企业响应下负面口碑线性阈值传播模型研究
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摘要
基于线性阈值模型和价值共创理论,构建了针对负面口碑(negative word-of-mouth,NWOM)特征的社会化网络传播以及企业价值共创策略的模型.通过对NWOM传播的仿真实验,发现了消费者感知阈值,退出概率以及消费者异质性等因素显著影响NWOM传播过程,指出了NWOM非常容易在社会网络中爆发,其传播依赖于在传播初期的低感知阈值的消费者数目.进一步模拟了两种企业策略响应行为,两种响应因素对NWOM传播的影响,显示企业应在传播早期迅速执行价值共创响应策略,在传播后期的价值共创策略响应不能有效遏制NWOM对企业带来的负面影响,甚至适得其反.
According to the features of negative word-of-mouth(NWOM), this paper constructs a diffusion model to describe the NWOM diffusion pattern and companies' responding strategies by combining linear threshold model and value co-creation theory together in social networks. Experimental results imply that the threshold of awareness, probability of states transition, and users' heterogeneity affect NWOM diffusion. Most of the users will be infected by NWOM. The diffusion process deeply relies on the number of users who is easily infected. Two kinds of company's value co-creation responding strategies and two kinds of responding variables have been simulated further. Simulation results suggest that companies should make a response to NWOM as fast as possible. Value co-creation responding behaviors in the later stages cannot restrain the negative influence of NWOM, and even makes it worse.
According to the features of negative word-of-mouth(NWOM), this paper constructs a diffusion model to describe the NWOM diffusion pattern and companies' responding strategies by combining linear threshold model and value co-creation theory together in social networks. Experimental results imply that the threshold of awareness, probability of states transition, and users' heterogeneity affect NWOM diffusion. Most of the users will be infected by NWOM. The diffusion process deeply relies on the number of users who is easily infected. Two kinds of company's value co-creation responding strategies and two kinds of responding variables have been simulated further. Simulation results suggest that companies should make a response to NWOM as fast as possible. Value co-creation responding behaviors in the later stages cannot restrain the negative influence of NWOM, and even makes it worse.
引文
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[24]Etzion D.Diffusion as classification.Organization Science,2014,25(3):420–437
[2]Luo X M.Quantifying the long-term impact of negative word of mouth on cash flows and stock prices.Marketing Science,2009,28(1):148–165.
[3]Aggarwal R,Gopal R,Gupta A,et al.Putting money where the mouths are:The relation between venture financing and electronic word-of-mouth.Information Systems Research,2012,23(32):976–992.
[4]韩少春,刘云,张彦超等.基于动态演化博弈论的舆论传播羊群效应.系统工程学报,2011,26(2):275–281.Han S C,Liu Y,Zhang Y C,et al.Herd instinct of opinion based on dynamic evolutionary game theory.Journal of Systems Engineering,2011,26(2):275–281.(in Chinese)
[5]夏承遗,马军海,陈增强.复杂网络上考虑感染媒介的SIR传播模型研究.系统工程学报,2010,25(6):818–823.Xia C Y,Ma J H,Chen Z Q.SIR epidemic model with infection medium on complex networks.Journal of Systems Engineering,2010,25(6):818–823.(in Chinese)
[6]王长春,陈超.基于复杂网络的谣言传播模型.系统工程理论与实践,2012,32(1):203–210.Wang C C,Chen C.Rumor propagation model based on complex network.Systems Engineering:Theory and Practice,2012,32(1):203–210.(in Chinese)
[7]程寅钊,徐涛,彭勤科,等.具有社团和个体差异性的舆论传播动力学分析.系统工程学报,2012,27(4):431–438.Cheng Y Z,Xu T,Peng Q K,et al.Opinion dynamics with the different community and individual characteristics.Journal of Systems Engineering,2012,27(4):431–438.(in Chinese)
[8]路兰,高齐圣.基于复杂网络的社会财富演化模型.系统工程学报,2012,27(6):719–726.Lu L,Gao Q S.Evolving model of social wealth based on complex networks.Journal of Systems Engineering,2012,27(6):719–726.(in Chinese)
[9]Chen Y B,Xie J H.Online consumer review:Word-of-mouth as a news element of marketing communication mix.Management Science,2008,54(3):477–491.
[10]Kim Y S,Tran V L.Assessing the ripple effects of online opinion leaders with trust and distrust metrics.Expert Systems with Applications,2013,40(9):3500–3511.
[11]Haenlein M,Libai B.Targeting revenue leaders for a new product.Journal of Marketing,2013,77(3):65–80.
[12]Kimura M,Saito K,Motoda H.Blocking links to minimize contamination spread in a social network.ACM Transactions on Knowledge Discovery from Data,2009,3(2):9–32.
[13]Watts D J.A simple model of global cascades on random networks.Proceedings of the National Academy of Sciences,2002,99(9):5766–5771.
[14]Granovetter M.Threshold models of collective behavior.American Journal of Sociology,1978,83(6):1420–1443.
[15]Watts D J,Dodds P S.Influentials,networks,and public opinion formation.Journal of Consumer Research,2007,34(4):441–458.
[16]Yaˇgan O,Gligor V.Analysis of complex contagions in random multiplex networks,Physical Review E,2012,86(3):036103.
[17]Kahneman D,Tversky A.Prospect theory:An analysis of decision under risk.Econometrica:Journal of the Econometric Society,1979,47(2):263–291.
[18]Baumeister R F,Bratslavsky E,Finkenauer C,et al.Bad is stronger than good.Review of General Psychology,2001,5(4):323.
[19]Vargo S L,Maglio P P,Akaka M A.On value and value co-creation:A service systems and service logic perspective.European Management Journal,2008,26(3):145–152.
[20]Cai S Q,Yuan Q,Zhou P.A resource mapping framework for value co-creation in social media//The 12th Wuhan International Conference on e-Business.Wuhan,2013:1–8.
[21]Mccoll-Kennedy J R,Vargo S L,Tagger T S,et al.Health care customer value cocreation practice styles.Journal of Service Research,2012,4(15):370–389.
[22]Thomas J B,Peters C O,Howell E G,et al.Social media and negative word of mouth:Strategies for handing unexpecting comments.Atlantic Marketing Journal,2012,1(2):87–108
[23]Cantador I,Brusilovsky P,Kuflik T.Second workshop on information heterogeneity and fusion in recommender systems//Recreational Systems Analysis.New York:ACM,2011:387–388.
[24]Etzion D.Diffusion as classification.Organization Science,2014,25(3):420–437