Marketing Research on Maslow’s Theory of Motivation

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INTRODUCTION

Abstract

In this project, we use the data mining concepts to analyze and classify the motivational level and average work pressure faced by the employees in a work environment. We created a survey by including potential questions that would reflect the motivation level of employees based on Maslow’s theory of Motivation/ Needs, along with other potential attributes which helps to identify the average strain or pressure faced in the Work. Based on the outcome of survey data we performed Supervised and Unsupervised Machine Learning techniques to identify hidden insights. We used several Classification algorithms to perform supervised learning in order to classify the motivation level and average work pressure of employees. Our results reveal that excellent Work Environment results in employees feeling highly motivated. Another finding based on the study was that highly motivated employee is more likely to be always under any kind of stress or pressure in the work.

INTRODUCTION

Motivating employees to complete their job duties at a satisfactory or better level can be challenging. Staff members show motivation when they are self-inspired to perform tasks and proud of their work product. Employees who do not have the drive to succeed at your company adversely impact others in the workplace, which can directly affect the success of your business. Dissatisfaction in the office environment, leading to no employee motivation, can cause negative consequences for the organization. In our study, we use machine learning techniques to classify motivation level and work pressure of employees. We have collected data from survey filled by 236 employees across demographics sectors and age groups through the internet medium. We built two classification models from the survey dataset to Classify Motivation level and Work pressure/strain faced by employees for performing supervised learning. We created two separate classification models for classifying Motivation and Work pressure /strain using supervised learning algorithms like Logistic regression

Interesting findings based on the classification model for Motivation was that Work environment and Work Life Balance where the two important features that had the greatest effect on employee motivation. An employee with less than one year of experience with the current organization will have negative effect on his motivation. Interesting findings based on the classification model for classifying the average work pressure/strain was that the Relationship with Supervisor, Travel time to office and Work life balance had a greatest effect on classifying the average work pressure faced by employees. Based on the data obtained we applied Association rule mining (Apriori Algorithm), an unsupervised machine learning technique to identify interesting associations based on the collected data. We tested various hypotheses using Apriori algorithm for different support and confidence