Psychological factors influencing customers’ acceptance of smartphone diet apps when ordering food at restaurants

Abstract This paper examines the adoption of smartphone diet apps by restaurant customers and, more specifically, the psychological factors that influence their intention to use such apps when ordering food at restaurants. Data was collected from 395 individuals and analyzed using partial least squares structural equation modeling. Results showed that customers’ intention to use smartphone diet apps is predicted by expected performance of the application, anticipated effort of usage, social influence, and degree of user innovativeness. Following the Unified Theory of Acceptance and Use of Technology (UTAUT), this study proposes five determinants of mobile diet apps’ usage intentions: performance expectancy, effort expectancy, social influence, facilitating conditions, and personal innovativeness. Based on the study results, theoretical and practical implications are provided for scholars, health professionals, restaurant operators, and smartphone application developers.

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

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

[3]  Linda Hasman,et al.  An Introduction to Consumer Health Apps for the iPhone , 2011 .

[4]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[5]  Yi-Shun Wang,et al.  Investigating the determinants and age and gender differences in the acceptance of mobile learning , 2009, Br. J. Educ. Technol..

[6]  Piet Kommers,et al.  Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender , 2015, Comput. Hum. Behav..

[7]  C. Weiß,et al.  Consumer demand for food diversity: evidence for Germany , 2003 .

[8]  Michael D. Buhrmester,et al.  Amazon's Mechanical Turk , 2011, Perspectives on psychological science : a journal of the Association for Psychological Science.

[9]  Sven Laumer,et al.  Who influences whom? Analyzing workplace referents' social influence on IT adoption and non-adoption , 2009, J. Inf. Technol..

[10]  I. Ajzen.  The theory of planned behavior , 1991 .

[11]  Martin Wetzels,et al.  A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects , 2007, Inf. Manag..

[12]  Henry Been-Lirn Duh,et al.  A study on Singaporean women's acceptance of using mobile phones to seek health information , 2011, Int. J. Medical Informatics.

[13]  John R. White,et al.  Smartphone-Based Glucose Monitors and Applications in the Management of Diabetes: An Overview of 10 Salient “Apps” and a Novel Smartphone-Connected Blood Glucose Monitor , 2012, Clinical Diabetes.

[14]  Erik M. van Raaij,et al.  The acceptance and use of a virtual learning environment in China , 2008, Comput. Educ..

[15]  M. Allman-Farinelli,et al.  The barriers and enablers of healthy eating among young adults: a missing piece of the obesity puzzle: A scoping review , 2017, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[16]  I. Ajzen,et al.  On construct validity: A critique of Miniard and Cohen's paper , 1981 .

[17]  Bruce G. Link,et al.  The impact of obesity on US mortality levels: the importance of age and cohort factors in population estimates. , 2013, American journal of public health.

[18]  Viswanath Venkatesh,et al.  Gender and age differences in employee decisions about new technology: an extension to the theory of planned behavior , 2005, IEEE Transactions on Engineering Management.

[19]  E. Robinson,et al.  Development and feasibility testing of a smart phone based attentive eating intervention , 2013, BMC Public Health.

[20]  Viswanath Venkatesh,et al.  Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior , 2000, MIS Q..

[21]  Behavioral Factors Associated with Disease, Injury, and Death among Men: Evidence and Implications for Prevention , 2000 .

[22]  F. Nieto,et al.  Developing and Implementing “Waupaca Eating Smart” , 2016, Health promotion practice.

[23]  G. Roos,et al.  Men, masculinity and food: interviews with Finnish carpenters and engineers , 2001, Appetite.

[24]  Cynthia Kratzke,et al.  Smartphone Technology and Apps: Rapidly Changing Health Promotion. , 2012 .

[25]  Hsiu-Yuan Wang,et al.  User acceptance of mobile internet based on the Unified Theory of Acceptance and Use of Technology: Investigating the determinants and gender differences , 2010 .

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

[27]  Ritu Agarwal,et al.  A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology , 1998, Inf. Syst. Res..

[28]  Mark Moran,et al.  Students' Acceptance of Tablet PCs and Implications for Educational Institutions , 2011, J. Educ. Technol. Soc..

[29]  Hager Khechine,et al.  Student behavioural intentions to use desktop video conferencing in a distance course: integration of autonomy to the UTAUT model , 2013, J. Comput. High. Educ..

[30]  Kakoli Bandyopadhyay,et al.  The Effect of Culture on User Acceptance of Information Technology , 2007, Commun. Assoc. Inf. Syst..

