Explaining and predicting perceived effectiveness and use continuance intention of a behaviour change support system for weight loss

Persuasive technologies are designed to influence people and induce them to change their attitudes and behaviours, bringing advantages to their users. Behaviour change support systems are at the heart of persuasive technology research. However, the sought benefits cannot be achieved if the systems fail to engage and retain the users. The present study provides a detailed description of a theory-driven effort to empirically (N=314) explain and predict users’ continuance intention towards a behaviour change support system for weight loss. Deriving from extant theories, a research model is constructed and tested through partial least-squares (PLS) analysis. In the proffered model, primary task support affects perceived effort and perceived effectiveness. Computer–human dialogue support has strong connections to primary task support, perceived social support and perceived effectiveness. Perceived credibility has a significant relationship to the continuance intention. Social identification has a strong connection to perceived social support, which, in turn, has a significant effect on perceived effectiveness and continuance intention. Finally, perceived effectiveness has a significant impact on use continuance. Investigating the aspects related to the continued use of behaviour change support systems is feasible, as it will guide future implementations of such systems.

[1]  B. J. Fogg,et al.  Silicon sycophants: the effects of computers that flatter , 1997, Int. J. Hum. Comput. Stud..

[2]  Rob Koper,et al.  Fostering trust in virtual project teams: Towards a design framework grounded in a TrustWorthiness ANtecedents (TWAN) schema , 2010, Int. J. Hum. Comput. Stud..

[3]  C. Nass,et al.  Machines and Mindlessness , 2000 .

[4]  René Riedl,et al.  Are There Neural Gender Differences in Online Trust? An fMRI Study on the Perceived Trustworthiness of eBay Offers , 2010, MIS Q..

[5]  Dale Goodhue,et al.  Task-Technology Fit and Individual Performance , 1995, MIS Q..

[6]  V. Vroom Work and motivation , 1964 .

[7]  Jay F. Nunamaker,et al.  Design Principles for Special Purpose, Embodied, Conversational Intelligence with Environmental Sensors (SPECIES) Agents , 2011 .

[8]  H. Tajfel Social identity and intergroup behaviour , 1974 .

[9]  Jonathan Klein,et al.  This computer responds to user frustration: Theory, design, and results , 2002, Interact. Comput..

[10]  Ritu Agarwal,et al.  Through a Glass Darkly: Information Technology Design, Identity Verification, and Knowledge Contribution in Online Communities , 2007, Inf. Syst. Res..

[11]  M. Neve,et al.  Weight Change in a Commercial Web-Based Weight Loss Program and its Association With Website Use: Cohort Study , 2011, Journal of medical Internet research.

[12]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[13]  Katrin Baumgartner,et al.  Persuasion Theory And Research , 2016 .

[14]  Damon Centola,et al.  The Spread of Behavior in an Online Social Network Experiment , 2010, Science.

[15]  Dennis F. Galletta,et al.  Cognitive Fit: An Empirical Study of Information Acquisition , 1991, Inf. Syst. Res..

[16]  Harri Oinas-Kukkonen,et al.  A foundation for the study of behavior change support systems , 2012, Personal and Ubiquitous Computing.

[17]  Straub,et al.  Editor's Comments: An Update and Extension to SEM Guidelines for Administrative and Social Science Research , 2011 .

[18]  Harri Oinas-Kukkonen,et al.  Native Mobile Applications For Personal Well-Being: A Persuasive Systems Design Evaluation , 2012, PACIS.

[19]  유재현,et al.  Technology Acceptance Model , 2020, Encyclopedia of Education and Information Technologies.

[20]  E. A. Locke,et al.  Building a practically useful theory of goal setting and task motivation. A 35-year odyssey. , 2002, The American psychologist.

[21]  Dong-Hee Shin,et al.  User experience in social commerce: in friends we trust , 2013, Behav. Inf. Technol..

[22]  S. Shumaker,et al.  Toward a Theory of Social Support: Closing Conceptual Gaps , 1984 .

[23]  Chin-Lung Hsu,et al.  Consumer behavior in online game communities: A motivational factor perspective , 2007, Comput. Hum. Behav..

[24]  N. Allen,et al.  Assessing dissimilarity relations under missing data conditions: evidence from computer simulations. , 2007, The Journal of applied psychology.

[25]  Viswanath Venkatesh,et al.  Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..

[26]  Detmar W. Straub,et al.  Trust and TAM in Online Shopping: An Integrated Model , 2003, MIS Q..

[27]  Matthew K. O. Lee,et al.  Predicting continuance in online communities: model development and empirical test , 2010, Behav. Inf. Technol..

[28]  Alain Forget,et al.  Persuasion for Stronger Passwords: Motivation and Pilot Study , 2008, PERSUASIVE.

[29]  Izak Benbasat,et al.  The Effects of Process and Outcome Similarity on Users' Evaluations of Decision Aids , 2008, Decis. Sci..

[30]  W. Wood Attitude change: persuasion and social influence. , 2000, Annual review of psychology.

[31]  BenbasatIzak,et al.  The effects of personalizaion and familiarity on trust and adoption of recommendation agents , 2006 .

[32]  Harri Oinas-Kukkonen,et al.  Influencing Individually: Fusing Personalization and Persuasion , 2012, TIIS.

[33]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[34]  Graham Pervan,et al.  Eight key issues for the decision support systems discipline , 2008, Decis. Support Syst..

[35]  H. Oinas-Kukkonen,et al.  Persuasive Features in Web-Based Alcohol and Smoking Interventions: A Systematic Review of the Literature , 2011, Journal of medical Internet research.

[36]  Harri Oinas-Kukkonen,et al.  Humanizing the Web: Change and Social Innovation , 2013 .

