Predictors of customer acceptance of and resistance to smart technologies in the retail sector

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

[2]  Johannes Schöning,et al.  The path-to-purchase is paved with digital opportunities: An inventory of shopper-oriented retail technologies , 2017 .

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

[4]  Daniel Baier,et al.  Enhancing the online decision-making process by using augmented reality: A two country comparison of youth markets , 2017 .

[5]  Mathew B. Chylinski,et al.  Augmenting the eye of the beholder: exploring the strategic potential of augmented reality to enhance online service experiences , 2017, Journal of the Academy of Marketing Science.

[6]  Michael Scholz,et al.  A configuration-based recommender system for supporting e-commerce decisions , 2017, Eur. J. Oper. Res..

[7]  M. Mital,et al.  Adoption of Internet of Things in India: A test of competing models using a structured equation modeling approach , 2017, Technological Forecasting and Social Change.

[8]  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 .

[9]  Zied Mani,et al.  Drivers of consumers’ resistance to smart products , 2017 .

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

[11]  J. Inman,et al.  Shopper-Facing Retail Technology: A Retailer Adoption Decision Framework Incorporating Shopper Attitudes and Privacy Concerns , 2016 .

[12]  José L. Roldán,et al.  Prediction-oriented modeling in business research by means of PLS path modeling: Introduction to a JBR special section , 2016 .

[13]  Udo Wagner,et al.  Shoppers' acceptance and perceptions of electronic shelf labels , 2016 .

[14]  Tommi Laukkanen.  Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking , 2016 .

[15]  Christopher M. Schlick,et al.  Development and validation of a technology acceptance model for safety-enhancing, wearable locating systems , 2016, Behav. Inf. Technol..

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

[17]  S. Boon-itt.  Managing self-service technology service quality to enhance e-satisfaction , 2015 .

[18]  N. Kock.  Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach , 2015, Int. J. e Collab..

[19]  Kristina Heinonen,et al.  “Futurizing” smart service: implications for service researchers and managers , 2015 .

[20]  IpKin Anthony Wong,et al.  If you install it, will they use it? Understanding why hospitality customers take “technological pauses” from self-service technology , 2015 .

[21]  Z. Rahman,et al.  An alternative model of self-service retail technology adoption , 2015 .

[22]  Rosanna Garcia,et al.  Consumer resistance to innovation—a behavioral reasoning perspective , 2015 .

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

[24]  Dimitrios Buhalis,et al.  Smart technologies for personalized experiences: a case study in the hospitality domain , 2015, Electronic Markets.

[25]  Ananthanarayanan Parasuraman,et al.  An Updated and Streamlined Technology Readiness Index , 2015 .

[26]  Ruud T. Frambach,et al.  Mandatory use of technology-based self-service: does expertise help or hurt? , 2015 .

[27]  Sanda Renko,et al.  Perceived usefulness of innovative technology in retailing: Consumers׳ and retailers׳ point of view , 2014 .

[28]  S. Heidenreich,et al.  How to overcome pro-change Bias: : Incorporating passive and active innovation resistance in innovation decision models , 2014 .

[29]  Arch G. Woodside,et al.  Applying complexity theory to deepen service dominant logic: Configural analysis of customer experience-and-outcome assessments of professional services for personal transformations , 2014 .

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

[31]  A. Parasuraman,et al.  When the Recipe Is More Important Than the Ingredients , 2014 .

[32]  Jorge Ferreira da Silva,et al.  Impacts of technology readiness on emotions and cognition in Brazil , 2014 .

[33]  A. Kara,et al.  Supermarket self-checkout service quality, customer satisfaction, and loyalty: Empirical evidence from an emerging market , 2014 .

[34]  Marko Sarstedt,et al.  An assessment of the use of partial least squares structural equation modeling in marketing research , 2012 .

[35]  Udo Konradt,et al.  Development and validation of a formative and a reflective measure for the assessment of online store usability , 2012, Behav. Inf. Technol..

[36]  Fujun Lai,et al.  Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research , 2012 .

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

[38]  C. Veloutsou,et al.  Loyalty and or disloyalty to a search engine: the case of young Millennials , 2012 .

[39]  Barbara Vis,et al.  The Comparative Advantages of fsQCA and Regression Analysis for Moderately Large-N Analyses , 2012 .

[40]  Cees J. Gelderman,et al.  Choosing self-service technologies or interpersonal services—The impact of situational factors and technology-related attitudes , 2011 .

[41]  J. Lin,et al.  The role of technology readiness in self‐service technology acceptance , 2011 .

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

[43]  Jung-Kuei Hsieh,et al.  The challenge for multichannel services: Cross-channel free-riding behavior , 2011, Electron. Commer. Res. Appl..

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

[45]  Philippe Fortemps,et al.  ACUTA: A novel method for eliciting additive value functions on the basis of holistic preference statements , 2010, Eur. J. Oper. Res..

[46]  M. Sarstedt,et al.  Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies , 2010 .

[47]  Julio Jiménez,et al.  Adoption vs acceptance of e‐commerce: two different decisions , 2009 .

[48]  Kerk F. Kee,et al.  Is There Social Capital in a Social Network Site?: Facebook Use and College Students’ Life Satisfaction, Trust, and Participation 1 , 2009 .

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

[50]  Tommi Laukkanen,et al.  Consumer resistance to internet banking: postponers, opponents and rejectors , 2008 .

[51]  Thomas F. Stafford,et al.  Consumer acceptance of online auctions: An extension and revision of the TAM , 2008 .

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

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

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

[55]  E. Rogers.  A Prospective and Retrospective Look at the Diffusion Model , 2004, Journal of health communication.

[56]  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.

[57]  M. Lindell,et al.  Accounting for common method variance in cross-sectional research designs. , 2001, The Journal of applied psychology.

[58]  A. Parasuraman,et al.  Technology Readiness Index (Tri) , 2000 .

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

[60]  William Boulding,et al.  A consumer-side experimental examination of signaling theory: Do , 1993 .

[61]  F. Damanpour.  Organizational Innovation: A Meta-Analysis Of Effects Of Determinants and Moderators , 1991 .

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

[63]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[64]  A. Bandura.  Social Foundations of Thought and Action: A Social Cognitive Theory , 1985 .

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

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

[67]  Charles C. Ragin,et al.  Qualitative Comparative Analysis Using Fuzzy Sets (fsQCA) , 2008 .

[68]  S. Shavitt,et al.  The Use of Cues Depends on Goals: Store Reputation Affects Product Judgments When Social Identity Goals Are Salient , 2006 .

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

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

[71]  Joydeep Srivastava,et al.  Effect of Manufacturer Reputation, Retailer Reputation, and Product Warranty on Consumer Judgments of Product Quality: A Cue Diagnosticity Framework , 2001 .

[72]  Wynne W. Chin,et al.  Structural equation modeling analysis with small samples using partial least squares , 1999 .

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