A multi-method approach to examining consumer intentions to use smart retail technology

Abstract This study examines the antecedents and consequences of customers' intentions to use smart retail technology (SRT), specifically Smart Retail Carts. We propose that perceived novelty, perceived efficacy, perceived compatibility, and perceived risk of SRT determine consumers' intentions to use SRT, which, in turn, influences their shopping value through and interaction with this SRT. Survey responses from 338 actual shoppers with prior experience of SRT were used to test the research model. In addition to structural equation modeling (SEM), the Fuzzy-Set Qualitative Comparative Analysis (fsQCA) technique was used to analyze the data. SEM analysis enabled us to investigate and hypothesize relationships among the above factors, while fsQCA helped develop configurations of factors and find the appropriate target consumers of SRT. The findings posit perceived novelty, perceived efficacy, perceived compatibility, and perceived risk of SRT as antecedents to, and shopping value through SRT and interaction with SRT as consequences of, customers' intentions to use SRT. Moreover, the study found configurations of factors, such as perceived novelty and perceived compatibility, resulted in improved intention to use this form of SRT. The present study offers a better understanding of consumers' intentions to adopt SRT that may help managers to develop adoption strategies for successful implementation of SRT in-store.

[1]  Miltiadis D. Lytras,et al.  Mobile shopping apps adoption and perceived risks: A cross-country perspective utilizing the Unified Theory of Acceptance and Use of Technology , 2018, Comput. Hum. Behav..

[2]  Juho Lindman,et al.  Opportunities and Risks of Blockchain Technologies (Dagstuhl Seminar 17132) , 2017, Dagstuhl Reports.

[3]  K. Faqih Exploring the Influence of Perceived Risk and Internet Self-Efficacy on Consumer Online Shopping Intentions: Perspective of Technology Acceptance Model , 2013 .

[4]  Uthayasankar Sivarajah,et al.  Investigating the effects of smart technology on customer dynamics and customer experience , 2018, Comput. Hum. Behav..

[5]  Michail N. Giannakos,et al.  Fuzzy set analysis as a means to understand users of 21st-century learning systems: The case of mobile learning and reflections on learning analytics research , 2017, Comput. Hum. Behav..

[6]  A. Ahuvia,et al.  Some antecedents and outcomes of brand love , 2006 .

[7]  Hsin-Chieh Wu,et al.  Determinants of RFID adoption intention: Evidence from Taiwanese retail chains , 2010, Inf. Manag..

[8]  Lingling Gao,et al.  Understanding consumers' continuance intention towards mobile purchase: A theoretical framework and empirical study - A case of China , 2015, Comput. Hum. Behav..

[9]  Francis Kasekende,et al.  The mediation role of intention in knowledge sharing behavior , 2017 .

[10]  Jan L. Plass,et al.  Interactivity in multimedia learning: An integrated model , 2010, Comput. Hum. Behav..

[11]  Nguyen Dinh Tho,et al.  Can knowledge be transferred from business schools to business organizations through in-service training students? SEM and fsQCA findings , 2015 .

[12]  Mika Immonen,et al.  Self-service technologies in health-care: Exploring drivers for adoption , 2018, Comput. Hum. Behav..

[13]  Scott G. Dacko Enabling smart retail settings via mobile augmented reality shopping apps , 2017 .

[14]  L. Whitmarsh,et al.  Social barriers to the adoption of smart homes , 2013 .

[15]  Jane Klobas,et al.  How perceived security risk affects intention to use smart home devices: A reasoned action explanation , 2019, Comput. Secur..

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

[17]  S. Roy,et al.  Consumer-computer interaction and in-store smart technology (IST) in the retail industry: the role of motivation, opportunity, and ability , 2020 .

[18]  John M. Carroll,et al.  A scenario-based approach for projecting user requirements for wireless proximal community networks , 2014, Comput. Hum. Behav..

[19]  U. Cebeci,et al.  Exploring the determinants of intention to use self-checkout systems in super market chain and its application , 2020, Management Science Letters.

