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Making Your Semantic
Application Addictive:
 Incentivizing Users
                Roberta Cuel
    Univeristy of Trento (Italy) – KIT (Germany)
   roberta.cuel@unitn.it – roberta.cuel@kit.edu
Topics of the session


• The role of human contributions in the creation of
  semantic descriptions of digital artifacts.

• Methods and principles for the design of
  incentives-compatible semantic-annotation
  technology.

• Case studies:
 • TID: Telefónica R&D corporate knowledge
 • “Taste it! Try it” mobile app
Semantic content authoring

• Rely on human inputs:
  • Modeling a domain.
  • Understanding text and media content
  • Integrating data sources originating from different
    contexts
  • …

• Motivating users to contribute is essential for semantic
  technologies to reach critical mass and ensure
  sustainable growth.

• Realize incentivized semantic applications.
What is the secret to
                sustainable success?




• Offer solution to a real problem: right solution at the
  right time – at least 50% of success
Our approach:
Ideally: field  desk  lab  field
A procedural ordering of methods to
develop incentive compatible applications
Motivations in the Web 2.0
                          • Motivation and incentives
                            – Reciprocity
                            – Reputation
                            – Competition
                            – Altruism
                            – Self-esteem
                            – Fun
                            – Money




2/23/11                                                 2
Intrinsic / Extrinsic motivations




               Kaufman, Schulze, Veit (Mannheim University)
Theories of motivation (latin move)

Performance : f (ability*motivation)    Content theories of motivation
Incentives  Motivation  Performance   •Need theories
                                        •Herzberg’s “two factor” theory
                                        •McClelland’s achievement-<
                                        power-affiliation theory


                                        Job characteristic approach
                                        (Skill variety, autonomy, .. )

Psychological meaning:                  Process Theories of motivation
internal mental state pertaining to:
-initiation,                            -Reinforcement theory
-direction,                             -Goal setting theory
-persistence,                           -Expectancy theory
-intensity and
                                        -Organizational justice theory,
-termination of behavior
                                        -…, …, ...
The incentive analytical tool
                                                                         Nature of
                                                            Social
            Goal                       Tasks                            good being
                                                          Structure
                                                                         produced
Communication       High                       High
  level (about     Medium                    Medium
                             Variety of
   the goal of                                                          Public good
   the tasks)       Low                        Low        Hierarchy      (non-rival
 Participation      High                       High        neutral         non-
 level (in the                                                           exclusive)
                   Medium   Specificity of   Medium
   definition
  of the goal)      Low                        Low
                            Identification      High
                    High
                                with            Low
                                                                        Private good
 Clarity level                                 Highly    Hierarchical      (rival,
                              Required        specific                   exclusive)
                    Low
                                skills        Trivial
                                             Common
TID: Telefónica R&D corporate knowledge

      “Taste it! Try it” mobile app for reviewing
                         restaurant and other PoI


TWO CASE STUDIES
Enterprise Knowledge
Management @ TID - Spain
               • Services of the intranet
                 portal
                 • Document management
                 • Corporate directories
                 • Pilot/Product/Service
                   catalogues
                 • News
                 • Bank of ideas
                 • Blogs, wikis, forums
                 • Search engines


               • Some info
                 • 1200 employees in 7 cities
                   and 3 countries (↑)
                 • ˜3050 visits per day, ˜56000
                   page views (impressions) per
                   day, average visit time: 20’
Field and domain analysis

Domain analysis
•Site visit, semi-structured, qualitative interviews (Communication processes,
Existing usage practices, problems, tools/solutions)
  • Tape recording, transcription
  • Data analysis per ex-post categorization
•Focus group discussion
  • Usability lab tests and Expert walkthroughs
•Lab experiment
  • Two payments
•Field experiment
  • Natural vs. semantic
    annotation
The incentive analytical tool and
   TID motivations




We need to design the “game” in a way that permits to
achieve the outcome in annotations but does not distruct too
much employees from their main job
The Mechanism design exercise in our case
                    study (I)
Interplay of two alternative games:
  • Principal agent game
    • No tools to check employees perform at their best
    • Management can implement various incentives:
      • Piece rate wages (labour intensive tasks)
      • Performance measurement (all levels of tasks)
      • Tournaments (internal labour market)
  • Public goods game
    • Semantic content creation is a public good (non-excludable and
      non-rival)
    • The problem of free riding
The prototype creation
PD workshops and HCI analysis
Lab experiment
36 students
Individual task: annotation of images
Time: 8 mins
Two rewarding/incentives systems
•Pay per click: 0,03 € per tag
•Winner takes all model: 20€
2/23/11   www.insemtives.eu   39
2/23/11   www.insemtives.eu   40
2/23/11   www.insemtives.eu   41
Some results
In WTA treatment, 76 % of subjects
make more annotations than the
average number of annotations in
PPT scenario.
Prototype refinement
Incentivizing the tool …making it fun
… harnessing the networks and
    reputation effects




