Workaholics work more and sleep less, but they don’t spend less time with their families

Workaholics work more and sleep less, but they don’t spend less time with their families

In a recent post, we showed that the activity that takes up the largest amount of our nation’s waking time is work. Work is clearly an important part of modern life, thus the way in which people’s work life is integrated and balanced with other life domains is an important concern.  In this post we look at what happens when people lose this balance.

Time is a finite resource, and when people spend more time at work, something else has to give. We were interested in finding out where these tradeoffs are made. For these analyses, we dove into data from a large national sample of Canadians [i] to evaluate the incidence of workaholism in the general population and explore differences in time use among workaholics and non-workaholics. To evaluate the impact that work has on workaholics, we focused on 8,635 people who were employed (part-time or full-time) at the time of the study.[ii]

Our first finding was that a considerable portion of the population identified with being a workaholic. Particularly, 29% of those employed label themselves as workaholics, and slightly more men than women do so [iii].

Do you consider yourself a workaholicDo workaholics generally see their work and home lives differently than non-workaholics? Yes, and in important ways.  A greater proportion of people who consider themselves workaholics say that work is a major source of stress in their lives (57% vs. 52%) [iv] and workaholics are also less satisfied with the balance between their work and family life [v].

Source of stress & work-life balance satisfactionNote: The error bars represent 95% confidence intervals around the mean of satisfaction ratings.

Why are workaholics are less satisfied with work-life balance? Do they spend too much time at work? Does their work take away from the time they could spend with their families? To shed light on these questions, we looked at how people spent their time during the previous day – and this is where things get interesting. Unsurprisingly, workaholics reported spending more time at work than non-workaholics, and the difference is statistically significant [vi]. But contrary to common wisdom, those who consider themselves workaholics did not spend any less time with their families (spouse or partner [vii], or children [viii]). This finding goes against the stereotype of workaholics who spend little to no time with their families. Instead, these data paint a much different picture of the typical workaholic – and it appears that quantity of time spent with family is not where they are making sacrifices.

Partner-child-workNote: The error bars represent 95% confidence intervals around the means of time spent on each activity.

People were also asked about specific reasons for their dissatisfaction with their work-life balance, and there were some important patterns in the responses of workaholics and non-workaholics. In particular, a greater proportion of workaholics (compared to non-workaholics) reported that spending too much time working is a source of dissatisfaction.[ix] On the other hand, roughly the same proportion of workaholics and non-workaholics named lack of time for family, lack of time for other activities, or other work-related reasons as a source of their dissatisfaction with work-life balance.

Reasons for dissatisfaction

We found that people who consider themselves workaholics are spending more time working, but not less time with their family. So what are people sacrificing to get that extra work time? The answer is…sleep. Workaholics reported getting nearly 20 minutes less sleep in the previous night. This accounts for about half of the extra work time put in by workaholics. The other statistically significant, albeit much smaller, differences between time use of workaholics and non-workaholics were found in the domain of leisure activities (watching TV, reading, and sports & leisure).[x]

Time spent sleepingNote: The error bars represent 95% confidence intervals around the means of time spent on each activity.

The finding that self-identified workaholics get less sleep is important because loss of sleep can have huge implications on people’s mental health, physical health, and their daily functioning at work and at home. Of course, with this being an observational study, we cannot rule out alternative causal pathways of the association between time spent sleeping and working. For example, it is possible that workaholics simply do not need as much sleep, and that they use the extra time to get some extra work done. In any case, it is evident that people who identify as workaholics are less satisfied with important domains of their lives (e.g., work and family). As such, understanding workaholism, its consequences, and its implications on individuals’ functioning at home and in the workplace should be an important concern for organizations and policy makers concerned with improving societal quality of life.

This analysis is based on the Statistics Canada General Social Survey. All computations, use and interpretation of these data are entirely that of the authors.

Details

 

[i] The data come from wave 24 (collected in 2010) of Statistics Canada General Social Survey, the latest wave in which information on time use was collected. 15,390 people participated in this wave. Survey weighting was applied to ensure the estimates are representative of Canadian population. More information can be found at http://www.statcan.gc.ca/pub/89f0115x/89f0115x2013001-eng.htm. [back]

[ii] Analyses involving time spent with partner/spouse included only participants who reported living with a partner/spouse. Analyses involving time spent with children included only participants who reported having at least one child in the household. Analyses involving time spent with family included only participants who satisfied at least one of these conditions. [back]

[iii] 27% of women and 31 % of men consider themselves workaholics: Χ2(1) = 19.2, p < .001.
Rao-Scott corrections were applied to all chi-squared tests to account for weighting of survey data. [back]

[iv] Table of chi-squared tests. [back]

Χ2   df p
Overall test 33.03 * 5 .024
Work vs. all other categories combined 22.81 * 1 .002
Financial vs. all other categories combined 0.27 1 .736
Family vs. all other categories combined 13.12 * 1 .017
School vs. all other categories combined 2.16 1 .440
Time vs. all other categories combined 2.14 1 .330
Other vs. all other categories combined 6.88 1 .076

Note: *p < .05.

