Mobile Apps Use and WOM in the Food Delivery Sector: The Role of Planned Behavior, Perceived Security and Customer Lifestyle Compatibility

This research examines the phenomenon of food delivery services from the mobile app user’s perspective and how consumers’ lifestyles are changing because of the convenience provided by the apps. By means of an online survey targeted at US food delivery app customers, our study analyzes the main motivations that lead them to use and recommend these technology-based services. The results of the study revealed that some of the theory of planned behavior model variables (i.e., attitude toward the behavior, subjective norms), influence customer use and word-of-mouth (WOM) intentions. Security influences intention to spread WOM, whereas customer lifestyle compatibility influences intention to use the food delivery apps. A post hoc analysis revealed that perceived control is only important for older customers, who need to perceive that they control the apps before they will recommend them to other customers. The findings of the study are discussed and contrasted with previous research in the field. The managerial implications derived from the findings provide practical guidance for food delivery app companies. Further research avenues are suggested to encourage scholars to continue investigating the challenge of the diffusion of mobile apps in the food delivery and related sectors.

[1]  Juan Sánchez-Fernández,et al.  Antecedents of the adoption of the new mobile payment systems: The moderating effect of age , 2014, Comput. Hum. Behav..

[2]  R. Bagozzi,et al.  Antecedents and purchase consequences of customer participation in small group brand communities , 2006 .

[3]  Jinsoo Hwang,et al.  Understanding the Eco-Friendly Role of Drone Food Delivery Services: Deepening the Theory of Planned Behavior , 2020, Sustainability.

[4]  R. Hallowell The relationships of customer satisfaction, customer loyalty, and profitability: an empirical study , 1996 .

[5]  Daniel Belanche,et al.  Instagram Stories versus Facebook Wall: an advertising effectiveness analysis , 2019, Spanish Journal of Marketing - ESIC.

[6]  Seounmi Youn,et al.  Antecedents of Consumer Attitudes toward Cause-Related Marketing , 2008, Journal of Advertising Research.

[7]  C. Fornell,et al.  Evaluating Structural Equation Models with Unobservable Variables and Measurement Error , 1981 .

[8]  Jan Fabian Ehmke,et al.  Vehicle Routing for Attended Home Delivery in City Logistics , 2012 .

[9]  M. Holbrook Aims, Concepts, and Methods for the Representation of Individual Differences in Esthetic Responses to Design Features , 1986 .

[10]  ChenCheng,et al.  Extending the theory of planned behavior , 2017 .

[11]  Jianxia Du,et al.  Gender and attitudes toward technology use: A meta-analysis , 2017, Comput. Educ..

[12]  Russell H. Fazio,et al.  Attitudes as object-evaluation associations: Determinants, consequences, and correlates of attitude accessibility. , 1995 .

[13]  Jinsoo Hwang,et al.  Investigating motivated consumer innovativeness in the context of drone food delivery services , 2019, Journal of Hospitality and Tourism Management.

[14]  Younghoon Chang,et al.  Determinants of continuance intention to use the smartphone banking services: An extension to the expectation-confirmation model , 2016, Ind. Manag. Data Syst..

[15]  C. Flavián,et al.  Providing online public services successfully: the role of confirmation of citizens’ expectations , 2010 .

[16]  Suk-won Lee,et al.  Determinants of Continuous Intention on Food Delivery Apps: Extending UTAUT2 with Information Quality , 2019, Sustainability.

[17]  Millissa F. Y. Cheung,et al.  The influence of the propensity to trust on mobile users' attitudes toward in-app advertisements: An extension of the theory of planned behavior , 2017, Comput. Hum. Behav..

[18]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[19]  Shan Liu,et al.  Mobile health service adoption in China , 2019, Online Inf. Rev..

[20]  Kum Fai Yuen,et al.  Consumer participation in last-mile logistics service: an investigation on cognitions and affects , 2019, International Journal of Physical Distribution & Logistics Management.

[21]  Garry Wei-Han Tan,et al.  Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card , 2016, Expert Syst. Appl..

