Kim S, Maslowska E, Tamaddoni A, (2019), The paradox of (dis)trust in sponsorship disclosure: The characteristics and effects of sponsored online consumer reviews, Decision Support Systems, Vol. 116, pp. 114-124
Mots clés : Bouche à oreille digital, Avis des consommateurs en ligne, Parrainage, Persuasion, Attitude, Intention d’achat.
Résumé : Les avis de consommateurs en ligne sont devenus l’un des messages et moyen majeur de persuasion en termes de décision d’achat, créant une influence certaine sur le consommateur. Dans cette mesure, les spécialistes en marketing ont commencé à inciter les consommateurs à rédiger des avis avec pour objectif d’augmenter le volume d’avis positifs. Cependant, peu de recherches existent sur les caractéristiques de contenu et les effets des avis sponsorisés. Cette étude examine les différentes caractéristiques et effets des avis sponsorisés et organiques, ainsi que les mécanismes par lesquels les consommateurs reconnaissent et traitent ces deux types d’avis, en utilisant notamment des méthodes mixtes dans deux études. Les résultats de l’analyse d’exploration de texte suggèrent que les revues sponsorisées fournissent un contenu considéré comme plus élaboré et évaluatif. Cependant, ces avis sont perçus comme moins utiles que les avis organiques. Les résultats d’une expérience aléatoire suggèrent que la divulgation de parrainage augmente les soupçons sur les arrière-pensées de l’examinateur/récepteur et diminue ainsi les attitudes et intentions d’achat des consommateurs lorsqu’un examen est positif. Par contre, divulgation de parrainage ne nuit pas aux attitudes ou aux intentions d’achat lorsque l’avis est négatif.
Conclusion : Cette étude a permis de démontrer l’effet du parrainage dans un contexte d’avis en ligne. De cet article a ainsi été conclu que les avis dits « sponsorisés » et les avis rédigés par les consommateurs mais percevant une compensation en échange, telle que du parrainage, sont considérés par le récepteur comme biaisés et/ou malhonnêtes. Cependant l’étude prouve également que les critiques sponsorisés sont généralement plus élaborées, développées, objectives, complexes, positives et moins extrêmes que les critiques dites « organiques ». Cependant ces avis sont tout de même perçus comme les moins utiles, dû à la mention « parrainage » décrédibilisant l’avis en lui-même, qui n’est pas sans intérêt. Les auteurs concluent ainsi que le système de parrainage, ça divulgation, engendre une augmentation des soupçons et nuit donc sur l’intention d’achat d’un consommateur.
Sources :
[1] The Nielsen Company, Global Trust in Advertising, Retrieved from, 2015. http:// www.nielsen.com/content/dam/corporate/us/en/reports-downloads/2015- reports/global-trust-in-advertising-report-sept-2015.pdf. [2] E. Dichter, How word-of-mouth advertising works, Harvard Business Review 44 (6) (1966) 147–166. [3] P. Chatterjee, Drivers of new product recommending and referral behaviour on social network sites, International Journal of Advertising 30 (1) (2011) 77–101. [4] P. Hugstad, J.W. Taylor, G.D. Bruce, The effects of social class and perceived risk on consumer, The Journal of Services Marketing 1 (1) (1987) 47–52. [5] I. Simonson, Mission (largely) accomplished: what’s next for consumer bdt-jdm researchers? Journal of Marketing Behavior 1 (1) (2015) 9–35, https://doi.org/10. 1561/107.00000001. [6] L.J. Abendroth, J.E. Heyman, Honesty is the best policy: the effects of disclosure in word-of-mouth marketing, Journal of Marketing Communications 19 (4) (2013) 245–257, https://doi.org/10.1080/13527266.2011.631567. [7] M. Petrescu, K. O’Leary, D. Goldring, S. Ben Mrad, Incentivized reviews: promising the moon for a few stars, Journal of Retailing and Consumer Services 41 (2018) 288–295, https://doi.org/10.1016/j.jretconser.2017.04.005. [8] W. Zhou, W. Duan, An empirical study of how third-party websites influence the feedback mechanism between online word-of-mouth and retail sales, Decision Support Systems 76 (2015) 14–23, https://doi.org/10.1016/j.dss.2015.03.010. [9] J. Kennett, S. Matthews, What’s the buzz? Undercover marketing and the corruption of friendship, Journal of Applied Philosophy 25 (1) (2008) 2–18, https://doi.org/ 10.1111/j.1468-5930.2008.00391.x. [10] K.K. Coker, D.S. Smith, S.A. Altobello, Buzzing with disclosure of social shopping rewards, Journal of Research in Interactive Marketing 9 (3) (2015) 170–189, https://doi.org/10.1108/jrim-06-2014-0030. [11] WOMMA, Word of Mouth Marketing Association Guide to Best Practices for Transparency and Disclosure in Gigital, Social, & Mobile Marketing, Retrieved from, http://www.womma.org/wp-content/uploads/2015/08/WOMMA-SocialMedia-Disclosure-Guidelines-2013.pdf. [12] R.B. Cialdini, 1st Collins business essentials (Ed.), Influence: The Psychology of Persuasion, Collins, New York: New York, 2007. [13] J. Breitsohl, M. Khammash, G. Griffiths, E-business complaint management: perceptions and perspectives of online credibility, Journal of Enterprise Information Management 23 (5) (2010) 653–660, https://doi.org/10.1108/ 17410391011083083. [14] E.R. Spangenberg, J.L. Giese, An exploratory study of word-of-mouth communication in a hierarchy of effects context, Communication Research Reports 14 (1) (1997) 88–96. [15] T. Hennig-Thurau, K.P. Gwinner, G. Walsh, D.D. Gremler, Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing 18 (1) (2004) 38–52, https://doi.org/10.1002/dir.10073. [16] K. Bawa, R. Shoemaker, The effects of free sample promotions on incremental brand sales, Marketing Science 23 (3) (2004) 345–363, https://doi.org/10.1287/mksc. 1030.0052. [17] B.P. Buunk, W.B. Schaufeli, Reciprocity in interpersonal relationships: an evolutionary perspective on its importance for health and well-being, European Review of Social Psychology 10 (1) (1999) 259–291, https://doi.org/10.1080/ 14792779943000080. [18] A.W. Gouldner, The norm of reciprocity: a preliminary statement, American Sociological Review 25 (2) (1960) 161–178, https://doi.org/10.2307/2092623. [19] N. Hu, J. Zhang, P.A. Pavlou, Overcoming the J-shaped distribution of product reviews, Communications of the ACM 52 (10) (2009) 144–147, https://doi.org/10. 1145/1562764.1562800. [20] G. Askalidis, S.J. Kim, E.C. Malthouse, Understanding and overcoming biases in online review systems, Decision Support Systems 97 (2017) 23–30, https://doi.org/ 10.1016/j.dss.2017.03.002. [21] V. Liljander, J. Gummerus, M. Söderlund, Young consumers’ responses to suspected covert and overt blog marketing, Internet Research 25 (4) (2015) 610–632, https:// doi.org/10.1108/IntR-02-2014-0041. [22] W.J. Carl, The role of disclosure in organized word-of-mouth marketing programs, Journal of Marketing Communications 14 (3) (2008) 225–241, https://doi.org/10. 1080/13527260701833839. [23] M.A. Tuk, P.W.J. Verlegh, A. Smidts, D.H.J. Wigboldus, Interpersonal relationships moderate the effect of faces on person judgments, European Journal of Social Psychology 39 (5) (2009) 757–767, https://doi.org/10.1002/ejsp.576. [24] J. Colliander, Socially acceptable? Exploring consumer responses to marketing in social media, Ph.D. Stockholm School of Economics, Stockholm, Sweden, 2012. [25] J. Colliander, S. Erlandsson, The blog and the bountiful: exploring the effects of disguised product placement on blogs that are revealed by a third party, Journal of Marketing Communications 21 (2) (2015) 110–124, https://doi.org/10.1080/
[26] M.C. Campbell, G.S. Mohr, P.W.J. Verlegh, Can disclosures lead consumers to resist covert persuasion? The important roles of disclosure timing and type of response, Journal of Consumer Psychology 23 (4) (2013) 483–495, https://doi.org/10.1016/ j.jcps.2012.10.012. [27] C. Du Plessis, A.T. Stephen, Y. Bart, D. Goncalves, When in Doubt, Elaborate? How Elaboration on Uncertainty Influences the Persuasiveness of Consumer-generated Product Reviews When Reviewers are Incentivized, Saïd Business School WP, 2016 Retrieved from http://ssrn.com/abstract=2821641. [28] M. Friestad, P. Wright, The persuasion knowledge model: how people cope with persuasion attempts, Journal of Consumer Research 21 (1) (1994) 1–31, https:// doi.org/10.1086/209380. [29] Y. Hwang, S.-H. Jeong, “This is a sponsored blog post, but all opinions are my own”: the effects of sponsorship disclosure on responses to sponsored blog posts, Computers in Human Behavior 62 (2016) 528–535, https://doi.org/10.1016/j.chb. 2016.04.026. [30] S.C. Boerman, E.A. van Reijmersdal, P.C. Neijens, Sponsorship disclosure: effects of duration on persuasion knowledge and brand responses, Journal of Communication 62 (6) (2012) 1047–1064, https://doi.org/10.1111/j.1460-2466.2012.01677.x. [31] B.W. Wojdynski, N.J. Evans, Going native: effects of disclosure position and language on the recognition and evaluation of online native advertising, Journal of Advertising 45 (2) (2016) 157–168, https://doi.org/10.1080/00913367.2015. 1115380. [32] M.C. Campbell, A. Kirmani, Consumers’ use of persuasion knowledge: the effects of accessibility and cognitive capacity on perceptions of an influence agent, Journal of Consumer Research 27 (1) (2000) 69–83, https://doi.org/10.1086/314309. [33] K.J. Main, D.W. Dahl, P.R. Darke, Deliberative and automatic bases of suspicion: empirical evidence of the sinister attribution error, Journal of Consumer Psychology 17 (1) (2007) 59–69, https://doi.org/10.1207/s15327663jcp1701_9. [34] H.H. Kelley, Causal Schemata and the Attribution Process, General Learning Press, Morristown, NJ, 1972. [35] Z.L. Tormala, P. Briñol, R.E. Petty, When credibility attacks: the reverse impact of source credibility on persuasion, Journal of Experimental Social Psychology 42 (5) (2006) 684–691, https://doi.org/10.1016/j.jesp.2005.10.005. [36] R.E. Petty, P. Briñol, Z.L. Tormala, Thought confidence as a determinant of persuasion: the self-validation hypothesis, Journal of Personality and Social Psychology 82 (5) (2002) 722–741, https://doi.org/10.1037/0022-3514.82.5.722. [37] V.F. Ordenes, S. Ludwig, K. De Ruyter, D. Grewal, M. Wetzels, Unveiling what is written in the stars: analyzing explicit, implicit, and discourse patterns of sentiment in social media, Journal of Consumer Research 43 (6) (2017) 875–894. [38] S. Ludwig, K. de Ruyter, M. Friedman, E.C. Brüggen, M. Wetzels, G. Pfann, More than words: the influence of affective content and linguistic style matches in online reviews on conversion rates, Journal of Marketing 77 (1) (2013) 87–103, https:// doi.org/10.1509/jm.11.0560. [39] J.W. Pennebaker, R.J. Booth, R.L. Boyd, M.E. Francis, Linguistic Inquiry and Word Count: LIWC2015, Pennebaker Conglomerates, Austin, TX, 2015. [40] N.T. Bendle, P. Farris, P.E. Pfeifer, D.J. Reibstein, Marketing Metrics: The Manager’s Guide to Measuring Marketing Performance, , Incorporated, 2016. [41] N. Hu, I. Bose, N.S. Koh, L. Liu, Manipulation of online reviews: an analysis of ratings, readability, and sentiments, Decision Support Systems 52 (3) (2012) 674–684, https://doi.org/10.1016/j.dss.2011.11.002. [42] T.L. Harris, R.E. Hodges, The Literacy Dictionary: The Vocabulary of Reading and Writing, (1995). [43] S. Banerjee, A.Y.K. Chua, J.-J. Kim, Don’t be deceived: using linguistic analysis to learn how to discern online review authenticity, Journal of the Association for Information Science and Technology 68 (6) (2017) 1525–1538, https://doi.org/10. 1002/asi.23784. [44] A.Y.K. Chua, S. Banerjee, Analyzing review efficacy on Amazon.com: does the rich grow richer? Computers in Human Behavior 75 (2017) 501–509, https://doi.org/ 10.1016/j.chb.2017.05.047. [45] R. Gunning, The fog index after twenty years, Journal of Business Communication 6 (2) (1969) 3–13, https://doi.org/10.1177/002194366900600202. [46] R. Senter, E.A. Smith, Automated Readability Index, (1967). [47] M. Coleman, T.L. Liau, A computer readability formula designed for machine scoring, Journal of Applied Psychology 60 (2) (1975) 283–284, https://doi.org/10. 1037/h0076540. [48] N. Korfiatis, E. García-Bariocanal, S. Sánchez-Alonso, Evaluating content quality and helpfulness of online product reviews: the interplay of review helpfulness vs. review content, Electronic Commerce Research and Applications 11 (3) (2012) 205–217, https://doi.org/10.1016/j.elerap.2011.10.003. [49] S. Park, J.L. Nicolau, Asymmetric effects of online consumer reviews, Annals of Tourism Research 50 (2015) 67–83, https://doi.org/10.1016/j.annals.2014.10. 007. [50] S.M. Mudambi, D. Schuff, What Makes a Helpful Review? (2010) (A study of customer reviews on Amazon. com). [51] L.M. Willemsen, P.C. Neijens, F. Bronner, J.A. de Ridder, ‘Highly recommended!’ the content characteristics and perceived usefulness of online consumer reviews, Journal of Computer-Mediated Communication 17 (1) (2011) 19–38, https://doi. org/10.1111/j.1083-6101.2011.01551.x. [52] P.B. Goes, M. Lin, Au Yeung, C.-M., “Popularity effect” in user-generated content: evidence from online product reviews, Information Systems Research 25 (2) (2014) 222–238. [53] Q. Cao, W. Duan, Q. Gan, Exploring determinants of voting for the “helpfulness” of online user reviews: a text mining approach, Decision Support Systems 50 (2) (2011) 511–521, https://doi.org/10.1016/j.dss.2010.11.009. [54] S.-M. Kim, P. Pantel, T. Chklovski, M. Pennacchiotti, Automatically assessing
[55] N.J. Salkind, Encyclopedia of Research Design, (2010). [56] B. Derrick, D. Toher, P. White, Why Welch’s test is type I error robust, The Quantitative Methods in Psychology 12 (1) (2016) 30–38. [57] S. Brady, M. Lerigo-Jones, An examinination into consumer attitudes towards sponsored online content from social media influencers, Journal of Research Studies in Business and Management (2017) 3. [58] J. Wang, A. Ghose, P. Ipeirotis, Bonus, disclosure, and choice: what motivates the creation of high-quality paid reviews? Paper Presented at the International Conference on Information Systems (ICIS), 2012. [59] S.C. Boerman, L.M. Willemsen, E.P. Van Der Aa, “This post is sponsored”: effects of sponsorship disclosure on persuasion knowledge and electronic word of mouth in the context of Facebook, Journal of Interactive Marketing 38 (2017) 82–92, https:// doi.org/10.1016/j.intmar.2016.12.002. [60] P.W.J. Verlegh, G. Ryu, M.A. Tuk, L. Feick, Receiver responses to rewarded referrals: the motive inferences framework, Journal of the Academy of Marketing Science 41 (6) (2013) 669–682, https://doi.org/10.1007/s11747-013-0327-8. [61] G. Reeder, Mindreading: judgments about intentionality and motives in dispositional inference, Psychological Inquiry 20 (1) (2009) 1–18, https://doi.org/10. 1080/10478400802615744. [62] H. Hong, D. Xu, G.A. Wang, W. Fan, Understanding the determinants of online review helpfulness: a meta-analytic investigation, Decision Support Systems (2017), https://doi.org/10.1016/j.dss.2017.06.007. [63] S. Banerjee, S. Bhattacharyya, I. Bose, Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business, Decision Support Systems 96 (2017) 17–26, https://doi.org/10.1016/j.dss.2017.01.006. [64] Y.-S. Lim, B. Van Der Heide, Evaluating the wisdom of strangers: the perceived credibility of online consumer reviews on yelp, Journal of Computer-Mediated Communication 20 (1) (2015) 67–82, https://doi.org/10.1111/jcc4.12093. [65] L. Bergkvist, J.R. Rossiter, Tailor-made single-item measures of doubly concrete constructs, International Journal of Advertising 28 (4) (2009) 607–621, https://doi. org/10.2501/S0265048709200783. [66] T.E. Decarlo, R.N. Laczniak, T.W. Leigh, Selling financial services: the effect of consumer product knowledge and salesperson commission on consumer suspicion and intentions, Journal of the Academy of Marketing Science 41 (4) (2013) 418–435, https://doi.org/10.1007/s11747-012-0319-0. [67] R.D. Ahuja, T.A. Michels, M.M. Walker, M. Weissbuch, Teen perceptions of disclosure in buzz marketing, Journal of Consumer Marketing 24 (3) (2007) 151–159, https://doi.org/10.1108/07363760710746157. [68] R.B. Cialdini, Influence: How and Why People Agree to Things, 1st ed., Morrow, New York, 1984. [69] D.S. Sundaram, K. Mitra, C. Webster, Word-of-mouth communications: a motivational analysis, Advances in Consumer Research 25 (1) (1998) 527–531. [70] M.R. Forehand, S. Grier, When is honesty the best policy? The effect of stated company intent on consumer skepticism, Journal of Consumer Psychology 13 (3) (2003) 349–356, https://doi.org/10.1207/s15327663jcp1303_15. [71] E. Nekmat, K.K. Gower, Effects of disclosure and message valence in online word-ofmouth (ewom) communication: implications for integrated marketing communication, International Journal of Integrated Marketing Communications 4 (1) (2012) 85–98.
[72] S.N. Ahmad, M. Laroche, Analyzing electronic word of mouth: a social commerce construct, International Journal of Information Management 37 (3) (2017) 202–213, https://doi.org/10.1016/j.ijinfomgt.2016.08.004. [73] S.N. Ahmad, Uncovering the paths to helpful reviews using fuzzy-set qualitative comparative analysis, Journal of Marketing Analytics 5 (2) (2017) 47–56, https:// doi.org/10.1057/s41270-017-0015-5. [74] C.T. Carr, R.A. Hayes, The effect of disclosure of third-party influence on an opinion leader’s credibility and electronic word of mouth in two-step flow, Journal of Interactive Advertising 14 (1) (2014) 38–50, https://doi.org/10.1080/15252019. 2014.909296. [75] Y. Trope, N. Liberman, Construal-level theory of psychological distance, Psychological Review 117 (2) (2010) 440–463, https://doi.org/10.1037/ a0018963. [76] Z. Liu, S. Park, What makes a useful online review? Implication for travel product websites, Tourism Management 47 (2015) 140–151, https://doi.org/10.1016/j. tourman.2014.09.020. [77] S. Perez, Amazon Bans Incentized Reviews Tied to Free or Discounted Products, Retrieved from, Oct 3, 2016. https://techcrunch.com/2016/10/03/amazon-bansincentivized-reviews-tied-to-free-or-discounted-products/. [78] D.J. Bosman, C. Boshoff, G.-J. van Rooyen, The review credibility of electronic word-of-mouth communication on e-commerce platforms, Management Dynamics 22 (3) (2013) 29–44. [79] P.M. Herr, F.R. Kardes, J. Kim, Effects of word-of-mouth and product-attribute information on persuasion: an accessibility-diagnosticity perspective, Journal of Consumer Research 17 (4) (1991) 454–462, https://doi.org/10.1086/208570.
Al-Natour S, Turetken O, (2020), A comparative assessment of sentiment analysis and star ratings for consumer reviews, International Journal of Information Management, Volume 54, October 2020, 102132
Mots clés : Analyse des sentiments, Bouche à oreille, Avis des consommateurs, Apprentissage automatique, Évaluation comparative
Résumé : Le bouche à oreille électronique est important et abondant dans les domaines de consommation. Les consommateurs et les fournisseurs de produits / services ont besoin d’aide pour comprendre et naviguer dans les espaces d’informations qui en résultent, qui sont vastes et dynamiques. Le ton général ou la polarité des avis, blogs ou tweets fournit une telle aide. Dans cet article, nous explorons la viabilité de l’analyse automatique des sentiments pour évaluer la polarité d’un produit ou d’un examen de service. Pour ce faire, nous examinons le potentiel des principales approches de l’analyse des sentiments, ainsi que les notes en étoiles, pour capturer le véritable sentiment d’un avis
Grandes lignes :
L’analyse des sentiments peut être utilisée pour déterminer le ton général d’un avis de consommateur.
Nous comparons la sortie de plusieurs outils d’analyse des sentiments entre eux et avec les notes en étoiles.
Les résultats montrent que l’analyse des sentiments peut remplacer, et surpasse souvent, les notes.
Facteurs contextuels, tels que le type de produit, une précision d’analyse des sentiments modérée.
