Structuring Employee Rewards Package - Conjoint Analysis

Introduction

Employee psychology is a branch involved in studying employees' behavior and the cognitive process behind how employees perceive various issues. Understanding employee psychology related to the compensation and benefits plans organizations provide is essential. The HR professional needs to understand the utility of different policies for health benefits, flexible schedules, retirement benefits, performance rewards, etc. The objective is to understand what employees consider the most critical benefits elements by individual, group, or segment. A key outcome is determining the relative importance of these benefits, as they impact attraction, retention, and performance levels, and identifying which elements can be cut back or minimized in building benefit programs.

Conjoint analysis is a statistical technique used in research to determine how people value different attributes (features, functions, benefits) related to any concept. It is used to understand the weight of the various attributes related to any plan. Conjoint analysis is generally used to determine which attributes are essential to a person (employee in HR, customer in marketing) and the importance of each attribute.

Conjoint analysis is a technique used to understand the preference or relative importance of various proposed plans related to any particular area. Four important attributes are considered when designing an adequate compensation and benefits plan: work-life balance, performance reward, retirement benefits, and health care benefits. In this article, we will see how conjoint analysis can be used to answer the following questions:

1. Which type of retirement benefits is preferred by employees?

2. What is employees' preference for flexibility in working hours?

3. Which type of health care benefit is employees’ preference?

4. Which performance reward system is preferred by employees?

Efforts are being made to understand the weightage given by the employees to each of the attributes and the level related to the compensation and benefits plan. An in-depth analysis of the utility of every combination of compensation and benefits is carried out to determine the importance of each combination.

Conjoint Analysis

Conjoint analysis is a valuable approach to measure the value that individuals place on features of a plan or policy. It combines real-life scenarios and statistical techniques with modeling actual decisions. Companies can ask employees to evaluate various factors such as wages, health benefits, flexible schedules, retirement benefits, wellness programs, life insurance, health savings accounts, and medical insurance to implement this approach in rewards.

Conjoint analysis is an excellent tool for answering these types of questions. However, it is essential to understand that conjoint analysis differs from surveys. A survey provides answers related to percentage analysis, such as “47% of employees have a preference for plan A,” “42% want plan B,” and “11% want plan C.”

The objective is to understand what employees consider the most critical benefits elements by individual, group, or segment. A key outcome is determining the relative importance of these benefits, as they impact attraction, retention, and performance levels, and identifying which elements can be cut back or minimized in building benefit programs. These are four types of conjoint analysis:

• Rating-based conjoint: The respondents are asked to rate the alternatives they are shown. This can be on a scale of 0 to 100. They may be required to allocate scores so that the scores sum to a certain number (e.g., all scores in each question must add up to 100).

• Best-worst conjoint: In each question, the respondents are asked to indicate which option is best and which is the worst among three or more alternatives.

• Ranking-based conjoint: The respondents are asked to rank alternatives from best to worst. It is similar to the best-worst scaling, but the respondents must also allocate rankings to the intermediate options.

• Choice-based conjoint: The respondents are asked which option they will choose. However, this is the most theoretically sound, practical, and popular method of conjoint analysis in the marketing domain.

Data Exploration and Visualization

All the employees' data regarding their preference for compensation and benefits were collected. Each employee was asked to give his or her preference for work-life balance for flexible and inflexible hours; for the type of performance rewards related to incentive system, pay plan, and commission; for retirement benefits related to benefits plan and contribution plan; and for health care benefits related to life insurance, medical insurance, and health savings account. Since there were two categories for work-life balance, three for performance rewards, two for retirement benefits, and three for medical benefits, 36 (2x3x2x3) combinations were created.

A conjoint model exposes respondents to combinations described in terms of the same attributes (with varying levels). The employees were asked to rank 36 different compensation and benefits schemes based on their preferences on an ordinal scale from 1 to 10. The rating column corresponds to the average score of all the employees. It should be noted that since there were only 36 different combinations, the employees were requested to evaluate all 36 combinations. However, if there had been more combinations, only the selected and preferred combinations would have been considered for getting the employees' choice.

Compensation Benefits Data

The CompensationBenefits.csv file helps us understand KNIME usage. This dataset can be downloaded from the “Data” folder on the KNIME Community Hub.

The Structuring Employee Rewards Package - Conjoint Analysis workflow can be downloaded from the KNIME Community Hub.

