Identifying the optimal initial adopters and adoption paths of the internet-based intangible network goods

Internet-based intangible network good (IING) has undergone rapid developments, even revolutionized multiple industries in recent years. IING is highly dependent on the rapid diffusion rates for development success. For firms, how to select the initial targets or “seeding points” to accelerate the adoption process is critical in network marketing campaigns. The purpose of this study is to provide a new method to identify the optimal initial adopters and adoption paths.,First, the author generalize three aspects influencing IING’s adoption, namely, innovation attributes, customer’s personality and word-of-mouth. Next, we establish a modified gravity model to describe how social interactions affect consumer’s adoption behavior. Then, simulate the adoption process by setting each agent as the initial adopter to identify the optimal initial adopters. Finally, trace the information flow to forecast the adoption paths.,The model reveals how individual interactions (micro level) aggregate into the diffusion process (macro level). The optimal initial adopters are determined by a combination of factors as follows: IING’s attributes, the adopter’s diffusion ability, the potential-adopter’s personality and the trust degree between adopters and potential-adopters. Among all these factors, trust degree plays a most important role.,This study proposes the conceptual model of IING’s adoption from a perspective of dyadic influence, in which an adopter’s influence on its peers depends on pairwise characteristics of both parties. The authors propose a new method to identify the optimal initial adopters and adoption paths based on the gravity model. It is the first time to introduce the gravity model to describe IING’s adoption, which is a creative application of social physics. The findings provide new insights in IING’s adoption and identifying the key nodes in networks.

[1]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[2]  J. Q. Stewart AN INVERSE DISTANCE VARIATION FOR CERTAIN SOCIAL INFLUENCES. , 1941, Science.

[3]  Li Wang,et al.  An agent-based simulation model for IING's adoption from a perspective of kinetic energy and potential energy , 2018, Kybernetes.

[4]  Johannes Palmer,et al.  Modeling the Diffusion of Residential Photovoltaic Systems in Italy: An Agent-Based Simulation , 2013 .

[5]  Ying Xie,et al.  The Role of Targeted Communication and Contagion in Product Adoption , 2008, Mark. Sci..

[6]  Xin Li,et al.  Effects of entrepreneurship and IT fashion on SMEs' transformation toward cloud service through mediation of trust , 2017, Inf. Manag..

[7]  Dylan Walker,et al.  Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment , 2014, Manag. Sci..

[8]  Liang Wang,et al.  Collaborative innovation in construction project: A social network perspective , 2017, KSCE Journal of Civil Engineering.

[9]  Rosanna Garcia Uses of Agent-Based Modeling in Innovation/New Product Development Research , 2005 .

[10]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[11]  Eric Sundstrom,et al.  Intelligence, “Big Five” personality traits, and work drive as predictors of course grade , 2003 .

[12]  Joseph Farrell,et al.  Standardization, Compatibility, and Innovation , 1985 .

[13]  Renbin Xiao,et al.  Modelling and simulation of new product diffusion with negative appraise based on system dynamics: a comparative perspective , 2010, Int. J. Comput. Appl. Technol..

[14]  Christine H. Roch The Dual Roots of Opinion Leadership , 2005 .

[15]  C. Shapiro,et al.  Network Externalities, Competition, and Compatibility , 1985 .

[16]  D. Watts,et al.  Influentials, Networks, and Public Opinion Formation , 2007 .

[17]  Elmar Kiesling,et al.  Agent-based simulation of innovation diffusion: a review , 2011, Central European Journal of Operations Research.

[18]  Georgiy Bobashev,et al.  Multiple peer effects in the diffusion of innovations on social networks: a simulation study , 2015 .

[19]  Joseph Farrell,et al.  Standardization and variety , 1986 .

[20]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[21]  Yonghe Zhang,et al.  An improved mix framework for opinion leader identification in online learning communities , 2013, Knowl. Based Syst..

[22]  Richard G. Netemeyer,et al.  Price Perceptions and Consumer Shopping Behavior: A Field Study , 1993 .

