User Acceptance of IoT Applications in Retail Industry

With the rapid advancements in the internet technology, many retailers are embracing internet of things technology to enhance customer experience and improve efficiency. Specifically, many customer-facing IoT technologies such as augmented reality, smart shopping carts, smart displays, and RFID tags are expected to change the way customers experience retailing shopping. Drawing on the technology acceptance model, trust perspective, task-technology fit, and organizational reputation perspective, this study examines the customer adoption of IoT applications in retail setting. Responses collected from 289 actual retail shoppers were analyzed using structural equation modeling. Results reveal that perceived usefulness, perceived ease of use, task-technology fit, retailer reputation, and initial trust are significant predictors of customer attitude and intentions to use IoT in retail stores. The study findings have key implications for academicians and retailers in improving customer acceptance and in delivering superior customer experience.

[1]  Hong Zhou,et al.  Design and Research of Urban Intelligent Transportation System Based on the Internet of Things , 2012 .

[2]  H. H. Cheung,et al.  Item-level RFID for enhancement of customer shopping experience in apparel retail , 2015, Comput. Ind..

[3]  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.

[4]  Hean Tat Keh,et al.  Corporate reputation and customer behavioral intentions: The roles of trust, identification and commitment ☆ , 2009 .

[5]  A. Chong,et al.  Online banking adoption: an empirical analysis , 2010 .

[6]  Himadri Roy Chaudhuri,et al.  Impact of firm ' s reputation and ethnocentrism on attitude towards foreign products , 2014 .

[7]  M.H.P. Kleijnen,et al.  Customer adoption of e‐service: an experimental study , 2001 .

[8]  David F. Larcker,et al.  Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics: , 1981 .

[9]  Li D. Xu Information architecture for supply chain quality management , 2011 .

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

[11]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[12]  Donna L. Hoffman,et al.  Emergent Experience and the Connected Consumer in the Smart Home Assemblage and the Internet of Things , 2015 .

[13]  Frédéric Thiesse,et al.  Understanding the value of integrated RFID systems: a case study from apparel retail , 2009, Eur. J. Inf. Syst..

[14]  Yu-Shan Chen,et al.  The effect of task-technology fit on purchase intention: The moderating role of perceived risks , 2017 .

[15]  Keng Siau,et al.  A qualitative investigation on consumer trust in mobile commerce , 2004, Int. J. Electron. Bus..

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

[17]  Hee-Dong Yang,et al.  Are All Fits Created Equal? A Nonlinear Perspective on Task-Technology Fit , 2013, J. Assoc. Inf. Syst..

[18]  Kim K. P. Johnson,et al.  Consumer adoption of smart in-store technology: assessing the predictive value of attitude versus beliefs in the technology acceptance model , 2017 .

[19]  Stuart J. Barnes,et al.  Initial trust and online buyer behaviour , 2007, Ind. Manag. Data Syst..

[20]  Bill N. Schilit,et al.  Enabling the Internet of Things , 2015, Computer.

[21]  Mehmet Akif Ocak,et al.  Augmented reality in science laboratories: The effects of augmented reality on university students' laboratory skills and attitudes toward science laboratories , 2016, Comput. Hum. Behav..

[22]  J. Rho,et al.  Investigating the role of task-technology fit along with attractiveness of alternative technology to utilize RFID system in the organization , 2015 .

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

[24]  Alastair M. Morrison,et al.  Online shopping motivations and pleasure travel products: a correspondence analysis , 2005 .

[25]  Alessandro Bassi,et al.  From today's INTRAnet of things to a future INTERnet of things: a wireless- and mobility-related view , 2010, IEEE Wireless Communications.

[26]  Shuang-Hua Yang,et al.  How the internet of things technology enhances emergency response operations , 2013 .

[27]  Wen-Shan Lin,et al.  Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives , 2012, Int. J. Hum. Comput. Stud..

[28]  P. Kenning,et al.  The Influence of Retailers’ Family Firm Image on New Product Acceptance: An Empirical Investigation in the German FMCG Market , 2015 .

[29]  Hyun-Joo Lee,et al.  Interpersonal service quality, self-service technology (SST) service quality, and retail patronage , 2013 .

[30]  B. Weijters,et al.  Determinants and Outcomes of Customers' Use of Self-Service Technology in a Retail Setting , 2007 .

[31]  Xin Li,et al.  Why do we trust new technology? A study of initial trust formation with organizational information systems , 2008, J. Strateg. Inf. Syst..

[32]  Lingling Gao,et al.  Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation , 2015, Information Systems Frontiers.

[33]  Gaetano Marrocco,et al.  RFID Technology for IoT-Based Personal Healthcare in Smart Spaces , 2014, IEEE Internet of Things Journal.

[34]  Icek Ajzen,et al.  From Intentions to Actions: A Theory of Planned Behavior , 1985 .

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

[36]  Hsin Hsin Chang,et al.  Task-technology fit and user acceptance of online auction , 2010, Int. J. Hum. Comput. Stud..

[37]  Tao Zhou,et al.  An Empirical Examination of Initial Trust in Mobile Payment , 2014, Wireless Personal Communications.

[38]  Nuno Correia,et al.  Mobile Augmented Reality for Environmental Management (MARE) , 2003, Eurographics.

