EID) tags to be placed on all sheep offers an opportunity for sheep farmers to adopt precision technologies to manage herd health and maximise production and profit. Although the charactistics of farmers that are associated with adoption or non adoption of technology have been identified little is known about the social processes, meanings and experiences that influence uptake. This paper is novel as it draws on data from 36 sheep farmers in the UK and applies Normalization Process Theory (NPT) to gain an understanding of the reasons they do or do not use EID related precision technology on their farms. The interviews were tape recorded, transcribed verbatim and analysed using NVivo. Although respondents acknowledged the potential value of precision technology to improve their farm businesses they appeared to have alternative beliefs that were counter productive. Their beliefs that using precision technology posed a threat to their role as a good stockman, that it could not replace the need for hands-on interaction with their animals and that it was costly and difficult to use created an implementation gap. The use of NPT as an evaluation framework provided a valuable tool for increasing the understanding of contextual characteristics that undermine the routine embedding of such technology by sheep farmers. The data suggests that normalisation of the use of precision technology amongst sheep farmers could potentially be increased if manufacturers/suppliers co-design and work with farmer's to ensure that the technology enables the farmer to be in control and operates as an aid to achieving high quality stockmanship rather than a mechanism for profit maximisation. Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.