More Web Proxy on the site http://driver.im/

research-article

Personalizing persuasive technologies

Authors:

Maurits Kaptein,

Panos Markopoulos,

Boris de Ruyter,

Emile AartsAuthors Info & Claims

International Journal of Human-Computer Studies, Volume 77, Issue C

Pages 38 - 51

https://doi.org/10.1016/j.ijhcs.2015.01.004

Published: 01 May 2015 Publication History

Abstract

This paper discusses how persuasive technologies can be made adaptive to users. We present persuasion profiling as a method to personalize the persuasive messages used by a system to influence its users. This type of personalization can be based on explicit measures of users tendencies to comply to distinct persuasive strategies: measures based on standardized questionnaire scores of users. However, persuasion profiling can also be implemented using implicit, behavioral measures of user traits. We present three case studies involving the design, implementation, and field deployment of personalized persuasive technologies, and we detail four design requirements. In each case study we show how these design requirements are implemented. In the discussion we highlight avenues for future research in the field of adaptive persuasive technologies. Author-HighlightsPersuasive technologies can be more effective if they are personalized.We introduce persuasion profiles to personalize persuasive messages.Persuasion profiles can be effective using implicit or explicit measures.In three case studies we show the effects of personalized persuasion.

References

[1]

E.H.L. Aarts, P. Markopoulos, B.E.R. Ruyter, The persuasiveness of ambient intelligence, in: Security, Privacy and Trust in Modern Data Management, Springer, Berlin, Heidelberg, 2007, pp. 367-381.

[2]

I. Ajzen, M. Fishbein, Understanding Attitudes and Predicting Social Behavior, Prentice-Hall, Englewood Cliffs, NJ, 1980.

[3]

A. Ansari, C.F. Mela, E-Customization, J. Mark. Res., 40 (2005) 131-145.

[4]

C.J. Armitage, M. Conner, Predictive validity of the theory of planned behaviour, J. Community Appl. Soc. Psychol., 9 (1999) 261-272.

[5]

S.E. Asch, Studies of independence and conformity, Psychol. Monogr., 70 (1956) 1-70.

[6]

J.-Y. Audibert, R. Munos, C. Szepesvári, Exploration-exploitation tradeoff using variance estimates in multi-armed bandits, Theor. Comput. Sci., 410 (2009) 1876-1902.

Digital Library

[7]

A. Bandura, Social cognitive theory of self-regulation, Organ. Behav. Hum. Decis. Process., 50 (1991) 248-287.

[8]

M. Bang, C. Torstensson, C. Katzeff, The PowerHouse, Persuas. Technol., 3962 (2006) 123-132.

[9]

J.N. Bassili, Meta-judgmental versus operative indexes of psychological attributes, J. Personal. Soc. Psychol., 71 (1996) 637-653.

[10]

D. Berdichevsky, E. Neuenschwander, Toward an ethics of persuasive technology, Commun. ACM, 42 (1999) 51-58.

Digital Library

[11]

D.A. Berry, B. Fristedt, Bandit Problems: Sequential Allocation of Experiments (Monographs on Statistics and Applied Probability), Chapman & Hall, London, 1985.

[12]

T. Blass, Understanding behavior in the Milgram obedience experiment, J. Personal. Soc. Psychol., 60 (1991) 398-413.

[13]

H. Bless, G. Bohner, N. Schwarz, F. Strack, Mood and persuasion, Personal. Soc. Psychol. Bull., 16 (1990) 331-345.

[14]

T.C. Brock, Implications of commodity theory for value change, in: Psychological Foundations of Attitudes, Academic Press, New York, 1968, pp. 243-275.

[15]

J. Brug, Computer-tailored nutrition education, Health Educ. Res., 14 (1999) 249-256.

[16]

J. Brug, M. Campbell, P. van Assema, The application and impact of computer-generated personalized nutrition education: a review of the literature, Patient Educ. Couns., 36 (1999) 145-156.

[17]

J. Brug, H.J. Hospers, G. Kok, Differences in psychosocial factors and fat consumption between stages of change for fat reduction, Psychol. Health, 12 (1997) 719-727.

[18]

J. Brug, K. Glanz, P. Van Assema, G. Kok, G.J.P. van Breukelen, The impact of computer-tailored feedback and iterative feedback on fat, fruit, and vegetable intake, Health Educ. Behav., 25 (1998) 517-531.