Agarwal et al., 2011A. Agarwal, B. Xie, I. Vovsha, O. Rambow, R.J. PassonneauSentiment analysis of twitter dataProceedings of the Workshop on Language in Social Media (LSM 2011) (2011), pp. 30-38
Alaei et al., 2019A.R. Alaei, S. Becken, B. StanticSentiment analysis in tourism: Capitalizing on big dataJournal of Travel Research, 58 (2) (2019), pp. 175-191
Annett and Kondrak, 2008M. Annett, G. KondrakA Comparison of Sentiment Analysis Techniques: Polarizing Movie Blogs, Paper Presented at the Conference of the Canadian Society for Computational Studies of Intelligence (2008)
Appel et al., 2016O. Appel, F. Chiclana, J. Carter, H. FujitaA hybrid approach to the sentiment analysis problem at the sentence levelKnowledge-Based Systems, 108 (2016), pp. 110-124
Bulbul et al., 2014C. Bulbul, N. Gross, S. Shin, J. KatzWhen the path to purchase becomes the path to purposeThink with google (2014
Chang et al., 2017Y.-C. Chang, C.-H. Ku, C.-H. ChenSocial media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisorInternational Journal of Information Management (2017)
Chatterjee, 2019S. ChatterjeeExplaining customer ratings and recommendations by combining qualitative and quantitative user generated contentsDecision Support Systems, 119 (2019), pp. 14-22
Chen and Xie, 2008Y. Chen, J. XieOnline consumer review: Word-of-mouth as a new element of marketing communication mixManagement Science, 54 (3) (2008), pp. 477-491
Cheung and Lee, 2012C.M. Cheung, M.K. LeeWhat drives consumers to spread electronic word of mouth in online consumer-opinion platformsDecision Support Systems, 53 (1) (2012), pp. 218-225
Chopra et al., 2016D. Chopra, N. Joshi, I. MathurMastering natural language processing with pythonPackt Publishing Ltd. (2016)
Cicchetti, 1994D.V. CicchettiGuidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychologyPsychological Assessment, 6 (4) (1994), p. 284
Dang et al., 2009Y. Dang, Y. Zhang, H. ChenA lexicon-enhanced method for sentiment classification: An experiment on online product reviewsIEEE Intelligent Systems, 25 (4) (2009), pp. 46-53
Dang et al., 2010Y. Dang, Y. Zhang, H. ChenA lexicon-enhanced method for sentiment classification: An experiment on online product reviewsIEEE Intelligent Systems, 25 (4) (2010), pp. 46-53
De Maeyer, 2012P. De MaeyerImpact of online consumer reviews on sales and price strategies: A review and directions for future researchJournal of Product and Brand Management, 21 (2) (2012), pp. 132-139
Feldman, 2013R. FeldmanTechniques and applications for sentiment analysisCommunications of the ACM, 56 (4) (2013), pp. 82-89
Gao et al., 2015S. Gao, J. Hao, Y. FuThe Application and Comparison of Web Services for Sentiment Analysis in Tourism, Paper Presented at the 2015 12th International Conference on Service Systems and Service Management (ICSSSM) (2015)
Ghasemaghaei et al., 2018M. Ghasemaghaei, S.P. Eslami, K. Deal, K. HassaneinReviews’ length and sentiment as correlates of online reviews’ ratingsInternet Research, 28 (3) (2018), pp. 544-563
Giatsoglou et al., 2017M. Giatsoglou, M.G. Vozalis, K. Diamantaras, A. Vakali, G. Sarigiannidis, K.C. ChatzisavvasSentiment analysis leveraging emotions and word embeddingsExpert Systems with Applications, 69 (2017), pp. 214-224
Greco and Polli, 2019F. Greco, A. PolliEmotional text mining: Customer profiling in brand managementInternational Journal of Information Management (2019)
Griffin and Gonzalez, 1995D. Griffin, R. GonzalezCorrelational analysis of dyad-level data in the exchangeable casePsychological Bulletin, 118 (3) (1995), p. 430
Hasan et al., 2018A. Hasan, S. Moin, A. Karim, S. ShamshirbandMachine learning-based sentiment analysis for twitter accountsMathematical and Computational Applications, 23 (1) (2018), p. 11
Hu and Chen, 2016Y.-H. Hu, K. ChenPredicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratingsInternational Journal of Information Management, 36 (6) (2016), pp. 929-944
Hutto and Gilbert, 2014C.J. Hutto, E. GilbertVader: A Parsimonious Rule-Based Model for Sentiment Analysis of Social media Text, Paper Presented at the Eighth International AAAI Conference on Weblogs and Social media (2014)
Jeong et al., 2017B. Jeong, J. Yoon, J.-M. LeeSocial media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysisInternational Journal of Information Management, 48 (2017), pp. 280-290
Kirilenko et al., 2018A.P. Kirilenko, S.O. Stepchenkova, H. Kim, X. LiAutomated sentiment analysis in tourism: Comparison of approachesJournal of Travel Research, 57 (8) (2018), pp. 1012-1025
Koo and Li, 2016T.K. Koo, M.Y. LiA guideline of selecting and reporting intraclass correlation coefficients for reliability researchJournal of Chiropractic Medicine, 15 (2) (2016), pp. 155-163
Kordzadeh, 2019N. KordzadehInvestigating bias in the online physician reviews published on healthcare organizations’ websitesDecision Support Systems, 118 (2019), pp. 70-82
Lak and Turetken, 2014P. Lak, O. TuretkenStar ratings versus sentiment analysis–a comparison of explicit and implicit measures of opinions2014 47th Hawaii International Conference on System Sciences, IEEE (2014), pp. 796-805
Lerner and Tiedens, 2006J.S. Lerner, L.Z. TiedensPortrait of the angry decision maker: How appraisal tendencies shape anger’s influence on cognitionJournal of Behavioral Decision Making, 19 (2) (2006), pp. 115-137
Liu, 2010B. LiuSentiment analysis and subjectivityHandbook of Natural Language Processing, 2 (2010) (2010), pp. 627-666
Liu, 2012B. LiuSentiment analysis and opinion miningSynthesis Lectures on Human Language Technologies, 5 (1) (2012), pp. 1-167
Maqsood et al., 2020H. Maqsood, I. Mehmood, M. Maqsood, M. Yasir, S. Afzal, F. Aadil, M. Selim, K. MuhammadA local and global event sentiment based efficient stock exchange forecasting using deep learningInternational Journal of Information Management, 50 (2020), pp. 432-451
Mills and Law, 2004J.E. Mills, R. LawHandbook of consumer behavior, tourism, and the internetPsychology Press (2004)
Moraes et al., 2013R. Moraes, J.F. Valiati, W.P.G. NetoDocument-level sentiment classification: An empirical comparison between SVM and ANNExpert Systems with Applications, 40 (2) (2013), pp. 621-633
Mudambi and Schuff, 2010S.M. Mudambi, D. SchuffWhat makes a helpful review? A study of customer reviews on Amazon. comMIS Quarterly, 34 (1) (2010), pp. 185-200CrossRef
Nasukawa and Yi, 2003T. Nasukawa, J. YiSentiment Analysis: Capturing Favorability Using Natural Language Processing, Paper Presented at the Proceedings of the 2nd International Conference on Knowledge Capture (2003)
Nelson, 1970P. NelsonInformation and consumer behaviorThe Journal of Political Economy, 78 (2) (1970), pp. 311-329
Nielsen, 2015M.A. NielsenNeural networks and deep learning, Vol. 2018, Determination press, San Francisco, CA, USA (2015)Pang and Lee, 2005B. Pang, L. LeeSeeing stars: Exploiting class relationships for sentiment categorization with respect to rating scalesProceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics (2005), pp. 115-124
Pavlou and Dimoka, 2006P.A. Pavlou, A. DimokaThe nature and role of feedback text comments in online marketplaces: Implications for trust building, price premiums, and seller differentiationInformation Systems Research, 17 (4) (2006), pp. 392-414
Payne et al., 1993J.W. Payne, J.R. Bettman, E.J. JohnsonThe adaptive decision makerCambridge University Press (1993)
Philander and Zhong, 2016K. Philander, Y. ZhongTwitter sentiment analysis: Capturing sentiment from integrated resort tweetsInternational Journal of Hospitality Management, 55 (2016) (2016), pp. 16-24
Pirolli, 2007P. PirolliInformation foraging theory: Adaptive interaction with informationOxford University Press (2007)Google Sch
olarPozzi et al., 2016F.A. Pozzi, E. Fersini, E. Messina, B. LiuSentiment analysis in social networksMorgan Kaufmann (2016)
Prabowo and Thelwall, 2009R. Prabowo, M. ThelwallSentiment analysis: A combined approachJournal of Informetrics, 3 (2) (2009), pp. 143-157
Pradhan et al., 2016V.M. Pradhan, J. Vala, P. BalaniA survey on Sentiment Analysis Algorithms for opinion miningInternational Journal of Computer Applications, 133 (9) (2016), pp. 7-11
Qiu et al., 2012L. Qiu, J. Pang, K.H. LimEffects of conflicting aggregated rating on eWOM review credibility and diagnosticity: The moderating role of review valenceDecision Support Systems, 54 (1) (2012), pp. 631-643
Ragini et al., 2018J.R. Ragini, P.R. Anand, V. BhaskarBig data analytics for disaster response and recovery through sentiment analysisInternational Journal of Information Management, 42 (2018), pp. 13-24
Rathore and Ilavarasan, 2020A.K. Rathore, P.V. IlavarasanPre-and post-launch emotions in new product development: Insights from twitter analytics of three productsInternational Journal of Information Management, 50 (2020), pp. 111-127
Ribeiro et al., 2016F.N. Ribeiro, M. Araújo, P. Gonçalves, M.A. Gonçalves, F. BenevenutoSentibench-a benchmark comparison of state-of-the-practice sentiment analysis methodsEPJ Data Science, 5 (1) (2016), p. 23
Roussinov and Turetken, 2009D. Roussinov, O. TuretkenExploring models for semantic category verificationInformation Systems, 34 (8) (2009), pp. 753-765
Saif et al., 2016H. Saif, Y. He, M. Fernandez, H. AlaniContextual semantics for sentiment analysis of TwitterInformation Processing & Management, 52 (1) (2016), pp. 5-19
Schuckert et al., 2015M. Schuckert, X. Liu, R. LawHospitality and tourism online reviews: Recent trends and future directionsJournal of Travel & Tourism Marketing, 32 (5) (2015), pp. 608-62
Schumaker et al., 2012R.P. Schumaker, Y. Zhang, C.-N. Huang, H. ChenEvaluating sentiment in financial news articlesDecision Support Systems, 53 (3) (2012), pp. 458-464
Taboada et al., 2011M. Taboada, J. Brooke, M. Tofiloski, K. Voll, M. StedeLexicon-based methods for sentiment analysisComputational Linguistics, 37 (2) (2011), pp. 267-307
Thavasimani and Missier, 2016P. Thavasimani, P. MissierFacilitating Reproducible Research by Investigating Computational Metadata, Paper Presented at the 2016 IEEE International Conference on Big Data (Big Data) (2016)
Valdivia et al., 2017A. Valdivia, M.V. Luzón, F. HerreraSentiment analysis in tripadvisorIEEE Intelligent Systems, 32 (4) (2017), pp. 72-77
Vijayarani and Janani, 2016S. Vijayarani, R. JananiText mining: Open source tokenization tools-an analysisAdvanced Computational Intelligence: An International Journal (ACII), 3 (1) (2016), pp. 37-47
Wang et al., 2015Y. Wang, J. Yuan, J. LuoAmerica Tweets China: A Fine-Grained Analysis of the State and Individual Characteristics Regarding Attitudes Towards China, Paper Presented at the 2015 IEEE International Conference on Big Data (Big Data). (2015)
Wilson et al., 2005T. Wilson, J. Wiebe, P. HoffmannRecognizing contextual polarity in phrase-level sentiment analysisProceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (2005), pp. 347-354
Wu et al., 2019P. Wu, X. Li, S. Shen, D. HeSocial media opinion summarization using emotion cognition and convolutional neural networksInternational Journal of Information Management (2019)
Ye et al., 2009Q. Ye, Z. Zhang, R. LawSentiment classification of online reviews to travel destinations by supervised machine learning approachesExpert Systems with Applications, 36 (3) (2009), pp. 6527-6535
Zhang et al., 2011Z. Zhang, Q. Ye, Z. Zhang, Y. LiSentiment classification of Internet restaurant reviews written in CantoneseExpert Systems with Applications, 38 (6) (2011), pp. 7674-7682
Zhao et al., 2012J. Zhao, L. Dong, J. Wu, K. XuMoodlens: An Emoticon-Based Sentiment Analysis System for Chinese Tweets, Paper Presented at the Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2012)
Zhu and Zhang, 2010F. Zhu, X. ZhangImpact of online consumer reviews on sales: The moderating role of product and consumer characteristicsJournal of Marketing, 74 (2) (2010), pp. 133-148
Eslami S, Ghasemaghaei M, Hassanein K, (2018), Which online reviews do consumers find most helpful? A multi-method investigation, Decision Support Systems, Volume 113, September 2018, Pages 32-42
Mots clés : Avis des consommateurs en ligne, Réseau neuronal artificiel, Analyse des sentiments, Durée de l’examen, Note d’évaluation
Résumé : Bien qu’il existe des preuves que la longueur de l’examen, le score de l’examen et le cadre d’argumentation peuvent avoir une incidence sur les perceptions des consommateurs concernant l’utilité des évaluations des consommateurs en ligne, les études n’ont pas encore identifié les niveaux les plus appropriés de ces facteurs en termes de maximisation de l’utilité perçue de ces évaluations. En nous appuyant sur les théories du biais de négativité et de la sommation des indices, nous proposons un modèle théorique qui explique l’utilité des revues en ligne en fonction des caractéristiques spécifiques de ces revues (c.-à-d. La longueur, le score, la trame d’argument). Le modèle est validé empiriquement à l’aide de deux ensembles de données d’avis de consommateurs en ligne liés aux produits et services d’Amazon.com et Insureye.com respectivement. De plus, nous utilisons un réseau neuronal artificiel comme approche pour prédire l’utilité d’un examen donné en fonction de ses caractéristiques. Les résultats révèlent que les avis de consommateurs en ligne les plus utiles sont ceux qui sont associés à une longueur moyenne, à des notes d’avis plus faibles et à un cadre d’arguments négatif ou neutre. Les résultats révèlent également qu’il n’y a pas de différence majeure entre les caractéristiques des avis de consommateurs en ligne les plus utiles concernant les produits ou services. Enfin, les résultats révèlent que le facteur le plus utile pour prédire l’utilité d’un avis de consommateur en ligne est la longueur de l’avis. Les contributions théoriques et pratiques sont décrites.
Grandes lignes :
Plusieurs méthodes ont été utilisées, notamment l’analyse des sentiments.
Les avis utiles ont une longueur moyenne, des scores inférieurs et un cadre d’arguments négatif.
Il n’y a aucune différence entre les avis les plus utiles sur les produits ou services.
Le facteur le plus utile pour prédire l’utilité d’un avis de consommateur est la longueur.
[1] M. Salehan, D.J. Kim, Predicting the performance of online consumer reviews: a
sentiment mining approach to big data analytics, Decision Support Systems 81
(2016) 30–40.
[2] N. Amblee, T. Bui, Freeware downloads: an empirical investigation into the impact
of expert and user reviews on demand for digital goods, AMCIS 2007 Proceedings,
2007, p. 21.
[3] J.A. Chevalier, D. Mayzlin, The effect of word of mouth on sales: online book reviews, Journal of Marketing Research 43 (3) (2006) 345–354.
[4] R. Kohli, S. Devaraj, M.A. Mahmood, Understanding determinants of online consumer satisfaction: a decision process perspective, Journal of Management
Information Systems 21 (1) (2004) 115–136.
[5] F. Zhu, X. Zhang, Impact of online consumer reviews on sales: the moderating role
of product and consumer characteristics, Journal of Marketing 74 (2) (2010)
133–148.
[6] D.D. Chaudhari, R.A. Deshmukh, A.B. Bagwan, P.K. Deshmukh, Feature based approach for review mining using appraisal words, Emerging Trends in
Communication, Control, Signal Processing & Computing Applications (C2SPCA),
2013 International Conference On, IEEE, 2013, pp. 1–5.
[7] J.P. Singh, S. Irani, N.P. Rana, Y.K. Dwivedi, S. Saumya, P.K. Roy, Predicting the
‘Helpfulness’ of online consumer reviews, Journal of Business Research 70 (2017)
346–355.
[8] K. Shrestha, 50 Important Online Reviews Stats You Need to Know [Infographic],
2016 Vendasta Blog, 2016 August 29 https://www.vendasta.com/blog/50-statsyou-need-to-know-about-online-reviews , Accessed date: 24 February 2018.
[9] M. Ghasemaghaei, S.P. Eslami, K. Deal, K. Hassanein, Consumers’ Attitude toward
Insurance Companies: A Sentiment Analysis of Online Consumer Reviews, (2016).
[10] S.S. Srinivasan, R. Anderson, K. Ponnavolu, Customer loyalty in e-commerce: an
exploration of its antecedents and consequences, Journal of Retailing 78 (1) (2002)
[12] C. Pongpatipat, Y. Liu-Thompkins, Beyond information: how consumers use online
reviews to manage social impressions, Let’s Get Engaged! Crossing the Threshold of
Marketing’s Engagement Era, Springer, 2016, pp. 103–104.
[13] A. Minnema, T.H. Bijmolt, S. Gensler, T. Wiesel, To keep or not to keep: effects of
online customer reviews on product returns, Journal of Retailing 92 (3) (2016)
253–267.
[14] Q. Cao, W. Duan, Q. Gan, Exploring determinants of voting for the ‘Helpfulness’ of
online user reviews: a text mining approach, Decision Support Systems 50 (2)
(2011) 511–521.
[15] X. Liu, M. He, F. Gao, P. Xie, An empirical study of online shopping customer satisfaction in China: a holistic perspective, International Journal of Retail &
Distribution Management 36 (11) (2008) 919–940.
[16] D. Yin, S. Bond, H. Zhang, Anxious or Angry? Effects of Discrete Emotions on the
Perceived Helpfulness of Online Reviews, (2013).
[17] R. Filieri, What makes online reviews helpful? A diagnosticity-adoption framework
to explain informational and normative influences in e-WOM, Journal of Business
Research 68 (6) (2015) 1261–1270.
[18] L. Qiu, J. Pang, K.H. Lim, Effects of conflicting aggregated rating on EWOM review
credibility and diagnosticity: the moderating role of review valence, Decision
Support Systems 54 (1) (2012) 631–643.
[19] A. Ghose, P.G. Ipeirotis, Estimating the helpfulness and economic impact of product
reviews: mining text and reviewer characteristics, IEEE Transactions on Knowledge
and Data Engineering 23 (10) (2011) 1498–1512.
[20] N. Korfiatis, E. García-Bariocanal, S. Sánchez-Alonso, Evaluating content quality
and helpfulness of online product reviews: the interplay of review helpfulness vs.
review content, Electronic Commerce Research and Applications 11 (3) (2012)
205–217.
[21] N. Hu, N.S. Koh, S.K. Reddy, Ratings lead you to the product, reviews help you
clinch it? The mediating role of online review sentiments on product sales, Decision
Support Systems 57 (2014) 42–53.
[22] S.M. Mudambi, D. Schuff, What Makes a Helpful Review? A Study of Customer
Reviews on Amazon.com. (2010).
[23] A. Qazi, K.B.S. Syed, R.G. Raj, E. Cambria, M. Tahir, D. Alghazzawi, A concept-level
approach to the analysis of online review helpfulness, Computers in Human
Behavior 58 (2016) 75–81.
[24] K.Z. Zhang, S.J. Zhao, C.M. Cheung, M.K. Lee, Examining the influence of online
reviews on consumers’ decision-making: a heuristic–systematic model, Decision
Support Systems 67 (2014) 78–89.
[25] M. Kim, J.-H. Kim, S.J. Lennon, Online service attributes available on apparel retail
web sites: an ES-QUAL approach, Managing Service Quality: An International
Journal 16 (1) (2006) 51–77.
[26] S.-M. Kim, E. Hovy, Automatic identification of pro and con reasons in online
reviews, Proceedings of the COLING/ACL on Main Conference Poster Sessions,
Association for Computational Linguistics, 2006, pp. 483–490.
[27] R.M. Schindler, B. Bickart, Perceived helpfulness of online consumer reviews: the
role of message content and style, Journal of Consumer Behaviour 11 (3) (2012)
[39] N.E. Miller, Graphic communication and the crisis in education, Audio Visual
Communication Review (1957) 1–120.
[40] J.-K. Hsieh, Y.-C. Hsieh, Y.-C. Tang, Exploring the disseminating behaviors of
EWOM marketing: persuasion in online video, Electronic Commerce Research 12
(2) (2012) 201–224.
[41] G. Wang, X. Liu, W. Fan, A Knowledge Adoption Model Based Framework for
Finding Helpful User-generated Contents in Online Communities, (2011).
[42] L.M. Willemsen, P.C. Neijens, F. Bronner, J.A. De Ridder, ‘Highly Recommended!’
The content characteristics and perceived usefulness of online consumer reviews,
Journal of Computer-Mediated Communication 17 (1) (2011) 19–38.
[43] D.E. Kanouse, L.R. Hanson Jr., Negativity in evaluations, Preparation of This Paper
Grew out of a Workshop on Attribution Theory Held at University of California, Los
Angeles, Aug 1969, Lawrence Erlbaum Associates, Inc., 1987.
[44] P. Rozin, E.B. Royzman, Negativity bias, negativity dominance, and contagion,
Personality and Social Psychology Review 5 (4) (2001) 296–320.
[45] S. Sen, D. Lerman, Why are you telling me this? An examination into negative
consumer reviews on the web, Journal of Interactive Marketing 21 (4) (2007)
76–94.
[46] P.M. Herr, F.R. Kardes, J. Kim, Effects of word-of-mouth and product-attribute information on persuasion: an accessibility-diagnosticity perspective, Journal of
Consumer Research 17 (4) (1991) 454–462.
[47] M. Lee, S. Youn, Electronic word of mouth (EWOM) how EWOM platforms influence consumer product judgement, International Journal of Advertising 28 (3)
(2009) 473–499.
[48] Z. Zhang, Q. Ye, R. Law, Y. Li, The impact of e-word-of-mouth on the online popularity of restaurants: a comparison of consumer reviews and editor reviews,
International Journal of Hospitality Management 29 (4) (2010) 694–700.
[49] M.S. Eastin, Credibility assessments of online health information: the effects of
source expertise and knowledge of content, Journal of Computer-Mediated
Communication 6 (4) (2001) (0–0).
[50] C. Park, T.M. Lee, Antecedents of online reviews’ usage and purchase influence: an
empirical comparison of US and Korean consumers, Journal of Interactive
Marketing 23 (4) (2009) 332–340.
[51] M.-A. Reinhard, S.L. Sporer, Content versus source cue information as a basis for
credibility judgments, Social Psychology 41 (2010) 93–104.
[52] C. Park, T.M. Lee, Information direction, website reputation and EWOM effect: a
moderating role of product type, Journal of Business Research 62 (1) (2009) 61–67.
[53] P. Racherla, W. Friske, Perceived ‘Usefulness’ of online consumer reviews: an exploratory investigation across three services categories, Electronic Commerce
Research and Applications 11 (6) (2012) 548–559.
[54] X. Bai, Predicting consumer sentiments from online text, Decision Support Systems
50 (4) (2011) 732–742.
[55] S. Brody, N. Elhadad, An unsupervised aspect-sentiment model for online reviews,
Human Language Technologies: The 2010 Annual Conference of the North
American Chapter of the Association for Computational Linguistics, Association for
Computational Linguistics, 2010, pp. 804–812.
[56] Y. Jo, A.H. Oh, Aspect and sentiment unification model for online review analysis,
Proceedings of the Fourth ACM International Conference on Web Search and Data
Mining, ACM, 2011, pp. 815–824.
[57] N. Li, D.D. Wu, Using text mining and sentiment analysis for online forums hotspot
detection and forecast, Decision Support Systems 48 (2) (2010) 354–368.
S.P. Eslami et al. Decision Support Systems 113 (2018) 32–42
41
[58] I. Maks, P. Vossen, A lexicon model for deep sentiment analysis and opinion mining
applications, Decision Support Systems 53 (4) (2012) 680–688.
[59] Q. Ye, R. Law, B. Gu, The impact of online user reviews on hotel room sales,
International Journal of Hospitality Management 28 (1) (2009) 180–182.
[60] J.B. Walther, K.P. D’Addario, The impacts of emoticons on message interpretation in
computer-mediated communication, Social Science Computer Review 19 (3) (2001)
324–347.
[61] N. Hu, I. Bose, N.S. Koh, L. Liu, Manipulation of online reviews: an analysis of
ratings, readability, and sentiments, Decision Support Systems 52 (3) (2012)
674–684.
[62] T.A. Ito, J.T. Larsen, N.K. Smith, J.T. Cacioppo, Negative information weighs more
heavily on the brain: the negativity bias in evaluative categorizations, Journal of
Personality and Social Psychology 75 (4) (1998) 887.
[63] R.F. Baumeister, E. Bratslavsky, C. Finkenauer, K.D. Vohs, Bad is stronger than
good, Review of General Psychology 5 (4) (2001) 323.
[64] B. Rimé, Emotion elicits the social sharing of emotion: theory and empirical review,
Emotion Review 1 (1) (2009) 60–85.
[65] B. Rimé, C. Finkenauer, O. Luminet, E. Zech, P. Philippot, Social sharing of emotion:
new evidence and new questions, European Review of Social Psychology 9 (1)
(1998) 145–189.
[66] C.E. Osgood, P.H. Tannenbaum, The principle of congruity in the prediction of
[67] T.M. Newcomb, Interpersonal balance, Theories of Cognitive Consistency: A
Sourcebook, 1968, pp. 28–51.