[20]  E. Carvajal-Trujillo,et al.  Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model , 2014 .

[21]  Ingrid Poncin,et al.  The impact of "e-atmospherics" on physical stores , 2014 .

[22]  Nikolaos Stylos,et al.  Generation Z consumers' expectations of interactions in smart retailing: A future agenda , 2017, Comput. Hum. Behav..

[23]  G. Mortimer,et al.  Examining the antecedents and consequences of perceived shopping value through smart retail technology , 2020, Journal of Retailing and Consumer Services.

[24]  S. Roy,et al.  The rise of smart consumers: role of smart servicescape and smart consumer experience co-creation , 2019, Journal of Marketing Management.

[25]  Botjan umak,et al.  The acceptance and use of interactive whiteboards among teachers , 2016 .

[26]  Eleonora Pantano,et al.  Exploring the forms of sociality mediated by innovative technologies in retail settings , 2017, Comput. Hum. Behav..

[27]  S. Lennon,et al.  Effects of reputation and website quality on online consumers' emotion, perceived risk and purchase intention , 2013 .

[28]  Patrick Mikalef,et al.  Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA , 2017 .

[29]  Ankit Kesharwani,et al.  The impact of trust and perceived risk on internet banking adoption in India : An extension of technology acceptance model , 2022 .

[30]  G. Odekerken-Schröder,et al.  Investments in Consumer Relationships: A Cross-Country and Cross-Industry Exploration , 2001 .

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

[32]  Satish Krishnan,et al.  Personality and espoused cultural differences in technostress creators , 2017, Comput. Hum. Behav..

[33]  Lincoln C. Wood,et al.  Consumers' Perceptions of Item-Level RFID Use in FMCG: A Balanced Perspective of Benefits and Risks , 2016, J. Glob. Inf. Manag..

[34]  C. Fornell,et al.  Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics , 1981 .

[35]  Michail N. Giannakos,et al.  Supporting school leadership decision making with holistic school analytics: Bridging the qualitative-quantitative divide using fuzzy-set qualitative comparative analysis , 2018, Comput. Hum. Behav..

[36]  Chao Wen,et al.  Transactional quality, relational quality, and consumer e-loyalty: Evidence from SEM and fsQCA , 2016, Int. J. Inf. Manag..

[37]  Chia-Chi Chang,et al.  Effects of individuals' locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies , 2014, Behav. Inf. Technol..

[38]  E. Pantano,et al.  Demand pull and technology push perspective in technology-based innovations for the points of sale: the retailers evaluation , 2014 .

[39]  Joseph Goodman,et al.  Crowdsourcing Consumer Research , 2017 .

[40]  Yong Liu,et al.  Understanding perceived risks in mobile payment acceptance , 2015, Ind. Manag. Data Syst..

[41]  Alan J. Dubinsky,et al.  A conceptual model of perceived customer value in e-commerce: A preliminary investigation , 2003 .

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

[43]  Xitong Guo,et al.  User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance , 2017, Informatics for health & social care.

[44]  Hsiu-Ping Yueh,et al.  Employees' acceptance of mobile technology in a workplace: An empirical study using SEM and fsQCA , 2016 .

[45]  A. Woodside Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory , 2013 .

[46]  Muhanna Muhanna,et al.  Virtual reality and the CAVE: Taxonomy, interaction challenges and research directions , 2015, J. King Saud Univ. Comput. Inf. Sci..

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

[48]  J. W. Hutchinson,et al.  The Influence of Unity and Prototypicality on Aesthetic Responses to New Product Designs , 1998 .

[49]  Ju-Young M. Kang,et al.  In-store mobile usage: Downloading and usage intention toward mobile location-based retail apps , 2015, Comput. Hum. Behav..

[50]  Lorraine Whitmarsh,et al.  The development of smart homes market in the UK , 2013 .