[31]  Marko Sarstedt,et al.  Management of multi-purpose stadiums: importance and performance measurement of service interfaces , 2010, Int. J. Serv. Technol. Manag..

[32]  Fevzi Okumus,et al.  International Journal of Hospitality Management , 2022 .

[33]  D. Midgley,et al.  Innovativeness: The Concept and Its Measurement , 1978 .

[34]  J. Kandampully,et al.  Generation Y's dining information seeking and sharing behavior on social networking sites: An exploratory study , 2014 .

[35]  M. Conner,et al.  Barriers to healthy eating amongst men: a qualitative analysis. , 2006, Social science & medicine.

[36]  Xiaohui Liu,et al.  The effects of individual differences on e-learning users' behaviour in developing countries: A structural equation model , 2014, Comput. Hum. Behav..

[37]  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.

[38]  Aaron C. T. Smith.  Older adults and technology use , 2014 .

[39]  Adam J. Berinsky,et al.  Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk , 2012, Political Analysis.

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

[41]  Eric T. G. Wang,et al.  Understanding Web-based learning continuance intention: The role of subjective task value , 2008, Inf. Manag..

[42]  Shuiqing Yang,et al.  A study on factors that affect users' behavioral intention to transfer usage from the offline to the online channel , 2011, Comput. Hum. Behav..

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

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

[45]  Chang Liu,et al.  Mobile Data Service Demographics in Urban China , 2009, J. Comput. Inf. Syst..

[46]  Christine Williams,et al.  Differences in Eating Behavior, Physical Activity, and Health-related Lifestyle Choices between Users and Nonusers of Mobile Health Apps , 2017 .

[47]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

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

[49]  Ángel Herrero Crespo,et al.  Explaining B2C e-commerce acceptance: An integrative model based on the framework by Gatignon and Robertson , 2008, Interact. Comput..

[50]  Faizan Ali,et al.  An assessment of students' acceptance and usage of computer supported collaborative classrooms in hospitality and tourism schools , 2016 .

[51]  T. C. Edwin Cheng,et al.  Adoption of internet banking: An empirical study in Hong Kong , 2006, Decis. Support Syst..

[52]  Marko Sarstedt,et al.  Editorial - Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance , 2013 .

[53]  Brent McFerran,et al.  I’ll Have What She’s Having: Effects of Social Influence and Body Type on the Food Choices of Others , 2010 .

[54]  Jane M. Howell,et al.  Personal Computing: Toward a Conceptual Model of Utilization , 1991, MIS Q..

[55]  Héctor San Martín,et al.  Influence of the user's psychological factors on the online purchase intention in rural tourism: integrating innovativeness to the UTAUT framework. , 2012 .

[56]  M. Conner,et al.  The Theory of Planned Behaviour , 2004 .

[57]  Per E. Pedersen,et al.  Explaining intention to use mobile chat services: moderating effects of gender , 2005 .

[58]  P. Krebs,et al.  Health App Use Among US Mobile Phone Owners: A National Survey , 2015, JMIR mHealth and uHealth.

[59]  Anil Bilgihan,et al.  Factors Affecting the Acceptance of Smartphone Diet Applications , 2016 .

[60]  Douglas C. Nelson,et al.  A Qualitative Assessment of Yelp.Com Users’ Motivations to Submit and Read Restaurant Reviews , 2015 .

[61]  Richard J. Holden,et al.  The Technology Acceptance Model: Its past and its future in health care , 2010, J. Biomed. Informatics.

[62]  Fevzi Okumus,et al.  The Role of Online Social Network Travel Websites in Creating Social Interaction for Gen Y Travelers , 2013 .

[63]  J. J. Cronin,et al.  The Role of Corporate Social Responsibility in Consumer Evaluation of Nutrition Information Disclosure by Retail Restaurants , 2014, Journal of Business Ethics.

[64]  Ki Won Lee,et al.  The role of perceived corporate social responsibility on providing healthful foods and nutrition information with health-consciousness as a moderator , 2014 .

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

[66]  Jylana L. Sheats,et al.  Harnessing Different Motivational Frames via Mobile Phones to Promote Daily Physical Activity and Reduce Sedentary Behavior in Aging Adults , 2013, PloS one.

[67]  Petra Povalej Brzan,et al.  Mobile Applications for Control and Self Management of Diabetes: A Systematic Review , 2016, Journal of Medical Systems.

[68]  E. Rogers.  Diffusion of Innovations , 1962 .

[69]  W. Willett,et al.  Global obesity: trends, risk factors and policy implications , 2013, Nature Reviews Endocrinology.

