Neuro linguistic programming automation for improvement of organisational performance

Amirhosseini, Mohammad Hossein (2018) Neuro linguistic programming automation for improvement of organisational performance. Doctoral thesis, London Metropolitan University.


Neuro Linguistic Programming (NLP) is a methodology used for recognition of human behavioural patterns and the modification of the behaviour. A significant part of this process is influenced by the theory of representational systems which based on the five main senses. Meta model is another important technique in this process. This technique can be adopted to allow an individual to gain a better understanding of their own issues as well as those of others. Another vital factor in NLP are Meta programs, which are habitual ways of inputting sorting and filtering the information found in the world around us. The difference in Meta programs results in significant differences in behaviour from one person to another, the type of personality can be recognised through utilising and analysing the Meta programs. There are different methods to predict the personality type based on Meta programs and Myers-Briggs Type Indicator® (MBTI) is currently considered to be one of the most popular and reliable methods. Traditionally, the application of NLP relies on consultation with a profession qualified in implementation of this technique. To circumvent the limitations in reliability of this process, attempts of automation of this technique have been carried out. These attempts aim to eliminate the effect of human error such as lack of skill and experience, inconsistency in judgement, inaccuracy or mistakes as well as the impact of personal opinion. Nonetheless, many shortcomings are integral of the methodologies adopted in these attempts. Primarily, these automations are in the format of computerisation of the NLP practice and no artificial intelligence techniques have been implemented to substitute the role of the human practitioner. Hence, improvement of reliability and accuracy remain a challenge for application of NLP, which this research aims to address using artificial intelligence techniques such as natural language processing. The second challenge in this field is the opportunity of applying NLP to benefit a group of people in order to make NLP applicable for organisations rather than individuals alone. This research aims to create this prospect in order to extend the application of NLP for improvement of organisational performance.

The focus of this research is on the automation of the three main branches of NLP, which includes (1) identification of the preferred representational system, (2) the Meta model and (3) personality type prediction based on the Meta programs. Hence, it aims to generate an intelligent software for recognising the preferred representational system and personality type of employees as individuals and also as a group. This recognition offers organisations a specific output of information and relevant advice to improve task allocation, communication and teamwork. Moreover, this research also aims to significantly increase the efficiency, accuracy and reliability of using NLP by substituting the dependence on human judgement by an automated software. Limitations of previous computerisations of NLP are also aimed to be responded to by incorporation of artificial intelligence. To achieve these objectives, the means of analysing the behavioural pattern of individuals by software is to be explored. Moreover, the implementation of natural language processing for identifying the preferred representational system, personality type and application of the NLP Meta model during a human-computer conversation will be investigated.

To examine the function of the software and the reliability of its output, three evaluations are to be conducted. Firstly, the results of using the software is to be compared to the use of a questionnaire, which the responses to would be analysed by an experienced NLP practitioner. Both of these methods are to focus on the identification of the preferred representational system. Secondly, the application of the Meta model in a human-computer conversation is to be compared to an NLP practitioner’s analysis of the same conversation. Thirdly, the analysis of personality type is to be evaluated by comparing the use of the intelligent software to the use of a computerised questionnaire.

Natural Language Processing and machine learning techniques were used for the automation process and an intelligent software has been developed. The automation is successful in eliminating human errors, thereby the software is able to perform with a higher level accuracy, reliability and efficiency. The performance of the software has been tested and compared to the performance of humans and existing methods. Regarding the representational system identification, the results of the software are similar to an experienced NLP practitioner. However, in various parts of the process, the software responded more accurately than a human practitioner. The results of the automated Meta model have shown increased accuracy in identification of the language patterns used in conversation. The recovery of information has shown to be more efficient in the software in comparison to an NLP practitioner. Finally, the results of the software regarding the personality type prediction was highly accurate and reliable after comparing with an official MBTI questionnaire. The novel methodology created in this research will assist the NLP practitioners and psychologists to obtain an improved understanding of their clients’ behavioural patterns and the associated cognitive and emotional processes. It can also facilitate the organisational performance improvement in organisations.

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