Adoption and non-adoption motivational risk beliefs in the use of mobile services for health promotion

The purpose of this paper is to validate empirically a theoretical model that integrates an innovative construct capturing consumers’ non-adoption risk belief associated with not using a mobile service designed to support them in a non-leisure activity.,A theoretical model contrasting perceived non-adoption risk to perceived adoption risk of a mobile service supporting health promotion was developed and tested with a sample of potential consumers in North America.,Results show that non-adoption risk is a moderately strong antecedent of motivational factors in contrast to adoption risk that hinders the acceptance of a mobile service supporting health promotion.,Healthcare is a highly sensitive social sector, so possible negative consequences of not using the support of a mobile service are an additional motivation for adopting this service. Future research should test the role of non-adoption risk in other contexts of technology use, including non-leisure settings.,Making potential users see the possible negative consequences of not using a mobile service designed to support them in a non-leisure activity increases their motivation and, subsequently, intention to use the service.,Educational efforts to making consumers see the risks of not using a supporting technology application appear to be justified.,This study demonstrates the significant role of non-adoption risk belief that captures the negative consequences individuals may perceive if they fail to use as expected a mobile service application designed specifically to help them.

[1]  Ronald Neville,et al.  Mobile phone text messaging can help young people manage asthma , 2002, BMJ : British Medical Journal.

[2]  Marzena Cedzynski,et al.  Partial Least Squares: a Critical Review and a Potential Alternative , 2005 .

[3]  J. Weinman,et al.  The beliefs about medicines questionnaire: The development and evaluation of a new method for assessing the cognitive representation of medication , 1999 .

[4]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[5]  John G Meara,et al.  Use of SMS text messaging to improve outpatient attendance , 2005, The Medical journal of Australia.

[6]  Ming-Tien Tsai,et al.  Predicting intention to purchase on group buying website in Taiwan: Virtual community, critical mass, and risk , 2012, Online Inf. Rev..

[7]  Alexander Serenko,et al.  The benefits and dangers of enjoyment with social networking websites , 2012, Eur. J. Inf. Syst..

[8]  Sirkka L. Jarvenpaa,et al.  Perils of Internet fraud: an empirical investigation of deception and trust with experienced Internet consumers , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[9]  M. Becker,et al.  The Health Belief Model: A Decade Later , 1984, Health education quarterly.

[10]  Rajeev Sharma,et al.  Estimating the effect of common method variance: the method-method pair technique with an illustration from TAM research , 2009 .

[11]  C. Møldrup,et al.  Individualised health marketing using SMS - a smoking cessation case , 2007 .

[12]  Suzanne Rivard,et al.  A Multilevel Model of Resistance to Information Technology Implementation , 2005, MIS Q..

[13]  Jan-Bernd Lohmöller,et al.  Latent Variable Path Modeling with Partial Least Squares , 1989 .

[14]  Tao Zhou,et al.  Understanding location-based services users' privacy concern: An elaboration likelihood model perspective , 2017, Internet Res..

[15]  N. Bontis Intellectual capital: an exploratory study that develops measures and models , 1998 .

[16]  Shan Liu,et al.  The effect of intrinsic and extrinsic motivations on mobile coupon sharing in social network sites: The role of coupon proneness , 2016, Internet Res..

[17]  R. Whittaker,et al.  Mobile phone-based interventions for smoking cessation , 2010 .

[18]  Detmar W. Straub,et al.  A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example , 2005, Commun. Assoc. Inf. Syst..

[19]  G. Hillis,et al.  Effect of Lifestyle-Focused Text Messaging on Risk Factor Modification in Patients With Coronary Heart Disease: A Randomized Clinical Trial. , 2015, JAMA.

[20]  Alexander Serenko,et al.  User acceptance of hedonic digital artifacts: A theory of consumption values perspective , 2010, Inf. Manag..

[21]  Yuan Sun,et al.  Extending technology usage to work settings: The role of perceived work compatibility in ERP implementation , 2009, Inf. Manag..

[22]  Ronald T. Cenfetelli,et al.  Identifying and Testing the Inhibitors of Technology Usage Intentions , 2011, Inf. Syst. Res..

[23]  Michel Laroche,et al.  Exploring How Intangibility Affects Perceived Risk , 2004 .

[24]  M. Sobel Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models , 1982 .

[25]  E. Deci,et al.  Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. , 2000, Contemporary educational psychology.

[26]  G. Zinkhan,et al.  An integrated framework for the conceptualization of consumers’ perceived-risk processing , 2004 .

[27]  Edward L. Deci,et al.  Intrinsic Motivation and Self-Determination in Human Behavior , 1975, Perspectives in Social Psychology.

[28]  Paul A. Pavlou,et al.  Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model , 2003, Int. J. Electron. Commer..

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

[30]  D. Sandy Staples,et al.  Toward Contextualized Theories of Trust: The Role of Trust in Global Virtual Teams , 2004, Inf. Syst. Res..