You are ready after downloading the CSV file from the Data folder.

Reading the CSV-based dataset

The first step in our analysis will be to load the data for our exploratory analyses. We will do this first step using the CSV Reader node before we persist our analysis in a KNIME table.

The KNIME table is created by loading the CompensationBenefits.csv CSV dataset. The above table shows that the employee dataset has 36 observations and 5 columns. The names of the five columns are ‘Rating’, ‘WorkLifeBalance’, ‘PerformanceReward’, ‘RetirementBenefits’, and ‘HealthCareBenefits’. It should be understood that the experimental design consists of four attributes: work-life balance, performance reward, retirement benefits, and health benefits. The occurrences table from the Statistics node shows that there are two categories in work-life balance: ‘InflexibleHours’, ‘FlexibleHours’; three categories in performance reward: ‘IncentiveSystem’, ‘PayPlan’, ‘Commission’; two categories in retirement benefits: ‘BenefitPlan, ‘ContributionPlan’; and three categories in health benefits: ‘LifeInsurance’, ‘MedicalInsurance’, ‘HealthSavingAccount’.

The dataset has 36 combinations, showing the rating for every combination. The highest rating is given to the scheme that has flexible hours and commission for performance rewards. However, retirement benefits can be either a benefit plan or a contribution plan, and health care benefits can be a health savings account or medical insurance.

Measure Part-Worth Utilities of Each Level Using Conjoint Analysis

Objective: To determine the importance of each level using conjoint analysis

Conjoint analysis is carrying out linear regression where the target variable could be binary (choice-based conjoint analysis) 1-10 Likert scale (rating conjoint analysis), or ranking (rank-based conjoint analysis). The part-worth utilities measure both attribute importance score and level preference score. The beta coefficient of the regression equation helps to calculate the part-worth utility (PWU) of the variants of variables for the attribute importance scores. It should be noted that large part-worth utilities are assigned to the most preferred levels, and small part-worth utilities are assigned to the least preferred levels. In this section, the importance of every predictor variable is determined using the ordinary least squares regression method considering rank-based conjoint analysis.

Part-worth utilities (conjoint analysis utilities) measure attribute importance and level values. The utilities are numerical scores that measure how much each feature influences the decision to make that choice. It should be noted here that the part-worth utilities allow us to have a deeper understanding of which specific features within an attribute influence the choice. Strongly preferred levels are assigned higher scores compared to the less preferred ones.

In this section, conjoint analysis is used to assign utility values for each sublevel of each attribute: performance reward, health care benefits, retirement benefits, and work-life balance. It should be noted that the sublevel getting the highest utility value is the one that the employee most favors for that attribute.

It is known that the ordinary least squares (OLS) model cannot be created on categorical variables. Also, the OLS model works well on binary data; hence, binary dummy data are made using the One to Many node. This will create new independent variables for all the categories of independent variables.

The One to Many node creates new columns according to the number of categories for each variable. Three columns are made since there are three categories for health care benefits (health savings account, life insurance, and medical insurance). Similarly, three new columns are designed for performance rewards, and two are for retirement benefits and work-life balance. The result clearly shows that there are 36 observations with 15 columns. Among these, 10 new columns have binary integer values.

In conjoint analysis, one category for each categorical variable is considered a reference, and others are considered dummy variables. For this process, the following dummy and reference variables are created for each attribute:

1. “InflexibleHours” is a dummy variable for work-life balance, and “FlexibleHours” is the reference variable.

2. For health care benefits, “LifeInsurance” and “MedicalInsurance” are dummy variables, and “HealthSavingsAccount” is a reference variable.

3. For performance reward, “IncentiveSystem” and “PayPlan” are dummy variables, and “Commission” is a reference variable.

4. “ContributionPlan” is a dummy variable for retirement benefits, and “BenefitPlan” is the reference variable.

Dropping Reference Variables

It is essential to retain only requirement columns for the analysis. Hence, the reference and categorical variables are removed using the Column Filter node before creating the regression model. Rating is considered the dependent variable.

After dropping the unwanted columns, the table has 36 rows and 7 columns.

Creating the Linear Regression Model

The regression model uses the Linear Regression Learner node with six independent variables and considers the Rating column the dependent variable.