[51]  Michael D. Williams,et al.  Recipes for success: Conditions for knowledge transfer across open innovation ecosystems , 2019, Int. J. Inf. Manag..

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

[53]  Richard P. Bagozzi,et al.  The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift , 2007, J. Assoc. Inf. Syst..

[54]  D. Štreimikienė,et al.  A new model for customer purchase intention in e-commerce recommendation agents , 2018, Journal of International Studies.

[55]  Josep Crespo Hervás,et al.  Predicting future intentions of basketball spectators using SEM and fsQCA , 2016 .

[56]  Kun Chang Lee,et al.  Scenario-based management of individual creativity , 2015, Comput. Hum. Behav..

[57]  David W. Gerbing,et al.  An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment , 1988 .

[58]  E. Pantano,et al.  Engaging consumers on new integrated multichannel retail settings: challenges for retailers , 2015 .

[59]  Terry Ryan,et al.  The Role of Social Presence and Moderating Role of Computer Self-Efficacy in Predicting the Continuance Usage of E-Learning Systems , 2004, J. Inf. Syst. Educ..

[60]  S. Forsythe,et al.  Consumer patronage and risk perceptions in Internet shopping , 2003 .

[61]  M. Quaddus,et al.  Predictors of customer acceptance of and resistance to smart technologies in the retail sector , 2018 .

[62]  Michael Bourlakis,et al.  Preferences of smart shopping channels and their impact on perceived wellbeing and social inclusion , 2017, Comput. Hum. Behav..

[63]  Louis Raymond,et al.  Enabling innovation in the face of uncertainty through IT ambidexterity: A fuzzy set qualitative comparative analysis of industrial service SMEs , 2020, Int. J. Inf. Manag..

[64]  Hans-Werner Wahl,et al.  The role of internet self-efficacy, innovativeness and technology avoidance in breadth of internet use: Comparing older technology experts and non-experts , 2020, Comput. Hum. Behav..

[65]  J. Hulland,et al.  “Keep on Turkin’”? , 2018 .

[66]  Eleonora Pantano,et al.  Innovation drivers in retail industry , 2014, Int. J. Inf. Manag..

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

[68]  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 .

[69]  Timothy Teo,et al.  Unpacking teachers' acceptance of technology: Tests of measurement invariance and latent mean differences , 2014, Comput. Educ..

[70]  Kim Witte,et al.  Fear appeals and persuasion: A review and update of the Extended Parallel Process Model. , 2011 .

[71]  Judith C. Forney,et al.  The Moderating Role of Consumer Technology Anxiety in Mobile Shopping Adoption: Differential Effects of Facilitating Conditions and Social Influences , 2013 .

[72]  E. Pantano,et al.  To what extent luxury retailing can be smart? , 2018, Journal of Retailing and Consumer Services.

[73]  J. Ahmad,et al.  The effect of innovation and consumer related factors on consumer resistance to innovation , 2017 .

[74]  Jesse Chandler,et al.  Lie for a Dime , 2017 .

[75]  Tiago Oliveira,et al.  Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application , 2014, Int. J. Inf. Manag..

[76]  John G. Lynch,et al.  Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis , 2010 .

[77]  Yann Truong A cross-country study of consumer innovativeness and technological service innovation , 2013 .

[78]  M. K. Raja,et al.  A unified model of knowledge sharing behaviours: theoretical development and empirical test , 2012, Behav. Inf. Technol..

[79]  Vassilis Kostakos,et al.  Applying configurational analysis to IS behavioural research: a methodological alternative for modelling combinatorial complexities , 2017, Inf. Syst. J..

[80]  Yogesh Kumar Dwivedi,et al.  Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model , 2017, Information Systems Frontiers.

[81]  Princely Ifinedo,et al.  Determinants of students’ continuance intention to use blogs to learn: an empirical investigation , 2018, Behav. Inf. Technol..

[82]  S. Roy,et al.  Constituents and consequences of smart customer experience in retailing , 2017 .

[83]  Peer C. Fiss Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research , 2011 .