Metrics of Emergence, Self-Organization, and Complexity for EWOM Research

In a recent round table organized by the Santa Fe Institute, the complexity of commerce captured the attention of those interested in understanding how complex systems science can be applicable for settings where consumers and providers interact. Despite the usefulness of applied complexity for commerce-related phenomena, few works have attempted to provide insightful ideas. This mini-review aims at providing a succinct discussion of how the metrics of emergence, self-organization, and complexity might benefit the research agenda of applied complexity and commerce/consumer studies. In particular, the paper argues possible pragmatic ways to understanding the valuable information present in word-of-mouth data found on electronic commerce platforms.

[1]  S. Hewitt,et al.  Reproducibility , 2019, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[2]  Beom Jun Kim,et al.  Consumer referral in a small world network , 2006, Soc. Networks.

[3]  Satchidananda Dehuri,et al.  Enhancing scalability and accuracy of recommendation systems using unsupervised learning and particle swarm optimization , 2014, Appl. Soft Comput..

[4]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[5]  Michel Tuan Pham.  The Seven Sins of Consumer Psychology , 2013 .

[6]  J. Correa,et al.  Evaluation of collaborative consumption of food delivery services through web mining techniques , 2019, Journal of Retailing and Consumer Services.

[7]  Sitabhra Sinha,et al.  "Hits" emerge through self-organized coordination in collective response of free agents. , 2013, Physical review. E.

[8]  Carlos Gershenson,et al.  Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis , 2013, ArXiv.

[9]  Jimeng Sun,et al.  A Survey of Models and Algorithms for Social Influence Analysis , 2011, Social Network Data Analytics.

[10]  Dawn Iacobucci,et al.  Brand diagnostics: Mapping branding effects using consumer associative networks , 1998, Eur. J. Oper. Res..

[11]  Simon Munzert,et al.  Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining , 2014 .

[12]  Yasuko Kawahata,et al.  Sociophysics Analysis of the Dynamics of Peoples' Interests in Society , 2018, Front. Phys..

[13]  Jonah A. Berger.  Word of mouth and interpersonal communication: A review and directions for future research , 2014 .

[14]  Jingdong Chen,et al.  Research on the Influence Mechanism of eWOM on Selection of Tourist Destinations—The Intermediary Role of Psychological Contract , 2019, Advances in Intelligent Systems and Computing.

[15]  Jerome B. Kernan,et al.  Analysis of Referral Networks in Marketing: Methods and Illustration , 1986 .

[16]  Carlos Gershenson,et al.  A Package for Measuring Emergence, Self-organization, and Complexity Based on Shannon Entropy , 2017, Front. Robot. AI.

[17]  Daniel Polani,et al.  Information and Self-Organization of Behavior , 2013, Adv. Complex Syst..

[18]  Hadley Wickham,et al.  R for Data Science: Import, Tidy, Transform, Visualize, and Model Data , 2014 .

[19]  Manoj Kumar Tiwari,et al.  Predicting the consumer's purchase intention of durable goods: An attribute-level analysis , 2017, Journal of Business Research.

[20]  Geng Cui,et al.  Terms of Use , 2003 .

[21]  Carlos Gershenson,et al.  The World as Evolving Information , 2007, ArXiv.

[22]  Péter Érdi,et al.  Challenges in complex systems science , 2012, ArXiv.

[23]  Luciano da Fontoura Costa,et al.  Concentric network symmetry grasps authors' styles in word adjacency networks , 2015, ArXiv.

[24]  Samuel Thiriot,et al.  Word-of-mouth dynamics with information seeking: Information is not (only) epidemics , 2018 .

[25]  Petter Holme,et al.  The network organisation of consumer complaints , 2010 .

[26]  David Robinson,et al.  tidytext: Text Mining and Analysis Using Tidy Data Principles in R , 2016, J. Open Source Softw..

[27]  David Godes,et al.  Using Online Conversations to Study Word-of-Mouth Communication , 2004 .

[28]  Mikhail Prokopenko,et al.  An information-theoretic primer on complexity, self-organization, and emergence , 2009, Complex..

[29]  Sabrina Giordano,et al.  hmmm: An R Package for Hierarchical Multinomial Marginal Models , 2014 .

[30]  Ricard V. Solé,et al.  The morphospace of language networks , 2018, Scientific Reports.

[31]  Sang-Hee Kweon,et al.  A Semantic Network Analysis of the Newspaper articles on Big data , 2014 .

[32]  R. Jahn,et al.  Organization and dynamics of SNARE proteins in the presynaptic membrane , 2015, Front. Physiol..

[33]  Rakhi Thakur.  Customer engagement and online reviews , 2018 .

[34]  Carlos Gershenson,et al.  A Novel Antifragility Measure Based on Satisfaction and Its Application to Random and Biological Boolean Networks , 2018, Complex..

[35]  Haiyan Wang,et al.  quanteda: An R package for the quantitative analysis of textual data , 2018, J. Open Source Softw..

[36]  Arie W. Kruglanski,et al.  The dynamics of consumer behavior: A goal systemic perspective , 2012 .

[37]  Joao Antonio Pereira,et al.  Linked: The new science of networks , 2002 .