•   Competitive environment
•   Internal market of labour
•   Reputation in terms of expertise)
•   HR Department should be involved
Field experiment


Real users and tasks
should have
 – practical usefulness for
   users (search):
 – social implications,
   providing information about
   people, and their
   performances
• 2761 annotations,
                         Some results
     • 82% are semantic   Social rewards are as strong as
                          monetary rewards! (Man Whitney test )
                                                       Competition   Social
Competition: 200€           Number of annotation       1589          1172
                            % of semantic annotation   88,92%        71,84
                           Maximum number of annotation 439          262
                           Annotation of free text      180          326


                               Social: daily contributor
                                     on Yammer
Taste it! Try it!
Goals of the tool:
   • provide semantically-enabled reviews
Features
   • sufficiently easy to create for end-user acceptance
   • keep a user entertained - Facebook and badges
   • offer the personalized, semantic, context-aware recommendation process
Research context: (ontology-based) collaborative filtering and user clustering,
structuring and disambiguation of the reviews by using domain knowledge and
incentives
The application




Badges


         A scenario
Experiment

Hypothesis:
•Points vs. badge
•No information about others vs. information
•No information about herself vs. information

(6 groups) x (~ 25 students) = ~150 students
•Group 0: Points, Piece vise, no info on others private info, web based
•Group 1: Points, piece vise, median, public info
•Group 2: Points, piece vise, neighborhood, public info
•Group 3: Badge, piece vise, no info on others private info, web based
•Group 4: Badge, piece vise, median, public info - treatment
•Group 5. Badge, piece vise, neighborhood, public info - treatment

Points: max. 8 for creating reviews and 2 points for filling in the questionnaire
Average number    Average number                      Average number
              Average Score         reviews      semantic annotation   Average time    of actions *10
Group 0          7,4223              11,41              4,41                6,6                 4,85
Group 1          7,4904              12,08              3,76               5,26                 5,71
Group 2          10,3607             15,44              7,26               4,83                 7,14
Group 3          7,6246              12,08              4,98               4,26                10,42
Group 4          7,7612              12,32              4,48               6,46                 8,24
Group 5          8,1615               12                5,87               5,58                11,51
As proposed in game mechanics (showing the neighborhood performance) is more effective
    than the median story that is now the "top" at least in published economics papers ;-)
Any question?




         Thank you


            Roberta Cuel
      University of Trento & KIT
        roberta.cuel@unitn.it

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SemTech 2012 - Making your semantic app addictive: Incentivizing Users