[19]

J. Brug, A. Oenema, M. Campbell, Past, present, and future of computer-tailored nutrition education, Am. J. Clin. Nutr., 77 (2003) 1028-1034.

[20]

J.M. Burger, The foot-in-the-door compliance procedure, Personal. Soc. Psychol. Rev., 3 (1999) 303-325.

[21]

J.T. Cacioppo, R.E. Petty, The need for cognition, J. Personal. Soc. Psychol., 42 (1982) 116-131.

[22]

J.T. Cacioppo, R.E. Petty, C.F. Kao, R. Rodriguez, Central and peripheral routes to persuasion, J. Personal. Soc. Psychol., 51 (1986) 1032-1043.

[23]

J.T. Cacioppo, R.E. Petty, J.A. Feinstein, W.B.G. Jarvis, Dispositional differences in cognitive motivation, Psychol. Bull., 119 (1996) 197-253.

[24]

S. Chaiken, Heuristic versus systematic information processing and the use of source versus message cues in persuasion, J. Personal. Soc. Psychol., 39 (1980) 752.

[25]

Chaiken, S., Liberman, A., Eagly, A.H., 1989. Heuristic and Systematic Information Processing Within and Beyond the Persuasion Context. Unintended Thought 212.

[26]

E.F. Churchill, Putting the person back into personalization, Interactions, 20 (2013) 12-15.

Digital Library

[27]

R. Cialdini, Influence, Science and Practice, Allyn & Bacon, Boston, 2001.

[28]

R. Cialdini, The science of persuasion, Sci. Am. Mind, 284 (2004) 76-84.

[29]

R.B. Cialdini, M.R. Trost, Social influence, in: The Handbook of Social Psychology, vol. 2, McGraw-Hill, New York, 1998, pp. 151-192.

[30]

R.B. Cialdini, M.R. Trost, J.T. Newsom, Preference for consistency, J. Personal. Soc. Psychol., 69 (1995) 318-328.

[31]

Consolvo, S., Libby, R., Smith, I., Landay, J.A., McDonald, D.W., Toscos, T., Chen, M.Y., Froehlich, J., Harrison, B., Klasnja, P., LaMarca, A., LeGrand, L., 2008. Activity sensing in the wild. In: Proceeding of the 26th Annual CHI Conference on Human Factors in Computing Systems-CHI ¿08. ACM Press, New York, NY, USA, p. 1797.

[32]

Consolvo, S., McDonald, D.W., Landay, J.A., 2009. Theory-driven design strategies for technologies that support behavior change in everyday life. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems-CHI 09. ACM Press, New York, NY, USA, p. 405.

[33]

R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, J. Taylor, Emotion recognition in human-computer interaction, IEEE Signal Process. Mag., 18 (2001) 32-80.

[34]

W.D. Crano, R. Prislin, Attitudes and persuasion, Annu. Rev. Psychol., 57 (2006) 345-374.

[35]

K.W. Davidson, M. Goldstein, R.M. Kaplan, P.G. Kaufmann, G.L. Knatterud, C.T. Orleans, B. Spring, K.J. Trudeau, E.P. Whitlock, Evidence-based behavioral medicine, Ann. Behav. Med., 26 (2003) 161-171.

[36]

E.L. Deci, R.M. Ryan, Self-Determination, Wiley Online Library Hoboken (John Wiley & Sons), 2010.

[37]

S. Deterding, Gamification, Interactions, 19 (2012) 14.

Digital Library

[38]

M. Deutsch, H.B. Gerard, A study of normative and informational social influences upon individual judgment, J. Abnorm. Soc. Psychol., 51 (1955) 629-636.

[39]

H. de Vries, J. Brug, Computer-tailored interventions motivating people to adopt health promoting behaviours, Patient Educ. Couns., 36 (1999) 99-105.

[40]

C.C. DiClemente, A.S. Marinilli, M. Singh, L.E. Bellino, The role of feedback in the process of health behavior change, Am. J. Health Behav., 25 (2001) 217-227.

[41]

A. Dijkstra, Working mechanisms of computer-tailored health education, Health Educ. Res., 20 (2005) 527-539.

[42]

A. Dijkstra, H. De Vries, The development of computer-generated tailored interventions, Patient Educ. Couns., 36 (1999) 193-203.