[68] M. Ghasemaghaei, S.P. Eslami, K. Deal, K. Hassanein, Reviews’ length and sentiment
as correlates of online reviews’ ratings, Internet Research 28 (3) (2018) 544–563.
[69] R. He, J. McAuley, VBPR: Visual Bayesian Personalized Ranking From Implicit
Feedback, AAAI, 2016, pp. 144–150.
[70] J. McAuley, R. Pandey, J. Leskovec, Inferring networks of substitutable and complementary products, Proceedings of the 21th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, ACM, 2015, pp. 785–794.
[71] Y. Yu, W. Duan, Q. Cao, The impact of social and conventional media on firm equity
value: a sentiment analysis approach, Decision Support Systems 55 (4) (2013)
919–926.
[72] J. Berger, A.T. Sorensen, S.J. Rasmussen, Positive effects of negative publicity:
[73] W. Duan, B. Gu, A.B. Whinston, Do online reviews matter?—an empirical investigation of panel data, Decision Support Systems 45 (4) (2008) 1007–1016.
[74] S.R. Das, M.Y. Chen, Yahoo! for Amazon: sentiment extraction from small talk on
the web, Management Science 53 (9) (2007) 1375–1388.
[75] A. Collomb, C. Costea, D. Joyeux, O. Hasan, L. Brunie, A study and comparison of
sentiment analysis methods for reputation evaluation, Rapport de Recherche RRLIRIS-2014-002, 2014.
[76] B. Liu, Sentiment analysis and subjectivity, Handbook of Natural Language
Processing, 2 2010, pp. 627–666.
[77] E. Cambria, B. Schuller, Y. Xia, C. Havasi, New avenues in opinion mining and
sentiment analysis, IEEE Intelligent Systems 28 (2) (2013) 15–21.
[78] B. Pang, L. Lee, Opinion mining and sentiment analysis, Foundations and Trends® in
Information Retrieval 2 (1–2) (2008) 1–135.
[79] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, M. Stede, Lexicon-based methods for
Srivastana V, Karlo A, (2019), Enhancing the Helpfulness of Online Consumer Reviews: The Role of Latent (Content) Factors, Journal of Interactive Marketing, Volume 48, November 2019, Pages 33-50
Mots clés : Avis des consommateurs en ligne, Traitement du langage naturel, Exploration de texte, Analyse de contenu, Qualité de l’argument, Message valence
Résumé : Des études empiriques antérieures ont analysé l’influence des facteurs manifestes du contenu des avis en ligne et des facteurs liés aux examinateurs sur l’utilité des avis en ligne. Cependant, l’influence des facteurs de contenu latents, qui sont impliqués dans le texte et qui entraînent des perceptions différentielles de l’utilité des destinataires de l’avis, ont été ignorées. Par conséquent, en utilisant la lentille du modèle de vraisemblance d’élaboration, nous développons un modèle complet pour étudier l’influence des facteurs liés au contenu et aux réviseurs sur l’utilité de la révision. Cette étude comprend non seulement les facteurs manifestes liés au contenu et aux examinateurs, mais également les facteurs de contenu latents consistant en la qualité des arguments (exhaustivité, clarté, lisibilité et pertinence) et la valence du message. Les résultats montrent que les variables de contenu de révision latentes comme la qualité des arguments et la valence influencent mieux l’utilité des critiques et au-delà des facteurs liés au contenu de l’avis et aux réviseurs manifestes précédemment étudiés.. Les résultats montrent que les variables de contenu de révision latentes comme la qualité des arguments et la valence influencent mieux l’utilité des critiques et au-delà des facteurs liés au contenu de la revue et aux réviseurs manifestes précédemment étudiés. Les résultats montrent que les variables de contenu d’avis latents comme la qualité des arguments et la valence influencent mieux l’utilité des critiques et au-delà des facteurs liés au contenu de l’avis et aux examinateurs manifestes précédemment étudiés.
Grandes lignes :
Les facteurs de contenu latents (qualité des arguments et valence de la révision) sont des prédicteurs significatifs de l’utilité de l’avis.
L’étude définit opérationnellement la qualité de l’argument qui comprend l’exhaustivité, la clarté, la lisibilité et la pertinence.
Un examen complet réduit l’incertitude autour des différents attributs de produit / service.
La clarté et la lisibilité améliorées conduisent à une plus grande adoption de la recommandation de message dans un avis en ligne.
Le contenu non pertinent dans les avis réduit son utilité.
L’influence de la valence du message est complexe. Les avis positifs sont jugés moins utiles que les avis négatifs.
Ahluwalia, Rohini (2002), “How Prevalent Is the Negativity Effect in Consumer Environments?” Journal of Consumer Research, 29, 2, 270–9. Archer, Richard (1979), Self-Disclosure under Conditions of Self-Awareness. Baek, Hyunmi, JoongHo Ahn, and Youngseok Choi (2012), “Helpfulness of Online Consumer Reviews: Readers’ Objectives and Review Cues,” International Journal of Electronic Commerce, 17, 2, 99–126. Banerjee, Snehasish and Alton Chua (2016), “In Search of Patterns among Travellers’ Hotel Ratings in Tripadvisor,” Tourism Management, 53, 125–31. Belch, George E. and Michael A. Belch (2013), “A Content Analysis Study of the Use of Celebrity Endorsers in Magazine Advertising,” International Journal of Advertising, 32, 3, 369–89. Berger, Jonah (2014), “Word of Mouth and Interpersonal Communication: A Review and Directions for Future Research,” Journal of Consumer Psychology, 24, 4, 586–607. Bhattacherjee, Anol and Clive Sanford (2006), “Influence Processes for Information Technology Acceptance: An Elaboration Likelihood Model,” MIS Quarterly, 805–25. Bickart, Barbara and Robert Schindler (2001), “Internet Forums as Influential Sources of Consumer Information,” Journal of Interactive Marketing, 15, 3, 31–40. BrightLocal (2018), “Local Consumer Review Survey 2018,” retrieved from https://www.brightlocal.com/learn/local-consumer-review-survey/ (8th November). Cao, Qing, Wenjing Duan, and Qiwei Gan (2011), “Exploring Determinants of Voting for the “Helpfulness” of Online User Reviews: A Text Mining Approach,” Decision Support Systems, 50, 511–21. Cheng, Yi-Hsiu and Hui-Yi Ho (2015), “Social Influence’s Impact on Reader Perceptions of Online Reviews,” Journal of Business Research, 68, 4, 883–7. Cheung, Christy, Matthew Lee, and Neil Rabjohn (2008), “The Impact of Electronic Word-Of-Mouth: The Adoption of Online Opinions in Online Customer Communities,” Internet Research, 18, 3, 229–47. ——— and Dimple Thadani (2012), “The Impact of Electronic Word-of-Mouth Communication: A Literature Analysis and Integrative Model,” Decision Support Systems, 54, 1, 461–70. Cheung, Man Yee, Chuan Luo, Choon Ling Sia, and Huaping Chen (2009), “Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of online Consumer Recommendations,” International Journal of Electronic Commerce, 13, 4, 9–38. ———, Choon-Ling Sia, and Kevin K.Y. Kuan (2012), “Is this Review Believable? A Study of Factors Affecting the Credibility of Online Consumer Reviews from an ELM Perspective,” Journal of the Association for Information Systems, 13, 8, 618. Chintagunta, Pradeep, Shyam Gopinath, and Sriram Venkataraman (2010), “The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation across Local Markets,” Marketing Science, 29, 5, 944–57. Cohen, Stanley (1960), “Purification of a Nerve-Growth Promoting Protein from the Mouse Salivary Gland and its Neuro-Cytotoxic Antiserum,” Proceedings of the National Academy of Sciences, 46, 3, 302–11. Cui, Geng, Hon-Kwong Lui, and Xiaoning Guo (2012), “The Effect of Online Consumer Reviews on New Product Sales,” International Journal of Electronic Commerce, 17, 1, 39–58. Forman, Chris, Anindya Ghose, and Batia Wiesenfeld (2008), “Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity 48 V. Srivastava, A.D. Kalro / Journal of Interactive Marketing 48 (2019) 33–50 Disclosure in Electronic Markets,” Information Systems Research, 19, 3, 291–313. Fu, Xiaorong, Bin Zhang, Qinghong Xie, Liuli Xiao, and Yu Che (2011), Impact of Quantity and Timeliness of EWOM Information on Consumer’s Online Purchase Intention under C2C Environment. Geometry Globul (2018), “The Influence of Influencers: A Research Study,” available at http://gen.video/influencerstudy, Accessed date: 8 November 2018. Ghose, Anindya and Panagiotis Ipeirotis (2011), “Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics,” IEEE Transactions on Knowledge and Data Engineering, 23, 10, 1498–512. Gottschalk, Sabrina and Alexander Mafael (2017), “Cutting Through the Online Review Jungle- Investigating Selective eWOM Processing,” Journal of Interactive Marketing, 37, 89–104. Gretzel, Ulrike and Kyung Hyan Yoo (2008), Information and Communication Technologies in Tourism35–46 . Use and Impact of Online Travel Reviews. Hall, Edward T. (1976), Beyond Culture. New York: Double-day. Hennig-Thurau, Thorsten, Kevin Gwinner, Gianfranco Walsh, and Dwayne Gremler (2004), “Electronic Word-of-Mouth via Consumer-Opinion Platforms: What Motivates Consumers to Articulate Themselves on the Internet?” Journal of Interactive Marketing, 18, 1, 38–52. Herr, Paul M., Frank R. Kardes, and John Kim (1991), “Effects of Word-ofMouth and Product-Attribute Information on Persuasion: An AccessibilityDiagnosticity Perspective,” Journal of Consumer Research, 17, 4, 454–62. Hsieh, Hsiu-Fang and Sarah Shannon (2005), “Three Approaches to Qualitative Content Analysis,” Qualitative Health Research, 15, 9, 1277–88. Hu, Nan, Noi Sian Koh, and Srinivas K. Reddy (2014), “Ratings Lead you to the Product, Reviews Help You Clinch It? The Mediating Role of Online Review Sentiments on Product Sales,” Decision Support Systems, 57, January, 42–53. Huang, Albert, Kuanchin Chen, David Yen, and Trang Tran (2015), “A Study of Factors that Contribute to Online Review Helpfulness,” Computers in Human Behavior, 48, 17–27. Huang, James, Stephanie Rogers, and Eunkwang Joo (2014), Improving Restaurants by Extracting Subtopics from Yelp Reviews. iConference (Social Media Expo). Jiang, Yifan, Oscar de Bruijn, and Antonella de Angeli (2009), “The Perception of Cultural Differences in Online Self-Presentation,” IFIP Conference on Human-Computer Interaction, 672–85. Kim, Su Jung, Ewa Maslowska, and Edward C. Malthouse (2018), “Understanding the Effects of Different Review Features on Purchase Probability,” International Journal of Advertising, 37, 1, 29–53. King, Robert, Pradeep Racherla, and Victoria Bush (2014), “What We Know and Don’t Know about Online Word-of-Mouth: A Review and Synthesis of the Literature,” Journal of Interactive Marketing, 28, 3, 167–83. Korfiatis, Nikolaos, Elena García-Bariocanal, and Salvador Sánchez-Alonso (2012), “Evaluating Content Quality and Helpfulness of Online Product Reviews: The Interplay of Review Helpfulness vs. Review Content,” Electronic Commerce Research and Applications, 11, 3, 205–17. Kowner, Rotem and Richard Wiseman (2003), “Culture and Status-Related Behavior: Japanese and American Perceptions of Interaction in Asymmetric Dyads,” Cross-Cultural Research, 37, 2, 178–210. Krishnamoorthy, Srikumar (2015), “Linguistic Features for Review Helpfulness Prediction,” Expert Systems with Applications, 42, 7, 3751–9. Laroche, Michel (2010), “New Developments in Modeling Internet Consumer Behavior: Introduction to the Special Issue,” Journal of Business Research, 915–8. Lee, Chung Hun and David A. Cranage (2014), “”Toward Understanding Consumer Processing of Negative Word-of-Mouth Communication: The Roles of Opinion Consensus and Organizational Response Strategies”,” Journal of Hospitality & Tourism Research, 38, 3, 330–60. Lee, Hee Andy, Rob Law, and Jamie Murphy (2011), “Helpful Reviewers in TripAdvisor, an Online Travel Community,” Journal of Travel & Tourism Marketing, 28, 7, 675–88. Li, Mengxiang, Liqiang Huang, Chuan-Hoo Tan, and Kwok-Kee Wei (2013), “Helpfulness of Online Product Reviews as Seen by Consumers: Source and Content Features,” International Journal of Electronic Commerce, 17, 4, 101–36. Lim, Kai, Izak Benbasat, and Lawrence Ward (2000), “The Role of Multimedia in Changing First Impression Bias,” Information Systems Research, 11, 2, 115–36. Liu, Zhiwei and Sangwon Park (2015), “What Makes A Useful Online Review? Implication for Travel Product Websites,” Tourism Management, 47, 140–51. Luo, Chuan, Xin Luo, Laurie Schatzberg, and Choon Sia (2013), “Impact of Informational Factors on Online Recommendation Credibility: The Moderating Role of Source Credibility,” Decision Support Systems, 56, 92–102. Moe, Wendy and David Schweidel (2012), “Online Product Opinions: Incidence, Evaluation, and Evolution,” Marketing Science, 31, 3, 372–86. ——— and Michael Trusov (2011), “The Value of Social Dynamics in Online Product Ratings Forums,” Journal of Marketing Research, 48, 3, 444–56. Mohammad, Saif M. and Peter D. Turney (2013), “Crowdsourcing a Word– Emotion Association Lexicon,” Computational Intelligence, 29, 3, 436–65. Mudambi, Susan and David Schuff (2010), “What Makes a Helpful Review? A Study of Customer Reviews on Amazon. Com,” MIS Quarterly, 34, 1, 185–200. Muralidharan, Sidharth, Hye Jin Yoon, Yongjun Sung, Jessica Miller, and Arturo Lee (2017), “Following the Breadcrumbs: An Analysis of Online Product Review Characteristics by Online Shoppers,” Journal of Marketing Communications, 23, 2, 113–34. Pan, Lee-Yun and Jyh-Shen Chiou (2011), “How Much Can You Trust Online Information? Cues for Perceived Trustworthiness of Consumer-Generated Online Information,” Journal of Interactive Marketing, 25, 2, 67–74. Pan, Yue and Jason Zhang (2011), “Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews,” Journal of Retailing, 87, 4, 598–612. Park, Do-Hyung, Jumin Lee, and Ingoo Han (2007), “The Effect of On-line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement,” International Journal of Electronic Commerce, 11, 4, 125–48. ——— and ——— (2008), “eWOM Overload and its Effect on Consumer Behavioral Intention Depending on Consumer Involvement,” Electronic Commerce Research and Applications, 7, 4, 386–98. Pentina, Iryna, Ainsworth Anthony Bailey, and Lixuan Zhang (2018), “Exploring Effects of Source Similarity, Message Valence, and Receiver Regulatory Focus on Yelp Review Persuasiveness and Purchase Intentions,” Journal of Marketing Communications, 24, 2, 125–45. Petty, Richard and John Cacioppo (1986), “The Elaboration Likelihood Model of Persuasion,” Advances in Experimental Social Psychology, 11, 1, 673–5. Pornpitakpan, Chanthika (2004), “The Persuasiveness of Source Credibility: A Critical Review of Five Decades Evidence,” Journal of Applied Social Psychology, 34, 2, 243–81. Racherla, Pradeep and Wesley Friske (2012), “Perceived ‘Usefulness’ of Online Consumer Reviews: An Exploratory Investigation across Three Services Categories,” Electronic Commerce Research and Applications, 11, 6, 548–59. Ratchford, Brian, Myung-Soo Lee, and Debabrata Talukdar (2003), “The Impact of the Internet on Information Search for Automobiles,” Journal of Marketing Research, 40, 2, 193–209. Rui, Jian and Michael Stefanone (2013), “Strategic Self-Presentation Online: A Cross-Cultural Study,” Computers in Human Behavior, 29, 1, 110–8. Schlosser, Ann (2011), “Can Including Pros and Cons Increase the Helpfulness and Persuasiveness of Online Reviews? The Interactive Effects of Ratings and Arguments,” Journal of Consumer Psychology, 21, 3, 226–39. Sen, Shahana and Dawn Lerman (2007), “Why Are You Telling Me This? An Examination into Negative Consumer Reviews on the Web,” Journal of Interactive Marketing, 21, 4, 76–94. Shriver, Scott, Harikesh Nair, and Reto Hofstetter (2013), “Social Ties and User-Generated Content: Evidence from an Online Social Network,” Management Science, 59, 6, 1425–43. Skowronski, John J. and Donal E. Carlston (1989), “Negativity and Extremity Biases in Impression Formation: A Review of Explanations,” Psychological Bulletin, 105, 1, 131. V. Srivastava, A.D. Kalro / Journal of Interactive Marketing 48 (2019) 33–50 49 Smith, Malcolm and Richard Taffler (1992), “Readability and Understandability: Different Measures of the Textual Complexity of Accounting Narrative,” Accounting, Auditing & Accountability Journal, 5, 4. Sparks, Beverley, Helen Perkins, and Ralf Buckley (2013), “Online Travel Reviews as Persuasive Communication: The Effects of Content Type, Source, and Certification Logos on Consumer Behavior,” Tourism Management, 39, 1–9. Stringam, Betsy Bender and John Gerdes Jr. (2010), “An Analysis of Word-OfMouse Ratings and Guest Comments of Online Hotel Distribution Sites,” Journal of Hospitality Marketing & Management, 19, 7, 773–96. Sussman, Stephanie and Wendy Siegal (2003), “Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption,” Information Systems Research, 14, 1, 47–65. Vermeulen, Ivar and Daphne Seegers (2009), “Tried and Tested: The Impact of Online Hotel Reviews on Consumer Consideration,” Tourism Management, 30, 1, 123–7. Vessey, Iris and Dennis Galletta (1991), “Cognitive Fit: An Empirical Study of Information Acquisition,” Information Systems Research, 2, 1, 63–84. Wang, Xia, Chunling Yu, and Yujie Wei (2012), “Social Media Peer Communication and Impacts on Purchase Intentions: A Consumer Socialization Framework,” Journal of Interactive Marketing, 26, 4, 198–208. Ward, James and Amy Ostrom (2003), “The Internet as Information Minefield: An Analysis of the Source and Content of Brand Information Yielded by Net Searches,” Journal of Business Research, 56, 11, 907–14. Weiss, Allen, Nicholas Lurie, and Deborah MacInnis (2008), “Listening to Strangers: Whose Responses Are Valuable, How Valuable Are They, and Why?” Journal of Marketing Research, 45, 4, 425–36. Willemsen, Lotte, Peter Neijens, Fred Bronner, and Jan De Ridder (2011), ““Highly Recommended!” The Content Characteristics and Perceived Usefulness of Online Consumer Reviews,” Journal of Computer-Mediated Communication, 17, 1, 19–38. Würtz, Elizabeth (2005), “Intercultural Communication on Web Sites: A CrossCultural Analysis of Web Sites from High-Context Cultures and Low-Context Cultures,” Journal of Computer-Mediated Communication, 11, 1, 274–99. Xiaoping, Zheng (2008), An Empirical Study of the Impact of Online Review on Internet Consumer Purchasing Decision. China People’s University5. Xu, Pei, Liang Chen, and Radhika Santhanam (2015), “Will Video Be the Next Generation of eCommerce Product Reviews? Presentation Format and the Role of Product Type,” Decision Support Systems, 73, 85–96. Yang, Jun and Enping Shirley Mai (2010), “Experiential Goods with Network Externalities Effects: An Empirical Study of Online Rating System,” Journal of Business Research, 63, 9-10, 1050–7. Ye, Qiang, Rob Law, Bin Gu, and Wei Chen (2011), “The Influence of UserGenerated Content on Traveler Behavior: An Empirical Investigation on the Effects of e-Word-of-Mouth to Hotel Online Bookings,” Computers in Human Behavior, 27, 2, 634–9. Zakaluk, Beverly and S. Jay Samuels (1988), Readability: It’s Past, Present, and Future. 800 Barksdale Rd., PO Box 8139, Newark, DE l9714–8139: International Reading Association. Zhang, Jason, Georgiana Craciun, and Dongwoo Shin (2010), “When Does Electronic Word-of-Mouth Matter? A Study of Consumer Product Reviews,” Journal of Business Research, 63, 12, 1336–41. Zhang, Lun, Tai-Quan Peng, Ya-Peng Zhang, Xiao-Hong Wang, and Jonathan J.H. Zhu (2014), “Content or Context: Which Matters More in Information Processing on Microblogging Sites,” Computers in Human Behavior, 31, 242–9.
Ortega B, (2020), When the performance comes into play: The influence of positive online consumer reviews on individuals’ post-consumption responses, Journal of Business Research
Mots clés : Avis des consommateurs en ligne, Niveau de performance, Positivité perçue, Réponses post-consommation, Forme fonctionnelle en U
Résumé : Cet article vise à étudier l’influence des avis post achat positifs sur les réponses post-consommation, à savoir l’attitude envers les entreprises et les intentions de rachat. Il différencie si le niveau de performance pendant la consommation était élevé ou faible, c’est-à-dire si le produit a atteint les objectifs fixés par les consommateurs. À cette fin, le document aborde les avis post achat positifs dans une double approche. Premièrement, il analyse l’effet de l’intensité de valence positive, en tenant compte des avis post achat neutres-indifférents, modérément positifs et extrêmement positifs. Deuxièmement, il teste l’influence de la positivité perçue des avis post achat par les individus. Les résultats montrent que les mêmes avis post achat peuvent avoir une influence positive ou négative sur les individus.