[70]  Yogesh Kumar Dwivedi,et al.  Modeling Consumers’ Adoption Intentions of Remote Mobile Payments in the United Kingdom: Extending UTAUT with Innovativeness, Risk, and Trust , 2015 .

[71]  Harry Bouwman,et al.  Analysis of users and non-users of smartphone applications , 2010, Telematics Informatics.

[72]  R. Goldsmith.  Explaining and Predicting Consumer Intention to Purchase Over the Internet: An Exploratory Study , 2002 .

[73]  A Food Safety Belief Model for Latino(A) Employees in Foodservice , 2013 .

[74]  Emiliano Rodriguez-Sanchez,et al.  Effectiveness of a smartphone application for improving healthy lifestyles, a randomized clinical trial (EVIDENT II): study protocol , 2014, BMC Public Health.

[75]  C. Yu.  Factors Affecting Individuals to Adopt Mobile Banking: Empirical Evidence from the Utaut Model , 2012 .

[76]  Aaron Smith,et al.  U.S. Smartphone Use in 2015 , 2015 .

[77]  Russell A. McCann,et al.  mHealth for mental health: Integrating smartphone technology in behavioral healthcare. , 2011 .

[78]  Jijie Wang,et al.  Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT , 2007, Inf. Manag..

[79]  Peter A. Todd,et al.  Understanding Information Technology Usage: A Test of Competing Models , 1995, Inf. Syst. Res..

[80]  Christer Carlsson,et al.  Factors driving the adoption of m-learning: An empirical study , 2010, Comput. Educ..

[81]  Bruno M. C. Silva,et al.  A new mobile ubiquitous computing application to control obesity: SapoFit , 2013, Informatics for health & social care.

[82]  Bram Pynoo,et al.  Monitoring the PACS Implementation Process in a Large University Hospital—Discrepancies Between Radiologists and Physicians , 2008, Journal of Digital Imaging.

[83]  J. Michael Pearson,et al.  Internet banking in Jordan: The unified theory of acceptance and use of technology (UTAUT) perspective , 2007, J. Syst. Inf. Technol..

[84]  Jörg Henseler,et al.  Testing Moderating Effects in PLS Path Models. An Illustration of Available Procedures , 2005 .

[85]  Karen A. Forcht,et al.  Combining Visualization and Interaction for Scalable Detection of Anomalies in Network Data , 2010, J. Comput. Inf. Syst..

[86]  Wolfgang Wörndl,et al.  Evaluating the impact of proactivity in the user experience of a context-aware restaurant recommender for Android smartphones , 2013, J. Syst. Archit..

[87]  M. Gallant.  The Influence of Social Support on Chronic Illness Self-Management: A Review and Directions for Research , 2003, Health education & behavior : the official publication of the Society for Public Health Education.

[88]  C. Ruiz-Mafé,et al.  The role of consumer innovativeness and perceived risk in online banking usage , 2009 .

[89]  Scot Burton,et al.  What Consumers Don't Know Can Hurt Them: Consumer Evaluations and Disease Risk Perceptions of Restaurant Menu Items , 2004 .

[90]  M. Sarstedt,et al.  The relevance of reputation in the nonprofit sector: the moderating effect of socio-demographic characteristics , 2014 .

[91]  Steven N. Silverman,et al.  Adoption of Internet shopping: the role of consumer innovativeness , 2000, Ind. Manag. Data Syst..

[92]  K J Calfas,et al.  Design and implementation of a randomized controlled social and mobile weight loss trial for young adults (project SMART). , 2014, Contemporary clinical trials.

[93]  Boyd Swinburn,et al.  The Influence on Population Weight Gain and Obesity of the Macronutrient Composition and Energy Density of the Food Supply , 2015, Current Obesity Reports.

[94]  Wynne W. Chin,et al.  Extending the technology acceptance model: the influence of perceived user resources , 2001, DATB.

[95]  M. Sarstedt,et al.  A new criterion for assessing discriminant validity in variance-based structural equation modeling , 2015 .

[96]  Viswanath Venkatesh,et al.  Going beyond intention: Integrating behavioral expectation into the unified theory of acceptance and use of technology , 2017, J. Assoc. Inf. Sci. Technol..

[97]  P. Pavlou,et al.  Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model , 2003 .

[98]  Rex B. Kline,et al.  Principles and Practice of Structural Equation Modeling , 1998 .

[99]  Michael J. Maloni,et al.  Corporate Social Responsibility in the Supply Chain: An Application in the Food Industry , 2006 .