[v] Workaholics (M = 2.64, SE = 0.03) vs. non-workaholics (M = 3.00, SE = 0.01): t(8,633) = 11.56, p < .001. [back]

[vi] Time spent working: workaholics (M = 374.63, SE = 8.40) vs. non-workaholics (M = 334.83, SE = 4.75): difference of 39.08 minutes; t(8,633) = -4.13, p < .001. [back]

[vii] Time spent with the partner/spouse: workaholics (M = 299.04, SE = 8.24) vs. non-workaholics (M = 308.05, SE = 4.78): difference of 9.02 minutes; t(5,423) = 0.95, p = .344. [back]

[viii] Time spent with a child (in minutes): workaholics (M = 297.36, SE = 12.36) vs. non-workaholics (M = 305.31, SE = 7.04): difference of 7.95 minutes; t(2,795) = 0.56, p = .576. [back]

[ix] Reason for dissatisfaction with work-life balance. Statistical tests of comparisons between workaholics and non-workaholics. [back]

Χ2   df p
Too much time spent working 104.94 * 1 .006
Not enough time for family 2.68 1 .661
Not enough time for other activities 0.72 1 .819
Other work-related reasons 0.87 1 .800

Note: *p < .05.

[x] Differences in time use between workaholics and non-workaholics. [back]

Not workaholic Workaholic Difference t df p
With Partner/Spouse 308.05 299.04 9.02 0.95 5,423 .344
With Child(ren) 305.31 297.36 7.95 0.56 2,795 .576
Working 289.78 328.54 -38.75 * -4.56 8,633 .000
Education 23.09 22.53 0.56 0.17 8,633 .867
Volunteering 18.98 19.62 -0.64 -0.31 8,633 .755
Childcare 87.56 77.9 9.66 1.75 2,795 .081
Shopping 44.01 42.74 1.27 0.51 8,633 .611
Cooking 37.32 34.68 2.64 1.76 8,633 .079
Housekeeping 31.7 33.04 -1.34 -0.64 8,633 .524
Maintenance & repair 8.52 12.61 -4.09 * -2.02 8,633 .043
Meals at Home 57.61 56.58 1.03 0.53 8,633 .596
Restaurant Meals 18.35 16.85 1.5 1.02 8,633 .310
Watching TV 101.6 93.01 8.59 * 2.5 8,633 .012
Socializing 74.11 70.54 3.57 0.86 8,633 .390
Sports & leisure 62.68 55.49 7.19 * 2.21 8,633 .027
Reading 14.72 10.7 4.02 * 3.59 8,633 .000
Sleeping 488.05 468.64 19.42 * 5.17 8,633 .000

Note: *p < .05.

Buy on the Fly: Recent Trends in the Changing World of Mobile Commerce

Buy on the Fly: Recent Trends in the Changing World of Mobile Commerce

by Stevie Yap

As smartphone and tablet adoption continue to increase, the number of people with access to mobile internet continues to grow. As a result, mobile buying has entered the mainstream. We are not just talking about digital products either – recent research suggests that consumers are becoming more comfortable than ever buying tangible goods via mobile web. What are some of the emerging trends in mobile commerce?

Devices

Smartphone vs Tablet? Size matters

Recent data suggest that tablets are used more than smartphones for actual purchases, and smartphones are used more for browsing/researching products. However, with screen sizes on smartphones becoming larger with each generation of device, these differences are narrowing.

iOS vs. Android

Buying behavior varies across operating systems. Data suggests that iOS continues to lead Android in sales performance and conversion rates. This likely reflects several important demographic differences in people who use each OS rather than the characteristics of OS itself, with Apple users typically being younger, more educated and having higher incomes on average. Indeed, if you examine the behavior of just consumers that use high-end Android devices (e.g., Samsung Galaxy S6), sales and conversion rates are comparable to those seen in iOS users.

The importance of user interface

It is not surprising that user interface is linked to mobile commerce. For example, screen size of the mobile device has a direct linear relationship with conversion rates – the bigger the screen, the more likely a user will convert to purchase. One of the main barriers to mobile sales is frustration with checkout process – with difficulty entering credit card information and billing addresses on mobile devices leading to people leaving unpurchased products in their virtual carts. Providing “one-click” solutions to checkout – employing the use of stored credit card data (e.g., Amazon one-click and Apple Pay) reduces this friction and leads to higher rates of completed purchases.