Grandes lignes :
Les avis post achat positifs et les performances influencent conjointement les réponses post-consommation
Les avis post achat positifs peuvent avoir des effets négatifs sur les réponses post-consommation
Les relations entre la positivité perçue des avis post achat et les réponses post-consommation suivent des formes fonctionnelles curvilignes
Si les performances sont faibles, des relations en U inversé sont identifiées
Si les performances sont élevées, des relations en U sont identifiées
Aaker, J., Drolet, A., & Griffin, D. (2008). Recalling recall bias. Journal of Consumer Research, 35, 268–278. Abelson, R. P. (1995). Attitude extremity. In R. E. Petty, & J. A. Krosnick (Eds.). Attitude strength: Antecedents and consequences (pp. 25–42). Mahwah, NJ: Lawrence Erlbaum. Alexander, C. S., & Jay, B. H. (1978). The use of vignettes in survey research. Public Opinion Quarterly, 42(1), 93–104. Anderson, E., & Mittal, V. (2000). Strengthening the satisfaction-profit chain. Journal of Service Research, 3(2), 107–120. Aurier, P., & Siadou-Martin, B. (2007). Perceived justice and consumption experience evaluations. A qualitative and experimental investigation. International Journal of Service Industry Management, 18(5), 450–471. Beerli-Santana, A., & Martín-Santana, J. (2017). How does confirmation of motivations influence on the pre- and post-visit change of image of a destination? European Journal of Management and Business Economics, 26(7), 238–251. Bendapudi, N., & Leone, R. P. (2003). Psychological implications of customer participation in co-production. Journal of Marketing, 67(January), 14–28. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation confirmation model. MIS Quarterly, 25(3), 351–370. Blackwell, R., Miniard, P., & Engel, J. F. (2005). Consumer behavior (5ª ed.). Florida: Dryden Press. B. Hernández-Ortega Journal of Business Research 113 (2020) 422–435 433 Bone, P. F. (1995). Word-of-mouth effects on short-term and long-term product judgments. Journal of Business Research, 32, 213–223. Bridges, E. (1993). Service attributes: Expectations and judgments. Psychology and Marketing, 10(3), 185–197. BrightLocal (2017). Local consumer review survey. Available at https://www.brightlocal. com/learn/local-consumer-review-survey/. Bronner, A. E., & de Hoog, R. (2011). Vacationers and eWOM: Who posts, and why, where, and what? Journal of Travel Research, 50(1), 15–26. Buunk, A. P., & Gibbons, F. X. (2007). Social comparison: The end of a theory and the emergence of a field. Organizational Behavior and Human Decision Processes, 102, 3–21. Cadotte, E. R., Woodruff, R. B., & Jenkins, R. L. (1987). Expectations and norms in models of consumer satisfaction. Journal of Marketing Research, 19(4), 491–504. Casalo, L., Flavián, C., Guinaliu, M., & Ekinci, Y. (2015). Avoiding the dark side of positive online consumer reviews: Enhancing reviews’ usefulness for high risk-averse travelers. Journal of Business Research, 68(9), 1829–1835. Chan, H., & Cui, S. (2011). The contrasting effects of negative word of mouth in the postconsumption stage. Journal of Consumer Psychology, 21, 324–337. Chang, E.-C., Lv, Y., Chou, T.-J., He, Q., & Song, Z. (2014). Now or later: Delay’s effects on post-consumption emotions and consumer loyalty. Journal of Business Research, 67, 1368–1375. Chen, Y., & Xie, J. (2008). Online consumer review: Word of mouth as a new element of marketing communication mix. Management Science, 54, 447–491. Chua, B.-L., Lee, S., & Han, H. (2017). Consequences of cruise line involvement: A comparison of first-time and repeat passengers. International Journal of Contemporary Hospitality Management, 29(6), 1658–1683. Cohen, J. W. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. Cowley, E. (2014). Consumers telling consumption stories: Word-of-mouth and retrospective evaluations. Journal of Business Research, 67, 1522–1529. Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 19(2), 189–211. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1003. Dellarocas, C., Zhang, X., & Awd, N. (2007). Exploring the value of online product reviews in forecasting sale: The case of motion pictures. Journal of Interactive Marketing, 21(4), 23–45. Dias, L., & Lobel, M. (1997). Social comparison in medically high-risk pregnant women. Journal of Applied Social Psychology, 27, 1629–1649. Duan, W., Gu, B., & Whinston, A. B. (2008). The dynamics of online WOM and product sales – An empirical investigation of the movie industry. Journal of Retailing, 84(2), 233–242. Duverger, P. (2013). Curvilinear effects of user-generated content on hotels’ market share: A dynamic panel-data analysis. Journal of Travel Research, 52, 465–478. Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace College Publishers. Eisenbeiss, M., CorneliBen, M., Backhaus, K., & Hoyer, W. (2014). Nonlinear and asymmetric returns on customer satisfaction: Do they vary across situations and consumers? Journal of Academy of Marketing Science, 42, 242–263. Engel, J., Kollat, D., & Blackwell, R. (1968). Consumer behavior (1st ed.). New York: Holt, Rinehart and Winston. Fieller, E. C. (1954). Some problems in interval estimation. Journal of the Royal Statistical Society, Series B (Methodological), 16(2), 175–185. Floyd, K., Freling, R., Alhoqail, S., Young Cho, H., & Freling, T. (2014). How online product reviews affect retail sales: A meta-analysis. Journal of Retailing, 90(2), 217–232. Forman, C., Ghose, A., & Wiesenfield, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291–313. Gilly, M. C., Graham, J. L., Wolfinbarger, M. F., & Yale, L. J. (1998). A dyadic study of interpersonal information search. Journal of Academy of Marketing Science, 26, 83–100. Grace, D., & O’Cass, A. (2001). Examining service experiences and post-consumption evaluations. Journal of Services Marketing, 18(6), 450–461. Haans, R. F. J., Pieters, C., & He, Z.-L. (2016). Thinking about U: Theorizing and testing Uand inverted U-shaped relationships in strategy research. Strategic Management Journal, 37(7), 1177–1195. Hamby, A., Danisloski, K., & Brinberg, D. (2015). How consumer reviews persuade through narratives. Journal of Business Research, 68, 1242–1250. Heimbach, I., & Hinz, O. (2016). The impact of content sentiment and emotionality on content virality. International Journal of Research in Marketing, 33(3), 695–701. Helgeson, V. S., & Taylor, S. E. (1993). Social comparison and adjustment among cardiac patients. Journal of Applied Social Psychology, 23, 1171–1195. Herr, P., Kardes, F. R., & Kim, J. (1991). Effects of word-of-mouth and product-attribute information on persuasion: an accessibility-diagnosticity perspective. Journal of Consumer Research, 17(4), 454–462. Holbrook, M. B., & Moore, W. L. (1981). Feature interactions in consumer judgments of verbal versus pictorial presentations. Journal of Consumer Research, (June), 103–113. Homburg, C., Koschate, N., & Hoyer, W. D. (2005). Do satisfied customers really pay more? A study of the relationship between customer satisfaction and willingness to pay. Journal of Marketing, 69, 84–96. Hosany, S., & Prayag, G. (2013). Patterns of tourists’ emotional responses, satisfaction, and intention to recommend. Journal of Business Research, 66, 730–737. Hu, Y., & Li, X. (2011). Context-dependent product evaluations: A empirical analysis of Internet book reviews. Journal of Interactive Marketing, 25, 123–133. Jang, S., & Namkung, Y. (2009). Perceived quality, emotions, and behavioral intentions: Application of an extended Mehrabian-Russell model to restaurants. Journal of Business Research, 62, 451–460. Jones, P., & Chen, M. M. (2011). Factors determining hotel selection: Online behavior by leisure travelers. Tour Hospitality Research, 11, 83–95. Kahneman, D., & Tversky (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292. Kennedy, J. R., & Thirkell, P. C. (1988). An extended perspective on the antecedents of satisfaction. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 1, 2–9. Kim, S., Chung, J.-E., & Suh, Y. (2016). Multiple reference effects on restaurant evaluations: A cross-cultural study. International Journal of Contemporary Hospitality Management, 28(7), 1441.1466. Koo, D.-M. (2015). The strength of no tie relationship in an online recommendation. European Journal of Marketing, 49(7/8), 1163–1183. Kostyra, D. S., Reiner, J., Natter, M., & Klapper, D. (2016). Decomposing the effects of online customer reviews on brand, price, and product attributes. International Journal of Research in Marketing, 33(1), 11–26. Lambert, J. (2013). Digital storytelling: Capturing lives, creating community. Berkeley, CA: Digital Diner Press. Lee, D. (2012). Powerful storytelling techniques. Association for Talent Development. Lee, J., Lee, J., & Shin, H. (2011). The long tail or the short tail: The category-specific impact of eWOM on sales distribution. Decision Support Systems, 51(3), 466–479. Lee, J., Park, D.-H., & Han, I. (2008). The effect of negative online consumer reviews on product attitude: An information processing view. Electronic Commerce Research and Applications, 7, 341–352. Lee, J., & Pee, L. G. (2013). Reading consumer reviews to confirm my expectations: The accelerated impact of confirmation under extreme review tones. Pacific Asia conference on information systems (PACIS) proceedings (pp. 76). . Available in http://aisel. aisnet.org/cgi/viewcontent.cgi?article=1076&context=pacis2013 (12/19/2017) . Lee, M., & Youn, S. (2009). Electronic word of mouth (eWOM): How eWOM platforms influence consumer product judgment. International Journal of Advertising: The Quarterly Review of Marketing Communications, 28(3), 473–499. Lin, C.-P., Tsai, Y. H., & Chiu, C.-K. (2009). Modeling customer loyalty from an integrative perspective of self-determination theory and expectation-confirmation theory. Journal of Business Psychology, 24, 315–326. Lin, Z., & Heng, C.-S. (2015). The paradoxes of word of mouth in electronic commerce. Journal of Management Information Systems, 32(4), 246–284. Lind, J., & Mehlum, H. (2010). With or without you? The appropriate test for a u-shaped relationship. Oxford Bulletin of Economics and Statistics, 72(1), 109–118. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70, 74–89. Loftus, E. (1975). Leading questions and the eyewitness report. Cognitive Psychology, 7, 560–572. Loftus, E. (1979). Reactions to blatantly contradictory information. Memory & Cognition, 7, 368–374. Moe, W. W., & Trusov, M. (2011). The value of social dynamics in online product ratings forums. Journal of Marketing Research, 48(3), 444–456. Morales, A. C., Amir, O., & Lee, L. (2017). Keeping it real experimental, research – Understanding when, where, and how to enhance realism and measure consumer behavior. Journal of Consumer Research, 44(2), 465–476. Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185–200. Netemeyer, R. G., Maxham, J. G., & Pullig, C. (2005). Conflicts in the work-family interface: Links to job stress, customer service employee performance, and customer purchase intent. Journal of Marketing, 69, 130–143. Nicosia, F. M. (1966). Consumer decision process: Marketing and advertising complications. Englewood Cliffs, New York: Prentice Hall. Nunnally, J. (1978). Psychometric theory. New York: McGraw-Hill. Oghuma, A. P., Libaque-Saenz, C. F., & Chang, Y. (2016). An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics, 33, 34–47. Oliver, R. L. (1977). Effect of expectation and disconfirmation on post-exposure product evaluations: An alternative interpretation. Journal of Applied Psychology, 62(4), 480–486. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 46–49. Oliver, R. L., & DeSarbo, W. S. (1998). Response determinants in satisfaction judgments. Journal of Consumer Research, 14(2), 495–507. Olshavsky, R. W., & Miller, J. A. (1972). Consumer expectations, product performance, and perceived product quality. Journal of Marketing Research, 9, 19–21. ONTSI (2017). Perfil sociodemografico de los internautas. Analisis de datos INE 2017. Available in: Observatorio Nacional de las telecomunicaciones y de la sociedad de la informaciónhttps://www.ontsi.red.es/ontsi/sites/ontsi/files/Perfil%20sociodemogr %C3%A1fico%20de%20los%20internautas%202017.pdf. Pan, L.-Y., & Chiou, J.-S. (2011). How much can you trust online information? Cues for perceived trustworthiness of consumer-generated online information. Journal of Interactive Marketing, 25(2), 67–74. Pansari, A., & Kumar, V. (2017). Customer engagement: The construct, antecedents, and consequences. Journal of the Academy of Marketing Science, 45(3), 294–311. Park, C., & Lee, T. (2009). Information direction, website reputation and eWOM effect: A moderating role of product type. Journal of Business Research, 62(1), 61–67. Park, D., & Kim, S. (2008). The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic Commerce Research and Applications, 7(4), 399–410. Park, D., & Lee, J. (2008). eWOM overload and its effect on consumer behavioral B. Hernández-Ortega Journal of Business Research 113 (2020) 422–435 434 intention depending on consumer involvement. Electronic Commerce Research and Applications, 7(4), 386–398. Park, S., & Nicolau, J. L. (2015). Asymmetric effects of online consumer review. Annals of Tourism Research, 50, 67–83. Pavlou, P., & Dimoka, A. (2006). The nature and role of feedback text comments in online marketplace: Implications for trust building, price premiums, and seller differentiation. Information Systems Research, 17(4), 392–414. Purnawirawan, N., De Pelsmacker, P., & Dens, N. (2012). Balance and sequence in online reviews: How perceived usefulness affects attitudes and intentions. Journal of Interactive Marketing, 26(4), 244–255. Schindler, R. M., & Bickart, B. (2005). Published word of mouth: Referable, consumergenerated information on the Internet. In C. P. Haugtvedt, K. A. Machleit, & R. F. Yalch (Eds.). Online consumer psychology: Understanding and influencing consumer behavior in the virtual world (pp. 35–61). Mahwah, NJ: Lawrence Erlbaum. Seddon, P., & Kiew, M. Y. (1996). A partial test and development of the DeLone and McLean model of IS success. Australian Journal of Information Systems, 4, 90–109. Sherif, M., & Hovland, C. I. (1961). Social judgment: Assimilation and contrast effects in communication and attitude change. Oxford, England: Yale University Press. Shi, X., & Liao, Z. (2017). Online consumer review and group-buying participation: The mediating effects of consumer beliefs. Telematics and Informatics, 34, 605–617. Sirakaya, E., & Wooside, A. G. (2005). Building and testing theories of decision making by travelers. Tourism Management, 26(6), 815–832. Söderlund, M. (2002). Customer familiarity and its effects on satisfaction and behavioral intentions. Pyschology and Marketing, 19, 861–879. Sparks, B., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and trust. Tourism Management, 32(6), 1310–1323. Staples, S., Wong, I., & Seddon, P. B. (2002). Having expectations of information systems benefits: Does it really matter? Information & Management, 40, 115–131. Tang, T., Fang, E., & Wang (2014). Is neutral really neutral? The effects of neutral usergenerated content on product sales. Journal of Marketing, 78, 41–58. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. Triantafillidou, A., & Siomkos, G. (2014). Consumption experience outcomes: Satisfaction, nostalgia intensity, word-of-mouth communication and behavioral intentions. Journal of Consumer Marketing, 31(6/7), 526–540. Tsao, W.-H., Hsieh, M.-T., Shih, L.-W., & Lin, T. M. Y. (2015). Compliance with eWOM: The influence of hotel reviews on booking intention from the perspective of consumer conformity. International Journal of Hospitality Management, 46, 99–111. Tse, D. K., & Wilton, P. C. (1988). Models of consumer satisfaction formation: An extension. Journal of Marketing Research, XXV, 204–212. Viglia, G., Furlan, R., & Ladrón-de-Guevara, A. (2014). Please, talk about it! When hotel popularity boosts preferences. International Journal of Hospitality Management, 42, 155–164. Vlachos, P. A., Pramatari, K., & Vrechopolos, A. (2011). Too much of a good thing: Curvilinear effects of service of service evaluation constructs and the mediating role of trust. Journal of Service Marketing, 25(6), 440–450. Walsh, G., Shiu, E., Hassan, L. M., Michaelidou, N., & Beatty, S. (2011). Emotions, storeenvironmental cues, store-choice criteria, and marketing outcomes. Journal of Business Research, 64, 737–744. Wang, F., Liu, X., & Fang, E. (2015). User reviews variance, critic reviews variance, and product sales: An exploration of customer breadth and depth effects. Journal of Retailing, 91(3), 372–389. Wirtz, J., & Chew, P. (2002). The effects of incentives deal proneness, satisfaction and tie strength on word-of-mouth behaviour. International Journal of Service Industry Management, 13(2), 141–162. Xu, J., & Schwartz, N. (2009). Do we really need a reason to indulge? Journal of Marketing Research, 46, 25–36. Xu, X. (2019). Examining an asymmetric effect between online customer reviews emphasis and overall satisfaction determinants. Journal of Business Research. https://doi. org/10.1016/j.jbusres.2018.07.022. Ye, Q., Law, R., Gu, B., & Chen, W. (2011). The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings. Computers in Human Behavior, 27, 634–639. Yi, Y. (1990). A critical review of consumer satisfaction. In V. A. Zeithaml (Ed.). Review of marketing (pp. 68–123). Chicago: American Marketing Association. Yi, Y., & La, S. (2003). The moderating role of confidence in expectations and the asymmetric influence of disconfirmation on customer satisfaction. The Services Industries Journal, 23(5), 20–47. Yim, C. K., Chan, K. W., & Hung, K. (2007). Multiple reference effects in service evaluations: Roles of alternative attractiveness and self-image congruity. Journal of Retailing, 83(1), 147–157. Yoo, K. H., & Gretzel, U. (2008). What motivates consumers to write online travel reviews? Information Technology & Tourism, 10(4), 283–295. Zeelenberg, M., & Pieters, R. (1999). Comparing service delivery to what might have been behavioral responses to regret and disappointment. Journal of Service Research, 2(1), 86–97. Zeelenberg, M., van Dijk, W. W., Manstead, A. S. R., & van der Plight, J. (2000). On bad decisions and disconfirmed expectancies: The psychology of regret and disappointment. Cognition and Emotion, 14, 521–541. Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1993). The nature and determinants of customer expectations of service. Journal of the Academy of Marketing Science, 21(1), 1–12. Zhang, J. Q., Craciun, G., & Shin, D. (2010). When does electronic word-of-mouth matter? A study of consumer product reviews. Journal of Business Research, 63(12), 1336–1341.
Xu X, (2020), Examining the role of emotion in online consumer reviews of various attributes in the surprise box shopping model, Decision Support Systems
Mots clés : Émotion des consommateurs, Avis des consommateurs en ligne, Attributs de produit et de service, Boite surprise
Résumé : La concurrence féroce entre les détaillants exige la création de nouveaux modèles de vente au détail. Le marketing émotionnel visant à susciter les émotions positives des consommateurs attire la demande. Dans ce contexte, le modèle de la boîte surprise, dans lequel une entreprise notifie les consommateurs par le biais d’un abonnement, puis envoie des boîtes de courrier de nouveaux produits sans répétition, a émergé pour se développer rapidement. Cette étude examine le rôle des émotions des consommateurs dans leur comportement de rédaction d’avis en ligne dans le contexte du modèle de magasinage surprise. Nous trouvons pour les attributs du produit, du service et de l’accomplissement, mais pas pour la valeur; les consommateurs ont tendance à commenter davantage dans les avis lorsqu’ils ont une émotion extrême, positive ou négative. Les consommateurs commentent encore plus lorsqu’ils ont une émotion extrêmement négative que lorsqu’ils ont une émotion extrêmement positive. En outre, nous constatons que la période d’abonnement, l’expérience et la satisfaction globale des consommateurs affectent leur comportement de révision, qui dépend des attributs particuliers sur lesquels les consommateurs commentent.
Grandes lignes :
Nous exaltons l’émotion des consommateurs et les retours en ligne dans le modèle de boîte surprise.
L’émotion positive et négative des consommateurs affecte les évaluations en ligne de chaque attribut.
La période d’abonnement et l’expérience affectent les évaluations en ligne de chaque attribut.
La satisfaction globale des consommateurs affecte les évaluations en ligne de chaque attribut.
L’émotion a différents effets modérateurs sur les avis en fonction des attributions.
