Is TripAdvisor still relevant? The influence of review credibility, review usefulness, and ease of use on consumers’ continuance intention

Recent figures show that users are discontinuing their usage of TripAdvisor, the leading user-generated content (UGC) platform in the tourism sector. Hence, it is relevant to study the factors that influence travelers’ continued use of TripAdvisor.,The authors have integrated constructs from the technology acceptance model, information systems (IS) continuance model and electronic word of mouth literature. They used PLS-SEM (smartPLS V.3.2.8) to test the hypotheses using data from 297 users of TripAdvisor recruited through Prolific.,Findings reveal that perceived ease of use, online consumer review (OCR) credibility and OCR usefulness have a positive impact on customer satisfaction, which ultimately leads to continuance intention of UGC platforms. Customer satisfaction mediates the effect of the independent variables on continuance intention.,Managers of UGC platforms (i.e. TripAdvisor) can benefit from the findings of this study. Specifically, they should improve the ease of use of their platforms by facilitating travelers’ information searches. Moreover, they should use signals to make credible and helpful content stand out from the crowd of reviews.,This is the first study that adopts the IS continuance model in the travel and tourism literature to research the factors influencing consumers’ continued use of travel-based UGC platforms. Moreover, the authors have extended this model by including new constructs that are particularly relevant to UGC platforms, such as performance heuristics and OCR credibility.

[1]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[2]  R. Oliver A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions , 1980 .

[3]  R. Oliver A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions , 1980 .

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

[5]  J. Cacioppo,et al.  Central and Peripheral Routes to Advertising Effectiveness: The Moderating Role of Involvement , 1983 .

[6]  Michael A. Kamins,et al.  Two-Sided versus One-Sided Celebrity Endorsements: The Impact on Advertising Effectiveness and Credibility , 1989 .

[7]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

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

[9]  K. B. Murray,et al.  The impact of services versus goods on consumers’ assessment of perceived risk and variability , 1990 .

[10]  K. B. Murray,et al.  The impact of services versus goods on consumers’ assessment of perceived risk and variability , 1990 .

[11]  V. Mitchell,et al.  Risk Perception and Reduction in the Purchase of Consumer Services , 1993 .

[12]  Wynne W. Chin The partial least squares approach for structural equation modeling. , 1998 .

[13]  Shelly Chaiken,et al.  The heuristic-systematic model in its broader context. , 1999 .

[14]  Detmar W. Straub,et al.  Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs , 1999, MIS Q..

[15]  Anol Bhattacherjee,et al.  Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..

[16]  Raafat George Saadé,et al.  The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model , 2005, Inf. Manag..

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

[18]  Shuk Ying Ho,et al.  Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective , 2005, Inf. Syst. Res..

[19]  Gerald L. Lohse,et al.  Designing marketplaces of the artificial with consumers in mind: Four approaches to understanding consumer behavior in electronic environments , 2006 .

[20]  Izak Benbasat,et al.  Quo vadis TAM? , 2007, J. Assoc. Inf. Syst..

[21]  Ingoo Han,et al.  The impact of Web quality and playfulness on user acceptance of online retailing , 2007, Inf. Manag..

[22]  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..

[23]  Ulrike Gretzel,et al.  Measuring Web Site Quality for Online Travel Agencies , 2007 .

[24]  Moez Limayem,et al.  How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance , 2007, MIS Q..

[25]  J. Dawes Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5-Point, 7-Point and 10-Point Scales , 2008 .

[26]  Marylène Gagné,et al.  Understanding e-learning continuance intention in the workplace: A self-determination theory perspective , 2008, Comput. Hum. Behav..

[27]  Kyung Hyan Yoo,et al.  Use and Impact of Online Travel Reviews , 2008, ENTER.

[28]  Matthew K. O. Lee,et al.  The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities , 2008, Internet Res..

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

[30]  Huaping Chen,et al.  Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of On-line Consumer Recommendations , 2009, Int. J. Electron. Commer..

[31]  Eleri Jones,et al.  Perceived Risk and Risk-Relievers in Online Travel Purchase Intentions , 2009 .

[32]  Mark Sanderson,et al.  A review of factors influencing user satisfaction in information retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[33]  Kevin Moore,et al.  PROCESS STUDIES OF TOURISTS’ DECISION-MAKING , 2010 .

[34]  Mark Sanderson,et al.  A review of factors influencing user satisfaction in information retrieval , 2010 .

[35]  J. Hair Multivariate data analysis : a global perspective , 2010 .

[36]  Chao-Min Chiu,et al.  In justice we trust: Exploring knowledge-sharing continuance intentions in virtual communities of practice , 2010, Comput. Hum. Behav..

[37]  David Schuff,et al.  What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com , 2010 .

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

[39]  Heeseok Lee,et al.  Understanding the role of an IT artifact in online service continuance: An extended perspective of user satisfaction , 2010, Comput. Hum. Behav..

[40]  David Schuff,et al.  What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com , 2010 .

