A Multi-perspective Analysis of Social Context and Personal Factors in Office Settings for the Design of an Effective Mobile Notification System
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Mirco Musolesi | Peter Tino | Seyma Kucukozer Cavdar | Tugba Taskaya-Temizel | P. Tiňo | Mirco Musolesi | T. Taşkaya-Temizel | S. K. Cavdar | S. Cavdar
[1] M. Csíkszentmihályi,et al. The Experience Sampling Method , 2014 .
[2] Uichin Lee,et al. Multi-Stage Receptivity Model for Mobile Just-In-Time Health Intervention , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[3] LeeUichin,et al. Multi-Stage Receptivity Model for Mobile Just-In-Time Health Intervention , 2019 .
[4] Nitesh V. Chawla,et al. Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[5] Jeffery J. Mondak,et al. The Relationship Between Personality and Response Patterns on Public Opinion Surveys: The Big Five, Extreme Response Style, and Acquiescence Response Style , 2019 .
[6] Mohieddin Jafari,et al. Why, When and How to Adjust Your P Values? , 2018, Cell journal.
[7] Nazlena Mohamad Ali,et al. Smartphone Application for Physical Activity Enhancement at Workplace: Would Office Workers Actually Use It? , 2018 .
[8] Hazwani Mohd Mohadis,et al. Smartphone Application for Physical Activity Enhancement at Workplace: Would Office Workers Actually Use It? , 2018, 2018 International Conference on Information and Communication Technology for the Muslim World (ICT4M).
[9] Thomas Fritz,et al. Sensing Interruptibility in the Office: A Field Study on the Use of Biometric and Computer Interaction Sensors , 2018, CHI.
[10] Bongshin Lee,et al. Time for Break: Understanding Information Workers' Sedentary Behavior Through a Break Prompting System , 2018, CHI.
[11] Martin Pielot,et al. Beyond Interruptibility , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[12] Jorge Gonçalves,et al. Predicting interruptibility for manual data collection: a cluster-based user model , 2017, MobileHCI.
[13] Mark A Pereira,et al. Social ecological correlates of workplace sedentary behavior , 2017, International Journal of Behavioral Nutrition and Physical Activity.
[14] D. Nduhura,et al. When I chat online, I feel relaxed and work better , 2017 .
[15] Janne Lindqvist,et al. How Busy Are You?: Predicting the Interruptibility Intensity of Mobile Users , 2017, CHI.
[16] J. Bakdash,et al. Repeated Measures Correlation , 2017, Front. Psychol..
[17] K. Macky,et al. The demands and resources arising from shared office spaces. , 2017, Applied ergonomics.
[18] D. Spruijt-Metz,et al. Compliance With Mobile Ecological Momentary Assessment Protocols in Children and Adolescents: A Systematic Review and Meta-Analysis , 2017, Journal of medical Internet research.
[19] Takanori Ichikawa,et al. Attention and engagement-awareness in the wild: A large-scale study with adaptive notifications , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom).
[20] Cecilia Mascolo,et al. Mobile-Based Experience Sampling for Behaviour Research , 2015, Emotions and Personality in Personalized Services.
[21] Nicholas D. Lane,et al. Engagement-aware computing: modelling user engagement from mobile contexts , 2016, UbiComp.
[22] Veljko Pejovic,et al. TaskyApp: inferring task engagement via smartphone sensing , 2016, UbiComp Adjunct.
[23] R. Olmos,et al. Inconsistencies in Reported p-Values in Spanish Journals of Psychology , 2016 .
[24] E. Berntson,et al. Does Personality Have a Different Impact on Self-Rated Distraction, Job Satisfaction, and Job Performance in Different Office Types? , 2016, PloS one.
[25] Mirco Musolesi,et al. My Phone and Me: Understanding People's Receptivity to Mobile Notifications , 2016, CHI.
[26] Daniel A. Epstein,et al. Taking 5: Work-Breaks, Productivity, and Opportunities for Personal Informatics for Knowledge Workers , 2016, CHI.
[27] Harri Oinas-Kukkonen,et al. RightOnTime: The Role of Timing and Unobtrusiveness in Behavior Change Support Systems , 2016, PERSUASIVE.
[28] Mahesh Sooriyabandara,et al. HealthyOffice: Mood recognition at work using smartphones and wearable sensors , 2016, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).
[29] Albert Cheng,et al. When You Say Nothing at All: The Predictive Power of Student Effort on Surveys , 2015 .
[30] Martin Pielot,et al. Boredom-computer interaction: boredom proneness and the use of smartphone , 2015, UbiComp.
[31] Martin Pielot,et al. When attention is not scarce - detecting boredom from mobile phone usage , 2015, UbiComp.
[32] Mirco Musolesi,et al. Designing content-driven intelligent notification mechanisms for mobile applications , 2015, UbiComp.
[33] Martin Pielot,et al. Boredom-Triggered Proactive Recommendations , 2015, MobileHCI Adjunct.
[34] John C. Tang,et al. Investigating Mobile Users' Ringer Mode Usage and Attentiveness and Responsiveness to Communication , 2015, MobileHCI.
[35] Marija Ham,et al. The role of subjective norms in forming the intention to purchase green food , 2015 .
[36] B. Wallmann-Sperlich,et al. Socio-demographic, behavioural and cognitive correlates of work-related sitting time in German men and women , 2014, BMC Public Health.
[37] Anind K. Dey,et al. ProactiveTasks: the short of mobile device use sessions , 2014, MobileHCI '14.
