wpa (version 1.4.3)

flex_index: Compute a Flexibility Index based on the Hourly Collaboration Query

Description

Pass an Hourly Collaboration query and compute a Flexibility Index for the entire population. The Flexibility Index is a quantitative measure of the freedom for employees to work at a time of their choice.

Usage

flex_index(
  data,
  hrvar = NULL,
  signals = c("email", "IM"),
  active_threshold = 0,
  start_hour = "0900",
  end_hour = "1700",
  return = "plot",
  plot_method = "common"
)

Arguments

data

Hourly Collaboration query to be passed through as data frame.

hrvar

A string specifying the HR attribute to cut the data by. Defaults to NULL. This only affects the function when "table" is returned.

signals

Character vector to specify which collaboration metrics to use: You may use "email" for emails only, "IM" for Teams messages only, or a combination of the two c("email", "IM") (default).

active_threshold

A numeric value specifying the minimum number of signals to be greater than in order to qualify as active. Defaults to 0.

start_hour

A character vector specifying starting hours, e.g. "0900"

end_hour

A character vector specifying end hours, e.g. "1700"

return

String specifying what to return. This must be one of the following strings:

• "plot"

• "data"

• "table"

See Value for more information.

plot_method

Character string for determining which plot to return.

• "sample" plots a sample of ten working pattern

• "common" plots the ten most common working patterns

• "time" plots the Flexibility Index for the group over time

Value

A different output is returned depending on the value passed to the return argument:

• "plot": 'ggplot' object. A random of ten working patterns are displayed, with diagnostic data and the Flexibility Index shown on the plot.

• "data": data frame. The original input data appended with the Flexibility Index and the component scores. Can be used with plot_flex_index() to recreate visuals found in flex_index().

• "table": data frame. A summary table for the metric.

Context

The central feature of flexible working arrangements is that it is the employee rather the employer who chooses the working arrangement. Observed flexibility serves as a proxy to assess whether a flexible working arrangement are in place. The Flexibility Index is an attempt to create such a proxy for quantifying and measuring flexibility, using behavioural data from Workplace Analytics.

Details

The Flexibility Index is a score between 0 and 1, and is calculated based on three component measures:

• ChangeHours: this represents the freedom to define work start and end time. Teams that embrace flexibility allow members to start and end their workday at different times.

• TakeBreaks: this represents the freedom define one's own schedule. In teams that embrace flexibility, some members will choose to organize / split their day in different ways (e.g. take a long lunch-break, disconnect in the afternoon and reconnect in the evening, etc.).

• ControlHours: this represents the freedom to switch off. Members who choose alternative arrangements should be able to maintain a workload that is broadly equivalent to those that follow standard arrangements.

The Flexibility Index returns with one single score for each person-week, plus the three sub-component binary variables (TakeBreaks, ChangeHours, ControlHours). At the person-week level, each score can only have the values 0, 0.33, 0.66, and 1. The Flexibility Index should only be interpreted as a group of person-weeks, e.g. the average Flexibility Index of a team of 6 over time, where the possible values would range from 0 to 1.

See Also

Other Working Patterns: identify_shifts_wp(), identify_shifts(), plot_flex_index(), workpatterns_area(), workpatterns_classify_bw(), workpatterns_classify_pav(), workpatterns_classify(), workpatterns_hclust(), workpatterns_rank(), workpatterns_report()

Examples

# NOT RUN {
# Examples of how to test the plotting options individually
# Sample of 10 work patterns
em_data %>%
  flex_index(return = "plot", plot_method = "sample")

# 10 most common work patterns
em_data %>%
  flex_index(return = "plot", plot_method = "common")

# Plot Flexibility Index over time
em_data %>%
  flex_index(return = "plot", plot_method = "time")

# Return a summary table with the computed Flexibility Index
em_data %>%
  flex_index(hrvar = "Organization", return = "table")

# }
# NOT RUN {
# Return the raw data with the computed Flexibility Index
em_data %>%
  flex_index(return = "data")
# }
# NOT RUN {
# }