[31]  C. Guinovart,et al.  The role of mobile phones in improving vaccination rates in travelers. , 2004, Preventive medicine.

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

[33]  Wynne W. Chin The partial least squares approach for structural equation modeling. , 1998 .

[34]  J. Barry Mason,et al.  Attitude and risk: Exploring the relationship , 1995 .

[35]  M. Lindell,et al.  Accounting for common method variance in cross-sectional research designs. , 2001, The Journal of applied psychology.

[36]  Paul A. Pavlou,et al.  Predicting E-Services Adoption: A Perceived Risk Facets Perspective , 2002, Int. J. Hum. Comput. Stud..

[37]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .

[38]  R. W. Rogers,et al.  A Protection Motivation Theory of Fear Appeals and Attitude Change1. , 1975, The Journal of psychology.

[39]  Matthias Jarke Scenarios for modeling , 1999, CACM.

[40]  Jintae Lee,et al.  Relating motivation to information and communication technology acceptance: Self-determination theory perspective , 2015, Comput. Hum. Behav..

[41]  Il Im,et al.  The effects of perceived risk and technology type on users' acceptance of technologies , 2008, Inf. Manag..

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

[43]  Izak Benbasat,et al.  A Comprehensive Model of Perceived Risk of E-Commerce Transactions , 2010, Int. J. Electron. Commer..

[44]  Jerilyn K Allen,et al.  Mobile phone interventions to increase physical activity and reduce weight: a systematic review. , 2013, The Journal of cardiovascular nursing.

[45]  Xiangbin Yan,et al.  Information disclosure on social networking sites: An intrinsic-extrinsic motivation perspective , 2015, Comput. Hum. Behav..

[46]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[47]  R. Bagozzi,et al.  An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors , 2002 .

[48]  Neng-Pai Lin,et al.  Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions , 2018, Internet Res..

[49]  John Weinman,et al.  Medicine in a multi-cultural society: the effect of cultural background on beliefs about medications. , 2004, Social science & medicine.

[50]  N. Bontis,et al.  Intellectual capital and business performance in Malaysian industries , 2000 .

[51]  Stephen Sutton,et al.  Randomized controlled trial evaluation of a tailored leaflet and SMS text message self-help intervention for pregnant smokers (MiQuit). , 2012, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[52]  Zinta S. Byrne,et al.  From the user's perspective: Perceptions of risk relative to benefit associated with using the Internet , 2016, Comput. Hum. Behav..

[53]  N. Bontis National Intellectual Capital Index : A United Nations Initiative for the Arab Region , 2002 .

[54]  Paul Benjamin Lowry,et al.  Proposing the Multimotive Information Systems Continuance Model (MISC) to Better Explain End-User System Evaluations and Continuance Intentions , 2015, J. Assoc. Inf. Syst..

[55]  Viswanath Venkatesh,et al.  User Acceptance Enablers in Individual Decision Making About Technology: Toward an Integrated Model , 2002, Decis. Sci..

[56]  Magid Igbaria,et al.  A Motivational Model of Microcomputer Usage , 1996, J. Manag. Inf. Syst..

[57]  W. Riley,et al.  College Smoking-Cessation Using Cell Phone Text Messaging , 2004, Journal of American college health : J of ACH.

[58]  Terry L. Childers,et al.  HEDONIC AND UTILITARIAN MOTIVATIONS FOR ONLINE RETAIL SHOPPING BEHAVIOR , 2001 .

[59]  M. Kenward,et al.  Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomised trial , 2011, The Lancet.

[60]  Marko Sarstedt,et al.  An assessment of the use of partial least squares structural equation modeling in marketing research , 2012 .

[61]  Nick Bontis,et al.  User acceptance of wireless short messaging services: Deconstructing perceived value , 2007, Inf. Manag..

[62]  D. A. Kenny,et al.  The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. , 1986, Journal of personality and social psychology.

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

[64]  Hayeon Song,et al.  Anti-smoking educational game using avatars as visualized possible selves , 2013, Comput. Hum. Behav..

[65]  Ronald T. Cenfetelli,et al.  Interpretation of Formative Measurement in Information Systems Research , 2009, MIS Q..

[66]  Morten Hertzum,et al.  Making use of scenarios: a field study of conceptual design , 2003, Int. J. Hum. Comput. Stud..

[67]  Viswanath Venkatesh,et al.  Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation , 1999, MIS Q..

[68]  K. Patrick,et al.  A Text Message–Based Intervention for Weight Loss: Randomized Controlled Trial , 2009, Journal of medical Internet research.

[69]  Ronald T. Cenfetelli Inhibitors and Enablers as Dual Factor Concepts in Technology Usage , 2004, J. Assoc. Inf. Syst..

[70]  Nena Lim,et al.  Consumers' perceived risk: sources versus consequences , 2003, Electron. Commer. Res. Appl..

[71]  Norm Archer,et al.  Early Investigation of New Information Technology Acceptance: A Perceived Risk - Motivation Model , 2009, Commun. Assoc. Inf. Syst..