[38] Barry A. T. Brown,et al. 100 days of iPhone use: understanding the details of mobile device use , 2014, MobileHCI '14.
[39] Jocelyn E. Bolin,et al. Multilevel Modeling Using R , 2019 .
[40] Paul Johns,et al. Bored mondays and focused afternoons: the rhythm of attention and online activity in the workplace , 2014, CHI.
[41] Matthijs Verhage,et al. A solution to dependency: using multilevel analysis to accommodate nested data , 2014, Nature Neuroscience.
[42] G. Kolt,et al. Physical Activity and Sedentary Time , 2014, American journal of men's health.
[43] Paul Johns,et al. Capturing the mood: facebook and face-to-face encounters in the workplace , 2014, CSCW.
[44] Anne C. Frenzel,et al. Types of boredom: An experience sampling approach , 2014 .
[45] F. Vijver,et al. A general response style factor: Evidence from a multi-ethnic study in the Netherlands , 2013 .
[46] Cecilia Mascolo,et al. Smartphones for Large-Scale Behavior Change Interventions , 2013, IEEE Pervasive Computing.
[47] N. Lane,et al. MoodScope: building a mood sensor from smartphone usage patterns , 2013, MobiSys '13.
[48] Sidney K. D'Mello,et al. Detecting boredom and engagement during writing with keystroke analysis, task appraisals, and stable traits , 2013, IUI '13.
[49] N. Owen,et al. Addressing physical inactivity in Omani adults: perceptions of public health managers , 2013, Public Health Nutrition.
[50] Aart van Halteren,et al. Toward a persuasive mobile application to reduce sedentary behavior , 2013, Personal and Ubiquitous Computing.
[51] Harri Oinas-Kukkonen,et al. Behavior Change Support Systems: A Research Model and Agenda , 2010, PERSUASIVE.
[52] Jarrod D. Hadfield,et al. MCMC methods for multi-response generalized linear mixed models , 2010 .
[53] Jun Han,et al. Social context: Supporting interaction awareness in ubiquitous environments , 2009, 2009 6th Annual International Mobile and Ubiquitous Systems: Networking & Services, MobiQuitous.
[54] Ping Yu,et al. Health IT acceptance factors in long-term care facilities: A cross-sectional survey , 2009, Int. J. Medical Informatics.
[55] Dan Morris,et al. SuperBreak: using interactivity to enhance ergonomic typing breaks , 2008, CHI.
[56] Robin C. Dunkin,et al. Supplementary breaks and stretching exercises for data entry operators: a follow-up field study. , 2007, American journal of industrial medicine.
[57] R. Barredo,et al. The Effects of Exercise and Rest Breaks on Musculoskeletal Discomfort during Computer Tasks: An Evidence-Based Perspective , 2007 .
[58] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[59] A. Bakker,et al. The Measurement of Work Engagement With a Short Questionnaire , 2006 .
[60] Joyce Ho,et al. Using context-aware computing to reduce the perceived burden of interruptions from mobile devices , 2005, CHI.
[61] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[62] Melissa Monsey,et al. Increasing compliance with stretch breaks in computer users through reminder software. , 2003, Work.
[63] Bradley P. Carlin,et al. Bayesian measures of model complexity and fit , 2002 .
[64] Patrick Van Kenhove,et al. The influence of topic involvement on mail survey response behavior , 2002 .
[65] Jennifer Healey,et al. Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[66] S. R. Searle,et al. Generalized, Linear, and Mixed Models , 2005 .
[67] D. Spiegelhalter,et al. Bayesian measures of model omplexity and t , 2001 .
[68] Venkateshviswanath,et al. A Theoretical Extension of the Technology Acceptance Model , 2000 .
[69] Fred D. Davis,et al. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.
[70] H. de Vries,et al. Determinants of intention to quit smoking among Dutch employees: the influence of the social environment. , 1996, Preventive medicine.
[71] Giuseppe Mantovani,et al. Social Context in HCI: A New Framework for Mental Models, Cooperation, and Communication , 1996, Cogn. Sci..
[72] David B. Dunson,et al. Bayesian Data Analysis , 2010 .
[73] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[74] John Uebersax,et al. Statistical Modeling of Expert Ratings on Medical Treatment Appropriateness , 1993 .
[75] Cynthia D. Fisherl. Boredom at Work: A Neglected Concept , 1993 .
[76] N. Breslow,et al. Approximate inference in generalized linear mixed models , 1993 .
[77] I. Ajzen. The theory of planned behavior , 1991 .
[78] D. Turk,et al. Neglected topics in the treatment of chronic pain patients — relapse, noncompliance, and adherence enhancement , 1991, Pain.
[79] D. Paulhus. Measurement and control of response bias. , 1991 .
[80] John P. Robinson,et al. Measures Of Personality And Social Psychological Attitudes , 1991 .
[81] K J Rothman,et al. No Adjustments Are Needed for Multiple Comparisons , 1990, Epidemiology.
[82] I. Ajzen,et al. Understanding Attitudes and Predicting Social Behavior , 1980 .
[83] I. Ajzen,et al. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .
[84] Shalom H. Schwartz,et al. Awareness of Consequences and the Influence of Moral Norms on Interpersonal Behavior , 1968 .
[85] K. Keniston,et al. Yeasayers and naysayers: agreeing response set as a personality variable. , 1960, Journal of abnormal and social psychology.
[86] O. Fenichel. On the psychology of boredom. , 1951 .
[87] L. Cronbach. Response Sets and Test Validity , 1946 .