[39]  Ying Xie,et al.  Information technologies in retail supply chains: a comparison of Tesco and Asda , 2013, Int. J. Bus. Perform. Supply Chain Model..

[40]  William R. King,et al.  A meta-analysis of the technology acceptance model , 2006, Inf. Manag..

[41]  C. Veloutsou,et al.  Beyond technology acceptance: Brand relationships and online brand experience☆ , 2013 .

[42]  Thea van der Geest,et al.  A cue or two and I'll trust you: Determinants of trust in government organizations in terms of their processing and usage of citizens' personal information disclosed online , 2012, Gov. Inf. Q..

[43]  Lingling Gao,et al.  A unified perspective on the factors influencing consumer acceptance of internet of things technology , 2014 .

[44]  Wei-Tsong Wang,et al.  Factors influencing mobile services adoption: A brand-equity perspective , 2012, Internet Res..

[45]  Sung-Un Yang An Integrated Model for Organization—Public Relational Outcomes, Organizational Reputation, and Their Antecedents , 2007 .

[46]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[47]  Hao Li,et al.  The effects of trust assurances on consumers' initial online trust: A two-stage decision-making process perspective , 2014, Int. J. Inf. Manag..

[48]  Kyung Kyu Kim,et al.  Initial trust and the adoption of B2C e-commerce: The case of internet banking , 2004, DATB.

[49]  Katerina Pramatari,et al.  Deploying RFID-Enabled Services in the Retail Supply Chain: Lessons Learned toward the Internet of Things , 2012, Inf. Syst. Manag..

[50]  Kit Hong Wong,et al.  The effects of response strategies and severity of failure on consumer attribution with regard to negative word-of-mouth , 2015, Decis. Support Syst..

[51]  Charu C. Aggarwal,et al.  The Internet of Things: A Survey from the Data-Centric Perspective , 2013, Managing and Mining Sensor Data.

[52]  Mithu Bhattacharya,et al.  A conceptual framework of RFID adoption in retail using Rogers stage model , 2015, Bus. Process. Manag. J..

[53]  Hsi-Peng Lu,et al.  Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit , 2014, Comput. Hum. Behav..

[54]  Diane M. Strong,et al.  Extending the technology acceptance model with task-technology fit constructs , 1999, Inf. Manag..

[55]  Parijat Upadhyay,et al.  Analyzing user perspective on the factors affecting use intention of mobile based transfer payment , 2016, Internet Res..

[56]  M. Porter,et al.  How Smart, Connected Products Are Transforming Competition , 2014 .

[57]  Michael Bourlakis,et al.  Modelling the determinants of a simulated experience in a virtual retail store and users’ product purchasing intentions , 2013 .

[58]  Shibin Sheng,et al.  Motivating purchase of private brands: Effects of store image, product signatureness, and quality variation , 2011 .

[59]  Dan T. Dunn,et al.  Understanding how technology paradoxes affect customer satisfaction with self‐service technology: The role of performance ambiguity and trust in technology , 2008 .

[60]  L. Stoel,et al.  Consumer e-shopping acceptance: Antecedents in a technology acceptance model , 2009 .

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

[62]  John Ingham,et al.  Why do people use information technology? A critical review of the technology acceptance model , 2003, Inf. Manag..

[63]  Xiang Li,et al.  China's “smart tourism destination” initiative: A taste of the service-dominant logic , 2013 .

[64]  Irene C. L. Ng The future of pricing and revenue models , 2010 .

[65]  W. Reinartz,et al.  Retailing Innovations in a Globalizing Retail Market Environment , 2011 .

[66]  David C. Yen,et al.  Determinants of users' intention to adopt wireless technology: An empirical study by integrating TTF with TAM , 2010, Comput. Hum. Behav..

[67]  H. Timmermans,et al.  What is smart for retailing , 2014 .

[68]  Luis A. Hernández Gómez,et al.  Smart Cities at the Forefront of the Future Internet , 2011, Future Internet Assembly.

[69]  E. Giménez,et al.  Decision-driven marketing , 2014 .

[70]  Vishanth Weerakkody,et al.  E-government adoption: A cultural comparison , 2008, Inf. Syst. Frontiers.

[71]  Vincent Dutot,et al.  Factors influencing Near Field Communication (NFC) adoption: An extended TAM approach , 2015 .

[72]  P. Dion,et al.  The influence of retailer reputation on store patronage , 2006 .

[73]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[74]  E. McKinney,et al.  Extending the Technology Acceptance Model Extending the Technology Acceptance Model and the Task and the Task-Technology Fit Model to Technology Fit Model to Consumer E Consumer E- -Commerce Commerce , 2004 .

[75]  Tao Zhou,et al.  Integrating TTF and UTAUT to explain mobile banking user adoption , 2010, Comput. Hum. Behav..

[76]  Florian Michahelles,et al.  An Architectural Approach Towards the Future Internet of Things , 2011, Architecting the Internet of Things.

[77]  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 .

[78]  JungKun Park,et al.  Consumer acceptance of a revolutionary technology-driven product: The role of adoption in the industrial design development , 2015 .

[79]  Heiner Evanschitzky,et al.  Consumer Trial, Continuous Use, and Economic Benefits of a Retail Service Innovation: The Case of the Personal Shopping Assistant , 2015 .

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