Studying the Impact ofUbiquitous MonitoringTechnology on Office WorkerBehaviours: The Value ofSharing Research DataStuart ...
Introduction Pervasive Computing Benefits in the workplace Gaps on how to use data    ◦ Method/Guide to using data-----...
Mutual                Project 1                   Project 2                             AspectsBackground    Engineering  ...
Mutual                Project 1                   Project 2                             AspectsBackground    Engineering  ...
Mutual                Project 1                   Project 2                             AspectsBackground    Engineering  ...
Mutual                Project 1                   Project 2                             AspectsBackground    Engineering  ...
Methodology   Positivism / Mixed Methods    ◦ Reality is believed to be directly observable and measureable Thematic Con...
Mutual                Project 1                   Project 2                             AspectsBackground    Engineering  ...
Data Six week temporary pilot project (2005) Testing of wearable location tracking  technology in office Two wearable R...
Mutual                Project 1                   Project 2                             AspectsBackground    Engineering  ...
“I don’t understand how the                                         technology works and whatAnalysis                     ...
Mutual                Project 1                   Project 2                             AspectsBackground    Engineering  ...
Interview Conclusions   Cross Comparison demonstrated very similar    conclusions   Different approaches adopted by rese...
Mutual                Project 1                   Project 2                             AspectsBackground    Engineering  ...
Output   Project 1: developed the PSA-BI model for    predicting monitored user behaviour                                ...
Conclusions   Different projects, aims and methods   Same question, data and conclusions   Reminder that there is value...
Questions?
Discussion Questions Why is research data not more frequently  and widely shared? How can we (in this room) collaborate ...
Studying the Impact of Ubiquitous Monitoring Technology on Office Worker Behaviours: The Value of Sharing Research Data
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Studying the Impact of Ubiquitous Monitoring Technology on Office Worker Behaviours: The Value of Sharing Research Data

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Studying the Impact of Ubiquitous Monitoring Technology on Office Worker Behaviours: The Value of Sharing Research Data

  1. 1. Studying the Impact ofUbiquitous MonitoringTechnology on Office WorkerBehaviours: The Value ofSharing Research DataStuart Moran, Irene Lopez de Vallejo, KeiichiNakata, Ruth Conroy-Dalton, Rachael Luck,Peter McLennan and Steve Hailes
  2. 2. Introduction Pervasive Computing Benefits in the workplace Gaps on how to use data ◦ Method/Guide to using data------------------------------------------------------ Monitoring changes behaviour Undesirable effects ◦ Method for predicting behavioural responses
  3. 3. Mutual Project 1 Project 2 AspectsBackground Engineering Sociology Impact ofMotivation PerCom Questions Literature Real World Positivism InterpretivismMethodology /Mix.Method /Mix.Method Interview Data Data Analysis Deduction InductionConclusion Conclusions Predictive Output Methodology Model
  4. 4. Mutual Project 1 Project 2 AspectsBackground Engineering Sociology Impact ofMotivation PerCom Questions Literature Real World Positivism InterpretivismMethodology /Mix.Method /Mix.Method Interview Data Data Analysis Deduction InductionConclusion Conclusions Predictive Output Methodology Model
  5. 5. Mutual Project 1 Project 2 AspectsBackground Engineering Sociology Impact ofMotivation PerCom Questions Literature Real World Positivism InterpretivismMethodology /Mix.Method /Mix.Method Interview Data Data Analysis Deduction InductionConclusion Conclusions Predictive Output Methodology Model
  6. 6. Mutual Project 1 Project 2 AspectsBackground Engineering Sociology Impact ofMotivation PerCom Questions Literature Real World Positivism InterpretivismMethodology /Mix.Method /Mix.Method Interview Data Data Analysis Deduction InductionConclusion Conclusions Predictive Output Methodology Model
  7. 7. Methodology Positivism / Mixed Methods ◦ Reality is believed to be directly observable and measureable Thematic Content Analysis Questionnaire and Statistical Analysis Simulation developed in VenSim Triangulate VenSim prediction with Interview Data Interpretivism / Mixed Methods ◦ Reality is believed to only be understood through subjective interpretation Interviews and Observation data triangulated to portray complex socio technical system Research allowed to evolve and unfold, rather than constrain it through structure
  8. 8. Mutual Project 1 Project 2 AspectsBackground Engineering Sociology Impact ofMotivation PerCom Questions Literature Real World Positivism InterpretivismMethodology /Mix.Method /Mix.Method Interview Data Data Analysis Deduction InductionConclusion Conclusions Predictive Output Methodology Model
  9. 9. Data Six week temporary pilot project (2005) Testing of wearable location tracking technology in office Two wearable RFID tags 28 semi structured interviews This data was shared Buffer       Cell 2 Cell 1   Sensor    
  10. 10. Mutual Project 1 Project 2 AspectsBackground Engineering Sociology Impact ofMotivation PerCom Questions Literature Real World Positivism InterpretivismMethodology /Mix.Method /Mix.Method Interview Data Data Analysis Deduction InductionConclusion Conclusions Predictive Output Methodology Model
  11. 11. “I don’t understand how the technology works and whatAnalysis will happen at the end of the trial” Deduction ◦ Use factors as coding Induction ◦ Allow themes to emerge Vallejo et al. Themes Moran and Nakata Factors Understanding and Informed User, and Communication Application Assumptions Temporary Nature Temporal Effects of the Deployment Perceived Privacy Invasion, Privacy and Device Obtrusion and Intrusio Positioning of Device Perceived Usefulness, Attitudes and Undesirable Behaviours and Organisational Culture Influencing Attitude
  12. 12. Mutual Project 1 Project 2 AspectsBackground Engineering Sociology Impact ofMotivation PerCom Questions Literature Real World Positivism InterpretivismMethodology /Mix.Method /Mix.Method Interview Data Data Analysis Deduction InductionConclusion Conclusions Predictive Output Methodology Model
  13. 13. Interview Conclusions Cross Comparison demonstrated very similar conclusions Different approaches adopted by researchers add confidence to comparison Moran and Nakata’s factors confirmed as effective monitoring based coding scheme
  14. 14. Mutual Project 1 Project 2 AspectsBackground Engineering Sociology Impact ofMotivation PerCom Questions Literature Real World Positivism InterpretivismMethodology /Mix.Method /Mix.Method Interview Data Data Analysis Deduction InductionConclusion Conclusions Predictive Output Methodology Model
  15. 15. Output Project 1: developed the PSA-BI model for predicting monitored user behaviour Exogenous Moderating Variables Moderating Anchors Past Computer Context Age Gender Environment Role Culture Experience Skill Level Behavior Based Social Influence Attitudes External Variables Object Based Anchors Beliefs Object Based Attitudes Application Application Attitude toward Space Perceptions Application Attitude toward Behaviour Behaviour Intention Technology Technology Attitude toward Space Perceptions Technology Adjusters Facilitating Conditions New Experience Time Information Project 2: developed a guide on how to make use of accurate location data in understanding flow interaction dynamics in organisations
  16. 16. Conclusions Different projects, aims and methods Same question, data and conclusions Reminder that there is value in sharing research data between researchers and across disciplines Online repository of qualitative and quantitative to facilitate the sharing of data
  17. 17. Questions?
  18. 18. Discussion Questions Why is research data not more frequently and widely shared? How can we (in this room) collaborate and share resources? What immediate benefits can really be gained from Pervasive technology in the next 5 years? What social implications are we already aware of?

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