[1] H. Abdi, Singular value decomposition (SVD) and generalized singular value decomposition, Encyclopedia of Measurement and Statistics (2007) 907–912. [2] R.M. Al-dweeri, Z.M. Obeidat, M.A. Al-dwiry, M.T. Alshurideh, A.M. Alhorani, The impact of e-service quality and e-loyalty on online shopping: moderating effect of esatisfaction and e-trust, International Journal of Marketing Studies 9 (2) (2017) 92–103. [3] M. Antioco, K. Coussement, Misreading of consumer dissatisfaction in online product reviews: writing style as a cause for bias, Int. J. Inf. Manag. 38 (1) (2018) 301–310. [4] D. Arli, C. Leo, F. Tjiptono, Investigating the impact of guilt and shame proneness on consumer ethics: a cross national study, Int. J. Consum. Stud. 40 (1) (2016) 2–13. [5] P. Bagdasarian, What it Takes to Start a Subscription Box Company, Accessed from,
https://startupnation.com/start-your-business/start-subscription-boxcompany/. [6] K. Baker, Singular value decomposition tutorial, The Ohio State University, 2005 Accessed from http://site.iugaza.edu.ps/ahdrouss/files/2010/03/Singular_Value_ Decomposition_Tutorial.pdf (Accessed on May 1, 2020). [7] M. Bashir, R. Verma, Internal factors & consequences of business model innovation, Manag. Decis. 57 (1) (2019) 262–290. [8] M. Bui, A.S. Krishen, K. Bates, Modeling regret effects on consumer post-purchase decisions, Eur. J. Mark. 45 (7/8) (2011) 1068–1090. [9] A.S. Cantallops, F. Salvi, New consumer behavior: a review of research on eWOM and hotels, Int. J. Hosp. Manag. 36 (2014) 41–51. [10] L.M. Caro, J.A.M. García, Cognitive–affective model of consumer satisfaction. An exploratory study within the framework of a sporting event, J. Bus. Res. 60 (2) (2007) 108–114. [11] J. Chen, S. Dibb, Consumer trust in the online retail context: exploring the antecedents and consequences, Psychol. Mark. 27 (4) (2010) 323–346. [12] L. Chen, A. Baird, D. Straub, A linguistic signaling model of social support exchange in online health communities, Decis. Support. Syst. 130 (2020) 113233. [13] Y.L. Chen, C.L. Chang, C.S. Yeh, Emotion classification of YouTube videos, Decis. Support. Syst. 101 (2017) 40–50. [14] C.M. Cheung, M.K. Lee, What drives consumers to spread electronic word of mouth in online consumer-opinion platforms, Decis. Support. Syst. 53 (1) (2012) 218–225. [15] A.Y.L. Chong, E. Ch’ng, M.J. Liu, B. Li, Predicting consumer product demands via big data: the roles of online promotional marketing and online reviews, Int. J. Prod. Res. 55 (17) (2017) 5142–5156. [16] E.K. Clemons, P.F. Nunes, Carrying your long tail: delighting your consumers and managing your operations, Decis. Support. Syst. 51 (4) (2011) 884–893. [17] G. Craciun, W. Zhou, Z. Shan, Discrete emotions effects on electronic word-ofmouth helpfulness: the moderating role of reviewer gender and contextual emotional tone, Decis. Support. Syst. 130 (2020) 113226. [18] W.R. Darden, M.J. Dorsch, An action strategy approach to examining shopping behavior, J. Bus. Res. 21 (3) (1990) 289–308. [19] DesMarais, C., 2016. Here’s how much people like their subscription boxes. Accessed from https://www.inc.com/christina-desmarais/heresdata-showing-thecrazy-growth-of-subscription-box-services-infographic.html. (Accessed on May 2, 2020). [20] S.P. Eslami, M. Ghasemaghaei, K. Hassanein, Which online reviews do consumers find most helpful? A multi-method investigation, Decis. Support. Syst. 113 (2018) 32–42. [21] N. Evangelopoulos, Citing Taylor: tracing Taylorism’s technical and sociotechnical duality through latent semantic analysis, Journal of Business & Management 17 (1) (2011) 57–74. [22] R. Filieri, What makes an online consumer review trustworthy? Ann. Tour. Res. 58 (2016) 46–64. [23] A. Gupta, M. Eilert, J.W. Gentry, Can I surprise myself? A conceptual framework of surprise self-gifting among consumers, J. Retail. Consum. Serv. 54 (2020) 101712. [24] A. Gustafsson, M.D. Johnson, Determining attribute importance in a service satisfaction model, J. Serv. Res. 7 (2) (2004) 124–141. [25] J. Gutman, A means-end chain model based on consumer categorization processes, J. Mark. 46 (2) (1982) 60–72. [26] T.H. Ho, Y.S. Zheng, Setting customer expectation in service delivery: an integrated marketing-operations perspective, Manag. Sci. 50 (4) (2004) 479–488. [27] Hopcroft, J., & Kannan, R. (2012). Chapter 4 Singular value decomposition in the book of Computer Science Theory for the Information Age. Accessed from https:// www.cs.cmu.edu/~venkatg/teaching/CStheory-infoage/hopcroft-kannan-feb2012. pdf. (Accessed on May 1, 2020). [28] S. Hosany, G. Prayag, Patterns of tourists’ emotional responses, satisfaction, and intention to recommend, J. Bus. Res. 66 (6) (2013) 730–737. [29] J.A. Howard, Consumer Behavior: Application of Theory, Vol. 325 McGraw-Hill, New York, 1977. [30] P. Husbands, H. Simon, C.H.Q. Ding, On the use of the singular value decomposition for text retrieval, Computational Information Retrieval 5 (2001) 145–156. [31] K. Hutter, S. Hoffmann, Surprise, surprise. Ambient media as promotion tool for retailers, J. Retail. 90 (1) (2014) 93–110. [32] S. Jang, J. Chung, V.R. Rao, The importance of functional and emotional content in online consumer reviews for product sales: Evidence from the mobile gaming market, Journal of Business Research (2019) In Press. [33] N.A. Johnson, R.B. Cooper, W.W. Chin, Anger and flaming in computer-mediated negotiation among strangers, Decis. Support. Syst. 46 (3) (2009) 660–672. [34] M.A. Jones, K.E. Reynolds, D.L. Mothersbaugh, S.E. Beatty, The positive and negative effects of switching costs on relational outcomes, J. Serv. Res. 9 (4) (2007) 335–355. [35] M. Kandaurova, S.H.M. Lee, The effects of virtual reality (VR) on charitable giving: the role of empathy, guilt, responsibility, and social exclusion, J. Bus. Res. 100 (2019) 571–580. [36] J. Kim, P. Gupta, Emotional expressions in online user reviews: how they influence consumers’ product evaluations, J. Bus. Res. 65 (7) (2012) 985–992. [37] R.A. King, P. Racherla, V.D. Bush, What we know and don’t know about online word-of-mouth: a review and synthesis of the literature, J. Interact. Mark. 28 (3) (2014) 167–183. [38] H.S. Krishnan, R.W. Olshavsky, The Dual Role of Emotions in Consumer Satisfaction/Dissatisfaction, ACR North American Advances, 1995. [39] S.S. Kulkarni, Uday M. Apte, N.E. Evangelopoulos, The use of latent semantic analysis in operations management research, Decis. Sci. 45 (5) (2014) 971–994. [40] H. Kuokkanen, W. Sun, Companies, meet ethical consumers: strategic CSR management to impact consumer choice, Journal of Business Ethics (2019) In Press. [41] R. Ladhari, N. Souiden, B. Dufour, The role of emotions in utilitarian service settings: the effects of emotional satisfaction on product perception and behavioral intentions, J. Retail. Consum. Serv. 34 (2017) 10–18. [42] C.H. Lee, D.A. Cranage, Toward understanding consumer processing of negative online word-of-mouth communication: the roles of opinion consensus and organizational response strategies, Journal of Hospitality & Tourism Research 38 (3) (2014) 330–360. [43] M. Lee, M. Jeong, J. Lee, Roles of negative emotions in customers’ perceived helpfulness of hotel reviews on a user-generated review website, Int. J. Contemp. Hosp. Manag. 29 (2) (2017) 762–783. [44] Y.K. Lee, C.K. Lee, S.K. Lee, B.J. Babin, Festivalscapes and patrons’ emotions, satisfaction, and loyalty, J. Bus. Res. 61 (1) (2008) 56–64. [45] Q. Li, X. Guan, T. Shi, W. Jiao, Green product design with competition and fairness concerns in the circular economy era, Int. J. Prod. Res. 58 (1) (2020) 165–179. [46] Y.S. Lii, E. Sy, Internet differential pricing: effects on consumer price perception, emotions, and behavioral responses, Comput. Hum. Behav. 25 (3) (2009) 770–777. [47] Q. Liu, S. Shum, Pricing and capacity rationing with customer disappointment aversion, Prod. Oper. Manag. 22 (5) (2013) 1269–1286. [48] Y. Liu, C. Jiang, H. Zhao, Assessing product competitive advantages from the perspective of customers by mining user-generated content on social media, Decis. Support. Syst. 123 (2019) 113079. [49] N. López-Mosquera, M. Sánchez, The influence of personal values in the economicuse valuation of peri-urban green spaces: an application of the means-end chain theory, Tour. Manag. 32 (4) (2011) 875–889. [50] M.S.I. Malik, A. Hussain, Helpfulness of product reviews as a function of discrete positive and negative emotions, Comput. Hum. Behav. 73 (2017) 290–302. [51] E.C. Malthouse, M. Haenlein, B. Skiera, E. Wege, M. Zhang, Managing customer relationships in the social media era: introducing the social CRM house, J. Interact. Mark. 27 (4) (2013) 270–280. [52] C. Mathwick, J. Mosteller, Online reviewer engagement: a typology based on reviewer motivations, J. Serv. Res. 20 (2) (2017) 204–218. [53] J.R. McColl-Kennedy, P.G. Patterson, A.K. Smith, M.K. Brady, Customer rage episodes: emotions, expressions and behaviors, J. Retail. 85 (2) (2009) 222–237. [54] W.U. Meyer, R. Reisenzein, A. Schützwohl, Toward a process analysis of emotions: the case of surprise, Motiv. Emot. 21 (3) (1997) 251–274. [55] M. Miele, V. Parisi, Consumer concerns about animal welfare and food choice, X. Xu Decision Support Systems xxx (xxxx) xxxx 11 Italian Report on Laddering Interviews, 2000. [56] T.L. Ngo-Ye, A.P. Sinha, The influence of reviewer engagement characteristics on online review helpfulness: a text regression model, Decis. Support. Syst. 61 (2014) 47–58. [57] M. Niepel, U. Rudolph, A. Schützwohl, W.U. Meyer, Temporal characteristics of the surprise reaction induced by schema-discrepant visual and auditory events, Cognit. Emot. 8 (5) (1994) 433–452. [58] P.U. Nyer, M. Gopinath, Effects of complaining versus negative word of mouth on subsequent changes in satisfaction: the role of public commitment, Psychol. Mark. 22 (12) (2005) 937–953. [59] R.L. Oliver, A cognitive model of the antecedents and consequences of satisfaction decisions, J. Mark. Res. 17 (4) (1980) 460–469. [60] J.C. Olson, T.J. Reynolds, Understanding consumers’ cognitive structures: implications for advertising strategy, Advertising and Consumer Psychology 1 (1983) 77–90. [61] D.H. Park, J. Lee, eWOM overload and its effect on consumer behavioral intention depending on consumer involvement, Electron. Commer. Res. Appl. 7 (4) (2008) 386–398. [62] S. Park, J.L. Nicolau, Asymmetric effects of online consumer reviews, Ann. Tour. Res. 50 (2015) 67–83. [63] J. Previte, R. Russell-Bennett, R. Mulcahy, C. Hartel, The role of emotional value for reading and giving eWOM in altruistic services, J. Bus. Res. 99 (2019) 157–166. [64] A.T. Purcell, Abstract and specific physical attributes and the experience of landscape, J. Environ. Manag. 34 (3) (1992) 159–177. [65] T. Reimer, M. Benkenstein, Not just for the recommender: how eWOM incentives influence the recommendation audience, J. Bus. Res. 86 (2018) 11–21. [66] T.J. Reynolds, J. Gutman, Laddering theory, method, analysis, and interpretation, J. Advert. Res. 28 (1) (1988) 11–31. [67] T.J. Reynolds, J.C. Olson, Understanding Consumer Decision Making: The MeansEnd Approach to Marketing and Advertising Strategy, Psychology Press, 2001. [68] C. Ruiz-Mafe, K. Chatzipanagiotou, R. Curras-Perez, The role of emotions and conflicting online reviews on consumers’ purchase intentions, J. Bus. Res. 89 (2018) 336–344. [69] M. Salehan, D.J. Kim, Predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics, Decis. Support. Syst. 81 (2016) 30–40. [70] C.M. Sashi, Customer engagement, buyer-seller relationships, and social media, Manag. Decis. 50 (2) (2012) 253–272. [71] Segran, E. (2018). Inside the $2.6 billion subscription box wars. Accessed from https://www.fastcompany.com/90248232/inside-the-2-6-billion-subscription-boxwars. Accessed on May 2, 2020. [72] E.A. Selby, A. Kranzler, E. Panza, K.B. Fehling, Bidirectional-compounding effects of rumination and negative emotion in predicting impulsive behavior: implications for emotional cascades, J. Pers. 84 (2) (2016) 139–153. [73] A. Sidorova, N. Evangelopoulos, J.S. Valacich, T. Ramakrishnan, Uncovering the intellectual core of the information systems discipline, MIS Q. 32 (3) (2008) 467–482. [74] N. Stephens, K.P. Gwinner, Why don’t some people complain? A cognitive-emotive process model of consumer complaint behavior, J. Acad. Mark. Sci. 26 (3) (1998) 172–189. [75] Y. Sun, X. Dong, S. McIntyre, Motivation of user-generated content: social connectedness moderates the effects of monetary rewards, Mark. Sci. 36 (3) (2017) 329–337. [77] T. Suwelack, J. Hogreve, W.D. Hoyer, Understanding money-back guarantees: cognitive, affective, and behavioral outcomes, J. Retail. 87 (4) (2011) 462–478. [78] R. Sylvester, 30 Surprising Subscription Box Ideas, Accessed from, 2019. https:// www.realsimple.com/holidays-entertaining/gifts/gift-of-the-month. [79] R. Terpend, B.B. Tyler, D.R. Krause, R.B. Handfield, Buyer–supplier relationships: derived value over two decades, J. Supply Chain Manag. 44 (2) (2008) 28–55. [80] R. Ullah, N. Amblee, W. Kim, H. Lee, From valence to emotions: exploring the distribution of emotions in online product reviews, Decis. Support. Syst. 81 (2016) 41–53. [81] T. Verhagen, A. Nauta, F. Feldberg, Negative online word-of-mouth: Behavioral indicator or emotional release? Comput. Hum. Behav. 29 (4) (2013) 1430–1440. [82] Y.J. Wang, M.S. Minor, J. Wei, Aesthetics and the online shopping environment: understanding consumer responses, J. Retail. 87 (1) (2011) 46–58. [83] Y. Wang, B.T. Hazen, Consumer product knowledge and intention to purchase remanufactured products, Int. J. Prod. Econ. 181 (2016) 460–469. [84] R.P. Weber, Basic content analysis, Sage University Series on Quantitative Applications in the Social Sciences, Sage, Beverly Hills, CA, and London, 1985, pp. 7–49. [85] H. Woo, B. Ramkumar, Who seeks a surprise box? Predictors of consumers’ use of fashion and beauty subscription-based online services (SOS), J. Retail. Consum. Serv. 41 (2018) 121–130. [86] Q. Xu, S. Gregor, Q. Shen, Q. Ma, W. Zhang, A. Riaz, The power of emotions in online decision making: a study of seller reputation using fMRI, Decis. Support. Syst. 131 (2020) 113247. [87] X. Xu, How do consumers in the sharing economy value sharing? Evidence from online reviews, Decis. Support. Syst. 128 (2020) 113162. [88] X. Xu, C. Lee, Utilizing the platform economy effect through EWOM: does the platform matter? Int. J. Prod. Econ. 227 (2020) 107663. [89] Z. Yan, T. Wang, Y. Chen, H. Zhang, Knowledge sharing in online health communities: a social exchange theory perspective, Inf. Manag. 53 (5) (2016) 643–653. [90] D. Yin, S.D. Bond, H. Zhang, Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews, MIS Q. 38 (2) (2014) 539–560. [91] K.H. Yoo, U. Gretzel, What motivates consumers to write online travel reviews? Information Technology & Tourism 10 (4) (2008) 283–295. [92] S. Young, B. Feigin, Using the benefit chain for improved strategy formulation, J. Mark. 39 (3) (1975) 72–74. [93] W. Zhang, H. Xu, W. Wan, Weakness finder: find product weakness from Chinese reviews by using aspects based sentiment analysis, Expert Syst. Appl. 39 (11) (2012) 10283–10291. [94] Z. Zhang, C. Guo, P. Goes, Product comparison networks for competitive analysis of online word-of-mouth, ACM Transactions on Management Information Systems (TMIS) 3 (4) (2013) 1–22.
Helversen B, Abramczuk K, Kopeć W, Nielek R, (2018), Influence of consumer reviews on online purchasing decisions in older and younger adults, Decision Support Systems
Mots clés : Prise de décision des consommateurs, Adultes, Évaluations des consommateurs, Avis des consommateurs, Preuves anecdotiques
Résumé : Cet article montre que les attributs des produits, les notes moyennes des consommateurs et les avis de consommateurs positifs ou négatifs riches en effets ont influencé les décisions d’achat en ligne hypothétiques des jeunes et des personnes âgées. Conformément à des recherches antérieures, on voit que les jeunes adultes utilisaient les trois types d’information : ils préféraient clairement des produits avec de meilleurs attributs et des notes moyennes des consommateurs plus élevées. Si faire un choix était difficile car il impliquait des compromis entre les attributs du produit, la plupart des jeunes adultes ont choisi le produit le mieux noté. Cependant, la préférence pour le produit mieux noté pourrait être annulée par un seul examen négatif ou positif riche en effets. Les personnes âgées ont été fortement influencées par un seul examen négatif riche en effets et ont également pris en considération les attributs du produit; cependant, ils n’ont pas pris en compte les notes moyennes des consommateurs ou les avis positifs uniques riches en effets. Ces résultats suggèrent que les personnes âgées ne considèrent pas les informations agrégées des consommateurs et les avis positifs se concentrant sur les expériences positives avec le produit, mais sont facilement influencées par les avis faisant état d’expériences négatives.
Grandes lignes :
Sont étudiées les intentions d’achat en ligne chez les personnes âgées et les étudiants.
Est étudié l’impact des notes moyennes des consommateurs et des critiques émotionnelles uniques.
Les étudiants mais pas les adultes plus âgés ont été fortement influencés par les notes moyennes des consommateurs.
Chez les étudiants, les avis positifs et négatifs ont annulé l’effet des notes moyennes.
Les personnes âgées ont été influencées par des critiques individuelles négatives, mais pas par des critiques positives.
[1] J.W. Lian, D.C. Yen, Online shopping drivers and barriers for older adults: age and gender differences, Computers in Human Behavior 37 (2014) 133–143. [2] M. Law, M. Ng, Age and gender differences: understanding mature online users with the online purchase intention model, Journal of Global Scholars of Marketing Science 26 (3) (2016) 248–269. [3] Y.J. Ma, H. Kim, H.-h. Lee, Effect of individual differences on online review perception and usage behavior: the need for cognitive closure and demographics, Journal of the Korean Society of Clothing and Textiles 36 (12) (2012) 1270–1284. [4] P.Y. Chen, S.y. Wu, J. Yoon, The impact of online recommendations and consumer feedback on sales, ICIS 2004 Proceedings, 2004, p. 58. [5] K. Floyd, R. Freling, S. Alhoqail, H.Y. Cho, T. Freling, How online product reviews affect retail sales: a meta-analysis, Journal of Retailing 90 (2) (2014) 217–232, https://doi.org/10.1016/j.jretai.2014.04.004. [6] R.A. King, P. Racherla, V.D. Bush, What we know and don’t know about online word-of-mouth: a review and synthesis of the literature, Journal of Interactive Marketing 28 (3) (2014) 167–183. [7] N. Purnawirawan, M. Eisend, P. De Pelsmacker, N. Dens, A meta-analytic investigation of the role of valence in online reviews, Journal of Interactive Marketing 31 (2015) 17–27, https://doi.org/10.1016/j.intmar.2015.05.001. [8] Y. Liu, Word of mouth for movies: its dynamics and impact on box office revenue, Journal of Marketing 70 (3) (2006) 74–89. [9] E. Maslowska, E.C. Malthouse, V. Viswanathan, Do customer reviews drive purchase decisions? The moderating roles of review exposure and price, Decision Support Systems 98 (2017) 1–9. [10] S. Karimi, F. Wang, Online review helpfulness: impact of reviewer profile image, Decision Support Systems 96 (2017) 39–48. [11] J. Lee, D.h. Park, I. Han, The effect of negative online consumer reviews on product attitude: an information processing view, Electronic Commerce Research and Applications 7 (2008) 341–352. [12] S. Sen, D. Lerman, Why are you telling me this? An examination into negative consumer reviews on the web, Journal of Interactive Marketing 21 (4) (2007) 76–94. [13] C. Betsch, N. Haase, F. Renkewitz, P. Schmid, The narrative bias revisited: what drives the biasing influence of narrative information on risk perceptions? Judgment and Decision Making 10 (3) (2015) 241–264. [14] P. Rozin, E.B. Royzman, Negativity bias, negativity dominance, and contagion, Personality and Social Psychology Review 5 (4) (2001) 296–320. [15] P.F. Wu, In search of negativity bias: an empirical study of perceived helpfulness of online reviews, Psychology & Marketing 30 (11) (2013) 971–984. [16] BrightLocal, Local Consumer Review Survey 2016, (2016) https://www.brightlocal. com/learn/local-consumer-review-survey/. [17] S. Hong, H.S. Park, Computer-mediated persuasion in online reviews: statistical versus narrative evidence, Computers in Human Behavior 28 (3) (2012) 906–919. [18] M. Ziegele, M. Weber, Example, please! Comparing the effects of single customer reviews and aggregate review scores on online shoppers’ product evaluations, Journal of Consumer Behaviour 14 (2015) 103–114. [19] C. Betsch, C. Ulshöfer, F. Renkewitz, T. Betsch, The influence of narrative v. statistical information on perceiving vaccination risks, Medical Decision Making 31 (5) (2011) 742–753, https://doi.org/10.1177/0272989X11400419. [20] P.A. Ubel, C. Jepson, J. Baron, The inclusion of patient testimonials in decision aids, Medical Decision Making 21 (1) (2001) 60–68. [21] A. Winterbottom, H.L. Bekker, M. Conner, A. Mooney, Does narrative information bias individual’s decision making? A systematic review, Social Science and Medicine 67 (12) (2008) 2079–2088. [23] P.B. Baltes, U.M. Staudinger, U. Lindenberger, Lifespan psychology: theory and application to intellectual functioning. Annual Review of Psychology 50 (1999) 471–507, https://doi.org/10.1146/annurev.psych.50.1.471. [24] L.L. Carstensen, The influence of a sense of time on human development, Science 312 (5782) (2006) 1913–1915. [25] E. Peters, T.M. Hess, D. Västfjäll, C. Auman, Adult age differences in dual information processes: implications for the role of affective and deliberative processes in older adults’ decision making, Perspectives on Psychological Science 2 (1) (2007) 1–23. [26] R. Mata, T. Pachur, B. von Helversen, R. Hertwig, J. Rieskamp, L. Schooler, Ecological rationality: a framework for understanding and aiding the aging decision maker, Frontiers in Decision Neuroscience 6 (Article 19) (2012) 1–6, https://doi. org/10.3389/fnins.2012.00019. [27] W.A. Rogers, A.J. Stronge, A.D. Fisk, Technology and Aging, Reviews of human factors and ergonomics 1 (1) (2005) 130–171. [28] T.A. Salthouse, Mental exercise and mental aging: evaluating the validity of the “use it or lose it” hypothesis, Perspectives on Psychological Science 1 (1) (2006) 68–87. [29] T. Salthouse, Consequences of age-related cognitive declines, Annual Review of Psychology 63 (2012) 201–226. [30] M.L. Finucane, C.K. Mertz, P. Slovic, E.S. Schmidt, Task complexity and older adults’ decision-making competence, Psychology and Aging 20 (1) (2005) 71–84. [31] R. Frey, R. Mata, R. Hertwig, The role of cognitive abilities in decisions from experience: age differences emerge as a function of choice set size, Cognition 142 (2015) 60–80, https://doi.org/10.1016/j.cognition.2015.05.004. [32] R. Mata, L. Nunes, When less is enough: cognitive aging, information search, and decision quality in consumer choice, Psychology and Aging 25 (2010) 289–298, https://doi.org/10.1037/a0017927. [33] R. Mata, L.J. Schooler, J. Rieskamp, The aging decision maker: cognitive aging and the adaptive selection of decision strategies, Psychology & Aging 22 (2007) 101037/0882–7974224796. [34] B. von Helversen, R. Mata, Losing a dime with a satisfied mind: positive affect predicts less search in sequential decision making, Psychology and Aging 27 (4) (2012) 825–839, https://doi.org/10.1037/a0027845. [35] G. Gigerenzer, P.M. Toddthe ABC Research Group, Simple Heuristics That Make Us Smart, Oxford University Press, 1999. [36] R. Mata, B. von Helversen, J. Rieskamp, Learning to choose: cognitive aging and strategy selection learning in decision making, Psychology and Aging 25 (2) (2010) 299–309, https://doi.org/10.1037/a0018923. [37] J.A. Mikels, C.E. Löckenhoff, S.J. Maglio, L.L. Carstensen, M.K. Goldstein, A. Garber, Following your heart or your head: focusing on emotions versus information differentially influences the decisions of younger and older adults, Journal of Experimental Psychology: Applied 16 (1) (2010) 87. [38] C.A. Cole, S.K. Balasubramanian, Age differences in consumers’ search for information: public policy implications, Journal of Consumer Research 20 (1993) 157–169. [39] C.M. Schaninger, D. Sciglimpaglia, The influence of cognitive personality traits and demographics on consumer information acquisition, Journal of Consumer Research 8 (2) (1981) 208–216. [40] R. Lambert-Pandraud, G. Laurent, E. Lapersonne, Repeat purchasing of new automobiles by older consumers: empirical evidence and interpretations, Journal of Marketing 69 (2) (2005) 97–113. [41] S.M. Carpenter, C. Yoon, Aging and consumer decision making, Annals of the New York Academy of Sciences 1235 (1) (2011) 1–12. [42] Q. Ma, K. Chen, A.H.S. Chan, P.L. Teh, Acceptance of ICTs by older adults: a review of recent studies, International Conference on Human Aspects of IT for the Aged Population, Springer, 2015, pp. 239–249. [43] A. Ahmed, A.S. Sathish, Determinants of online shopping adoption: meta analysis and review, European Journal of Social Sciences 49 (4) (2015) 483–510. [44] G. Cohen, Language comprehension in old age, Cognitive Psychology 11 (4) (1979) 412–429. [45] R. De Beni, E. Borella, B. Carretti, Reading comprehension in aging: the role of working memory and metacomprehension, Aging, Neuropsychology, and Cognition 14 (2) (2007) 189–212, https://doi.org/10.1080/13825580500229213. [46] L.H. Phillips, R.D. MacLean, R. Allen, Age and the understanding of emotions neuropsychological and sociocognitive perspectives, The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences 57 (6) (2002) 526–P530. [47] L.L. Carstensen, Motivation for social contact across the life span: a theory of socioemotional selectivity, Nebraska symposium on motivation, vol. 40, 1993, pp. 209–254. [48] S.T. Charles, L.L. Carstensen, Social and emotional aging, Annual Review of Psychology 61 (2010) 383–409. [49] A.E. Reed, L. Chan, J.A. Mikels, Meta-analysis of the age-related positivity effect: age differences in preferences for positive over negative information, Psychology and Aging 29 (1) (2014) 1–15. [50] H.H. Fung, L.L. Carstensen, Sending memorable messages to the old: age differences in preferences and memory for advertisements, Journal of Personality and Social Psychology 85 (1) (2003) 163–178. [51] M.K. Depping, A.M. Freund, Normal aging and decision making: the role of motivation, Human Development 54 (6) (2011) 349–367. [52] M.J. Frank, L. Kong, Learning to avoid in older age, Psychology and Aging 23 (2) (2008) 392–398, https://doi.org/10.1037/0882-7974.23.2.392. [53] D. Hämmerer, S.C. Li, V. Müller, U. Lindenberger, Life span differences in electrophysiological correlates of monitoring gains and losses during probabilistic reinforcement learning, Journal of Cognitive Neuroscience 23 (3) (2011) 579–592. [54] B. Eppinger, N.W. Schuck, L.E. Nystrom, J.D. Cohen, Reduced striatal responses to B. von Helversen et al. Decision Support Systems 113 (2018) 1–10 9 reward prediction errors in older compared with younger adults, Journal of Neuroscience 33 (24) (2013) 9905–9912. [55] A. Mantonakis, P. Rodero, I. Lesschaeve, R. Hastie, Order in choice: effects of serial position on preferences, Psychological Science 20 (11) (2009) 1309–1312, https:// doi.org/10.1111/j.1467-9280.2009.02453.x. [56] C. Mogilner, B. Shiv, S.S. Iyengar, Eternal quest for the best: sequential (vs. simultaneous) option presentation undermines choice commitment, Journal of Consumer Research 39 (6) (2013) 1300–1312, https://doi.org/10.1086/668534. [57] A. Dieckmann, K. Dippold, Compensatory versus noncompensatory models for predicting consumer preferences, Judgment and Decision Making 4 (3) (2009) 200–213. [58] W. Kopeć, K. Skorupska, A. Jaskulska, K. Abramczuk, R. Nielek, A. Wierzbicki, LivingLab PJAIT: towards better urban participation of seniors, Proceedings of the International Conference on Web Intelligence, ACM, 2017, pp. 1085–1092. [59] W. Kopeć, B. Balcerzak, R. Nielek, G. Kowalik, A. Wierzbicki, F. Casati, Older adults and hackathons: a qualitative study, Empirical Software Engineering (2017) 1–36. [60] W. Kopeć, K. Abramczuk, B. Balcerzak, M. Juźwin, K. Gniadzik, G. Kowalik, R. Nielek, A location-based game for two generations: teaching mobile technology to the elderly with the support of young volunteers, eHealth 360, Springer, 2017, pp. 84–91. [61] D. Godes, D. Mayzlin, Using online conversations to study word-of-mouth communication, Marketing Science 23 (4) (2004) 545–560. [62] P. Resnick, R. Zeckhauser, J. Swanson, K. Lockwood, The value of reputation on eBay: a controlled experiment, Experimental Economics 9 (2) (2006) 79–101. [63] H. Singmann, B. Bolker, J. Westfall, F. Aust, afex: Analysis of Factorial Experiments, (2016) https://CRAN.R-project.org/package=afex r package version 0.16-1. [64] R.V. Lenth, Least-squares means: the R package lsmeans, Journal of Statistical Software 69 (1) (2016) 1–33, https://doi.org/10.18637/jss.v069.i01. [65] F. Schieber, Human factors and aging: identifying and compensating for age-related deficits in sensory and cognitive function, Impact of technology on successful aging, 2003, pp. 42–84. [66] L.H. Phillips, R. Allen, Adult aging and the perceived intensity of emotions in faces and stories, Aging Clinical and Experimental Research 16 (3) (2004) 190–199. [67] N. Hu, N.S. Koh, S.K. Reddy, Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales, Decision Support Systems 57 (2014) 42–53.