[23]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[24]  T. Valente,et al.  Accelerating the Diffusion of Innovations Using Opinion Leaders , 1999 .

[25]  Jianfeng Ma,et al.  Identifying opinion leader nodes in online social networks with a new closeness evaluation algorithm , 2018, Soft Comput..

[26]  Frank R. Kardes,et al.  The role of the social-identity function of attitudes in consumer innovativeness and opinion leadership , 2000 .

[27]  J. Goldenberg,et al.  The Role of Hubs in the Adoption Process , 2009 .

[28]  L. Mátyás The Gravity Model: Some Econometric Considerations , 1998 .

[29]  G. Serio,et al.  A generalization of the Kermack-McKendrick deterministic epidemic model☆ , 1978 .

[30]  Bo Li,et al.  What makes MOOC users persist in completing MOOCs? A perspective from network externalities and human factors , 2018, Comput. Hum. Behav..

[31]  Panagiotis Adamopoulos,et al.  The Impact of User Personality Traits on Word of Mouth: Text-Mining Social Media Platforms , 2018, Inf. Syst. Res..

[32]  Mark Batey,et al.  A tale of two sites: Twitter vs. Facebook and the personality predictors of social media usage , 2012, Comput. Hum. Behav..

[33]  R. Hurley,et al.  Alternative indexes for monitoring customer perceptions of service quality: A comparative evaluation in a retail context , 1998 .

[34]  P. Herr,et al.  Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective , 1991 .

[35]  José M. Molina López,et al.  Agent-based simulation with NetLogo to evaluate ambient intelligence scenarios , 2018, J. Simulation.

[36]  Gert Sabidussi,et al.  The centrality index of a graph , 1966 .

[37]  Sung-Byung Yang,et al.  Online consumers' reactions to price decreases: Amazon's Kindle 2 case , 2016, Internet Res..

[38]  Robert J. Kauffman,et al.  Opening the "Black Box" of Network Externalities in Network Adoption , 2000, Inf. Syst. Res..

[39]  E. S. Knowles,et al.  Social physics and the effects of others: Tests of the effects of audience size and distance on social judgments and behavior. , 1983 .

[40]  S. Gosling,et al.  A very brief measure of the Big-Five personality domains , 2003 .

[41]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[42]  A. Tellegen,et al.  An alternative "description of personality": the big-five factor structure. , 1990, Journal of personality and social psychology.

[43]  T. S. Robertson,et al.  Pop-ups, Ephemerality, and Consumer Experience: The Centrality of Buzz , 2018, Journal of the Association for Consumer Research.

[44]  Peter H. Reingen,et al.  Social Ties and Word-of-Mouth Referral Behavior , 1987 .

[45]  Miles Q. Ott,et al.  Strategic players for identifying optimal social network intervention subjects , 2018, Soc. Networks.

[46]  Weiren Shi,et al.  Evaluating the importance of nodes in complex networks , 2016 .

[47]  Peter Sheridan Dodds,et al.  Universal behavior in a generalized model of contagion. , 2004, Physical review letters.

[48]  Zun Liu,et al.  Identifying key nodes based on improved structural holes in complex networks , 2017 .

[49]  Jürgen Weigand,et al.  Agent-based simulation of policy induced diffusion of smart meters , 2014 .

[50]  Stephen P. Borgatti,et al.  Identifying sets of key players in a social network , 2006, Comput. Math. Organ. Theory.

[51]  M. Palo Rating satisfaction research: is it poor, fair, good, very good, or excellent? , 1997 .

[52]  Joong Hoon Kim,et al.  Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments , 2018, Artif. Intell. Medicine.

[53]  Teresa Correa,et al.  Who interacts on the Web?: The intersection of users' personality and social media use , 2010, Comput. Hum. Behav..

[54]  Paul Dwyer,et al.  Measuring the value of electronic word of mouth and its impact in consumer communities , 2007 .