Future of Work-From-Home

by Suvrajit Pal and Prithish Nandi

Executive Summary

 With the rise in COVID 19 cases, an increasing number of companies are allowing their employees to work from home. Big and small firms are ramping up their remote working orders to prevent the spread of corona virus. Gradually, these companies are also considering work-from-home (WFH) a viable long term option.

Research objectives

1. To predict whether an employee is content with the existing Work-from-home policy (as in pre-COVID scenario) or he/she will demand an increase in the work from home days when the offices re-open after COVID pandemic. 
2. Identify different types of employees and their willingness to work from home based on age, marital status, their behavior of utilizing WFH (pre-COVID) 
3. Management is asking some of the employees to start working from home permanently, as the restriction of COVID lockdown is decreasing. These employees may have identified based on less critical client requirements. However, many IT companies like TCS have indicated that not all 100 percent of the employees are required in the office on any given day post-Covid. Our objective is to identify people who can be clubbed together into various clusters based on their personality type into the following. 

• Employees who have no issue from working permanently from home for a prolonged period. 
• Employees who can work from home most of the time 
• Employees who have difficulty in WFH (not based on the work they do but their personality trait) 

If we can design the WFH facility according to the nature of the employee and client requirement, employees would be happy and can contribute more productively. Only if we get to know how employees may react in the future, we will be in a better position to hire people that may fit the organization's culture. This may not only help us design a more comprehensive WFH policy but also, predict whether we need to acquire more office spaces.

Major findings

·        Employees who had been regularly utilizing the WFH facility are more content with existing WFH policy and are less likely to apply for an increase in WFH when the office opens post-COVID. Employees who are currently living with family/friends during this course of lockdown are more likely to get used to the WFH permanently. These employees might be seeking more WFH when compared to the employees who have been living alone. Interestingly, married employees are less likely to agree to an increase in WFH, when the office opens post-COVID. People who can maintain a perfect balance between personal life and work, for example, relationship with a family member and taking time out from their daily routine to exercise or do yoga, are most likely to be clubbed into a group that can work permanently from home.  Personal nature defined by the items like being introverted, people who procrastinate, and people who do not enjoy traveling to the office is more likely to agree to an increase in WFH days. These people can be identified and clubbed to a group that can be set to work permanently from home post COVID without much decrease in their productivity. 

Assumptions and limitation 

·        We have assumed that respondents have completed the survey questionnaire without any biases.  

·        Limitation: The number of respondents (sample size) for this study was low (we have collected 196 valid responses)

Factors and Items

For this study, we have a total of three factors. The factors are as follows:

·        InfraAvg: This factor depicts the presence of adequate infrastructure (power backup and Wi-Fi) and available space at home

·        PersLifeAvg: This factor quantifies the effects of work-from-home on personal life.

·        PersonNatureAvg: This factor quantifies how Personal nature influences one's Work while doing WFH? Higher the score of this factor, the higher the chances are that he or she is an extrovert person.  

The scaling techniques used for the questionnaire was Likert scale. In the descriptive phase the tool used for data analysis were R programming, Tableau and Microsoft Excel 2016. The techniques used across several objectives were, logistic regression and one-way ANOVA.

Analysis

 We circulated online questionnaire to a set of people who fit our target demographic. We received a total of 196 clean responses.   

Logistic Regression -

We carried out Logistic Regression to determine whether or not working people would want the same policy for work from home as it is currently (represented by dummy variable ‘0’) or would like to see an increase in number of days for work from home (represented by dummy variable ‘1’).

Below is the output table of the logistic regression carried out in R.

Below is the script written in Tableau for logistic regression 

From the table we find that PersLifeAvg is statistically significant at significance level of 0.5, similarly, PersonNatureAvg is also statistically significant.

The logistic regression equation is as follow:

Ln(p/1-p) = 0.7035 + -.4598* (WFHPresent.status)1 + -.5830* (WFHPresent.status)2 +0.2817 * (Living)1 +-.1458* (Married)1 + 0.1177* InfraAvg + 0.6468* PersLifeAvg + (-0.6302) * PersonNatureAvg; ------------------------>1

Let us consider, -.4598* (WFHPresent.status)1 + -.5830* (WFHPresent.status)2 +0.2817 * (Living)1 +-.1458* (Married)1 + 0.1177* InfraAvg + 0.6468* PersLifeAvg + -.6302* PersonNatureAvg; as B1

Hence, Y= Ln(p/1-p) = 0.7035 + B1; ------------------------>2

Now, odds are equal to p/(1-p) =E^ (0.7035 + B1); ------------------------>3

Thus, odds could be now easily calculated using equation (3).

Odds, represents here chance of a working professional to choose increase in number of days for work from home as compared to chance of choosing the existing policy of work from home.

Now, we will analyse the individual factors in the equation. It shows some interesting facts. Let’s see some of these:

·        If we see “WFHPresent.status”, i.e. how people utilized the WFH facility (before Covid) i.e. either never, rarely or often,

·        Employees who used to utilize WFH rarely as compared to employees who did not utilize WFH before COVID are less likely to seek an increase in WFH. The odds are E^ (-0.4598) times to that of people who never availed earlier. Similarly, odds for people who would often avail WFH are E^ (-.5830) to that of who used to avail rarely. Hence, we can conclude people who earlier used to avail more WFH are now less likely to agree to an increase in WFH number of days. 

 ·        Factor: Living, which depicts whether a person is currently living with family/friends, we find that the odds in favor of family people seeking more WFH days is E^ (0.2817) times to that of people who are living alone. It suggests that people currently living with family/friends are more likely to agree to an increase in more number of WFH days as compared to those who are not living alone.

·        Now, let’s see for Married, it denotes whether a person is married/unmarried. For this, we find that odds in favor of people seeking more WFH days and are married is E^ (-0.1458) times to that of people who are not married. Thus, we can infer that people who are married are less likely to agree to an increase in more number of WFH days as compared to those who are unmarried.

·        Now, we will analyse the remaining three factors in the logistic regression equation. For, InfraAvg, which depicts presence of adequate infrastructure to support WFH, we find that as presence of proper infrastructure increases the log(odds) in favor of people agreeing to increase in more number of WFH days increases.

Similarly, for PersLifeAvg, which depicts the personal life of people, people who can manage both personal and professional life score high in the factor: PersLifeAvg. A high score in personal life influences in a positive way i.e. log(odds) in favor of people agreeing to increase in more number of WFH days increases. These people are comfortable in Working from home and can be asked to work permanently from home. And also assigned to a task which can work out remotely  

·        Finally, for PersonNatureAvg, depicting personal nature of a person on the basis of his/her reaction to different situations, we find that for people who are introvert, more they procrastinate and don’t like travelling to office, the log(odds) in favor of people agreeing to increase in more number of WFH days decreases.

Conclusion: 

As per most of the respondents, employees are in agreement with the organization viewing work from home as a viable long term plan.

 As per the logit analysis, we found that the most important factor for employees choosing the WFH option is the balance between their Personal life and Work. Employees consider the effect of family members on their work productivity and vice versa a driving force for choosing or rejecting a WFH. Also, they value maintaining a disciplined life to be a significant contributor in their decision to seek more WFH.

Another important factor for choosing WFH is the Personal Nature of the employees. Employees who correctly anticipate their time taken to complete a job and those who are introvert in nature are more likely to seek WFH. Another reason for applying WFH is how a person enjoys his ride to the office.

Organizations should allow employees to maintain a perfect balance between their personal life and work irrespective of their age. The employees choosing WFH based on their Personal Nature does not vary with age and hence organization should respect the same.