Mobile Apps Use and WOM in the Food Delivery Sector: The Role of Planned Behavior, Perceived Security and Customer Lifestyle Compatibility
暂无分享,去创建一个
[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 .