April 10, 2023

Preliminary Results

Measuring Motivation Activation in a Virtual World

Mark W. BellAnnie Lang

Thanks

• Annie Lang• Edward Castronova and Gert Wagner• This study was conducted in a virtual

environment created with funding from the German Federal Ministry of Education and Research (BMBF), Grant No. 01UW0706 - PT-DLR.

Motivation Activation Measure

• Motivation Activation Measure (MAM) MAM , developed by A. Lang and colleagues

• An indicator of individual differences in appetitive and aversive activation

• Approach system activation (ASA) and defense system activation (DSA)

Lunch With Annie

• Can we do MAM in Second Life?

Virtual Data Collection Interface (VDCI)• What, if any, differences will we find?

Lit Review

• MAM Literature

A. Lang; A. Lang, Bradley, Sparks, & Lee, 2007; Potter, Lee & Rubenking

Sensation Seeking, Substance Use, Media Choice

• VDCI Literature

Castronova & Wagner• VW as Research Sites Literature

Castronova & Falk

Work in Progress

• Accepted at ICA• Re-write in progress• Preliminary Results

Media Use

Age

Gender

Media Choice Framework• The previous MAM participants are mostly

college students.• These previous participants use all kinds of

media• Those high in DSA don’t like violence and gore• SL doesn’t have this• SL may be more attractive to those higher in

DSA• SL is still a new technology• High ASA individuals are less likely to try new

experiences

Gender and Age Framework

• The previous MAM participants are mostly college students.

• These previous participants were from a narrow age range

• Does ASA or DSA change over time in the individual?

• Does ASA or DSA change over time by gender over time?

• Predictions of ASA and DSA can be made

Hypothesis

• H1. The mean values, ranges and the normality of the distributions will differ significantly between SL and non SL data. SL should have lower DSA and higher ASA.

• H2. Different media choices will have different percentages in each of the four motivational groups. In SL, there should be more Risk avoiders, and Coactives and fewer risk takers and inactives.

• H3. Males will be higher in ASA and lower in DSA than females.

• H4. ASA will increase with age until a peak is reached and then decline.

• H5. DSA will increase with age.

Method

• Build the apparatus

As like Media Lab as possible• Samples

Convenience samples• Email Lists• Classified Ad

• Paid 250 L$ or .96 USD per for each participant

• 632 Attempts (N=373)

Results

18-25 26-35 36-45 46-55 > 55

25.3 29.3 32.9 6.0 6.5

Male Female

35.1 64.9

Asian Black Hispanic White Other Skip

8.7 7.9 12.5 27.7 14.7 28.5

Findings - ASA

Findings - DSA

Hypothesis 2 – Motivational Groups

Risk takers 18.2

Risk avoiders 18.2

Coactives 31.9

Inactives 31.6

Hypothesis 3 – ASA/DSA & Gender

p<.001

Hypothesis 4 – ASA/DSA & Age

Univariate Analysis of Variance

Gender p<.001

What we learned about research in virtual worlds• Excellent research skills• Virtual space literacy• New skills needed• Working with large data sets

Excellent research skills

• Possibly a higher level of skills needed • Novel environments may encounter bias

of their subject matter• Research that is solidly grounded (high

quality questions, theory and methodology) will provide no opportunity for reviewers to dismiss research too quickly.

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Virtual space literacy

• Virtual spaces demand an increased need for literacy about the spaces before research can be conducted

• Several instances of well-intentioned research conducted in virtual spaces that is spoiled due to a lack of literacy in the spaces by the researchers

• By learning, exploring, and becoming adapt in these environments, the researcher will improve their results.

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New skills needed

• A new set of skills beyond that of research skills are beginning to be needed.

• A greater knowledge of technical skills is moving from a desirable skill to an often requirement.

• This literacy and technical skill informs the researcher about  what is possible, allows them to expand their research repertoire, and make connections previously problematic or impossible to make.

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Working with large data sets

• A researcher must be accustomed to working with and understanding large data sets.

• A virtual environment allows us to collect data in new ways and in ways which might not be possible previously

• This may include the creation of new tools. • MAMinSL had > 130,000 data points

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Mark W. Bellbellmw@indiana.edu

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