[22]  Jen-Her Wu,et al.  What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model , 2005, Inf. Manag..

[23]  Kunal Swani,et al.  To “Free” or Not to “Free”: Trait Predictors of Mobile App Purchasing Tendencies , 2017 .

[24]  Kenneth L. Kraemer,et al.  Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies , 2006, Eur. J. Inf. Syst..

[25]  Kum Fai Yuen,et al.  The determinants of customers’ intention to use smart lockers for last-mile deliveries , 2019, Journal of Retailing and Consumer Services.

[26]  R. Bagozzi,et al.  Cultural and Situational Contingencies and the Theory of Reasoned Action: Application to Fast Food Restaurant Consumption , 2000 .

[27]  Xueming Luo,et al.  Personalized mobile marketing strategies , 2019, Journal of the Academy of Marketing Science.

[28]  Ángel Herrero Crespo,et al.  The effect of innovativeness on the adoption of B2C e-commerce: A model based on the Theory of Planned Behaviour , 2008, Comput. Hum. Behav..

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

[30]  Vess Johnson,et al.  Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services , 2018, Comput. Hum. Behav..

[31]  Kiseol Yang Consumer technology traits in determining mobile shopping adoption: An application of the extended theory of planned behavior , 2012 .

[32]  Youjae Yi,et al.  The effects of customer justice perception and affect on customer citizenship behavior and customer dysfunctional behavior , 2008 .

[33]  Swinder Janda,et al.  A phenomenological investigation of Internet usage among older individuals , 2000 .

[34]  Bianca C. Reisdorf,et al.  Internet (non-)use types and motivational access: Implications for digital inequalities research , 2015, New Media Soc..

[35]  Jinsoo Hwang,et al.  Merging the norm activation model and the theory of planned behavior in the context of drone food delivery services: Does the level of product knowledge really matter? , 2020 .

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

[37]  Alfredo Pérez-Rueda,et al.  Determinants of multi-service smartcard success for smart cities development: A study based on citizens' privacy and security perceptions , 2015, Gov. Inf. Q..

[38]  JungWon Yoon,et al.  The use of an online forum for health information by married Korean women in the United States , 2012, Inf. Res..

[39]  Kampan Mukherjee,et al.  The effect of perceived security and grievance redressal on continuance intention to use M-wallets in a developing country , 2018, International Journal of Bank Marketing.

[40]  Domingo Fernández-Uclés,et al.  Explanatory factors for efficiency in the use of social networking sites—The case of organic food products , 2017, Psychology & Marketing.

[41]  Ibrahim Arpaci,et al.  Understanding and predicting students' intention to use mobile cloud storage services , 2016, Comput. Hum. Behav..

[42]  Jinsoo Hwang,et al.  Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: The moderating role of gender and age , 2019, International Journal of Hospitality Management.

[43]  Elena Karahanna,et al.  Reconceptualizing Compatability Beliefs in Technology Acceptance Research , 2006, MIS Q..

[44]  Rudolf R. Sinkovics,et al.  The Use of Partial Least Squares Path Modeling in International Marketing , 2009 .

[45]  Jie Zhang,et al.  Impact of perceived technical protection on security behaviors , 2009, Inf. Manag. Comput. Secur..

[46]  Louis Leung,et al.  Extending the theory of planned behavior: A study of lifestyles, contextual factors, mobile viewing habits, TV content interest, and intention to adopt mobile TV , 2017, Telematics Informatics.

[47]  Jan U. Becker,et al.  Seeding Strategies for Viral Marketing: An Empirical Comparison , 2011 .

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

[49]  Manuel J. Sánchez-Franco,et al.  Variance-Based Structural Equation Modeling: Guidelines for Using Partial Least Squares in Information Systems Research , 2012 .

[50]  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..

[51]  K. King,et al.  The Effects of Interpersonal Tie Strength and Subjective Norms on Consumers' Brand-Related eWOM Referral Intentions , 2015 .

[52]  R. Peterson,et al.  A Meta-analysis of Online Trust Relationships in E-commerce , 2017 .