Milena Janjevic, Matthias Winkenbach, Characterizing urban last-mile distribution strategies in mature andemerging e-commerce markets Transportation Research Part A 133 (2020) 164–196
Introduction :
Les défis qui concernent la livraison du dernier kilomètre en milieu urbain sont de plus en plus importants sur les marchés émergents. Le développement d’économies se caractérise par des taux d’urbanisation élevés, ce qui entraîne l’émergence de villes particulièrement grandes et densément peuplées.
En raison de la croissance rapide de ces villes et de l’augmentation des niveaux de revenus, le développement d’infrastructures adéquates et la planification des transports ne peut pas suivre la forte augmentation du nombre de véhicules (Kin et al., 2017 ; Kutzbach, 2010). En raison des caractéristiques topologiques et de la qualité globale de l’infrastructure routière, certaines voies ne donnent pas l’accès aux grands véhicules commerciaux ou même aux voitures (Blanco et Fransoo, 2013).
Afin de répondre aux défis du commerce électronique urbain du dernier kilomètre, les entreprises doivent rechercher des modèles de distribution performants dans de multiples dimensions, telles que la rentabilité, la satisfaction du client et la durabilité.
Dans cet article, Milena Janjevic, Matthias Winkenbach abordent les questions suivantes : Quelles sont les variables qui caractérisent le commerce électronique urbain du dernier kilomètre ? Quelles interrelations peuvent être observées entre ces variables dans les pratiques actuelles du secteur ? Quels éléments du contexte local influencent le choix d’une stratégie de distribution du commerce électronique dans les zones urbaines du dernier kilomètre ?
Mots clés : commerce électronique, zones urbaines du dernier kilomètre, stratégies de distribution, structure logistique
Développement :
Les préférences des clients locaux ont une incidence sur les possibilités d’échange de produits. Par exemple, la livraison en points relais est une option préférée par 13 % des consommateurs en France contre seulement 4 % des consommateurs aux États-Unis (UPS, 2015b, 2015c).
Les préférences des clients locaux ont également une incidence sur les délais de livraison. Les consommateurs sont prêts à attendre 4 jours en moyenne pour la livraison en Asie et 8 jours pour une livraison au Brésil (UPS, 2015c). Au Japon, les consommateurs sur internet sont très sensibles au facteur temps, ce qui a poussé à offrir des services de livraison le jour même dès 2009 (Akimoto, 2009 ; Hayashi et al., 2014 ; UPS, 2015d). Enfin, comme indiqué par Gevaers et al. (2011), les sensibilités environnementales des clients peuvent avoir un impact sur les stratégies de distribution. Par exemple, les clients demandent de plus en plus aux prestataires logistiques de réduire leurs émissions de carbone, même s’ils ne sont pas toujours prêts à payer plus ou attendre plus longtemps pour leurs biens en échange d’un service plus écologique (Gevaers et al., 2011).
La structure du marché de la logistique locale influence le choix des modèles de gouvernance pour les nœuds d’approvisionnement, les nœuds de transbordement et les opérations de transport. Les marchés matures sont généralement caractérisés par un marché postal et des colis développé. Dans un tel contexte, les détaillants en ligne sont plus susceptibles d’externaliser les fonctions logistiques (Rao et al., 2009). Par exemple, au Japon, trois opérateurs de colis et de services postaux (Yamato Transport, Sagawa Express, Japan Post) s’occupent de 92,5 % de l’ensemble des livraisons (Yano et Saito, 2016). Le marché français de la logistique est très défragmenté. Les marchés émergents sont souvent caractérisés par une mauvaise qualité des services postaux, bien que les situations varient selon les pays et les régions. L’indice intégré pour le développement postal (2IPD) publié par l’Union postale universelle (2018) peut être utilisé pour illustrer ceci. Les pays dont les marchés sont matures ont généralement de bons résultats (par exemple, le Japon a un score de 91,6, l’Allemagne de 91,3 et les États-Unis de 91,3). ), alors que les pays des marchés émergents sont généralement moins performants (par exemple, le Brésil avec un score de 54,0, le Nigeria 50,9, le Kenya
33,7, Arabie Saoudite 39,7). Il convient toutefois de noter que les services postaux chinois et indiens obtiennent d’assez bons résultats, avec des scores de 69,5 et 66,1 respectivement. En outre, dans les pays émergents, les détaillants en ligne sont souvent confrontés à un marché logistique sous-développé. Par exemple, dans ces pays, une grande partie des marchandises est généralement acheminée par transport pour compte propre, c’est-à-dire par l’expéditeur.
Les entreprises de commerce électronique qui entrent sur ces marchés sont donc souvent obligés de développer leur propre réseau de distribution ou d’investir dans des acteurs du marché existants, comme l’illustrent les exemples de Alibaba, JingDong, Konga, Jumia, Flipkart et Souq.com. D’autres détaillants en ligne développent leur réseau de distribution en acquérant d’autres entreprises. Par exemple, B2W au Brésil a développé son réseau de distribution au fil du temps en acquérant des sociétés de distribution (par exemple, Direct services de colis et Click-Rodo) et a créé B2W Fullfilment. Les détaillants en ligne qui développent des capacités logistiques internes dans les pays émergents proposent souvent des services à d’autres entreprises, ce qui leur permet d’augmenter le volume global qu’elles traitent et de réaliser des économies d’échelles (Hayashi et al., 2014). Par exemple, la branche logistique de Konga offre un ensemble complet de services de logistique et de gestion de la chaîne d’approvisionnement (In, 2016). De même, Ekart, la branche logistique de Flipkart, dessert même des détaillants en ligne rivaux (par exemple, Paytm, Jabong et ShopClues).
Conclusion :
Ce document présente un cadre intégré permettant de caractériser et de comparer les stratégies de distribution du commerce électronique dans les zones urbaines du dernier kilomètre pour à la fois sur les marchés développés et émergents. Une analyse bibliographique exhaustive et une analyse complémentaire d’études de cas révèlent divers dispositifs opérationnels employés par les détaillants en ligne et les autres entreprises de distribution du commerce électronique dans le monde. Ainsi, le choix entre les vélos-cargo et les camionnettes comme le type de véhicule de livraison doit tenir compte de l’emplacement des installations logistiques, du délai de livraison requis, de l’accessibilité du service la disponibilité des infrastructures de stationnement, la densité des commandes, le coût de la main-d’œuvre et les préférences des consommateurs locaux.
Références :
Advangent, 2015. China Post: 5,000 Self-Collect Outlets for Alibaba’s Logistic Network Cainiao – Advangent [WWW Document]. URL http://www.advangent.com/
Agatz, N.A., Fleischmann, M., Van Nunen, J.A., 2008. E-fulfillment and multi-channel distribution–a review. Eur. J. Operat. Res. 187, 339–356.
Akimoto, A.A., 2009. Amazon Japan Starts “The Same Day” Delivery Service [WWW Document]. Asiajin. URL http://asiajin.com/blog/2009/10/03/amazon-japanstarts-
Akolawala, T., 2015. Flipkart launches 20 offline pickup centres in India to deal with last mile delivery issues in small cities [WWW Document]. URL http://www.bgr.
Allen, J., Browne, M., Woodburn, A., Leonardi, J., 2012. The role of urban consolidation centres in sustainable freight transport. Transp. Rev. 32, 473–490. https://doi.
org/10.1080/01441647.2012.688074.
Allen, J., Piecyk, M., Piotrowska, M., 2016. An Analysis of the Parcels Market and Parcel carriers’ operations in the UK (Carried out as part of the FTC2050 project).
University of Westminster, London.
Allen, J., Piecyk, M., Piotrowska, M., McLeod, F., Cherrett, T., Ghali, K., Nguyen, T., Bektas, T., Bates, O., Friday, A., Wise, S., Austwick, M., 2017. Understanding the
impact of e-commerce on last-mile light goods vehicle activity in urban areas: the case of London. Transport. Res. Part D: Transp. Environ. https://doi.org/10.
1016/j.trd.2017.07.020.
Allen, J., Browne, M., Piotrowska, M., Woodburn, A., 2010. Freight Quality Partnerships in the UK – an analysis of their work and achievements. Technical report
carried out as part of Green Logistics project. University of Westminster, London.
Alyoubi, A.A., 2015. E-commerce in developing countries and how to develop them during the introduction of modern systems. Proc. Comput. Sci. 65, 479–483.
Augereau, V., Dablanc, L., 2008. An evaluation of recent pick-up point experiments in European cities: the rise of two competing models. Innovat. City Logistics
303–320.
Bask, A., Lipponen, M., Tinnilä, M., 2012. E-Commerce logistics: a literature research review and topics for future research. Int. J. E-Serv. Mob. Appl. (IJESMA) 4 (3),
1–22.
Bhagat, S., Raju, R., Bhadange, S.P., 2018. Supply chain management – backbone of e-retailing: case of Flipkart. Int. J. Curr. Eng. Scientific Res. 5 (5), 1–4.
Bailey, J.P., Rabinovich, E., 2005. Internet book retailing and supply chain management: an analytical study of inventory location speculation and postponement.
Transport. Res. Part E: Logistics Transport. Rev. 41, 159–177. https://doi.org/10.1016/j.tre.2004.03.004.
Baindur, D., Macário, R.M., 2013. Mumbai lunch box delivery system: a transferable benchmark in urban logistics? Res. Transport. Econ., Econ. Sustain. Transport
India 38, 110–121. https://doi.org/10.1016/j.retrec.2012.05.002.
Bektas, T., Crainic, T.G., Van Woensel, T., 2015. From managing urban freight to smart city logistics networks (No. CIRRELT-2015-17). CIRRELT, Université de
Montréal.
Berthelot, B., 2016. Voici comment Amazon veut livrer les Parisiens en moins d’une heure [WWW Document]. Capital.fr. URL https://www.capital.fr/entreprisesmarches/
BESTFACT, 2015. Urban Delivery Centre (UDC) in Beaugrenelle, central Paris, France [WWW Document]. URL http://www.bestfact.net/wp-content/uploads/2016/
01/CL1_135_QuickInfo-Beaugrenelle-16Dec2015.pdf.
Bharucha, J.P., 2017. Issues in the Home Delivery Model in India. Int. J. Supply Chain Manage. 6, 145–151.
Blanco, E., Fransoo, J., 2013. Reaching 50 million Nanostores: Retail Distribution in Emerging Megacities. Technische Universiteit Eindhoven, Eindhoven, The
Netherlands.
Boccia, M., Crainic, T.G., Sforza, A., Sterle, C., 2011. Location-routing models for designing a two-echelon freight distribution system (No. CIRRELT-2011-06).
CIRRELT, Université de Montréal.
Botella, J., 2008. Kiala, le petit livreur qui défie La Poste [WWW Document]. Capital.fr. URL https://www.capital.fr/entreprises-marches/kiala-le-petit-livreur-quidefie-
la-poste-390729 (accessed 1.17.18).
Boudouin, D., 2012. Methodological Guide: Urban Logistics Spaces. CRET-LOG, Aix-Marseille, France.
Boyer, K.K., Frohlich, M.T., Hult, G.T.M., 2005. Extending the Supply Chain: How Cutting-edge Companies Bridge the Critical Last Mile Into Customers’ Homes.
AMACOM Div American Mgmt Assn.
Boyer, K.K., Prud’homme, A.M., Chung, Wenming, 2009. The last mile challenge: evaluating the effects of customer density and delivery window patterns. J. Bus.
Logist. 30, 185–201.
bpost, 2017a. bpost strengthens its position in sustainable logistics with the acquisition of Bubble Post [WWW Document]. accessed 12.8.17. http://corporate.bpost.
bpost, 2017b. Cubee, the largest Belgian parcel locker network open to all couriers [WWW Document]. accessed 1.15.18. http://corporate.bpost.be/media/pressreleases/
2017/04-10-2017?sc_lang=en.
bpost, 2013. European Commission Green Paper “An integrated parcel delivery market for the growth of e-commerce in the EU” – Answer of bpost to the public
Bruxelles Mobilité, 2013. Plan stratégique pour le transport de marchandises en région de Bruxelles-Capitale. Government report. Service public régional de Bruxelles-
Bruxelles Mobilité, Brussels, Belgium.
Browne, M., Allen, J., Leonardi, J., 2011. Evaluating the use of an urban consolidation centre and electric vehicles in central London. IATSS Res. 35, 1–6. https://doi.
org/10.1016/j.iatssr.2011.06.002.
Browne, M., Piotrowska, M., Woodburn, A., Allen, J., 2007. Literature review WM9: Part I-Urban freight transport (Carried out as part of Work Module 1 Green
Logistics Project). London.
Brynjolfsson, E., Hu, Y.J., Rahman, M.S., 2013. Competing in the age of omnichannel retailing. MIT Sloan Manage. Rev. 54, 23–29.
Bubble Post, 2017. Nespresso: “Right from the start our customers loved your innovative concept.” [WWW Document]. Bubble Post. URL http://bubblepost.eu/news/
Chanchani, M., Shrivastava, A., 11, E.B.| U.A., 2016, Ist, 09 17 Am, 2016. Flipkart logistics arm Ekart bets big on offline clients, bags Madura Fashion [WWW
Document]. The Economic Times. URL http://economictimes.indiatimes.com/industry/services/retail/flipkart-logistics-arm-ekart-bets-big-on-offline-clientsbags-
Chiejina, C., Olamide, S.E., 2014. Investigating the significance of the’pay on delivery’option in the emerging prosperity of the Nigerian e-commerce sector. J. Market.
Manage. 5, 120.
Chopra, S., 2003. Designing the distribution network in a supply chain. Transport. Res. Part E: Logistics Transport. Rev. 39, 123–140. https://doi.org/10.1016/S1366-
Clarke, S., Leonardi, J., 2017a. Multicarrier consolidation – Central London trial (Final Report). Greater London Authority – Mayor of London, London.
Clarke, S., Leonardi, J., 2017b. Parcel deliveries with electric vehicles in Central London – Category 3: Single carrier consolidation centre targeting poor air quality
zones enabling manual delivery methods. Data Report [WWW Document]. URL https://data.london.gov.uk/dataset/key-performance-indicators-of-demonstratorfreight-
Coker, O., 2015. Konga Adds Nigerian Postal Service to its Logistics Channels | TechCabal [WWW Document]. URL http://techcabal.com/2015/02/12/konga-addsnigerian-
Corongiu, A., 2013. Citylog – sustainability and efficiency of city logistics (Final Report). Centro Ricerche Fiat, Orbassano (TO), Italy.
Crainic, T.G., Ricciardi, N., Storchi, G., 2009. Models for evaluating and planning city logistics systems. Transport. Sci. 43, 432–454. https://doi.org/10.1287/trsc.
1090.0279.
Dablanc, L., 2007. Goods transport in large European cities: difficult to organize, difficult to modernize. Transport. Res. Part A: Policy Practice 41 (3), 280–285.
Dablanc, L., Morganti, E., Arvidsson, N., Woxenius, J., Browne, M., Saidi, N., 2017. The rise of on-demand ‘Instant Deliveries’ in European cities. In: Supply Chain
Forum: An International Journal. Taylor & Francis, pp. 203–217.
Dablanc, L., Lozano, A., 2013. Commercial Goods Transport, Mexico City.
Daduna, J.R., Lenz, B., 2005. Online shopping and changes in mobility. In: Distribution Logistics. Springer, pp. 65–84.
Datainsight, 2017. Fulfillment for e-commerce [WWW Document]. Datainsight. URL http://logistics.datainsight.ru/sites/default/files/di-fulfillment2017_eng.pdf
(accessed 20.1.19).
Das, K., Ara, A., 2015. Growth of E-Commerce in India. Growth, available at: http://ijcem.in/wp-content/uploads/2015/08/Growth_of_E_Commerce_in_India.pdf
(accessed 9 December 2015).
De Corbière, F., Durand, B., Rowe, F., 2011. Effets économiques et environnementaux de la mutualisation des informations logistiques de distribution: avis d’experts et
voies de recherche. Management & Avenir 326–348.
de Koster, R., Marinus, B., 2002. Distribution structures for food home shopping. Int. J. Phys. Distrib. Logist. Manage. 32, 362–380.
de Visseyrias, M., 2011. Colis: la bataille des points de livraison [WWW Document]. FIGARO. URL http://www.lefigaro.fr/conso/2011/12/22/05007-
Ding, Z., 2014. Evaluating different last mile logistics solutions: A case study of SF Express (Master Thesis). Högskolan i Gävle, Gävle, Sweden.
Dizain, D., Taniguchi, E., Dablanc, L., 2013. Urban logistics by rail and waterways in France and Japan. 8th International Conference on City Logistics. 15p.
Diziain, D., Ripert, C., Dablanc, L., 2012. How can we bring logistics back into cities? The case of Paris Metropolitan Area. Proc. Social Behav. Sci. 39, 267–281.
Ducass, A., Kwadjane, J.-M., 2015. E-commerce in Africa: Morocco, Tunisia, Senegal and Ivory Coast. Recommendations for regional integration in the Mediterranean.
IPEMED.
Ducret, R., 2014. Parcel deliveries and urban logistics: changes and challenges in the courier express and parcel sector in Europe — The French case. Res. Transport.
Bus. Manage., Managing Freight in Urban Areas 11, 15–22. https://doi.org/10.1016/j.rtbm.2014.06.009.
Ducret, R., Delaître, L., 2013. Parcel delivery and urban logistics-changes in urban courier, express and parcel services: the French case. In: 13th World Conference on
Transport Research, July 15-18, 2013-Rio de Janeiro, Brazil.
Durand, B., 2010. E-commerce et logistique urbaine : quand le développement durable s’en mêle…. Revue Française de Gestion Industrielle 29, 7–26.
Durand, B., Gonzalez-Féliu, J., 2012. Impacts of proximity deliveries on e-Grocery trips. Supply Chain Forum: Int. J. 13, 10–19. https://doi.org/10.1080/16258312.
2012.11517284.
East-West Digital News, 2016. “Ulmart is part IKEA and part Amazon,” says CEO Sergey Fedorinov [WWW Document]. East-West Digital News. URL http://www.
East-West Digital News, 2017a. E-commerce warehousing and fulfillment in Russia [WWW Document]. East-West Digital News. URL http://www.ewdn.com/ecommerce/
East-West Digital News, 2017b. E-commerce in Russia [WWW Document]. East-West Digital News. URL http://www.ewdn.com/files/ecom-rus-download.pdf (accessed
20.1.19).
Ecommerce News, 2014. Hybrid model Ulmart successful: sales grow by 31% [WWW Document]. Ecommerce News. URL https://ecommercenews.eu/hybrid-modelulmart-
successful-sales-grow-by-31/ (accessed 11.18.16).
Ecommerce Worldwide, 2017. Logistics in the Russia eCommerce market [WWW Document]. Ecommerce Worldwide. URL https://www.ecommerceworldwide.com/
Edwards, J., McKinnon, A., Cherrett, T., McLeod, F., Song, L., 2010a. Carbon dioxide benefits of using collection-delivery points for failed home deliveries in the United
Edwards, J., McKinnon, A., Cullinane, S., 2010b. Comparative analysis of the carbon footprints of conventional and online retailing: A “last mile” perspective. Int. J.
Phys. Distrib. Logist. Manage. 40, 103–123.
Efendioglu, A.M., Yip, V.F., Murray, W.L., 2005. January. E-Commerce in developing countries: issues and influences. In: Proceedings of the IBEC Annual Conference,
pp. 10–15.
El-Sofany, H., Al-Malki, T., Alzamel, A.A., Alharbi, A.A., 2012. Impact of trust factors in improvement and development of E-commerce in Saudi Arabia. Int. J. Comput.
Appl. 55 (9).
eMarketer, 2017. A Brief Overview of the Global Ecommerce Market [WWW Document]. eMarketer Retail. URL https://retail.emarketer.com/article/brief-overviewof-
Erisman, P., 2017. Six Billion Shoppers: The Companies Winning the Global E-Commerce Boom. St. Martin’s Press.
Esper, T.L., Jensen, T.D., Turnipseed, F.L., Burton, S., 2003. The last mile: an examination of effects of online retail delivery strategies on consumers. J. Bus. Logist. 24,
Esselaar, P., Miller, J., 2002. Towards electronic commerce in Africa: a perspective from three country studies. Southern African J. Inform. Commun. 2 (1) 1 1.
Esser, K., Kurte, J., 2006. B2C e-commerce: impact on transport in urban areas. In: Recent Advances in City Logistics. The 4th International Conference on City
Logistics.
