Measuring the Level of Automation and its Impact on Organizational Performance and Employees’ Job Performance

The purpose of this research is to propose the methodology for the measurement of levels of automation with the help of a dynamo reference scale for dynamic levels of automation and to find its relationship with organizational performance and employee job performance while bifurcating the tasks performed by humans and machines. The present research is quantitative in nature and conducted to find out the hypothetical relationship between levels of automation and organizational performance, and employee’s job performance with the survey questionnaire. The data was collected from managers and employees working directly with machinery involved in production, particularly the Kotri textile mill in the industrial zone of Kotri, Pakistan. The hypotheses testing suggests that the proposed model achieved an acceptable fit with the data (i.e., out of 7 hypotheses, 6 hypotheses were significantly accepted). The study has limitations in generalization, in terms of the survey questionnaire, and the targeted audience (employees of the firms & managers of the concerned department) of textile mills of the Kotri industrial zone. The first research contributes to the methodology in business studies for measuring levels of automation by employing a dynamo reference scale for levels of automation from industrial engineering which is totally a new concept in business studies for measurement of automation and can easily bifurcate a number of tasks performed by humans and machines. The second contribution of the study provides insight into how organizations are performing at an optimal level with the help of machinery only with a minimal number of employees. Finally, future research strongly suggests implementing the methodology for measurement of automation coupled with business research methods in other industries to understand their level of automation and task performance by employees and machinery in this modern age where all tasks are performed by machines only part of supervision is performed by humans this may bring about interesting possible outcomes.