[53]  Ramiro Gonçalves,et al.  How smartphone advertising influences consumers' purchase intention , 2019, Journal of Business Research.

[54]  Carlos Flavián,et al.  Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers , 2019, Ind. Manag. Data Syst..

[55]  Zhaohua Wang,et al.  Product recommendation in online social networking communities: An empirical study of antecedents and a mediator , 2019, Inf. Manag..

[56]  OoiKeng-Boon,et al.  Mobile technology acceptance model , 2016 .

[57]  Andreas B. Eisingerich,et al.  Why recommend a brand face-to-face but not on Facebook? How word-of-mouth on online social sites differs from traditional word-of-mouth , 2015 .

[58]  Jinsoo Hwang,et al.  Exploring perceived risk in building successful drone food delivery services , 2019, International Journal of Contemporary Hospitality Management.

[59]  Michael Browne,et al.  Home Delivery and the Impacts on Urban Freight Transport: A Review , 2014 .

[60]  J. Petrick,et al.  Wellness Pursuit and Slow Life Seeking Behaviors: Moderating Role of Festival Attachment , 2019, Sustainability.

[61]  Payam Hanafizadeh,et al.  A systematic review of Internet banking adoption , 2014, Telematics Informatics.

[62]  Hongwei Chris Yang,et al.  Bon Appétit for Apps: Young American Consumers' Acceptance of Mobile Applications , 2013, J. Comput. Inf. Syst..

[63]  H. Raghav Rao,et al.  A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents , 2008, Decis. Support Syst..

[64]  S. Geisser A predictive approach to the random effect model , 1974 .

[65]  Carlos Flavián,et al.  Trust transfer in the continued usage of public e-services , 2014, Inf. Manag..

[66]  Stephen C. Cosmas Life Styles and Consumption Patterns , 1982 .

[67]  Ki Hoon Lee,et al.  Influences of motivations and lifestyles on intentions to use smartphone applications , 2018 .

[68]  Peter A. Dacin,et al.  Spreading the word: Investigating antecedents of consumers’ positive word-of-mouth intentions and behaviors in a retailing context , 2005 .

[69]  Ronan de Kervenoael,et al.  E-retailers and the engagement of delivery workers in urban last-mile delivery for sustainable logistics value creation: Leveraging legitimate concerns under time-based marketing promise , 2020 .

[70]  Carlos Flavián,et al.  Understanding the influence of social information sources on e-government adoption , 2012, Inf. Res..

[71]  Fred D. Davis,et al.  Disentangling behavioral intention and behavioral expectation , 1985 .

[72]  L. Phillips,et al.  Age Differences in Information Processing: A Perspective on the Aged Consumer , 1977 .

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

[74]  Chien Yu,et al.  The Comparison of Three Major Occupations for User Acceptance of Information Technology: Applying the UTAUT Model , 2011 .

[75]  Rajiv Sabherwal,et al.  Mobile application security: Role of perceived privacy as the predictor of security perceptions , 2020, Int. J. Inf. Manag..

[76]  Jinsoo Hwang,et al.  Consequences of a green image of drone food delivery services: The moderating role of gender and age , 2019, Business Strategy and the Environment.

[77]  L. J. Harrison‐Walker The Measurement of Word-of-Mouth Communication and an Investigation of Service Quality and Customer Commitment As Potential Antecedents , 2001 .

[78]  R. Gurrea,et al.  The impact of consumers’ positive online recommendations on the omnichannel webrooming experience , 2019, Spanish Journal of Marketing - ESIC.

[79]  J. Drahokoupil,et al.  Work in the Platform Economy: Deliveroo Riders in Belgium and the SMart Arrangement , 2019, SSRN Electronic Journal.

[80]  Miguel Guinalíu Blasco,et al.  The Effect of Culture in Forming E-Loyalty Intentions: A Cross-Cultural Analysis between Argentina and Spain , 2015 .

[81]  A. Najmi,et al.  Understanding the impact of service convenience on customer satisfaction in home delivery: evidence from Pakistan , 2017 .

[82]  Mónica Cortiñas,et al.  Omni-channel users and omni-channel customers: a segmentation analysis using distribution services , 2019 .

