Understanding the Impact of Personal Feedback on Face-to-Face Interactions in the Workplace

The Quantified Self

Quantified Self Quantified Team Quantified Enterprise

People Analytics

Productivity Management

Space Management

Peopleá

nPlaces

7Activity

Quantified EnterpriseUnderstand and quantify how people interact and work together

in the real enterprise for personal, group and larger organization efficiency.

Awareness + Collaboration = Productivity

Spontaneous Interactions Key to Flow of Ideas

A third of team performance can be predicted merely by the number of Face

to Face exchanges among team members.

The “data signature” of natural leaders can be discovered.

Daily Productivity and Creativity can be rightly assessed.

The aware employee Key to voluntary self reflection

Happy management techniques = happy interactions = happy experiences = happy stock price.

raise in self knowledge

change in interaction behavior

Informal contact that results form frequent contact often leads to

collaboration

- Hagstrom & Kraut

Only 25 percent of job success is based upon IQ. Seventy-five percent is about how your brain believes your behavior matters, connects to other people, and

manages stress.

- Shawn Achor

- Blake Morgan

“Only of large companies can make meaningful predictions about their workforces, while can accurately predict business metrics such as budgets, financial

results, and expenses”

4%90%

- Bersin Research

Employee Survey

Active Badge - Cambridge UniversityComputer Laboratory

Happiness Badge - Hitachi

That Privacy Thingy!

Watching you Bro

Quantified Enterprise

Personal Data Analytics

Anonymous Data Analytics

People Analytics

ánSpace Analytics

• Personal Interaction Reflection • Personal Network Scale and Diversity • Personal Time and Activity Management • Personal Connection Extension

For Employees

• Quantifying Collaboration • Discover Emerging Leaders • Build High Performance Team • Develop Empathic Relationship

For Employers

• Predictive Maintenance • Better Space Arrangement & Management • Personalised Space Recommendation • Better Resource Management

For Building Managers

Implications

Small Data Driven Engagement

ÝAnonymous Data

Analytics

- Tiny Habits in the Giant Enterprise: Understanding the Dynamics of a Quantified Workplace

Small Data Driven Engagement

• Color • Noise • Air Quality • SunLight

Privacy Exposure

Usef

ulne

ss

Air Quality

Interaction

Color

Noise

Mood

Activity

Space Metrics

Quantified Enterprise Dashboard

• Mood • Activity • Crowded area

People Metrics

Playful Mood Display for Higher Engagement

Research QuestionIs short-term feedback on face-to-face interactions at a workplace an effective external

cue to raise employees self-knowledge about their workplace

Personal Data Analytics

Ü

Understanding the Impact of Personal Feedback on Face-to-Face Interactions in the Workplace

Research QuestionIs short-term feedback on face-to-face interactions at a workplace an effective external

cue to raise employees self-knowledge about their workplace

Network SensingBy observing an individual’s engagement with network annotated with temporal and spatial information, we can learn and infer behavior.

“Your Noise is my Signal”

Sense

Learn

Act

Share

Small DataA small set of well selected data gathered with participatory mobile sensing using experience sampling.

Design Philosophy

Minimal Sensing

Capture environment metrics with minimal sensing infrastructure

48% of office workers has smartphone within arm-reach

A Network and Small Data Driven Solution

iPhone/AndroidMaps and Sensors

5EF5

Enterprise Applications

ôQuantified Enterprise Platform

Advanced Models and Algorithms

API

Real time, network-based indoor localization

Location history and vectors are the key, and behaviour models can extract higher order contexts.

• Dramatically reduce deployment and management cost

• Reduce the energy consumption of mobile devices

RSS fingerprint matching

Co-location Detection

Face-to-faceInteraction

Exploiting Every Day Radio Signals to Detect Human Social Interactions

A network-centric architecture that captures existing radio signals (WiFi

probes) from the user’s device.

Co-location detection based on temporal variations of location

An empirically defined model grounded upon sociology theories, by leveraging the size and duration of the encounter.

80% of Face-to-face Encounters Detected

Face to Face Interaction Detection: Leverage on Size + Duration

Behaviour ModellingExtracting high order behavioral traits

Location -> Face to Face Interaction

Location -> PersonalityF2F Encounter Diversity, Number, Frequency, regularity and Spatial Behaviour are used to extract Big Five Personality Traits

Location -> Happiness

Spatial Behaviour and Movement Trajectory are used to estimate Physical Activeness and then map to mental wellbeing (baseline Happiness Index Survey)

This has been used to build connectivity graph and show collaboration intensity in the application.

Interaction Intensity

Interaction Intensity represents the relative exchange between different individuals and captures two aspects Interaction Frequency: Number of times of Face to Face Interaction. Interaction Duration: Total Durations of of Face to Face Interaction

A Higher P Value indicates a intense interaction, and a lower P value indicates the reverse

Total Number of Face to Face Interaction

Number of Face to Face Interaction of Specific Person | Program

Total Duration of a specific Face to Face Interaction

Maximum Duration for a single Face to Face Interaction across all

P = waid + (1� w)aif

Personal dashboard

5

Interaction History

Interaction Intensity

Locating Colleagues and Empty Rooms at Realtime

Realtime Mood Map of the Workspace

Realtime Recommendation to New Contacts

Ranking based on Interaction Intensity and Diversity

Study Methodology

Semi-Structured Interview

Application Usage

47USERS

87% male

72% 31-55 Years

Gender Generation

31-54

< 30

> 55

Application usage

4 Months

Usage log ..

Interactions

Impressions

5iPhoneAndroid

Application usage

7059 interactions5210 impressions657.50 total hours

Totals/User Impressions/User/Month

Gender difference (Mann Whitney U test)Impressions : (U = 89.5, Z = −1.36, p > 0.05) Interactions : (U = 63, Z = −2.19, p < 0.05)

females had more interactions than males

20USERS

80% male

60% 31-54 Years

Gender Generation

31-54

< 30> 55

Structured interview

Interview ..

Demographic

Personality assessment

Semi-Structured Discussion(laddering)

Sketching exercise

Introverts

Extraverts

155

Vis feedback effective external. cue

Reflect on workplace behaviour

Public leaderboards

Private leaderboards

Long term view

0 4.5 9 13.5 18

515

317

416

10

Mean Impression count

159 Introverts

10

173

315 Extraverts

Mean Interaction count

96 Introverts141 Extraverts

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

If you could add how productive my collaborations with a teammate where, it would be useful. Knowing the amount of interactions is not enough

Some interview remarks

For me these charts wouldn’t be the reason to go and grab a person for a coffee or talk to him.

I find the interaction history extremely useful, As I am new in the company and the app helps me to make new connections (High impression minority group)

Design recommendationsInsights from Quantified BL Dublin and Antwerp Workplaces

3 Secrets Revealed

Community awareness

Different user communities require different patterns.

Personality, gender and occupation call for different

feedback

Long term feedback

All users desired a long term feedback on their workplace behavior vs our

short term feedback

Actionable attributes

It is critical to incorporate actionable attributes in the feedback system

40

The delicate lineGood communication in advance is key

Claudio Forlivesi Utku Acer

Afra Mashhadi

Fahim Kawsar

Akhil Mathur

Marc Van Den BroeckGeert Vanderhulst Marc Godon

Nic Lane Sourav Bhattacharaya Aidan Boran

Antwerp

Dublin

Who made all of these possible..

T.HanksMarc Van den Broeck

@Dakawa