[43]

Dillahunt, T., Becker, G., Mankoff, J., Kraut., R., 2008. Motivating environmentally sustainable behavior changes with a virtual polar bear. In: Pervasive 2008 Workshop Proceedings, pp. 58-62

[44]

M. Eisend, Explaining the impact of scarcity appeals in advertising, J. Advert., 37 (2008) 33-40.

[45]

J.P. Faber, P. Markopoulos, P. Dadlani, A. van Halteren, AULURA, in: Ambient Intelligence. Lecture Notes in Computer Science, vol. 7040, Springer, Berlin, Heidelberg, 2011, pp. 215-221.

Digital Library

[46]

L. Festinger, A Theory of Cognitive Dissonance, Stanford University Press, Stanford, 1957.

[47]

M. Fishbein, I. Ajzen, Predicting and Changing Behavior: The Reasoned Action Approach, Taylor & Francis, London, 2011.

[48]

Fogg, B.J., 1998. Persuasive computers: perspectives and research directions. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM Press/Addison-Wesley Publishing Co., Boston, pp. 225-232.

[49]

B.J. Fogg, Persuasive Technology: Using Computers to Change What We Think and Do, Morgan Kaufmann, Burlington, Massachusetts, 2002.

Digital Library

[50]

Fogg, B.J., 2009. Creating Persuasive Technologies : An Eight-Step Design Process.

[51]

B.J. Fogg, D. Eckles, Mobile persuasion, in: Mobile Persuasion, Stanford Captology Media, Stanford, 2007, pp. 1-166.

[52]

B. Fogg, C. Nass, Silicon sycophants, Int. J. Hum.-Comput. Stud., 46 (1997) 551-561.

Digital Library

[53]

Fogg, B.J., Nass, C., 1997b. How users reciprocate to computers: an experiment that demonstrates behavior change. In: CHI¿97 Extended Abstracts on Human Factors in Computing Systems: Looking to the Future, March. ACM, New York, pp. 331-332.

[54]

Fuegen, K., Brehm, J.W., 2004. The Intensity of Affect and Resistance to Social Influence. Lawrence Erlbaum, Hillsdale, pp. 39-63

[55]

Garde-Perik, E.M., 2009. Ambient Intelligence & Personalization: People's Perspectives on Information Privacy (Ph.D. thesis). Technical University of Eindhoven.

[56]

R. Garner, What's in a name? Persuasion perhaps, J. Consum. Psychol., 15 (2005) 108-116.

[57]

B.S. Gerber, M.R. Stolley, A.L. Thompson, L.K. Sharp, M.L. Fitzgibbon, Mobile phone text messaging to promote healthy behaviors and weight loss maintenance, Health Informatics J., 15 (2009) 17-25.

[58]

N.J. Goldstein, R.B. Cialdini, V. Griskevicius, A room with a viewpoint, J. Consum. Res., 35 (2008) 472-482.

[59]

S.D. Gosling, P.J. Rentfrow, W.B.J. Swann, A very brief measure of the big-five personality domains, J. Res. Personal., 37 (2003) 504-528.

[60]

Greenberg, M.S., 1980. A theory of indebtedness. In: Social exchange. Springer, Berlin, Heidelberg, pp. 3-26

[61]

U. Gretzel, D. Fesenmaier, Persuasion in recommender systems, Int. J. Electron. Commer., 11 (2006) 81-100.

Digital Library

[62]

R. Guadagno, R. Cialdini, Online persuasion and compliance, in: The Social Net, Oxford University Press, Oxford, New York, 2005, pp. 296.

[63]

R.E. Guadagno, T. Asher, L.J. Demaine, R.B. Cialdini, When saying yes leads to saying no, Personal. Soc. Psychol. Bull., 27 (2001) 859-867.

[64]

Halko, S., Kientz, J.A., 2010. Personality and persuasive technology: an exploratory study on health-promoting mobile applications. In: Persuasive technology. Springer, Berlin, Heidelberg, pp. 150-161.

[65]

C.D. Hardin, E.T. Higgins, Guilford, New York, 1996.

[66]

C.P. Haugtvedt, R.E. Petty, Personality and persuasion, J. Personal. Soc. Psychol., 63 (1992) 308-319.

[67]

C.P. Haugtvedt, R.E. Petty, J.T. Cacioppo, Need for cognition and advertising, J. Consum. Psychol., 1 (1992) 239-260.