Since we have created one variable as a reference for each category, the value of the coefficients will be considered concerning the base variable. Thus, the results can be interpreted as follows:

Coefficients of independent variables from the regression model and their interpretation

It should be understood that the predictor variable's importance depends on the coefficient's value. Large part-worth utilities are assigned to the most preferred levels, and small part-worth utilities are transferred to the least preferred levels. However, since these coefficients are for the base variable, the next step is determining the utilities of each level of the attributes. This is done by selecting the relative utility of each level separately for each attribute.

Utility of Levels of Work-Life Balance

It is clear from the model that the coefficient of InflexibleHours is -2.1984. Thus, the utilities for both the levels of the work-life balance will be:

• Utility of FlexibleHours = 0 – Average(0,-2.1984) = 0-(-1.0992) = 1.0992

• Utility of InflexibleHours = -2.1984 – Average(0,-2.1984) = -2.1984-(-1.0992) = -1.0992

Thus, it is clear that the utility of FlexibleHours + utility of InflexibleHours = 0.

Utility of Levels of Performance Reward

The model shows that the coefficients of IncentiveSystem and PayPlan are 0.7024 and 2.2024, respectively. Thus, the utilities for all the levels of performance reward will be:

• Utility of Commission: 0-Average(0.7024, 2.2024) = 0-0.968 = -0.968

• Utility of IncentiveSystem: 0.7024-Average(0, 0.7024, 2.2024) = 0.7024-0.968 = -0.265

• Utility of PayPlan: 2.2024-Average(0,0.7024,2.2024) = 2.2024-0.968 = 1.234

Thus, it is clear that the utility of PayPlan + the utility of IncentivePlan + the utility of Commission = 0.

Utility of Levels of Retirement Benefits

The model shows that the coefficients of BenefitPlan and ContributionPlan are 0 and 2.690, respectively. Thus, the equations that can be framed for determining the utilities for both the levels of retirement benefits will be:

• Utility of BenefitPlan: 0-Average(0,2.690) = 0-2.690 = -1.345

• Utility of ContributionPlan: 2.690-Average(0,2.690) = 0-2.690 = 1.345

Thus, it is clear that utility of BenefitPlan + utility of ContributionPlan = 0.

Utility of Levels of Health Care Benefits

The model shows that HealthSavingsAccount, LifeInsurance, and MedicalInsurance coefficients are 0, 3.369, and 3.7857, respectively. Thus, the utilities for all the levels of the health care benefits will be:

• Utility of HealthSavingsAccount: 0-Average(0,3.369,3.7857) = 0-2.3849 = -2.3849

• Utility of LifeInsurance: 3.369-Average(0,3.369,3.7857) = 3.369-2.3849 = 0.9841

• Utility of MedicalInsurance: 3.7857-Average(0,3.369,3.7857) = 3.7857-2.3849 = 1.4008

Thus, it is clear that the utility of HealthSavingsAccount + utility of LifeInsurance + utility of MedicalInsurance = 0.

The above calculations are performed using various data manipulation nodes, which are then combined into the Utility Value of Levels meta node.

The resultant table provides the Utility value of each Level of the Attribute.

Utility of All the Levels of Each Attribute

Displaying the Utilities of Each Category

We will use the Python View node to generate a subplot chart displaying the utility of different plans/levels of each attribute.

import knime.scripting.io as knio

# This script plots a histogram using matplotlib and assigns it to the output view
import matplotlib.pyplot as plt
from io import BytesIO

# Creating subplot chart for displaying the utilities of each category
fig = plt.figure(figsize=(20,20))
plt.subplot(221)
plt.bar(["Flexible Hours","Inflexible Hours"],[1.0992,-1.0992])
plt.title("Work Life Balance")
plt.subplot(222)
plt.bar(["Commission","Incentive System","Pay Plan"],[-0.968,-0.266,1.234])
plt.title("Performance Reward")
plt.subplot(223)
plt.bar(["Benefits Plan","Contribution Plan"],[-1.345,1.345])
plt.title("Retirement Benefits")
plt.subplot(224)
plt.bar(["Health Saving Account","Life Insurance","Medical Insurance"],[-2.385,0.984,1.401])
plt.title("Health Care")
plt.suptitle("Utility of Different Plans")