[37]  Mark A. Neerincx,et al.  Persuasive robotic assistant for health self-management of older adults: Design and evaluation of social behaviors , 2010, Int. J. Hum. Comput. Stud..

[38]  L. Fredman,et al.  Weight Change , 1997, Journal of aging and health.

[39]  Sumaira Malik,et al.  Gender differences in computer-mediated communication: a systematic literature review of online health-related support groups. , 2009, Patient education and counseling.

[40]  Charles J. Kacmar,et al.  Developing and Validating Trust Measures for e-Commerce: An Integrative Typology , 2002, Inf. Syst. Res..

[41]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[42]  Eun-Ju Lee,et al.  I like you, but I won't listen to you: Effects of rationality on affective and behavioral responses to computers that flatter , 2009, Int. J. Hum. Comput. Stud..

[43]  Kalle Lyytinen HCI Research: Future Directions that Matter , 2010 .

[44]  Deborah Compeau,et al.  Computer Self-Efficacy: Development of a Measure and Initial Test , 1995, MIS Q..

[45]  Ana Ortiz de Guinea,et al.  Why break the habit of a lifetime? rethinking the roles of intention, habit, and emotion in continuing information technology use , 2009 .

[46]  M. Neve,et al.  Effectiveness of web‐based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta‐analysis , 2010, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[47]  Tuure Tuunanen,et al.  A Conceptual Framework for Consumer Information Systems Development , 2010, Pac. Asia J. Assoc. Inf. Syst..

[48]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[49]  Viswanath Venkatesh,et al.  Technology Acceptance Model 3 and a Research Agenda on Interventions , 2008, Decis. Sci..

[50]  Detmar W. Straub,et al.  Structural Equation Modeling and Regression: Guidelines for Research Practice , 2000, Commun. Assoc. Inf. Syst..

[51]  Youngjin Yoo,et al.  Media and Group Cohesion: Relative Influences on Social Presence, Task Participation, and Group Consensus , 2001, MIS Q..

[52]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[53]  B. Shneiderman,et al.  The Reader-to-Leader Framework: Motivating Technology-Mediated Social Participation , 2009 .

[54]  Elmer V. Bernstam,et al.  Social support in an Internet weight loss community , 2010, Int. J. Medical Informatics.

[55]  Michael A. Hogg,et al.  The Social Psychology of Group Cohesiveness: From Attraction to Social Identity , 1992 .

[56]  Izak Benbasat,et al.  The Adoption and Use of IT Artifacts: A New Interaction-Centric Model for the Study of User-Artifact Relationships , 2009, J. Assoc. Inf. Syst..

[57]  Bert N. Uchino,et al.  Social Support and Health: A Review of Physiological Processes Potentially Underlying Links to Disease Outcomes , 2006, Journal of Behavioral Medicine.

[58]  Anol Bhattacherjee,et al.  Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..

[59]  Detmar W. Straub,et al.  Validation in Information Systems Research: A State-of-the-Art Assessment , 2001, MIS Q..

[60]  Brian S. Butler,et al.  Membership Size, Communication Activity, and Sustainability: A Resource-Based Model of Online Social Structures , 2001, Inf. Syst. Res..

[61]  Izak Benbasat,et al.  Interactive Decision Aids for Consumer Decision Making in E-Commerce: The Influence of Perceived Strategy Restrictiveness , 2009, MIS Q..

[62]  Dean Eckles,et al.  Heterogeneity in the Effects of Online Persuasion , 2012 .

[63]  Andrea Everard,et al.  How Presentation Flaws Affect Perceived Site Quality, Trust, and Intention to Purchase from an Online Store , 2005, J. Manag. Inf. Syst..

[64]  B. Kahn,et al.  The Influence of Positive Affect on Variety Seeking among Safe, Enjoyable Products , 1993 .

[65]  Wynne W. Chin,et al.  A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic - Mail Emotion/Adoption Study , 2003, Inf. Syst. Res..

[66]  Alexander Hars,et al.  Web Based Knowledge Infrastructures for the Sciences: An Adaptive Document , 2000, Commun. Assoc. Inf. Syst..

[67]  B. J. Fogg Healthy Living with Persuasive Technologies : Framework , Issues , and Challenges , 2009 .

[68]  Eric T. G. Wang,et al.  Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories , 2006, Decis. Support Syst..

[69]  ZhangPing,et al.  A person-artefact-task (PAT) model of flow antecedents in computer-mediated environments , 2003 .

[70]  Harri Oinas-Kukkonen,et al.  Behavior Change Support Systems: A Research Model and Agenda , 2010, PERSUASIVE.

[71]  Harri Oinas-Kukkonen,et al.  Factors Affecting Perceived Persuasiveness of a Behavior Change Support System , 2012, ICIS.

[72]  Harri Oinas-Kukkonen,et al.  Humanizing the Web , 2013 .

[73]  Harri Oinas-Kukkonen,et al.  Persuasive Systems Design: Key Issues, Process Model, and System Features , 2009, Commun. Assoc. Inf. Syst..

[74]  Paul A. Pavlou,et al.  Understanding and Mitigating Uncertainty in Online Exchange Relationships: A Principal-Agent Perspective , 2007, MIS Q..

[75]  Luca Chittaro,et al.  Passengers' Safety in Aircraft Evacuations: Employing Serious Games to Educate and Persuade , 2012, PERSUASIVE.

[76]  Milena M. Head,et al.  Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping , 2007, Int. J. Hum. Comput. Stud..

[77]  Jane Webster,et al.  An agenda for 'Green' information technology and systems research , 2011, Inf. Organ..