  • 1. Making Your Semantic Application Addictive: Incentivizing Users Roberta Cuel Univeristy of Trento (Italy) – KIT (Germany) roberta.cuel@unitn.it – roberta.cuel@kit.edu
  • 2. Topics of the session • The role of human contributions in the creation of semantic descriptions of digital artifacts. • Methods and principles for the design of incentives-compatible semantic-annotation technology. • Case studies: • TID: Telefónica R&D corporate knowledge • “Taste it! Try it” mobile app
  • 3.
  • 4. Semantic content authoring • Rely on human inputs: • Modeling a domain. • Understanding text and media content • Integrating data sources originating from different contexts • … • Motivating users to contribute is essential for semantic technologies to reach critical mass and ensure sustainable growth. • Realize incentivized semantic applications.
  • 5. What is the secret to sustainable success? • Offer solution to a real problem: right solution at the right time – at least 50% of success
  • 6. Our approach: Ideally: field  desk  lab  field A procedural ordering of methods to develop incentive compatible applications
  • 7. Motivations in the Web 2.0 • Motivation and incentives – Reciprocity – Reputation – Competition – Altruism – Self-esteem – Fun – Money 2/23/11 2
  • 8. Intrinsic / Extrinsic motivations Kaufman, Schulze, Veit (Mannheim University)
  • 9. Theories of motivation (latin move) Performance : f (ability*motivation) Content theories of motivation Incentives  Motivation  Performance •Need theories •Herzberg’s “two factor” theory •McClelland’s achievement-< power-affiliation theory Job characteristic approach (Skill variety, autonomy, .. ) Psychological meaning: Process Theories of motivation internal mental state pertaining to: -initiation, -Reinforcement theory -direction, -Goal setting theory -persistence, -Expectancy theory -intensity and -Organizational justice theory, -termination of behavior -…, …, ...
  • 10. The incentive analytical tool Nature of Social Goal Tasks good being Structure produced Communication High High level (about Medium Medium Variety of the goal of Public good the tasks) Low Low Hierarchy (non-rival Participation High High neutral non- level (in the exclusive) Medium Specificity of Medium definition of the goal) Low Low Identification High High with Low Private good Clarity level Highly Hierarchical (rival, Required specific exclusive) Low skills Trivial Common
  • 11. TID: Telefónica R&D corporate knowledge “Taste it! Try it” mobile app for reviewing restaurant and other PoI TWO CASE STUDIES
  • 12. Enterprise Knowledge Management @ TID - Spain • Services of the intranet portal • Document management • Corporate directories • Pilot/Product/Service catalogues • News • Bank of ideas • Blogs, wikis, forums • Search engines • Some info • 1200 employees in 7 cities and 3 countries (↑) • ˜3050 visits per day, ˜56000 page views (impressions) per day, average visit time: 20’
  • 13. Field and domain analysis Domain analysis •Site visit, semi-structured, qualitative interviews (Communication processes, Existing usage practices, problems, tools/solutions) • Tape recording, transcription • Data analysis per ex-post categorization •Focus group discussion • Usability lab tests and Expert walkthroughs •Lab experiment • Two payments •Field experiment • Natural vs. semantic annotation
  • 14. The incentive analytical tool and TID motivations We need to design the “game” in a way that permits to achieve the outcome in annotations but does not distruct too much employees from their main job
  • 15. The Mechanism design exercise in our case study (I) Interplay of two alternative games: • Principal agent game • No tools to check employees perform at their best • Management can implement various incentives: • Piece rate wages (labour intensive tasks) • Performance measurement (all levels of tasks) • Tournaments (internal labour market) • Public goods game • Semantic content creation is a public good (non-excludable and non-rival) • The problem of free riding
  • 17. PD workshops and HCI analysis
  • 18. Lab experiment 36 students Individual task: annotation of images Time: 8 mins Two rewarding/incentives systems •Pay per click: 0,03 € per tag •Winner takes all model: 20€
  • 19.
  • 20. 2/23/11 www.insemtives.eu 39
  • 21. 2/23/11 www.insemtives.eu 40
  • 22. 2/23/11 www.insemtives.eu 41
  • 23. Some results In WTA treatment, 76 % of subjects make more annotations than the average number of annotations in PPT scenario.
  • 25. Incentivizing the tool …making it fun
  • 26. … harnessing the networks and reputation effects • Competitive environment • Internal market of labour • Reputation in terms of expertise) • HR Department should be involved
  • 27. Field experiment Real users and tasks should have – practical usefulness for users (search): – social implications, providing information about people, and their performances
  • 28. • 2761 annotations, Some results • 82% are semantic Social rewards are as strong as monetary rewards! (Man Whitney test ) Competition Social Competition: 200€ Number of annotation 1589 1172 % of semantic annotation 88,92% 71,84 Maximum number of annotation 439 262 Annotation of free text 180 326 Social: daily contributor on Yammer
  • 29. Taste it! Try it! Goals of the tool: • provide semantically-enabled reviews Features • sufficiently easy to create for end-user acceptance • keep a user entertained - Facebook and badges • offer the personalized, semantic, context-aware recommendation process Research context: (ontology-based) collaborative filtering and user clustering, structuring and disambiguation of the reviews by using domain knowledge and incentives
  • 31. Experiment Hypothesis: •Points vs. badge •No information about others vs. information •No information about herself vs. information (6 groups) x (~ 25 students) = ~150 students •Group 0: Points, Piece vise, no info on others private info, web based •Group 1: Points, piece vise, median, public info •Group 2: Points, piece vise, neighborhood, public info •Group 3: Badge, piece vise, no info on others private info, web based •Group 4: Badge, piece vise, median, public info - treatment •Group 5. Badge, piece vise, neighborhood, public info - treatment Points: max. 8 for creating reviews and 2 points for filling in the questionnaire
  • 32. Average number Average number Average number Average Score reviews semantic annotation Average time of actions *10 Group 0 7,4223 11,41 4,41 6,6 4,85 Group 1 7,4904 12,08 3,76 5,26 5,71 Group 2 10,3607 15,44 7,26 4,83 7,14 Group 3 7,6246 12,08 4,98 4,26 10,42 Group 4 7,7612 12,32 4,48 6,46 8,24 Group 5 8,1615 12 5,87 5,58 11,51 As proposed in game mechanics (showing the neighborhood performance) is more effective than the median story that is now the "top" at least in published economics papers ;-)
  • 33. Any question? Thank you Roberta Cuel University of Trento & KIT roberta.cuel@unitn.it

Notas do Editor

  1. Not the whole success
  2. We start with the laboratory experiment with University students Doing the experiment in experimental laboratory requires us to provide incentives to students to participate. They are not our friends that give us a favor to test the software or students in our course that earn course credit – it is not what we want. We want subjects that are neutral to us and to the task and that will reply only to the incentive structure that we provide. There is a fee that we need to pay to students no matter what just to maintain reputation of the laboratory to be sure that students will keep coming to the experiments organized also by other researchers. You can think of environments where you can run the test and don’t pay the participation fee but offer only the flexible part (Mechanicle Turk?) Don’t look at the €5 but concentrate on the flexible part of the payment
  3. (Leon Festinger, 1954) (Bram Buunk and Thomas Mussweiler 2001, Jerry Suls, Rene Martin, and Ladd Wheeler 2002), (Solomon E. Asch 1956, George A. Akerlof 1980, Stephen R. G. Jones 1984, Douglas Bernheim 1994).