# Create buffer to write into
buffer = BytesIO()

# Create plot and write it into the buffer
fig.savefig(buffer, format='svg')

# The output is the content of the buffer
output_image = buffer.getvalue()

# Assign the figure to the output_view variable
knio.output_view = knio.view(fig)  # alternative: knio.view_matplotlib()

It is clear from the chart and summary of the model that the utility of flexible hours is more than that of inflexible hours; the utility pay plan is more than that of the commission and incentive system; the utility of the contribution plan is more than that of benefits plan; and the utility of medical insurance is more than that of life insurance and health savings account. Hence, these are considered preferred variables by the employees. The higher the value, the higher the importance of that attribute in employees' perception. Since, for the work-life balance attribute, the flexible hours level has the maximum utility, and for the performance reward attribute, the pay plan level has the maximum utility, this means that when selecting compensation and benefits plan by the employees, the highest preference is given to flexible hours and pay plan.

It should be noted that the HR professional will prefer a scheme that maximizes utility. This will lead to employee satisfaction with the organization. The figures show that performance rewards with incentive systems, pay plans, and health care benefits with health savings accounts and life insurance are the least preferred approaches.

Measure Part-Worth Utilities of Each Attribute Using Conjoint Analysis

Objective: To determine the importance of each attribute using conjoint analysis

We need to determine to what extent each component (attribute) contributes to the total utility of the plan. What is more important for the employees should be known- health care benefits, performance rewards, work-life balance, or retirement benefits- and how much weight should be given to these attributes. It provides us with an idea about which attribute is most important. In this section, we will first determine the utility values for each attribute (performance reward, retirement benefits, work-life balance, and health care benefits). The utility per attribute is the range of coefficients in a feature. The difference between the maximum and minimum utility of variants calculates the range. The total utility is computed by determining the sum of all partial utilities. The relative importance per feature is calculated by dividing the utility of each level by the total utilities. Thus,

• Range = Maximum(Utility of variants) – Minimum(Utility of variants)

• Importance of attribute = Range of attribute/Total range of all attributes

The above calculations are performed using various data manipulation nodes, which are then combined into the Attribute Importance meta-node.

The resultant table provides the Relative importance of all attributes as shown in the AttributeImportance column.

Pie Chart Depicting the Significance of Attributes

We will use the PieChart node to generate an interactive chart depicting the importance of the attributes.

The total range of all attributes is 10.877. Hence, the importance of work-life balance, performance rewards, retirement benefits, and health care benefits is 20%, 20%, 24.7%, and 34.8%, respectively. The results clearly show that the employees gave the maximum preference to health care benefits.

Compare Different Compensation and Benefits Plans

Objective: To determine the total utilities of different plans for compensation and benefits

To arrive at the total utility value of all 36 plans, we perform the calculations using various data manipulation nodes combined into the Total Utility meta-node.

This section determines the total utility, as represented by the TotalUtility column, of all 36 plans related to compensation and benefits that can be executed. It should be noted that the attribute and sublevel getting the highest utility value will be most favored by employees. The total utility of each plan is the sum of the utilities of each criterion. Thus, the total utility of the plan with inflexible hours, incentive system, benefit plan, and life insurance is the sum of their utilities.

Listing Top Five Plans

We will use the Top k-row Filter node to find the top 5 plans by TotalUtility.

It is clear from the results that the highest utility (5.079) is for flexible hours choice in work-life balance, pay plan in performance reward, contribution plan in retirement, and medical insurance in health care benefits. The second highest utility (4.663) is for flexible hours choice in work-life balance, pay plan in performance reward, contribution plan in retirement, and life insurance in health care benefits. The lowest utility (-5.798) is for inflexible hours, choice in work-life balance, commission in performance reward, benefit plan in retirement, and health savings account in health care benefits. Hence, the organization can decide strategies accordingly. This will help improve employees' satisfaction level and make them happy with the organization’s policies, thereby contributing to their better performance and finally fulfilling the organization’s objectives.

Summary

In conclusion, structuring an employee rewards package using conjoint analysis provides a comprehensive understanding of employee preferences regarding compensation and benefits. By evaluating attributes such as work-life balance, performance rewards, retirement benefits, and health care benefits, organizations can identify which elements are most valued by employees. This approach allows HR professionals to design benefit programs that maximize employee satisfaction and retention. The analysis reveals that employees highly prefer flexible working hours, pay plans, contribution plans, and medical insurance. By aligning reward packages with these preferences, organizations can effectively enhance employee satisfaction, improve performance, and achieve organizational objectives.