Drives

About

Receptiviti’s Drives framework contains five measures that provide insight into what motivates people. Drives can be strong predictors of individual or group behaviour, offering insight into whether a person is driven by a need for achievement and self actualization, a need for domination, a need for reward, a need to avoid risk, or if they are driven by a need to engage in risk-seeking behaviour.

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"personality": {...},
"social_dynamics": {...},
"drives": {
"affiliation": 0.1299903474239308,
"achievement": 0.05912495867410771,
"risk_seeking": 2.259372672349582,
"risk_aversion": 52.21799222047504,
"power": 0.007224311584875494,
"reward": 0.03516344836040124
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"cognition": {...},
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Measures

MeasureSummaryHigh Score DefinitionLow Score Definition
affiliationThe degree to which a person is driven by their own internal need for affiliation with other individuals or groups.Suggests significant affiliation with others, or a significant need for affiliation with others.Suggests little-to-no affiliation with others, or little-to-no need for affiliation with others.
achievementThe degree to which a person is driven by an internal need for achievement.Suggests a person with a significant internal need for achievement and self-actualization.Suggests a person with little-to-no internal need for achievement and self-actualization.
risk_seekingThe degree to which a person is focused on engaging in risky behaviors or activities.Reflects a significant focus on seeking or engaging in risky behaviours or activities.Reflects minimal focus on seeking or engaging in risky behaviours or activities.
risk_aversionThe degree to which a person is focused on avoiding risk.Reflects a significant focus on avoiding risk.Reflects minimal focus on avoiding risk.
powerThe degree to which a person is driven by an internal need for power or domination.Suggests a significant internal need for power or domination.Suggests an individual with little-to-no internal need need for power or domination.
rewardThe degree to which a person is driven by an internal need for reward.Suggests a significant internal need for reward.Suggests an individual with little-to-no internal need for reward.

Additional Information on the Drives Framework

Affiliation

The Affiliation measure includes language that relates to connecting and being in the presence of other people. Words in this category are related to the Social measure, but measure different phenomena. The Social measure is a marker of social engagement and is associated with awareness of other people.

Examples of related research:

The Affiliation indicator has been used extensively in research. For example, it has been used to examine gender differences in evaluations of emergency medicine residents and their approach to patient care. Research has also shown that the feeling of affiliation, or the need to affiliate with others, can also play a role in promoting positive or negative health behaviours.

High-scoring example: “We just donated a small box to the food bank in our local community. We figured it was the least we could do to help our neighbours.”

Low-scoring example: “I wonder what time I’ll get home tonight - this traffic is absurd.”

Achievement

The Achievement measure includes language related to actualization and fulfillment. Words in this category include certain achievement-related verbs (i.e., advance, obtain), and nouns (i.e., plan, award, prize).

Examples of related research:

While the research related to Achievement and Drives is large, one study has shown that marker words can be important in detecting implicit motives, such as Achievement and Power.

High-scoring example: “If we’re diligent, we can make some efficient progress today.”

Low-scoring example: “I really need a vacation.”

Risk Seeking & Risk Aversion

The Risk Seeking and Risk Aversion measures evaluate certain language related to caution (i.e., avoid, danger), failure (i.e., lose, disaster), and behavior (i.e., apprehensive, reluctant, tentative). When combined with other Drive measures such as Reward, as well as Social Dynamics measures, we can begin to understand how individuals evaluate and assess risk related to objects, events, experiences, other people, and the world around them.

Examples of related research:

This measure has been used in research to evaluate many aspects of personality and behaviour. For example, researchers have used it to investigate how risk-taking evolves with age, how risk interacts with certainty, and more.

High-scoring Risk Seeking example: “We should be confident in our wager, it’s a perfect opportunity to score big.”

Low-scoring Risk Seeking example: “I think we should ask the group before moving along.”

High-scoring Risk Aversion example: “I’m a little apprehensive of our plan - I think we should tread carefully.”

Low-scoring Risk Aversion example: “Let’s try it out first and make our decision a little later.”

Power

The Power measure is broad, and contains language related to status (i.e., beginner, president, authority), dominance (i.e., conquest, destroy), wealth (i.e., rich, poor), and fame (i.e., famous, royal).

​Examples of related research:

​Power can be an important driver of behaviour. For example, some research has shown that marker words can be important in detecting implicit motives, such as Achievement and Power.

High-scoring example: “Defeat them while they’re still weak.”

Low-scoring example: “I’d prefer if we could talk about this in person.”

Reward

The Reward measure is narrower in scope as compared to the Power and Achievement categories. This category includes certain language related to benefits (i.e., award, goal), opportunity (i.e., bet, wager, score), and feelings (i.e., eager, fearless, excited).

​Examples of related research:

Studies investigating the relationship between Reward and behaviour are vast. For example, some researchers have used the Rewards measure to investigate the relationship between goal-setting, hopes, duties, and rewards.

High-scoring example: “I just earned another achievement - I’m so excited!”

Low-scoring example: “Sorry I’m late, I missed my bus this morning.”

Specs and Examples

Measures in the Drives framework are always in the range of 0 to 100. Our measures are baselined against our proprietary datasets, which consist of language samples that exceed 350 words.

A language sample that generates a score of 80 implies that 80% of all samples in our curated baseline dataset have scores that are less than the score of language sample being analyzed.

Note

  • Results will be most reliable when your text sample is >350 words in length.
  • All Drives framework measures can directly be used for comparisons since they have already been baselined.

Let's look at a couple of examples:

Example 1: Let me tell a fantastic story about how I made $100 on the stock market. I was, of course, completely new to the world of stocks and shares and financial what nots. But I had a plan! I had just lost my job and had $50 in my bank account. I was so close to broke, that the only place I had to go from there was up. I had a sound financial strategy - I subscribed to google trends on keywords relating to the market, coded up a script that sent me minute by minute alerts on the stock mentioned and the sentiment attached to its ticker. From there, I began putting a few cents into each ticker that had a positive sentiment and made $20 on my first day. On day 2, I learnt how to short sell - so I did the opposite with tickers that had a negative sentiment. Et voila! By the end of 5 days, I had grown my $50 to $150!

// partial response
{
"receptiviti_measures": {
"risk_aversion": 51.41048422923146
}
}

The paragraph above describes an individual’s growing investment in the stock market and a modest risk appetite. This is reflected in a near average risk_aversion score of 51.41. This means that 51.41% of all samples in our curated baseline dataset scores less than this sample paragraph.

Example 2: Let me tell a story about how I lost all my savings in the stock market. I was, of course, completely new to the world of stocks and shares and financial what nots. And I didn't have a clue. I had just lost my job and had $50 in my bank account. I was so close to broke, that I thought that only place I had to go from there was up. I was wrong - so very wrong. I used a silly gimmick - find a ticker I liked the sound of and bet $10 on it going up that day. Before I knew it, I had lost all my $50 and was borrowing from friends and family to invest some more. My losing streak continued for a week until I finally came to my senses - $500 in debt, but no more. What did I learn from all of this? Gambling - be it on the stock market or in a casino amounts to the same thing - a waste of time and money.

// partial response
{
"receptiviti_measures": {
"risk_aversion": 96.62372278987117
}
}

In this paragraph, we describe a different scenario — an individual who lost a significant portion of their savings by investing in the stock market. Consequently, one would expect this individual to appear risk averse. Accordingly, the risk_aversion measure returns a high score of 96.62. This means that 96.62% of all samples in our curated baseline dataset scored below this paragraph on risk_aversion. We could surmise that the individual in Example 2 is very focused on avoiding risk.