[72]  Varun Grover,et al.  Building a Model of Technology Preference: The Case of Channel Choices , 2011, Decis. Sci..

[73]  Jae Eun Chung,et al.  Antismoking campaign videos on YouTube and audience response: Application of social media assessment metrics , 2015, Comput. Hum. Behav..

[74]  Hans van der Heijden,et al.  Effects of Context Relevance and Perceived Risk on User Acceptance of Mobile Information Services , 2005, ECIS.

[75]  Mala Srivastava,et al.  Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India , 2014, Internet Res..

[76]  Young-Gul Kim,et al.  Extending the TAM for a World-Wide-Web context , 2000, Inf. Manag..

[77]  Rolph E. Anderson,et al.  Multivariate Data Analysis (7th ed. , 2009 .

[78]  Li Qin,et al.  Impact of Privacy Concern in Social Networking Websites , 2012, Internet Res..

[79]  N. Bontis National Intellectual Capital Index , 2004 .

[80]  Robert W. Zmud,et al.  Behavioral Intention Formation in Knowledge Sharing: Examining the Roles of Extrinsic Motivators, Social-Psychological Factors, and Organizational Climate , 2005, MIS Q..

[81]  Merrill Warkentin,et al.  Fear Appeals and Information Security Behaviors: An Empirical Study , 2010, MIS Q..

[82]  K. Grønhaug,et al.  Perceived Risk: Further Considerations for the Marketing Discipline , 1993 .

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

[84]  Ben S. Gerber,et al.  Mobile phone text messaging to promote healthy behaviors and weight loss maintenance: a feasibility study , 2009, Health Informatics J..

[85]  Dawei Liu,et al.  Exploring gender differences in acceptance of mobile computing devices among college students , 2017, Inf. Syst. E Bus. Manag..

[86]  Angelika Dimoka,et al.  On Product Uncertainty in Online Markets: Theory and Evidence , 2011, MIS Q..

[87]  Paul A. Pavlou,et al.  Swift Guanxi in Online Marketplaces: The Role of Computer-Mediated Communication Technologies , 2014, MIS Q..

[88]  Hans van der Heijden,et al.  User Acceptance of Hedonic Information Systems , 2004, MIS Q..

[89]  W. Chou,et al.  Social Media Use in the United States: Implications for Health Communication , 2009, Journal of medical Internet research.

[90]  Sirkka L. Jarvenpaa,et al.  Consumer trust in an Internet store , 2000, Inf. Technol. Manag..

[91]  K. Miller,et al.  Intrinsic Motivation and Self-Determination in Human Behavior , 1975, Perspectives in Social Psychology.

[92]  H. Cole-Lewis,et al.  Text messaging as a tool for behavior change in disease prevention and management. , 2010, Epidemiologic reviews.

[93]  I-Cheng Chang,et al.  The effects of hedonic/utilitarian expectations and social influence on continuance intention to play online games , 2014, Internet Res..

[94]  Fred D. Davis,et al.  Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1 , 1992 .

[95]  Guy Dewsbury,et al.  Nursing and information and communication technology (ICT): a discussion of trends and future directions. , 2011, International journal of nursing studies.

[96]  Liu Liu,et al.  Exploring consumer perceived risk and trust for online payments: An empirical study in China's younger generation , 2015, Comput. Hum. Behav..

[97]  Joseph S. Valacich,et al.  The Effect of Perceived Novelty on the Adoption of Information Technology Innovations: A Risk/Reward Perspective , 2010, Decis. Sci..

[98]  Juhani Iivari,et al.  Why do individuals use computer technology? A Finnish case study , 1995, Inf. Manag..

[99]  Detmar W. Straub,et al.  An Update and Extension to SEM Guidelines for Admnistrative and Social Science Research , 2011 .

[100]  Marko Sarstedt,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .

[101]  J. Shearer,et al.  Cost effectiveness analysis of smoking cessation interventions , 2006, Australian and New Zealand journal of public health.

[102]  S. Rivard,et al.  Getting physicians to accept new information technology: insights from case studies , 2006, Canadian Medical Association Journal.

[103]  S. Walker,et al.  Development and testing of the Health Promotion Model. , 1988, Cardio-vascular nursing.

[104]  J. Hulland,et al.  Managing An Organizational Learning System By Aligning Stocks and Flows , 2002 .

[105]  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.

[106]  B. Fjeldsoe,et al.  Behavior change interventions delivered by mobile telephone short-message service. , 2009, American journal of preventive medicine.

[107]  Mary J Wills,et al.  Do u smoke after txt? Results of a randomised trial of smoking cessation using mobile phone text messaging , 2005, Tobacco Control.

[108]  Wynne W. Chin How to Write Up and Report PLS Analyses , 2010 .

[109]  P. R. Warshaw A New Model for Predicting Behavioral Intentions: An Alternative to Fishbein , 1980 .