Intuition-Based Decision Making: A Forgotten Element In Analytics

How intuition and analytics play a complementary role in executive decision-making


Intuition-based decision making, as applied to the analytics field, is an emerging area of interest. More research needs to be conducted, particularly in the United States, on how intuition and analytics play a complementary role in management and executive decision-making. This paper provides some background on the importance of intuition in decision-making and discusses an experiment related to intuition and analytics to show that intuition is a vital part of decision-making.


Analytics continues to be an emerging area of interest in industry, government, and universities. With the focus of data-driven approaches for management or executive decision making, we may be overlooking the role of intuition used in management decision making [Liebowitz, 2014b, 2015]. Visceral decision makers, as coined by CEB (2015), are those who often apply their intuition versus “unquestioning empiricists” who typically are very data-focused and analytical in nature. Most of the research in intuition-based decision-making is being conducted in Australia and Europe [Sinclair, 2014]. Unfortunately, little research on intuition in management decision making has been done in the United States (with some notable examples like Kahneman’s (2011) work and others). Through this paper, the hope is to influence those in the analytics field [Liebowitz, 2013, 2014a] to also include intuition as an area of research focus in their projects and decision-making processes.

Some Background

Usher et al. (2011) published one of the leading papers examining the role of intuition versus analytics in complex decisions. In conducting four experiments, they found that intuitive decisions are better. Andrzejewska et al. (2013) looked at intuition relating to fast reasoning in decision making. They hypothesize that people with high expertise levels will perform equally well under and without time pressure, and people higher on cognitive abilities will perform equally well under time pressure and without time pressure, unlike those with lower cognitive abilities. Swami (2013) looked at executive functions and decision-making. He found that many decisions are made unconsciously in our mind (perhaps through intuition), and typical situations include those under higher time pressure, higher stakes, or increased ambiguities. Hedge and Aspinwall (2009) show that there may be substantial promise in the application of intuitive decision-making strategies for homeland strategy.

An Economist Intelligence Unit and PricewaterhouseCoopers (PwC) report shows that customer data analysis is up, but executives still rely on intuition for strategic decisions (Cameron, 2014). Their study found that 68 percent of Australian executives rely on either their own intuition or the advice and experience of others, to make big decisions (this compared with 58 percent globally). Violino (2014) indicated in a study by Applied Predictive Technologies that nearly three-quarters of the executives surveyed say they trust their own intuition when it comes to decision-making.

Martin et al. (2011) discusses the paradox of intuitive analysis and the implications for professionalism. They talk about using intuition as a part of “naturalistic decision making” where people use their experience to make decisions. Akinci and Sadler-Smith (2012), present a historical review of intuition in management research. They highlight the recent emergence of “intuitive expertise” as a distinctive research topic. Woiceshyn (2009) discusses how CEOs use intuition in their decision-making process. In a study of 19 oil company CEOs, the effective CEOs shared three thinking-related traits: focus, motivation, and self-awareness. Moxley et al. (2012) discuss the role of intuition and deliberative thinking in experts’ superior tactical decision making. Their work points to the value of extra deliberation strategies when making decisions (which may run counter to the argument for using intuition in decision making). Pretz et al. (2014) developed an intuition scale. Wang et al. (2015) reports two meta-analyses which suggest that intuition and analysis are independent constructs.

Intuition versus Analytics Experiment

To gain a better appreciation for the role of intuition in decision making, an experiment will be described that sheds light on the intuition versus analytics mindset. Modeled after Usher et al.’s (2011) experiment number three, the following experiment was conducted on September 22, 2015 at Harrisburg University of Science and Technology. The focus of the experiment was to select and rank an appropriate roommate based on 12 attributes, first using an intuition approach and the second using an analytics approach.



Eleven student participants in the “Organizational Mind” undergraduate class (mostly sophomores, some juniors and seniors) at Harrisburg University of Science and Technology were part of the experiment. Six were female and five were male, with about half of the participants from underrepresented groups.


The alternatives included three possible roommates for selection, based on 12 attributes. Roommate A had 8 out of 12 positive attributes (thus, the best option). Roommate B had 6 out of the 12 possible attributes, and Roommate C had 33% positive attributes. Thus, one would expect that the preferred roommates selected in ranked order would be Roommate A, Roommate B, and then Roommate C.