[68]

Haugtvedt, C.P., Liu, K., Min, K.S., 2008. Individual differences: tools for theory testing and understanding in consumer psychology research. In: Handbook of Consumer Psychology. CRC Press, Boca Raton, pp. 1161-1176.

[69]

J.R. Hauser, G.L. Urban, G. Liberali, M. Braun, Website morphing, Mark. Sci., 28 (2009) 202-223.

Digital Library

[70]

Hauser, J.R., Liberali, G., Urban, G.L., 2014. Website morphing 2.0: Switching costs, partial exposure, random exit, and when to morph. Manag. Sci.

[71]

F. Heider, Social perception and phenomenal causality, Psychol. Rev., 51 (1944) 358.

[72]

H.A. Hornstein, E. Fisch, M. Holmes, Influence of a model's feeling about his behavior and his relevance as a comparison other on observers¿ helping behavior, J. Personal. Soc. Psychol. (1968).

[73]

J. Inman, A. Peter, P. Raghubir, Framing the deal, J. Consum. Res., 24 (1997) 68-79.

[74]

J.M. James, R. Bolstein, The effect of monetary incentives and follow-up mailings on the response rate and response quality in mail surveys, Public Opin. Q., 54 (1990) 346.

[75]

W.B.G. Jarvis, R.E. Petty, The need to evaluate, J. Personal. Soc. Psychol., 70 (1996) 172-194.

[76]

Johnson, B.T., Eagly, A.H., 1989. Effects of Involvement on Persuasion : A Meta-Analysis.

[77]

D. Kahneman, A. Tversky, Prospect theory, Econometrica, 54 (1979) 263-291.

[78]

P.B. Kantor, L. Rokach, F. Ricci, B. Shapira, Springer, Berlin, Heidelberg, 2011.

[79]

Kaptein, M.C., 2011. Adaptive Persuasive Messages in an E-commerce Setting: the use of Persuasion Profiles. In: Proceedings of ECIS 2011, Helsinki.

[80]

M.C. Kaptein, S. Duplinsky, Combining multiple influence strategies to increase consumer compliance, Int. J. Internet Mark. Advert., 8 (2013) 32-53.

[81]

Kaptein, M.C., Eckles, D., 2010. Selecting Effective Means to Any End: Futures and Ethics of Persuasion Profiling. In: Persuasive Technology (pp. 82-93). Springer, Berlin, Heidelberg.

[82]

M.C. Kaptein, D. Eckles, Heterogeneity in the effects of online persuasion, J. Interact. Mark., 26 (2012) 176-188.

[83]

M.C. Kaptein, A. van Halteren, Adaptive persuasive messaging to increase service retention, J. Personal Ubiquitous Comput., 17 (2013) 1173-1185.

Digital Library

[84]

M.C. Kaptein, P. Markopoulos, B. Ruyter, E. Aarts, Persuasion in ambient intelligence, J. Ambient Intell. Humaniz. Comput., 1 (2009) 43-56.

[85]

M.C. Kaptein, D. Eckles, J. Davis, Envisioning persuasion profiles, ACM Interact., 18 (2011) 66-69.

Digital Library

[86]

M.C. Kaptein, B. de Ruyter, P. Markopoulos, E. Aarts, Tailored persuasive text messages to reduce snacking, Trans. Interact. Intell. Syst., 2 (2012) 10-35.

Digital Library

[87]

K. Kellermann, T. Cole, Classifying compliance gaining messages, Commun. Theory, 4 (1994) 3-60.

[88]

C. Kelman, V.L. Hamilton, Crimes of Obedience: Toward a Social Psychology of Authority and Responsibility, Yale University Press, New Haven, 1989.

[89]

E.S. Knowles, J.A. Linn, Resistance and Persuasion, Psychology Press, Abingdon, 2004.

[90]

S.S. Komorita, J.A. Hilty, C.D. Parks, Reciprocity and cooperation in social dilemmas, J. Confl. Resolut., 35 (1991) 494-518.

[91]

M. Kreuter, Tailoring Health Messages: Customizing Communication with Computer Technology, Routledge, 2000.

[92]

M.W. Kreuter, V.J. Strecher, Do tailored behavior change messages enhance the effectiveness of health risk appraisal? Results from a randomized trial, Health Educ. Res., 11 (1996) 97-105.