[41]  Ohbyung Kwon,et al.  Intimacy, familiarity and continuance intention: An extended expectation-confirmation model in web-based services , 2011, Electron. Commer. Res. Appl..

[42]  Charles Dennis,et al.  Antecedents of continuance intentions towards e-shopping: the case of Saudi Arabia , 2011, J. Enterp. Inf. Manag..

[43]  Wei Chen,et al.  The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings , 2011, Comput. Hum. Behav..

[44]  Efraim Turban,et al.  What Drives Social Commerce: The Role of Social Support and Relationship Quality , 2011, Int. J. Electron. Commer..

[45]  B. Sparks,et al.  The impact of online reviews on hotel booking intentions and perception of trust. , 2011 .

[46]  Byoungsoo Kim,et al.  Understanding Antecedents of Continuance Intention in Social-Networking Services , 2011, Cyberpsychology Behav. Soc. Netw..

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

[48]  Panagiotis G. Ipeirotis,et al.  Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics , 2010, IEEE Transactions on Knowledge and Data Engineering.

[49]  Florian Zach,et al.  The role of geo-based technology in place experiences , 2012 .

[50]  Ya-Ping Chang,et al.  The role of perceived social capital and flow experience in building users' continuance intention to social networking sites in China , 2012, Comput. Hum. Behav..

[51]  David C. Yen,et al.  Factors influencing the continuance intention to the usage of Web 2.0: An empirical study , 2012, Comput. Hum. Behav..

[52]  Wen-Lung Shiau,et al.  Continuance intention of blog users: the impact of perceived enjoyment, habit, user involvement and blogging time , 2013, Behav. Inf. Technol..

[53]  A. Chong Mobile commerce usage activities: The roles of demographic and motivation variables , 2013 .

[54]  Marko Sarstedt,et al.  Editorial - Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance , 2013 .

[55]  Kexin Zhao,et al.  The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: An empirical investigation , 2013, Decis. Support Syst..

[56]  Julian K. Ayeh,et al.  Predicting the intention to use consumer-generated media for travel planning , 2013 .

[57]  X. Leung,et al.  How Motivation, Opportunity, and Ability Impact Travelers' Social Media Involvement and Revisit Intention , 2013 .

[58]  P. Patterson,et al.  The Roles of Habit, Self-Efficacy, and Satisfaction in Driving Continued Use of Self-Service Technologies , 2013 .

[59]  Ken Kwong-Kay Wong,et al.  Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS , 2013 .

[60]  C. Morosan Toward an integrated model of adoption of mobile phones for purchasing ancillary services in air travel , 2014 .

[61]  Raffaele Filieri,et al.  E-WOM and Accommodation , 2014 .

[62]  Weiguo Fan,et al.  Social Presence, Trust, and Social Commerce Purchase Intention: an Empirical Research , 2016, PACIS.

[63]  Jeung-tai Eddie Tang,et al.  Blog learning: effects of users' usefulness and efficiency towards continuance intention , 2014, Behav. Inf. Technol..

[64]  Joseph F. Hair,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .

[65]  M. Hancer,et al.  Shaping travelers’ attitude toward travel mobile applications , 2014 .

[66]  Karen L. Xie,et al.  The business value of online consumer reviews and management response to hotel performance. , 2014 .

[67]  Ned Kock,et al.  A Note on How to Conduct a Factor-Based PLS-SEM Analysis , 2015, Int. J. e Collab..

[68]  Heng-Li Yang,et al.  User continuance intention to use cloud storage service , 2015, Comput. Hum. Behav..

[69]  Samar Mouakket,et al.  Factors influencing continuance intention to use social network sites: The Facebook case , 2015, Comput. Hum. Behav..

[70]  Z. Mao,et al.  Goodbye maps, hello apps? Exploring the influential determinants of travel app adoption , 2015 .

[71]  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..

[72]  Raffaele Filieri What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM , 2015 .

[73]  Zhibin Lin,et al.  Airline passengers’ continuance intention towards online check-in services: The role of personal innovativeness and subjective knowledge , 2015 .

[74]  Juan Luis Nicolau,et al.  Asymmetric effects of online consumer reviews , 2015 .

[75]  Raffaele Filieri,et al.  Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth , 2015 .

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

[77]  Raffaele Filieri What makes an online consumer review trustworthy , 2016 .

[78]  Fevzi Okumus,et al.  What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience , 2016, Int. J. Inf. Manag..

[79]  Geoffrey S. Hubona,et al.  Using PLS path modeling in new technology research: updated guidelines , 2016, Ind. Manag. Data Syst..

[80]  A. Mattila,et al.  Online Reviews , 2016 .

[81]  Dimitrios Buhalis,et al.  The influence of e-word-of-mouth on hotel occupancy rate , 2016 .

[82]  Zhibin Lin,et al.  Factors driving young users' engagement with Facebook: Evidence from Brazil , 2016, Comput. Hum. Behav..

[83]  Fevzi Okumus,et al.  International Journal of Hospitality Management , 2022 .

[84]  Ahmet Bulent Ozturk,et al.  Customer acceptance of cashless payment systems in the hospitality industry , 2016 .

[85]  M. Schraefel,et al.  “Now that you mention it”: A survey experiment on information, inattention and online privacy , 2017 .

[86]  Stefan Palan,et al.  Prolific.ac—A subject pool for online experiments , 2017 .

[87]  T. Jung,et al.  Hotel Guests’ Social Media Acceptance in Luxury Hotels , 2017 .

[88]  Claudio Vitari,et al.  The Effect of Brand on the Impact of e-WOM on Hotels’ Financial Performance , 2017, Int. J. Electron. Commer..

[89]  Faizan Ali,et al.  An Assessment of the Use of Partial Least Squares Structural Equation Modeling (PLS-SEM) in Hospitality Research , 2017 .

[90]  Young Ju Joo,et al.  Students' expectation, satisfaction, and continuance intention to use digital textbooks , 2017, Comput. Hum. Behav..

[91]  Tun-Min Jai,et al.  The impact of green experience on customer satisfaction: evidence from TripAdvisor , 2017 .

[92]  B. Pan,et al.  A retrospective view of electronic word-of-mouth in hospitality and tourism management , 2017 .

[93]  Hyunji Kim,et al.  The Interrelations Between Social Class, Personal Relative Deprivation, and Prosociality , 2016, Social psychological and personality science.

[94]  Xiaohui Chen,et al.  Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model , 2017, Comput. Hum. Behav..

[95]  Fraser McLeay,et al.  Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services , 2018, Inf. Manag..

[96]  Hongxiu Li,et al.  Understanding the effects of gratifications on the continuance intention to use WeChat in China: A perspective on uses and gratifications , 2018, Comput. Hum. Behav..

[97]  Babu P. George,et al.  What determines tourist adoption of smartphone apps?: An analysis based on the UTAUT-2 framework , 2018 .

[98]  M. C. tom Dieck,et al.  Determinants of Hotel Social Media Continued Usage , 2018 .

[99]  Charles F. Hofacker,et al.  What makes information in online consumer reviews diagnostic over time? The role of review relevancy, factuality, currency, source credibility and ranking score , 2018, Comput. Hum. Behav..

[100]  Faizan Ali,et al.  Psychological factors influencing customers’ acceptance of smartphone diet apps when ordering food at restaurants , 2018, International Journal of Hospitality Management.

[101]  Uttam Chakraborty Perceived credibility of online hotel reviews and its impact on hotel booking intentions , 2019, International Journal of Contemporary Hospitality Management.

[102]  Emmanuel Mogaji,et al.  Influence of Offline Activities and Customer Value Creation on Online Travel Community Continuance Usage Intention , 2018, ENTER.

[103]  R. Law,et al.  Progression and development of information and communication technology research in hospitality and tourism , 2019, International Journal of Contemporary Hospitality Management.

[104]  Jiantong Zhang,et al.  Users' continued participation behavior in social Q&A communities: A motivation perspective , 2019, Comput. Hum. Behav..

[105]  R. Merli,et al.  Why should hotels go green? Insights from guests experience in green hotels , 2019, International Journal of Hospitality Management.

[106]  Jee-Won Kang,et al.  The role of personalization on continuance intention in food service mobile apps , 2019, International Journal of Contemporary Hospitality Management.

[107]  R. Leon Hotel’s online reviews and ratings: a cross-cultural approach , 2019, International Journal of Contemporary Hospitality Management.

[108]  Z. Ouyang,et al.  Understanding bike sharing use over time by employing extended technology continuance theory , 2019, Transportation Research Part A: Policy and Practice.

[109]  A. Lo,et al.  What makes hotel online reviews credible? , 2019, International Journal of Contemporary Hospitality Management.

[110]  Marcello M. Mariani,et al.  Online Review Helpfulness and Firms’ Financial Performance: An Empirical Study in a Service Industry , 2020, Int. J. Electron. Commer..

[111]  A. Islam,et al.  Point of adoption and beyond. Initial trust and mobile-payment continuation intention , 2020 .

[112]  Jiang Yun,et al.  I, Chatbot: Modeling the determinants of users' satisfaction and continuance intention of AI-powered service agents , 2020, Telematics Informatics.

[113]  F. Okumus,et al.  Understanding the importance that consumers attach to social media sharing (ISMS): Scale development and validation , 2020 .

[114]  Jessie Pallud,et al.  Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews , 2020 .

[115]  M. Garrido,et al.  The “ins” and “outs” of product and services marketing: The influence of consonant wanderings in consumer decision‐making , 2020 .

[116]  Elisabetta Raguseo,et al.  The impact of service attributes and category on eWOM helpfulness: An investigation of extremely negative and positive ratings using latent semantic analytics and regression analysis , 2021, Comput. Hum. Behav..

[117]  P. Hellyer Google reviews. , 2022, British dental journal.