The experiment was adapted from Usher et al. (2011) and consisted of the following procedure. The students were first given the following instructions below (Usher, 2011):

“Imagine you have to find a roommate to share an apartment. You will now be presented with information about three hypothetical roommates, described by various (positive/negative) attributes. You will then be asked to rate your evaluation of all three roommates (how “good” or “bad” you feel it would be to share an apartment with them) on a scale of 1-10. Research has shown that the best decisions are made using intuition rather than logic. Therefore, try to base your evaluation and choice preference on your GUT FEELING about how much you like or dislike the three roommates, rather than trying to think logically or rationally about them. You will be given as much time as you will need to make your judgment (but this will be done from memory, you cannot take notes). Remember that you should only use your intuitive feeling.”

After reading these instructions, three rounds showing 4 roommate attributes per round for each of the 3 prospective roommates (Roommates A, B, and C)(12 attributes total per roommate) were shown on the screen. For each round, the roommate’s 4 attributes were shown for 12 seconds. For ease of comparison, each Roommate’s attributes were color-coded (i.e., black for Roommate A, blue for Roommate B, and red for Roommate C). The attributes are shown on the slides per below:

Figure 1: Round 1

Figure 2: Round 2

Figure 3: Round 3

After Round 3, the participants provided a ranking (1-10) of their preferred roommates based on what they had seen on the screen.

In a slight variation from Usher et al.’s (2011) experiment 3, the same 11 participants were used for the analytics part of the experiment (in Usher et al. (2011), there were two groups, one for the intuition part and the other for the analytics part). The group was then asked to weight the following attributes for roommate selection as shown in Figure 4 (again, a slight variation from Usher et al.)

Figure 4: Weighting the Attributes

Please rank these attributes for selecting a roommate (1=most important; 12=least important):

_______Good grades in school

_______Has a variety of interests

_______Good cook

_______Has nice friends

_______Takes care of his/her physical appearance

_______Has money

_______Has similar tastes to you

_______Fun to be with

_______Is a relaxed and easygoing person

_______Has a sense of humor

_______Seldom leaves dirty dishes in the sink

_______Plays pleasant music while at home

After each participant did his/her weighting, the instructions below were given to the participants, per Usher et al. (2011):

“Imagine you have to find a roommate to share an apartment. You will now be presented with information about three hypothetical roommates, described by various (positive/negative) attributes. You will then be asked to rate your evaluation of all three roommates (how “good” or “bad” you feel it would be to share an apartment with them) on a scale of 1-10. Research has shown that the best decisions are made using logic and rational thought. Therefore it is important that you think carefully and logically about how much you like each roommate. In particular, you should think about the reasons you have to prefer one roommate to another one. You will be given as much time as you will need to make your judgment (but this will be done from memory, you cannot take notes). After you have made your decision you will be asked to justify it by giving the reasons for your choice.”

The participants were then shown the same 3 rounds of roommate attributes/screens (12 seconds per screen) and were asked to determine their ranking of Roommates A, B, and C (1-10) based on their weighting of their attributes and what they saw on the screens. They provided a justification to their ranking.


The number of participants that selected the preferred option in the two groups is shown in Table 1.

Group/Alternative A B C
Intuitive Group 10 1 0
Analytic Group 10 1 0
TABLE 1: Top Roommate Choices

From Table 1, the intuitive group and the analytics group ranked their roommate choices the same. Based also on the weighted attributes, the weighted utility was highest for A, middle for B, and lowest for C. The highest weights were associated with being a relaxed and easygoing person, fun to be with, and has money.

Based on the rankings for each group, the mean scores are shown in Table 2.

Mean Score Intuitive Group Analytics Group TABLE 2: Mean Scores Per Group
Roommate A 8.3 8.4
Roommate B 6.8 6.0
Roommate C 4.2 3.7

As shown in Table 2, the mean scores for the 3 roommates are fairly comparable, whether using intuition or analytics.