[93]

M. Kukar-Kinney, A.G. Close, The determinants of consumers¿ online shopping cart abandonment, J. Acad. Mark. Sci., 38 (2009) 240-250.

[94]

C.L. Lambert, Baby think it over, Pediatrics, 107 (2001) 806.

[95]

C. Leone, H. Wallace, K. Modglin, The need for closure and the need for structure, J. Psychol., 133 (1999) 553-562.

[96]

Long, J.D., Stevens, K.R., 2004. Using Technology to Promote Self-Efficacy for Healthy Eating in Adolescents. Technical Report 2, Lubbock Christian University, 5601 W. 19th Street, Lubbock, TX 79407, USA. [email protected].

[97]

M. Lynn, Scarcity effects on desirability, J. Econ. Psychol., 10 (1989) 257-274.

[98]

M. Lynn, Scarcity effects on value, Psychol. Mark., 8 (1991) 43-57.

[99]

T.J. Madden, P.S. Ellen, I. Ajzen, A comparison of the theory of planned behavior and the theory of reasoned action, Personal. Soc. Psychol. Bull., 18 (1992) 3-9.

[100]

G.R. Maio, V.M. Esses, The need for affect, J. Personal., 69 (2001) 583-615.

[101]

R. Martin, M. Hewstone, Social influence processes of control and change, in: Psychological Foundations of Attitudes, Sage, London, 2003, pp. 347-366.

[102]

Maslow, A., Herzeberg, A., 1954. Hierarchy of needs. In: Maslow, A.H. (Ed.), Motivation and Personality. Harper, New York.

[103]

R.G. McFarland, G.N. Challagalla, T.a. Shervani, Influence tactics for effective adaptive selling, J. Mark., 70 (2006) 103-117.

[104]

McGraa, K.L., 2010. The Effects of Persuasive Motivational Text Messaging on Adherence to Diet and Exercise Programs Across Different Personality Traits. (Ph.D. thesis). Fielding Graduate University.

[105]

S. Michie, M. Richardson, M. Johnston, C. Abraham, J. Francis, W. Hardeman, M.P. Eccles, J. Cane, C.E. Wood, The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions, Ann. Behav. Med., 46 (2013) 81-95.

[106]

Midden, C., Mccalley, T., Ham, J., Zaalberg, R., 2008. Using persuasive technology to encourage sustainable behavior. In: Workshop paper at Sixth International Conference on Pervasive Computing, no. 1. Eindhoven University of Technology, pp. 83-86.

[107]

S. Milgram, Obedience to Authority, Tavistock, London, 1974.

[108]

A. Modigliani, F. Rochat, The role of interaction sequences and the timing of resistance in shaping obedience and defiance to authority, J. Soc. Issues, 51 (1995) 107-123.

[109]

M. Morris, F. Guilak, Mobile heart health, IEEE Pervasive Comput., 8 (2009) 57-61.

Digital Library

[110]

C. Nass, B.J. Fogg, Y. Moon, Can computers be teammates?, Int. J. Hum.-Comput. Stud., 45 (1996) 669-678.

Digital Library

[111]

S.M. Noar, C.N. Benac, M.S. Harris, Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions, Psychol. Bull., 133 (2007) 673-693.

[112]

Nov, O., Arazy, O., 2013. Personality-targeted design: theory, experimental procedure, and preliminary results. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work. ACM, New York, pp. 977-984.

[113]

D.J. O'Keefe, From strategy-based to feature-based analyses of compliance gaining message classification and production, Commun. Theory, 4 (1994) 61-69.

[114]

P. Ochi, S. Rao, L. Takayama, C. Nass, Predictors of user perceptions of web recommender systems, Int. J. Hum.-Comput. Stud., 68 (2010) 472-482.

Digital Library

[115]

Oinas-Kukkonen, H., Harjumaa, M., 2008. A systematic framework for designing and evaluating persuasive systems. In: Oinas-Kukkonen, H., Hasle, P. F.V., Harjumaa, M., Segerståhl, K., Ohrstrøm, P. (Eds.), Persuasive Technology, Proceedings of the Third International Conference {PERSUASIVE} 2008, Oulu, Finland, June 4-6, 2008. Lecture Notes in Computer Science, vol. 5033. Springer, pp. 164-176.