Fernie, J., McKinnon, A.C., 2009. The development of e-tail logistics. In: Emerging Issues and New Challenges in the Retail Supply Chain. John Fernie and Leigh
Sparks, London and Philadelphia.
Fortune, 2017. Amazon Prime Now: Here’s How It Works | Fortune [WWW Document]. accessed 11.29.17. http://fortune.com/2016/12/22/a-look-inside-amazonstwo-
hour-delivery-warehouse-in-midtown-manhattan/.
Ganguly, P., 2016. Flipkart taps into kirana stores to extend last-mile delivery [WWW Document]. The Economic Times. URL https://economictimes.indiatimes.com/
Gérardin, B., 2007. Dix ans d’expérimentations en matière de livraisons en ville – premier bilan critique. Les collections du Certu, CERTU, Pantin.
Gerardin Conseil, 2005. Projet ELP “Espaces Logistiques de Proximité” – Deuxième Phase d’expérimentation – Suivi et évaluation technico-economique et environnementale
(Rapport Final). Chambre de Commerce et d’Industrie de Bordeaux.
Gevaers, R., Van De Voorde, E., Vanelslander, T., 2011. Characteristics and typology of lastmile logistics from an innovation perspective in an Urban context. City
Ghezzi, A., Mangiaracina, R., Perego, A., 2012. Shaping the E-commerce logistics strategy: a decision framework. Int. J. Eng. Bus. Manage. 4, 13. https://doi.org/10.
5772/51647.
Gonzalez-Feliu, J., 2013. Multi-stage LTL transport systems in supply chain management. In: Cheung, J., Song, H. (Eds.), Logistics: Perspectives, Approaches and
Challenges, pp. 65–86.
Gonzalez-Feliu, J., 2008. Models and methods for the city logistics: The two-echelon capacitated vehicle routing problem (Doctoral thesis). Politecnico di Torino,
Torino.
Gonzalez-Feliu, J., Ambrosini, C., Routhier, J.-L., 2012. New trends on urban goods movement: modelling and simulation of e-commerce distribution. Eur. Transp. 50
Paper–N6, 23p.
Gonzalez-Feliu, J., Malhéné, N., Morganti, E., Trentini, A., 2013. Développement des espaces logistiques urbains. CDU et ELP dans l’europe du sud-ouest. Revue
Française de Gestion Industrielle 73–92.
Guerrero, J.C., Díaz-Ramírez, J., 2017. A review on transportation last-mile network design and urban freight vehicles. In: Proceedings of the 2017 International
Symposium on Industrial Engineering and Operations Management (IEOM).
Guillaume, J.-P., 2010. NESPRESSO: Une Supply Chain sous haute pression. Supply Chain Mag. 47, 24–28.
Han, Y., 2014. Study on Development strategies for express delivery services industry in Yangtze River Delta based on internet of things. In: Advanced Materials
Research. Trans Tech Publ, pp. 3970–3973.
Hausmann, L., Herrmann, N.-A., Krause, J., Netzer, T., 2014. Same-day delivery: The next evolutionary step in parcel logistics | McKinsey & Company [WWW
Hayashi, K., Nemoto, T., Visser, J.J., 2014. E-commerce and city logistics solution. City Logist.: Mapping Future 55.
Herson, M., 2015. Metamorphosis of UK Parcels Market [WWW Document]. Post&Parcel. URL https://postandparcel.info/65831/news/metamorphosis-of-uk-parcelsmarket/
(accessed 12.8.17).
Houde, J.-F., Newberry, P., Seim, K., 2017. Economies of Density in E-Commerce: A Study of Amazon’s Fulfillment Center Network (Working Paper No. 23361).
National Bureau of Economic Research. https://doi.org/10.3386/w23361.
Huang, Z., 2015. The last mile delivery in China – Adoption of last mile delivery modes by customers (Master Thesis). Résultats de recherche Erasmus School of
Hübner, A., Holzapfel, A., Kuhn, H., 2016a. Distribution systems in omni-channel retailing. Business Res. 9, 255–296.
Hübner, A., Kuhn, H., Wollenburg, J., 2016b. Last mile fulfillment and distribution in omni-channel grocery retailing: a strategic planning framework. Int. J. Retail
Hwarng, B., Mouri, M., 2015. TA-Q-BIN, The Last-Mile Delivery. In: TA-Q-BIN, Management for Professionals. Springer, Singapore, pp. 23–44. https://doi.org/10.
1007/978-981-287-673-7_3.
Ibikunle, O., 2013. E-commerce in Developing Nations: Issues and Challenges. Consumer Attitude In the Nigerian Market.
In, L., 2016. Encyclopedia of E-Commerce Development, Implementation, and Management. IGI Global.
Interface Transport, 2013. Bilan des points-relais (Rapport définitif). ADEME.
Janjevic, M., Kaminsky, P., Ballé Ndiaye, A., 2013. Downscaling the consolidation of goods – state of the art and transferability of micro-consolidation initiatives.
European Transport. Trasporti Europei 54, Paper n° 4.
Janjevic, M., Ndiaye, A., 2017a. Investigating the theoretical cost-relationships of urban consolidation centres for their users. Transport. Res. Part A: Policy Practice, SI:
Janjevic, M., Ndiaye, A., 2017b. Investigating the financial viability of urban consolidation centre projects. Res. Transport. Bus. Manage. Sustain. Efficiency Manage.
Urban Goods Transport: New Trends Appl. 24, 101–113. https://doi.org/10.1016/j.rtbm.2017.05.001.
Janjevic, M., Ndiaye, A.B., 2014. Inland waterways transport for city logistics: a review of experiences and the role of local public authorities. Urban Transport 138,
279.
Jiao, Z., 2016. Service mode and development trend of the “last-mile delivery” of E-commerce logistics. In: Contemporary Logistics in China. Springer, pp. 239–261.
Jingzhu, L., 2018. Electric 3-wheelers in urban delivery in China and Germany – a bumpy road ahead? [WWW Document]. SustainableTransport.org. URL https://
Kikuta, J., Ito, T., Tomiyama, I., Yamamoto, S., Yamada, T., 2012. New Subway-Integrated City Logistics Szystem. In: Procedia – Social and Behavioral Sciences,
Seventh International Conference on City Logistics which was held on June 7- 9, 2011, Mallorca, Spain 39, pp. 476–489. https://doi.org/10.1016/j.sbspro.2012.
03.123.
Kitukutha, N., Oláh, J., 2018. Trust And E-Commerce, Case Study On Jumia Company. The Annals of the University of Oradea. Econ. Sci. 27, 313–319.
Janjevic, M., Winkenbach, M., Merchán, D., 2019. Integrating collection-and-delivery points in the strategic design of urban last-mile e-commerce distribution
networks. Transport. Res. Part E: Logist. Transport. Rev. 131, 37–67.
Kin, B., Verlinde, S., Macharis, C., 2017. Sustainable urban freight transport in megacities in emerging markets. Sustain. Cities Soc. 32, 31–41.
Kshetri, Nir, 2007. Barriers to E-commerce and competitive business models in developing countries: a case study. Electron. Commer. Res. Appl. 6, 443–452.
Lewis, A., Lagrange, A., Patterson, D., Gallop, N., 2007. South London Freight Consolidation Centre Feasibility Study (No. Final Report). Transport & Travel Research
Ltd.
Liang, B., Tu, Y., Cline, T., Ma, Z., 2016. China’s E-tailing blossom: a case study. In: E-Retailing Challenges and Opportunities in the Global Marketplace. IGI Global, pp.
72–98.
Libeskind, J., 2014. Faites vos courses à vélo!. Stratégies Logistique 147, 24–28.
Lindholm, M., Browne, M., 2013. Local authority cooperation with urban freight stakeholders: a comparison of partnership approaches. Eur. J. Transp. Infrastruct. Res.
13 (1), 20–38.
Lim, S.F., Rabinovich, E., Rogers, D.S., Laseter, T.M., 2016. Last-mile supply network distribution in omnichannel retailing: a configuration-based typology.
Foundations and trends® in technology. Inform. Operat. Manage. 10, 1–87.
Liu, Q., Goh, M., 2015. TA-Q-BIN – Service Excellence and Innovation in Urban Logistics. Management for Professionals, Springer Singapore.
Lowe, R., Rigby, M., 2014. The Last Mile-Exploring the online purchasing and delivery journey (Technical report). Barclays, London.
Lukic, V., Souza, R., Wolfgang, M., 2013. Same-day delivery-Not yet ready for prime time, bcg. perspectives. The Boston Consulting Group, Boston, April.
Lunden, I., 2017. Amazon launches ‘The Hub’, parcel delivery lockers for apartment buildings [WWW Document]. TechCrunch
Madichie, N.O., 2014. An interview with Femi Asiwaju-Head of Sourcing, Konga Online Shopping Limited (Konga. com), Lagos, Nigeria: executive opinion. African J.
Bus. Econ. Res. 9, 135–141.
Mailonline, B.C.P.F., 2015. Amazon using SUBWAY to deliver Prime Now items in an hour of buying [WWW Document]. Mail Online. URL http://www.dailymail.co.
Mairie de Paris, 2005. Expérimentation de vélos triporteurs électriques à Paris – Evaluation à 24 mois – Synthèse. Mairie de Paris, Paris.
Marujo, L.G., Goes, G.V., D’Agosto, M.A., Ferreira, A.F., Winkenbach, M., Bandeira, R.A., 2018. Assessing the sustainability of mobile depots: the case of urban freight
distribution in Rio de Janeiro. Transport. Res. Part D: Transp. Environ. 62, 256–267.
McKinnon, A.C., Tallam, D., 2003. Unattended delivery to the home: an assessment of the security implications. Int. J. Retail Distrib. Mgt. 31, 30–41. https://doi.org/
10.1108/09590550310457827.
Meldner, R., 2017. UPS Expands Experiments with Electric Powered Cargo Bikes [WWW Document]. eSellerCafe. URL https://esellercafe.com/ups-expandsexperiments-
electric-powered-cargo-bikes/ (accessed 12.1.17).
Merchán, D., Blanco, E., 2015. The Near Future of Megacity Logistics Overview of Best-Practices, Innovative Strategies and Technology Trends for Last-Mile Delivery.
https://doi.org/10.13140/RG.2.2.28441.42083.
Merchán, D., Blanco, E.E., Winkenbach, M., 2016. Transshipment Networks for Last-Mile Delivery in Congested Urban Areas (Master Thesis). Massachusetts Institute of
Technology, Cambridge, MA.
Montini, L., 2015. Meet Souq, the Amazon of the Middle East [WWW Document]. Inc.com. URL https://www.inc.com/laura-montini/meet-souq-the-e-commercegiant-
of-the-middle-east.html (accessed 1.17.18).
Morganti, E., Dablanc, L., Fortin, F., 2014a. Final deliveries for online shopping: The deployment of pickup point networks in urban and suburban areas. Res.
Transport. Bus. Manage., Managing Freight Urban Areas 11, 23–31. https://doi.org/10.1016/j.rtbm.2014.03.002.
Morganti, E., Seidel, S., Blanquart, C., Dablanc, L., Lenz, B., 2014b. The impact of e-commerce on final deliveries: alternative parcel delivery services in France and
Germany. In: Transportation Research Procedia, Sustainable Mobility in Metropolitan Regions. mobil.TUM 2014. International Scientific Conference on Mobility
and Transport. Conference Proceedings vol. 4, pp. 178–190. https://doi.org/10.1016/j.trpro.2014.11.014.
Mouchawar, R., 2017. How Souq.com’s CEO Built an E-Commerce Powerhouse in a Region Where Cash Still Rules [WWW Document]. Harvard Business Review. URL
Muñuzuri, J., Larrañeta, J., Onieva, L., Cortés, P., 2005. Solutions applicable by local administrations for urban logistics improvement. Cities 22 (1), 15–28.
Nair, A., 2016a. Flipkart’s logistics arm Ekart unveils alternative delivery model [WWW Document]. YourStory.com. URL https://yourstory.com/2016/05/ekartlockers/
(accessed 10.31.16).
Nair, A., 2016b. Will CoD kill the Indian e-commerce star? [WWW Document]. YourStory.com. URL https://yourstory.com/2016/04/cod-kill-indian-e-commerce/
(accessed 10.31.16).
Nair, A., 2016c. Flipkart goes beyond e-commerce; eKart to serve offline players now [WWW Document]. YourStory.com. URL https://yourstory.com/2016/04/
flipkart-ekart/ (accessed 11.23.16).
Nair, V., 2018. Flipkart’s eBikes are pedalling for change, one delivery at a time [WWW Document]. Flipkart URL https://stories.flipkart.com/flipkart-ebikes/ (accessed
21.1.19).
Kordic, N., 2014. The extent of e-commerce presence in developing countries. In: International Scientific Conference of IT and Business-Related Research-SINTEZA.
Singidunum University, pp. 10–15308.
Oliver Wyman, 2015. Amazon’s Move Into Delivery Logistics [WWW Document]. URL http://www.oliverwyman.com/content/dam/oliver-wyman/europe/germany/
Parr, T., 2017. International Cargo Bike Festival: UPS – Reducing vehicle movements with city centre container hubs [WWW Document]. International Cargo Bike
Peng, R., Xu, A., 2016. Crowdsourced Logistics—Its Development and Potential A Case Study of JD Crowdsourced Logistics in China.
Perboli, G., Rosano, M., Gobbato, L., 2017. Parcel Delivery in Urban Areas: Opportunities and Threats for the Mix of Traditional and. Green Business Models.
Pontiroli, T., 2014. Livraison sur rendez-vous : Chronopost rachète Colizen à 100% [WWW Document]. Clubic.com. URL http://www.clubic.com/pro/e-commerce/
Prologis, 2017. Global E-Commerce Impact on Logistics Real Estate [WWW Document]. Prologis. URL /logistics-industry-research/global-e-commerce-impact-logistics-
real-estate (accessed 1.18.18).
Quak, H.J., 2008. Sustainability of Urban Freight Transport: Retail Distribution and Local Regulations in Cities. Erasmus University Rotterdam. http://repub.eur.nl/
resource/pub_11990/.
Quak, H., Balm, S., Posthumus, B., 2014. Evaluation of city logistics solutions with business model analysis. Proc.-Social Behav. Sci. 125, 111–124.
Quak, H.J., 2012. Improving Urban Freight Transport Sustainability by Carriers – Best Practices from The Netherlands and the EU Project CityLog. Procedia – Social
and Behavioral Sciences, Seventh International Conference on City Logistics which was held on June 7–9, 2011, Mallorca, Spain vol. 39, pp. 158–171. https://doi.
org/10.1016/j.sbspro.2012.03.098.
Rai, H.B., Verlinde, S., Merckx, J., Macharis, C., 2017. Crowd logistics: an opportunity for more sustainable urban freight transport? Eur. Transp. Res. Rev. 9, 39.
https://doi.org/10.1007/s12544-017-0256-6.
Rao, S., Goldsby, T.J., Iyengar, D., 2009. The marketing and logistics efficacy of online sales channels. Int. J. Phys. Distrib. Logist. Manage. 39, 106–130. https://doi.
org/10.1108/09600030910942386.
Reul, M., 2012. UPS acquires Belgian delivery service Kiala [WWW Document]. RetailDetail. URL https://www.retaildetail.eu/en/news/m-tail/ups-acquires-belgiandelivery-
service-kiala (accessed 2.28.18).
Reuters, 2015. Flipkart ties up with Mumbai dabbawalas to navigate city streets [WWW Document]. accessed 11.29.17. https://in.reuters.com/article/india-flipkartdabbawalas/
Ricker, F., Kalakota, R., 1999. Order fulfillment: the hidden key to e-commerce success. Supply Chain Manage. Rev. 11, 60–70.
Roberson, C., 2014. As the Middle East e-commerce Market grows, what are the opportunities for logistics providers? Aramex.
Rodrigue, J.P., 2016. Residential Parcel Deliveries: Evidence from a Large Apartment Complex. MetroFreight Center of Excellence.
Rodrigue, J.P., 2015. E-Commerce as a Driver for City Logistics in China. MetroFreight Center of Excellence Dept. of Global Studies & Geography, Hofstra University,
Hempstead, New York, USA.
Rougès, J.-F., Montreuil, B., 2014. Crowdsourcing delivery: New interconnected business models to reinvent delivery. In: 1 St International Physical Internet
Conference, pp. 28–30.
Royal Mail, 2014. Delivery Matters: Understanding the changing landscape of online shopping in 2014. Royal Mail.
Ruesch, M., Petz, C., 2008. Best Practice Update: E-Commerce and urban freight distribution (home shopping). BESTUFS II, Best Urban Freight Solutions II 101.
Samoilenko, A., 2016. E-commerce in Russia: Challenges and Opportunities: Russian e-commerce market for local and foreign enterpreneurs (Master Thesis). Helsinki
Metropolia University of Applied Sciences, Helsinki.
Sato, Y., 2016. Tokyo metro to test parcel operation [WWW Document]. URL http://www.railjournal.com/index.php/metros/tokyo-metro-to-test-parcel-operation.
html (accessed 11.30.17).
Schliwa, G., Armitage, R., Aziz, S., Evans, J., Rhoades, J., 2015. Sustainable city logistics—making cargo cycles viable for urban freight transport. Res. Transport. Bus.
Manage. 15, 50–57.
Shabanova, L., Zyuzina, S., 2016. Traditional and electronic commerce of consumer goods and services: strengths, weaknesses and prospects of development. Business.
Education. Law. Bulletin of Volgograd Business Institute, pp. 78–83.
M. Janjevic and M. Winkenbach Transportation Research Part A 133 (2020) 164–196
Sillitoe, B., 2015. Doddle aiming for 100 stores by end of year [WWW Document]. Essential Retail. URL http://www.essentialretail.com/ecommerce/article/
Sinha, A., Weitzel, P., 2015. eCommerce Supply Chain Insights in Groceries and Consumer Packaged Goods in the United States (White paper). Technology and
Operations Department, Ross School of Business, University of Michigan, Ann Arbor MI, USA.
Soni, A., 2015. Flipkart ties-up with Mumbai Dabbawalas, dabbles with an on-demand logistics service too [WWW Document]. YourStory.com. URL https://yourstory.
Souq.com, 2016. SOUQ.com introduces Same Day Delivery Service [WWW Document]. Souq.com newsroom. URL http://pr.souq.com/139204-souq-com-introducessame-
day-delivery-service (accessed 1.17.18).
Staricco, L., Brovarone, E.V., 2016. The spatial dimension of cycle logistics. Tema. J. Land Use, Mobility Environ. 9, 173–190.
Statista, 2019a. Preferred methods of payment as a share of online transaction volume across India in 2015 and 2020 by type [WWW Document]. Statista. URL https://
Statista, 2019b, Share of preferred online payment methods amongst customers in Saudi Arabia as of 2018. Statista. [WWW Document]. Statista. URL https://www.
Statista, 2019d, Percentage of population using the internet in Russia from 2000 to 2016. Statista [WWW Document]. URL https://www.statista.com/statistics/
Statista, 2019e, Percentage of population using the internet in Nigeria from 2000 to 2016. Statista [WWW Document]. URL https://www.statista.com/statistics/
Statista, 2019f, Internet penetration rate in India from 2007 to 2017. Statista [WWW Document]. URL https://www.statista.com/statistics/792074/india-internetpenetration-
STRAIGHTSOL, 2014. Final evaluation of all STRAIGHTSOL city distribution concepts by use of the MAMCA.
Taniguchi, E., Thompson, R.G., Yamada, T., 2016. New Opportunities and Challenges for City Logistics. Transportation Research Procedia. In: Tenth International
Conference on City Logistics 17–19 June 2015, Tenerife, Spain 12, pp. 5–13. https://doi.org/10.1016/j.trpro.2016.02.004.
TechMoran, 2013. Rocket Internet’s Lamoda Receives USD130M In New Funding [WWW Document]. TechMoran. URL https://techmoran.com/rocket-internetslamoda-
Transport for London, 2016. Rethinking deliveries report. Transport for London, London.
Universal Post Union, 2018. Postal development report [WWW Document]. accessed 21.1.2019. http://www.upu.int/uploads/tx_sbdownloader/
postalDevelopmentReport2018En.pdf.
UPS, 2016. UPS Pulse of the Online Shopper – U.S. Study.
UPS, 2015a. A UPS White Paper – Brazil study.
UPS, 2015b. UPS Pulse of the Online Shopper – Europe Study [WWW Document]. accessed 11.23.16. https://www.ups.com/media/en/gb/
OnlineComScoreWhitepaper.pdf.
UPS, 2015c. UPS Pulse of the Online Shopper – Global Study.
UPS, 2015d. UPS Pulse of the Online Shopper – Asia Study.
Vanelslander, T., Deketele, L., Hove, D.V., 2013. Commonly used e-commerce supply chains for fast moving consumer goods: comparison and suggestions for
improvement. Int. J. Logist. Res. Appl. 16, 243–256. https://doi.org/10.1080/13675567.2013.813444.
Verlinde, S., Macharis, C., Milan, L., Kin, B., 2014. Does a Mobile Depot Make Urban Deliveries Faster, More Sustainable and More Economically Viable: Results of a
Pilot Test in Brussels. Transportation Research Procedia, Sustainable Mobility in Metropolitan Regions. mobil.TUM 2014. International Scientific Conference on
Mobility and Transport. Conference Proceedings, vol.4, pp. 361–373. https://doi.org/10.1016/j.trpro.2014.11.027.
Vert Chez Vous, 2012. Une inauguration « Au fil de l’Eau » réussie pour VERT CHEZ VOUS ! [WWW Document]. http://vertchezvous.com. URL http://vertchezvous.
Vétois, P., Raimbault, N., 2017. L’«uberisation» de la logistique: disruption ou continuité? Le cas de l’Île-de-France, Technologie et innovation.
Vieira, J.G.V., Fransoo, J.C., Carvalho, C.D., 2015. Freight distribution in megacities: perspectives of shippers, logistics service providers and carriers. J. Transp. Geogr.
46, 46–54.
Visser, J., Nemoto, T., 2003. E-commerce and the consequences for freight transport. Innovations in Freight Transport. WIT Press, Boston.
Visser, J., Nemoto, T., Browne, M., 2014a. Home delivery and the impacts on urban freight transport: a review. Proc.-Social Behav. Sci. 125, 15–27.
Visser, J., Nemoto, T., Browne, M., 2014b. Home delivery and the impacts on urban freight transport: a review. In: Procedia – Social and Behavioral Sciences, Eighth
International Conference on City Logistics 17–19 June 2013, Bali, Indonesia, vol. 125, pp. 15–27. https://doi.org/10.1016/j.sbspro.2014.01.1452.
Voies Navigables de France, 2013. Urban waterway logistics [WWW Document]. accessed 5.5.14. http://www.vnf.fr/vnf/img/cms/Tourisme_et_domainehidden/
Urban_waterway_logistics_20131004143330.pdf.
Wang, J.J., Xiao, Z., 2015. Co-evolution between etailing and parcel express industry and its geographical imprints: the case of China. J. Transp. Geogr. 46, 20–34.
Wakoba, S., 2013b. Jumia Kenya Goes After Offline Retailers | Launches Initiative To Help Them Run Physical Shops [WWW Document]. TechMoran. URL http://
Weltevreden, J.W.J., 2008. B2c e-commerce logistics: the rise of collection-and-delivery points in The Netherlands. Intl. J. Retail Distrib. Mgt. 36, 638–660. https://
doi.org/10.1108/09590550810883487.
Willemsen, R., Abraham, J., van Richard, W., 2015. Global B2C E-commerce report 2015. Ecommerce Foundation, Amsterdam.
Winkenbach, M., Kleindorfer, P.R., Spinler, S., 2016. Enabling urban logistics services at La Poste through multi-echelon location-routing. Transport. Sci. 50, 520–540.
https://doi.org/10.1287/trsc.2015.0624.
World Bank, 2019. Aggregated LPI 2012-2018, [WWW Document]. URL https://lpi.worldbank.org/international/ (accessed 21.1.19).
Xiao, Z., Wang, J.J., Lenzer, J., Sun, Y., 2017. Understanding the diversity of final delivery solutions for online retailing: a case of Shenzhen, China. Transp. Res. Proc.
25, 985–998.
Xing, Y., Grant, D.B., 2006. Developing a framework for measuring physical distribution service quality of multi-channel and “pure player” internet retailers. Intl. J.
M. Janjevic and M. Winkenbach Transportation Research Part A 133 (2020) 164–196
Xing, Y., Grant, D.B., McKinnon, A.C., Fernie, J., 2010. Physical distribution service quality in online retailing. Int. J. Phys. Distrib. Logist. Manage. 40, 415–432.
Xu, M., Ferrand, B., Roberts, M., 2008. The last mile of e-commerce–unattended delivery from the consumers and eTailers’ perspectives. Int. J. Electron. Market.
Yano, Y., Saito, M., 2016. Making an efficient last mile delivery system in Japan. In: Presented at the International Conference on Industrial Logistics, Zakopane,
Poland.
Yu, Y., Wang, X., Zhong, R.Y., Huang, G.Q., 2016. E-commerce logistics in supply chain management: practice perspective. In: Procedia CIRP, The Sixth International
Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV2016), vol. 52, pp. 179–185. https://doi.org/10.1016/j.procir.2016.08.002.
Zacharias, J., Zhang, B., 2015. Local distribution and collection for environmental and social sustainability–tricycles in central Beijing. J. Transp. Geogr. 49, 9–15.
Zhang, H., Liu, K., 2015. Convenience store development mode of express enterprises in China. J. Logist. Manage. 4 (1), 9–14.
M. Janjevic and M. Winkenbach Transportation Research Part A 133 (2020) 164–196
Renata Albergaria de Mello Bandeiraa, George Vasconcelos Goesa, Daniel Neves Schmitz Goncalvesa, Marcio de Almeida D’Agostoa, Cintia Machado de Oliveiraa, Electric vehicles in the last mile of urban freight transportation: A sustainability assessment of postal deliveries in Rio de Janeiro- Brazil, Transportation Research Part D 67 (2019) 491–502
Introduction :
La croissance de la population urbaine et l’essor des activités de commerce électronique augmentent la complexité du dernier kilomètre des livraisons de colis et ses impacts sur l’environnement et la qualité de vie.
Cet article propose une méthode pour évaluer les stratégies mises en place sur le dernier kilomètre de livraison des colis, en prenant compte des enjeux sociaux, environnementaux et économiques. Les recherches effectuées font l’état de la migration des énergies fossiles de propulsion vers des énergies électriques dans les zones urbaines.
Les auteurs ont choisi d’évaluer les alternatives possibles avec l’utilisation des petits véhicules électriques et des vélos cargos.
Mots clés : dernier kilomètre de livraison, énergies électriques, tricycle electric
Développement:
Dans la stratégie « Distribution by Electric Tricycles » (DET), le messager effectue les livraisons à l’aide d’un tricycle électrique sur tout le trajet. Dans ce cas, le poids limite est la capacité du tricycle (50 kg). Cette stratégie ne nécessite le soutien d’un véhicule léger et l’utilisation d’un « Mobile Depot » (MD). Le messager se déplace du Centre de distribution au premier point de distribution en utilisant uniquement le tricycle. Une fois arrivé sur la zone de livraison, il gare le tricycle et livre à pied. Afin d’évaluer cette alternative, les auteurs ont testé l’utilisation de tricycles électriques dans une zone de distribution postale située dans la ville de Rio de Janeiro pendant deux semaines.
Les recherches menées dans ce document indiquent une tendance vers des alternatives plus durables sur le dernier kilomètre des livraisons urbaines, avec un changement de source d’énergie des véhicules et la réduction de la taille des véhicules, parallèlement à l’adoption de bicyclettes, de tricycles et de VUL.
Dans cette optique, les auteurs ont proposé une procédure d’évaluation qui cherche à concilier les aspects économiques, environnementaux et sociaux dans le choix des alternatives pour les livraisons du dernier kilomètre.
Dans la stratégie DET, il a été vérifié qu’une réduction de 27,9 % du coût total de livraison par itinéraire était réalisée, en plus d’une diminution des gaz à effet de serre. Néanmoins, il est important de souligner que l’entreprise postale devrait envisager une élimination et un programme de recyclage des piles utilisées par les tricycles électriques.
Conclusion :
Les résultats indiquent que l’utilisation de tricycles électriques est une alternative plus réalisable du point de vue économique, les aspects environnementaux et sociaux, n’exigeant aucune incitation publique.
Références :
Alessandrini, A., Campagna, A., Site, P.D., Filippi, F., Persia, L., 2015. Automated vehicles and the rethinking of mobility and cities. Transp. Res. Procedia 5, 145–160.
Andaloro, L., Napoli, G., Sergi, F., Micari, S., Agnello, G., Antonucci, V., 2015. Development of a new concept electric vehicle for last mile transportations. In: EVS28 Int. Electr.
Veh. Symp. Exhib. pp. 1–7.
Anderluh, A., Hemmelmayr, V.C., Nolz, P.C., 2016. Synchronizing vans and cargo bikes in a city distribution network. Cent. Eur. J. Oper. Res Springer Berlin Heidelberg.
Arena, R., Myers, J., Kaminsky, L.A., 2016. Revisiting age-predicted maximal heart rate: can it be used as a valid measure of effort? Am. Heart J. 173, 49–56.
Bussab, W.O., e Morettin, P.A., 1987. Estatistica Basica, 4a ed. Atual Editora.
Campos, V., Ramos, R., Correia, D., 2009. Multi-criteria analysis procedure for sustainable mobility evaluation in urban areas. J. Adv. Transp. 43 (4), 371–390.
Cook, D.J., Mulrow, C.D., Haynes, R.B., 1997. Systematic reviews: synthesis of best evidence for clinical decisions. Ann. Int. Med. 126 (5), 376–380.
Table 9
Comparative assessment between DET and AID strategies to TID.
Aspect Indicator (% variation) DET AID
Economic daily cost of the deliveries −28,0% 6,1%
Environmental Tier 2 approach CO2e −98,5% −25%
End-use approach CO2e – −28%
Social <57 27,0% 0%
57≤FC < 64 −43,0% 0%
64≤FC < 77 −95,0% 0%
R.A. de Mello Bandeira et al. Transportation Research Part D 67 (2019) 491–502
501
Crainic, T., Ricciardi, N., Storchi, G., 2004. Advanced freight transportation systems for congested urban areas. Transp. Res. Part C 12 (2004), 119–137.
Dablanc, L., 2009. Freight Transport for Development Toolkit. Urban Freight.
Dampier, A., Marinov, M., 2015. A study of the feasibility and potential implementation of metro-based freight transportation in newcastle upon tyne. Urban Rail Transit 1,
164–182.
Delft, 2013. Zero emissions trucks – an overview of state-of-the-art technologies and their potential.
Dennis, W. Parcel and small package delivery industry paperback. Available online: https://www.amazon.com/Parcel-Small-Package-Delivery-Industry/dp/1461021545 (accessed
in August 04, 2017).
Diziain, D., Taniguchi, E., Dablanc, L., 2014. Urban logistics by rail and waterways in France and Japan. Procedia – Soc. Behav. Sci. 151 (2014), 257–265.
Faccio, M., Gamberi, M., 2015. New city logistics paradigm: from the “Last Mile” to the “Last 50 Miles” sustainable distribution. Sustain 7, 14873–14894.
Fernandes, V.A., D’Agosto, M.A., Oliveira, C.M., Assumpcao, F.C., Deveza, A.C.P., 2015. Eco-driving: uma ferramenta para aprimorar a sustentabilidade do transporte de residuos
urbanos. Transportes 23, 1–8.
Foltyński, M., 2014. Electric fleets in urban logistics. Procedia Soc. Behav. Sci. 151, 48–59. https://doi.org/10.1016/j.sbspro.2014.10.007.
Froelicher, V.F., Myers, J., Follansbee, W.P., Labovitz, A.J., 1998. Exercicio e o coracao, third ed. Revinter, Rio de Janeiro.
Garber, C.E., Blissmer, B., Deschenes, M.R., Franklin, B.A., Lamonte, M.J., Lee, I.M., Nieman, D.C., Swain, D.P., 2011. ACSM – quantity and quality of exercise for developing and
maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med. Sci. Sports Exerc. 43,
1334–1359.
Global Opportunities for European SMEs – GO4SEM, 2015. Eletric vehicle supply chain – global opportunities for electric mobility: Brazil. Disponivel em: http://www.go4sem.
eu/public/global-opportunities/brazil-1.
Gruber, J., Kihm, A., 2016. Reject or embrace? Messengers and electric cargo bikes. Transp. Res. Procedia 12, 900–910.
Gruber, J., Kihm, A., Lenz, B., 2014. A new vehicle for urban freight? An ex-ante evaluation of electric cargo bikes in courier services. Res. Transp. Bus. Manage. 11, 53–62.
Heitz, A., Beziat, A., 2016. The parcel industry in the spatial organization of logistics activities in the Paris Region: inherited spatial patterns and innovations in urban logistics
systems. Transp. Res. Procedia 12, 812–824.
Hinde, S., Dixon, J., 2005. Changing The Obesogenic Environment: insights from a cultural economy of car reliance. Transp. Res. D 10, 31–53.
IPPC – Intergovernmental Panel on Climate Change, 2014. Fifth assessment report (AR5). Chapter 8, pp. 73–79. Available at: https://www.ipcc.ch/pdf/assessmentreport/ar5/
wg1/WG1AR5_Chapter08_FINAL.pdf.
IPCC – Intergovernmental Panel on Climate Change, 2006. IPCC Guidelines for National Greenhouse Gas Inventories. Prepared by the National Greenhouse Gas Inventories
Programme. Kanagawa: Institute for Global Environmental Strategies.
Joerss, M., Schroder, J., Neuhaus, F., Klink, C., Mann, F., 2016. McKinsey & Company Parcel delivery: the future of last mile, pp. 1–32.
Lebeau, P., Macharis, C., van Mierlo, J., Maes, G., 2013. Implementing electric vehicles in urban distribution: a discrete event simulation. World Electr. Veh. J. 6, 38–47.
Lebeau, P., De Cauwer, C., Van Mierlo, J., Macharis, C., Verbeke, W., Coosemans, T., 2015. Conventional, hybrid, or electric vehicles: which technology for an urban distribution
centre? Sci. World J. 2015.
Margaritis, D., Anagnostopoulou, A., Tromaras, A., Boile, M., 2016. Electric commercial vehicles: practical perspectives and future research directions. Research in Transportation
Business & Management.
MMA, 2013. Inventario Nacional de Emissoes Atmosfericas por Veiculos Automotores Rodoviarios 2013: Ano-base 2012. Ministerio do Meio Ambiente, Brasilia, DF.
Montwi, A., 2014. The role of seaports as logistics centers in the modelling of the sustainable system for distribution of goods in urban areas. Procedia – Soc. Behav. Sci. 151,
257–265.
Navarro, C., Roca-Riu, M., Furio, S.a., Estrada, M., 2015. Designing new models for energy efficiency in urban freight transport for smart cities and its application to the Spanish
case. Transp. Res. Procedia 12, 314–324.
Ngai, E.W.T., Wat, F.K.T., 2002. A literature review and classification of electronic commerce research. Inform. Manage. 39, 415–429.
Nord, J.H., Nord, G.D., 1995. MIS Research: Journal status assessment and analysis. Inform. Manage. 29, 29–42.
NTC, 2014. Manual de Calculo de Custos e Formacao de Precos do Transporte Rodoviario de Cargas. DECOPE – Departamento de Custos Operacionais, Estudos Tecnicos e
Economicos, Sao Paulo.
Oliveira, C.M., D’Agosto, M.A., Rosa, R.A., Assuncao, F.C., 2016. Low carbon logistics, green logistics & sustainable logistics: establishing concepts and scope. Int. J. Innov. Sci.
Res. 26, 47–64.
Oliveira, L.K., Pinto e Oliveira, B.R., Correia, V.A., 2014. Simulation of an urban logistic space for the distribution of goods in Belo Horizonte, Brazil. Procedia – Soc. Behav. Sci.
125, 496–505.
ONU, 2013. ONU: mais de 70 da populacao mundial vivera em cidades ate 2050. Disponivel em: http://www.onu.org.br/onu-mais-de-70-da-populacao-mundial-vivera-emcidades-
ate-2050/ (acesso em 08 de Outubro de 2015).
Peres, L.A.P., Ferreira, A.P.F., Krempser, A.R. e Ferreira, T.S., 2012. Beneficios Energeticos e Ambientais da Utilizacao de Triciclos Eletricos em Centros Urbanos no Brasil Cleiton
Magalhaes. XIV CBE Congresso Brasileiro de Energia Rio de Janeiro. Disponivel em: http://www.gruve.eng.uerj.br/download/
BeneficiosdaUtilizacaodeTriciclosEletricosFINAL (Acesso em 28 de Janeiro de 2016).
Rezvani, Z., Jansson, J., Bodin, J., 2015. Advances in consumer electric vehicle adoption research: a review and research agenda. Transport. Res. Part D: Transport Environ. 34,
Rizet, C., Cruzb, C., Vromantc, M., 2015. The constraints of vehicle range and congestion for the use of electric vehicles for urban freight in France. Transp. Res. Procedia 12,
500–507.
Rothengatter, W., Hensher, D.A., Button, K.J., 2003. Environmental concepts – physical and economic. Handbook of transportation and the environment, first ed. Elsevier Ltd.,
Amsterdam: Netherlands, pp. 827.
Roumboutsos, A., Kapros, S., Vanelslander, T., 2014. Green city logistics: systems of innovation to assess the potential of E-vehicles. Res. Transp. Bus. Manage. 11, 43–52.
SarmaSadhu, S.L.N., Tiwari, G., Jain, H., 2014. Impact of cycle rickshaw trolley (CRT) as non-motorised freight transport in Delhi. Transp. Policy 35, 64–70.
Schau, V., Rossak, W., Hempel, H., Spathe, S., 2015. Smart City Logistik Erfurt (SCL): ICT-support for managing fully electric vehicles in the domain of inner city freight traffic: a
Look at an ongoing federal project in the City of Erfurt, Germany. IEOM 2015 – 5th Int. Conf. Ind. Eng. Oper. Manag. Proceeding.
Schier, M., Offermann, B., Weigl, J.D., Maag, T., Mayer, B., Rudolph, C., Gruber, J., 2016. Innovative two wheeler technologies for future mobility concepts. In: 2016 11th Int.
Conf. Ecol. Veh. Renew. Energies, EVER 2016.
Schliwa, G., Armitage, R., Aziz, S., Evans, J., Rhoades, J., 2015. Sustainable city logistics — making cargo cycles viable for urban freight transport. Res. Transp. Bus. Manage. 15,
50–57.
Schoemaker, J., Allen, J., Huschebeck M. e Monigl, J., 2006. Quantification of urban freight transport effects I. Best urban freight solutions II.
Taniguchi, E., Imanishi, Y., Barber, R., Jamesd, J., Debauchee, W., 2014. Public sector governance to implement freight vehicle transport management. Procedia – Soc. Behav. Sci.
125, 345–357.
Thome, A.M., Scavarda, L.F., Scavarda, A.J., 2016. Conducting systematic literature review in operations management. Prod. Plan. Cont. https://doi.org/10.1080/09537287.
2015.1129464.
Thompson, R.G., Hassall, K., 2014. Implementing high productivity freight vehicles in urban areas. Procedia – Soc. Behav. Sci. 151, 318–332.
Tozzi, M., Corazza, M.V., Musso, A., 2013. Recurring patterns of commercial vehicles movements in urban areas: the Parma case study. Procedia – Soc. Behav. Sci. 87, 306–320.
Tranfield, D., Denyer, D., Smart, P., 2003. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br. J. Manage. 14,
207–222.
UNFCCC – United Nations Framework Convention on Climate Change, 2015. Mobilise your city local governments in developing countries take high road to low-carbon. http://
newsroom.unfccc.int/lpaa/transport/mobiliseyourcity-taking-the-high-road-to-low-carbon/ (acesso em 07.06.2016).
Visser, J., Nemoto, J., Browne, M., 2014. Home delivery and the impacts on urban freight transport: a review. Procedia – Soc. Behav. Sci. 125, 15–27.
Weiss, M., Dekker, P., Moro, A., Scholz, H., Patel, M.K., 2015. On the electrification of road transportation – a review of the environmental, economic, and social performance of
electric two-wheelers. Transport. Res. Part D: Transport Environ. 41, 348–366.
Lei Zhanga, Tilman Matteisb, Carina Thallerc, Gernot Liedtkeb, Simulation-based Assessment of Cargo Bicycle and Pick-up Point in Urban Parcel Delivery, The 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018)
Introduction :
Avec le développement d’internet et les nouvelles technologies, les attentes des consommateurs ont changé, faisant ainsi évoluer la logistique et la livraison à domicile. Actuellement, la livraison de colis urbains est devenue le goulot d’étranglement du commerce électronique. Ce goulot d’étranglement n’est pas seulement créé par le coût de la livraison des colis, mais également causé par la difficulté de réaliser des livraisons plus rapides et plus souples et précises.
La livraison du dernier kilomètre en milieu urbain est l’élément le plus critique de la distribution du commerce électronique. Les destinataires sont de plus en plus exigeants en termes de flexibilité, de rapidité et de fiabilité.
Cet article propose une simulation de la logistique urbaine du dernier kilomètre visant à résoudre les goulots d’étranglement rencontré par l’industrie de la livraison en introduisant des micro-dépôts et les vélos cargos dans le processus de livraison des colis.
Mots clés : Livraison à domicile, dernier kilomètre, e-commerce
Développement :
L’étude de cas sur le vélo cargo menée par les auteurs se penche sur deux types de scénarios :
Le premier scénario reflète le système de livraison actuel dans lequel les clients privés et commerciaux sont livrés directement à partir des centres de distribution par des véhicules de transport de colis typiques.
Dans le second scénario, les clients commerciaux sont approvisionnés par des véhicules écologiques – les vélos cargo.
Dans le scénario 1 les colis sont livrés directement à tous les clients à partir d’un centre de distribution par des véhicules de transport de colis typiques. Selon les statistiques de DHL, 77 % des colis privés sont livrés directement aux ménages. Les 23 % restants des colis privés sont livrés directement aux Packstations, où les clients privés peuvent les retirer à tout moment. Alors que tous les paquets commerciaux sont livrés avec succès dans les locaux des clients commerciaux.
Dans le scénario 2, le problème est de synchroniser le service de colis de deux échelons d’acheminement, où le premier échelon est livré par des véhicules de transport de colis typiques, et le second par des vélos-cargo. Tous les clients privés sont desservis par les véhicules de transport de colis typiques, tandis que tous les clients commerciaux sont desservis par des vélos cargo. Le mode de livraison des colis privés est le même que dans le scénario 1, mais les colis commerciaux sont d’abord livrés dans des micro-dépôts, qui sont le bureau de poste le plus proche des lieux où se trouvent les clients commerciaux, puis triés et livrés par des vélos cargo.
L’article démontre la faisabilité de nouvelles opérations de livraison basées sur le vélo-cargo et le point de ramassage.
Selon les résultats de la simulation, la livraison des clients commerciaux par vélo-cargo peut réduire d’environ 28% les émissions. En raison des limites de la précision des données, les vélos-cargo ne sont utilisés que pour la livraison de colis commerciaux. Toutefois, cela ne signifie pas que la distribution de colis commerciaux ne produira aucune émission. Ces colis doivent encore être distribués par le centre de distribution à des micro-dépôts en utilisant des véhicules de transport de colis typiques.
Conclusion :
A l’aide de simulations, les auteurs tendent à explorer des nouvelles solutions concernant la logistique urbaine et la livraison à domicile et permet de les comparer aisément au système de livraison actuelle.
Références :
1. Esser K, Kurte J. Kurier-, Express- und Paketdienste. Wachstumsmarkt und Beschäftigungsmotor. KEP-Studie 2016 – Analyse des Marktes in Deutschland. Köln; 2016. 2. Gruber J. Ich ersetze ein Auto (Schlussbericht) Elektro-Lastenräder für den klimafreundlichen Einsatz im Kuriermarkt. Vorhaben 03KSF029 der Nationalen Klimaschutzinitiative des BMUB. 2015. 3. Nesterova N, Quak H. A city logistics living lab: a methodological approach. In: Transportation Research Procedia 2016; 16: 403 – 417. 4. Balmer M, Rieser M, Meister K, Charypar D, Lefebvre N, Nagel K, Axhausen KW. MATSim-T: Architecture and Simulation Times. In: Bazzan ALC, Klügl F, editors. Information Science Reference. Hershey; 2009. p. 57-78. 5. Schröder S, Dabidian P, Liedtke G. A conceptual proposal for an expert system to analyze smart policy options for urban parcel transports. Proceedings of the Smart Cities Symposium Prague (SCSP). Prague; 2015. 6. Schröder S, Liedtke G. Modelling and analyzing the effects of differentiated urban freight measures – a case study of the food retailing industry. Transportation Research Board 93rd Annual Meeting. 2013. 7. Schröder S, Zilske M, Liedtke G, Nagel K. Towards a multi-agent logistics and commercial transport model: The transport service provider’s view. Procedia – Social and Behavioral Sciences 2012; 39: 649-663. 8. OpenStreetMap. Berlin 2016, Online in Internet, URL: http://www.openstreetmap.org/relation/62422#map=10/52.5072/13.4248. 9. Olson P, Nolan K. In Depth: Europe’s Most Congested Cities 2008. http://www.forbes.com/2008/04/21/europe-commute-congestion-forbeslife-cx_po_0421congestion_slide.html. 10. Paketda Alle Paketzentren in Deutschland 2016. Online in Internet, URL: http://www.paketda.de/paketdepot-alle.html. 11. Amt für Statistik Berlin-Brandenburg. Zensus Berlin 2011 für Berlin und Brandenburg. Berlin. 12. Statistisches Bundesamt. Gliederung der Klassifikation der Wirtschaftszweige. Ausgabe 2008 (WZ 2008). Online in Internet, URL: https://www.destatis.de/DE/Methoden/Klassifikationen/GueterWirtschaftklassifikationen/klassifikationenwz2008.pdf?_blob=publicationFile. 13. Wittenbrink P. Transportmanagement: Kostenoptimierung, Green Logistics und Herausforderungen an der Schnittstelle Rampe. Springer Gabler. Wiesbaden; 2014. 14. Ahrens A. Tabellenbericht zum Forschungsprojekt “Mobilität in Städten –SrV 2013”. Berlin; 2014. 15. Statistisches Bundesamt (2017a). BruttoinlandsProdukt 2016 für Deutschland, Begleitmaterial zur Pressekonferenz am 12. Januar 2017 in Berlin. Online in Internet, URL: https://www.destatis.de/DE/PresseService/Presse/Pressekonferenzen/2017/BIP2016/Pressebroschuere_BIP 2016.pdf?__blob=publicationFile accessed on 2017/07/31.