[83]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[84]  Ran Wei,et al.  Lifestyles and new media: adoption and use of wireless communication technologies in China , 2006, New Media Soc..

[85]  Carlos Flavián,et al.  The role of security, privacy, usability and reputation in the development of online banking , 2007, Online Inf. Rev..

[86]  Douglas R. May,et al.  The Impact of Aging on Self-Efficacy and Computer Skill Acquisition , 2005 .

[87]  Timothy Teo,et al.  Explaining the Intention to Use Technology among Student Teachers: An Application of the Theory of Planned Behavior (TPB) , 2010 .

[88]  Mark A. Bonn,et al.  Differences in perceptions about food delivery apps between single-person and multi-person households , 2019, International Journal of Hospitality Management.

[89]  Nripendra P. Rana,et al.  Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model , 2019, Int. J. Inf. Manag..

[90]  A. Lobo,et al.  Organic food products in China: determinants of consumers’ purchase intentions , 2012 .

[91]  A. Parasuraman,et al.  Problems and Strategies in Services Marketing , 1985 .

[92]  Yogesh Kumar Dwivedi,et al.  Social media in marketing: A review and analysis of the existing literature , 2017, Telematics Informatics.

[93]  William J. McDonald Time use in shopping: The role of personal characteristics , 1994 .

[94]  Andraz Petrovcic,et al.  Smart but not adapted enough: Heuristic evaluation of smartphone launchers with an adapted interface and assistive technologies for older adults , 2018, Comput. Hum. Behav..

[95]  Jinsoo Hwang,et al.  Consequences of psychological benefits of using eco-friendly services in the context of drone food delivery services , 2019, Future of Tourism Marketing.

[96]  Mu-Chen Chen,et al.  Ensuring the quality of e-shopping specialty foods through efficient logistics service , 2014 .

[97]  P. Bentler,et al.  Fit indices in covariance structure modeling : Sensitivity to underparameterized model misspecification , 1998 .

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

[99]  Norman Shaw,et al.  The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value , 2019, Int. J. Inf. Manag..

[100]  E. B. Jurado,et al.  Evaluation of Corporate Websites and Their Influence on the Performance of Olive Oil Companies , 2018 .

[101]  M. A. Harris,et al.  Identifying factors influencing consumers' intent to install mobile applications , 2016, Int. J. Inf. Manag..

[102]  Carlos Flavián,et al.  Users' motivations and attitude towards the online press , 2009 .

[103]  William Nick Street,et al.  Modeling and maximizing influence diffusion in social networks for viral marketing , 2018, Applied Network Science.

[104]  Jinsoo Hwang,et al.  Application of the value-belief-norm model to environmentally friendly drone food delivery services , 2020 .

[105]  Andrea Pérez,et al.  Values and Lifestyles in the Adoption of New Technologies Applying VALS Scale , 2014 .

[106]  V. Venkatesh,et al.  AGE DIFFERENCES IN TECHNOLOGY ADOPTION DECISIONS: IMPLICATIONS FOR A CHANGING WORK FORCE , 2000 .

[107]  R. Kanter,et al.  The Differentiation of Life-Styles , 1976 .

[108]  Amita Goyal Chin,et al.  A bidirectional perspective of trust and risk in determining factors that influence mobile app installation , 2018, Int. J. Inf. Manag..

[109]  Nathalie T. M. Demoulin,et al.  Adoption of in-store mobile payment: Are perceived risk and convenience the only drivers? , 2016 .

[110]  Ali Abdallah Alalwan,et al.  Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse , 2020, Int. J. Inf. Manag..

[111]  WashingtonRonald,et al.  Limitations to the rapid adoption of M-payment services , 2018 .

[112]  David C. Yen,et al.  Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model , 2007, Comput. Hum. Behav..

[113]  Jonathan Levav,et al.  The Compensatory Consumer Behavior Model: How Self-Discrepancies Drive Consumer Behavior , 2016 .

[114]  Carlos Flavián,et al.  The Role of Place Identity in Smart Card Adoption , 2014 .