[116]

H. Oinas-kukkonen, M. Harjumaa, Persuasive systems design, Commun. Assoc. Inf. Syst., Berlin, Heidelberg, 24 (2009) 28.

[117]

Ortega, P., Braun, D., 2013. Generalized Thompson Sampling for Sequential Decision-Making and Causal Inference. arXiv preprint: arXiv:1303.4431.

[118]

R.E. Petty, J.T. Cacioppo, The elaboration likelihood model of persuasion, Adv. Exp. Soc. Psychol., 19 (1986) 123-205.

[119]

R.E. Petty, D.T. Wegener, The elaboration likelihood model, in: Dual-process Theories in Social Psychology, Guilford Press, New York, 1999, pp. 41-72.

[120]

J.T. Plummer, The concept and application of life style segmentation, J. Mark., 38 (1974) 33-37.

[121]

Preece, J.J., 2010. I Persuade, They Persuade, It Persuades!. In: Persuasive Technology, 2010, pp. 2-3.

[122]

J.O. Prochaska, W.F. Velicer, The transtheoretical model of health behavior change, Am. J. Health Promot., 12 (1997) 38-48.

[123]

Rhoads, K., 2007. How Many Influence, Persuasion, Compliance Tactics & Strategies Are There? {http://www.workingpsychology.com/numbertactics.html}.

[124]

Sakai, R., van Peteghem, S., van de Sande, L., Banach, P., Kaptein, M.C., 2011. Personalized persuasion in ambient intelligence: the APStairs system. In: Proceedings of Ambient Intelligence (AmI) 2011, Amsterdam.

[125]

M.J. Salganik, D.J. Watts, Web-based experiments for the study of collective social dynamics in cultural markets, Top. Cogn. Sci., 1 (2009) 439-468.

[126]

Scherini, T., Melo, P., van Craenendonck, T., Zou, W., Kaptein, M.C., August 2010. Enhancing the sleeping quality of partners living apart. In: Proceedings of the Eighth ACM Conference on Designing Interactive Systems-DIS ¿10. ACM Press, New York, NY, USA, pp. 171-179.

[127]

Schmidt, A., Gellersen, H.-W., Merz, C., 2000. Enabling implicit human computer interaction: a wearable RFID-tag reader. In: Digest of Papers. Fourth International Symposium on Wearable Computers. IEEE Computer Society, Piscataway, pp. 193-194.

[128]

Schwartz, E.M., Bradlow, E., Fader, P., 2013. Working Paper Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments (1217).

[129]

Scott, S.L., 2010. A modern Bayesian look at the multi-armed bandit. In: Applied Stochastic Models in Business and Industry, vol. 26, no. 6, pp. 639-658.

[130]

B.F. Skinner, About Behaviorism, Vintage Books, New York, 1976.

[131]

T.A.B. Snijders, R.J. Bosker, Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, Sage Publications Ltd, Thousand Oaks, 1999.

[132]

B. Stallworth, Future imperfect, Fed. Commun. Law J., 62 (2010) 465-491.

[133]

V.J. Strecher, M. Kreuter, D.-J. Den Boer, S. Kobrin, H.J. Hospers, C.S. Skinner, The effects of computer-tailored smoking cessation messages in family practice settings, J. Fam. Pract., 39 (1994) 262-270.

[134]

R.S. Sutton, A.G. Barto, Introduction to Reinforcement Learning, MIT Press, Cambridge, Massachusetts, 1998.

[135]

O. Svane, Helping, informing or coaxing the consumer? Exploring persuasive technology as applied to households energy use, Policy, 1 (2007) 1-11.

[136]

Z.L. Tormala, V.L. DeSensi, The effects of minority/majority source status on attitude certainty, Personal. Soc. Psychol. Bull., 35 (2009) 114-125.

[137]

A.C. Tynan, J. Drayton, Market segmentation, J. Mark. Manag., 2 (1987) 301-335.

[138]

F. Vanharreveld, Attitudes as stable and transparent constructions, J. Exp. Soc. Psychol., 40 (2004) 666-674.

[139]

S.G. West, Increasing the attractiveness of college cafeteria food: a reactance theory perspective, J. Appl. Psychol., 60 (1975) 656-658.

[140]

P. Whittle, Multi-armed bandits and the Gittins index, J. R. Stat. Soc. Ser. B Methodol., 42 (1980) 143-149.

[141]

Yue, Y., Hong, S., Guestrin, C., 2012. Hierarchical Exploration for Accelerating Contextual Bandits. arXiv preprint: arXiv:1206.6454.