Mobile web vs. in-App

Recent data suggest that mobile web continues to dominate mobile shopping and purchasing behavior. As such, mobile optimization is an absolute must if e-retailers wish to capitalize on consumers’ growing mobile commerce behaviors. Further, even for purchases that will ultimately happen on a desktop computer, many of the shopping, browsing and researching behaviors that come before actual purchases happen on mobile devices. In contrast, apps do offer advantages to users such as camera integration, notifications, faster log-in and checkout (reducing the checkout frustration discussed above), but are typically used by only regular customers and heavy buyers from a particular retailer or site.

The evolution of search

The way in which consumers can search for items is evolving to fit mobile, and innovations like visual search and predictive search are changing the path of mobile shopping. For example, Slyce Inc., a Toronto based company, produces technology that allows consumers to search through an online catalogue using simply a photo. Imagine walking down the street and seeing a coat you like – you snap a picture of it and without knowing the brand or the name of the coat you are able to find it or something similar to it on your smartphone. Now that’s shopping on the fly! See how this works below:

Similarly, predictive search technology like Google Now is beginning to take the guess work out of mobile shopping for consumers by surfacing products and services to consumers before they even consciously decide that they need them. Here’s an example:

Overall, this type of technology is clearly shaping e-commerce, and the way people spend money on their mobile devices.

About insighta

Insighta is a boutique research firm specializing in understanding people and using data to surface insights about your customers, clients, and larger consumer base. At insighta, we are behavioral scientists with real expertise you can leverage to answer real world questions. We combine the the rigor of true science with psychologists’ broad understanding of people, their thought processes, and their behavior to deliver data-driven insights you can trust to drive action and growth in your organization.

What the “Mozart Effect” can teach us about making good data driven decisions

What the “Mozart Effect” can teach us about making good data driven decisions

by Stevie Yap & Ivana Anusic

In 1998, the state of Georgia passed a bill that provided a recording of classical music to each baby born in the state – costing the state $105,000 a year. The following year, the state of Florida passed a bill requiring that every state-funded early childhood education and care facility play 30 min of classical music each day.

baby

These major policy decisions came following a widely publicized research study showing that people who listened to 10 minutes of music by Mozart did better on a spatial reasoning task typically seen in IQ tests than the control group. Dubbed the “Mozart Effect”, this finding made waves in the media, with headlines claiming that listening to Mozart makes you smarter. It also created many opportunities for marketers in the music industry to create products that capitalize on the idea that listening to Mozart may enhance people’s mental ability. The fact that music influences people’s behavior and the decisions they make (e.g., the types of wine they select in a wine store) is not surprising to most people – but the idea that listening to music by Mozart may make you smarter was definitely grabbing the attention of parents, educators, and policy makers across the country.

example of the spatial reasoning task
Example of the type of task used in
this research.

It is important to use data to drive the big decisions for your organization. But it is also crucial that the decisions you make are justified by the data that you have. In the original Mozart Effect study, the people who listened to Mozart were compared to people who sat in silence. Knowing this detail changes our interpretation of the findings. Is listening to Mozart, in particular, the driving factor of enhanced performance? What if the music the control group listened to was not written by Mozart, but by another classical composer? Or a rock band? Would there still be a “Mozart Effect”? Would we see the same effect if the control group did some other task that was more engaging than simply sitting in silence, but not necessarily musical?

The research that followed this initial study evaluated all these questions, and found evidence that the “Mozart Effect” was not limited to music by Mozart (or music at all). Similar temporary gains in mental ability can be achieved by listening to other classical composers ( the “Schubert Effect”), rock music (the “Blur Effect”), or even a narrated story (the “Stephen King Effect”). But policy decisions were made, and the “Mozart Effect” was where state legislators were going to put their money.

It is wonderful to see policy makers using research and data to drive their decision making – but what we can learn here is that asking the right questions and proper study design are also vitally important. It turns out that this finding may not be limited to Mozart, classical, or even music at all. Further, it is not even certain that music exposure in infancy has any long term impact on babies’ mental ability, as the study’s participants completed the spatial reasoning task only 10-15 minutes after they listened to music. Simply reading an engaging story to one’s child or just flipping the radio on to a classical or top-40 station may lead to the same effects , and those public funds may have been spent on programs with better ROI for children, families and the community.

Partner with insighta to design research that optimizes your decision making, and make the best data-driven decisions for your organization. Rely on us to help you ask the right questions and make sure your insights fit the story your data tell.

 

About insighta

Insighta is a boutique research firm specializing in understanding people and using data to surface insights about your customers, clients, and larger consumer base. At insighta, we are behavioral scientists with real expertise you can leverage to answer real world questions. We combine the the rigor of true science with psychologists’ broad understanding of people, their thought processes, and their behavior to deliver data-driven insights you can trust to drive action and growth in your organization.