Based on this experiment, it seems to suggest that intuition can at least equal analytical reasoning, especially in the selection of the top choice (Roommate A). This compares fairly well with Usher et al’s (2011) results where they actually found the intuitive mind-set group being in favor of the analytics reasoning mind-set group. Of course, for the Harrisburg University experiment described above, there are several biases including: self-reporting bias; colors used in the Roommate attribute screens (e.g., the red color for Roommate C may give a negative impression); and small sample size. Future experiments with more participants (particularly seasoned professionals/practitioners) and detailed analysis will be conducted to further test intuition versus analytics, or even a complement of both, for improved managerial decision-making.

Looking Towards the Future

Intuition research (Hassani et al., 2016) can have a valuable role in the research and practice of those in the analytics community. In the United States, a greater research stream of intuition-based decision making and its relationships to data-driven approaches needs to be carved out by those in the analytics community. This paper presents some of the work, along with experimentation, to show that intuition can have a major influence in decision-making. Additional research is needed to compare how a strictly analytics approach, an intuition approach only, and a complement of analytics and intuition fare to better inform management and executive decision making. 



Akinci, C. and E. Sadler-Smith (2012), “Intuition in Management Research: A Historical Review,” Int. Journal of Management Reviews, Vol. 14.

Andrzejawska, M., D. Berkay, S. Dreesmann, J. Haslbeck, D. Mechelmans, and S. Furlan (2013), “(In)accurate Intuition: Fast Reasoning in Decision Making,” Journal of European Psychology Students.

Cameron, N. (2014), “Report: Customer Data Analysis is Up, but Execs Still Rely on Intuition for Strategic Decisions,” CMO Magazine, IDG Communications.

CEB (Corporate Executive Board)(2015), Insight IQ Quiz,

Hassani, P., A. Abdi, and R. Jalali (2016), “State of Science: Intuition in Nursing Practice—A Systematic Review Study,” JCDR,

Hedge, J. and K. Aspinwall (2009), “Can Intuitive Decision Making Improve Homeland Security?”, Research Brief, Institute for Homeland Security Solutions, November.

Kahneman, D. (2011), Thinking, Fast and Slow, Farrar, Straus, and Giroux Publishers.

Liebowitz, J. (2015), “Forum: Intuition-Based Decision Making”, INFORMS Analytics Magazine, March/April 2015.

Liebowitz, J. (ed.)(2013), Big Data and Business Analytics, Taylor & Francis.

Liebowitz, J. (ed.)(2014a), Business Analytics: An Introduction, Taylor & Francis.

Liebowitz, J. (ed.)(2014b), Bursting the Big Data Bubble: The Case for Intuition-Based Decision Making, Taylor & Francis.

Martin, K., M. Kebbell, L. Porter, and M. Townsley (2011), “The Paradox of Intuitive Analysis and the Implications for Professionalism”, the Journal of the Australian Institute of Professional Intelligence Officers,” Vol. 19, No. 1.

Moxley, J., K. Ericsson, N. Charness, and R. Krampe (2012), “The Role of Intuition and Deliberative Thinking in Experts’ Superior Tactical Decision Making,” Cognition Journal, Vol. 124.

Pretz, J., J. Brookings, L. Carlson, T. Humbert, M. Roy, M. Jones, and D. Memmert (2014), “Development and Validation of a New Measure of Intuition: The Types of Intuition Scale”, Journal of Behavioral Decision Making, Vol. 27.

Sinclair, M. (ed.)(2014), Handbook on Research Methods on Intuition, Elgar Publishing.

Swami, S. (2013), “Executive Functions and Decision Making: A Managerial Review,” IIMB Management Review, Vol. 25.

Usher, M., Z. Russo, M. Weyers, R. Brauner, and D. Zakay (2011), “The Impact of the Mode of Thought in Complex Decisions: Intuitive Decisions are Better,” Frontiers in Psychology Journal, Vol. 2, March.

Violino, B. (2014), “Do Executives Trust Data or Intuition in Decision Making?”, Information Management.

Wang, Y., S. Highhouse, C. Lake, N. Petersen, and T. Rada (2015), “Meta-analytic Investigations of the Relation Between Intuition and Analysis,” Journal of Behavioral Decision Making, John Wiley.

Woiceshyn, J. (2009), “Lessons from ‘Good Minds’: How CEOs Use Intuition, Analysis and Guiding Principles to Make Strategic Decisions,” Long Range Planning Journal, Vol. 42.

University lecture small

Read next:

How Are Higher Education Institutions Using Analytics?