[142]

Zanker, M., Bricman, M., Jessenitschnig, M., Damiani, E., Jeong, J., Howlett, R., Jain, L., 2009. New Directions in Intelligent Interactive Multimedia Systems and Services-2. Studies in Computational Intelligence, vol. 226. Springer, Berlin, Heidelberg.

[143]

F. Zhu, X.M. Zhang, Impact of online consumer reviews on sales, J. Mark., 74 (2010) 133-148.

Cited By

Wenting LHussain WXinlin JNa MAlam S(2024)Analyzing the Impact on Talent Acquisition and Performance ManagementJournal of Organizational and End User Computing10.4018/JOEUC.34260336:1(1-30)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.4018/JOEUC.342603
van Renswouw LBogers SLallemand Cvan Wesemael PVos S(2024)Changing Perspective on Data in Designing for Active EnvironmentsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661635(2403-2419)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661635
Sobey ACarter L(2024)The Harmful Fetishisation of Reductive Personal Tracking Metrics in Digital SystemsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658943(899-908)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658943
Show More Cited By

Personalizing persuasive technologies
1. Human-centered computing

Recommendations

Adaptive Persuasive Systems: A Study of Tailored Persuasive Text Messages to Reduce Snacking

This article describes the use of personalized short text messages (SMS) to reduce snacking. First, we describe the development and validation (N = 215) of a questionnaire to measure individual susceptibility to different social influence strategies. To ...
Goal-setting considerations for persuasive technologies that encourage physical activity
Persuasive '09: Proceedings of the 4th International Conference on Persuasive Technology

Goal-setting has been shown to be an effective strategy for changing behavior; therefore employing goal-setting in persuasive technologies could be an effective way to encourage behavior change. In our work, we are developing persuasive technologies to ...
Personalizing Persuasive Technologies: Do Gender and Age Affect Susceptibility to Persuasive Strategies?
UMAP '18: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

Personalizing Persuasive Technologies (PTs) increase their effectiveness at motivating desired behavioral change. However, most existing efforts towards personalizing PTs and developing personalization models were focused on people from the western ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image International Journal of Human-Computer Studies

International Journal of Human-Computer Studies Volume 77, Issue C

May 2015

66 pages

ISSN:1071-5819

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 May 2015

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

81
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wenting LHussain WXinlin JNa MAlam S(2024)Analyzing the Impact on Talent Acquisition and Performance ManagementJournal of Organizational and End User Computing10.4018/JOEUC.34260336:1(1-30)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.4018/JOEUC.342603
van Renswouw LBogers SLallemand Cvan Wesemael PVos S(2024)Changing Perspective on Data in Designing for Active EnvironmentsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661635(2403-2419)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661635
Sobey ACarter L(2024)The Harmful Fetishisation of Reductive Personal Tracking Metrics in Digital SystemsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658943(899-908)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658943
Dritsa Dvan Renswouw LColombo SVäänänen KBogers SMartinez AHolbrook JBrombacher A(2024)Designing (with) AI for WellbeingExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3636282(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3636282
Wu RYu CPan XLiu YZhang NFu YWang YZheng ZChen LJiang QXu XShi Y(2024)MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use InterventionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642790(1-24)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642790
Oyebode OSteeves DOrji R(2024)Persuasive strategies and emotional states: towards designing personalized and emotion-adaptive persuasive systemsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09390-x34:4(1175-1225)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11257-023-09390-x
Sánchez-Corcuera RCasado-Mansilla DBorges CLópez-de-Ipiña D(2024)Persuasion-based recommender system ensambling matrix factorisation and active learning modelsPersonal and Ubiquitous Computing10.1007/s00779-020-01382-728:1(247-257)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s00779-020-01382-7
Zeng DLegaspi RSun YDong XIkeda KSpirtes PZhang K(2024)Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion OutcomePersuasive Technology10.1007/978-3-031-58226-4_22(287-300)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1007/978-3-031-58226-4_22
Albers NNeerincx MBrinkman WAgmon NAn BRicci AYeoh W(2023)Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict BehaviorProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598704(717-726)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3598704
Xu DFan SKankanhalli MEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Combating Misinformation in the Era of Generative AI ModelsProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612704(9291-9298)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612704
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents