Distinctive Roles of Lead Users and Opinion Leaders in the Social Networks of Schoolchildren

Kratzer, Jan; Lettl, Christopher

doi:10.1086/599324

Cited by 114 publications

(111 citation statements)

References 70 publications

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“…For self-reported opinion leadership and the control variables commitment and involvement, we adapted existing scales to reduce the length of the survey and increase the expected response rate. Existing literature shows that opinion leadership is a broad concept and several authors argue that innovativeness and perceived knowledge are part of the broad opinion leadership construct (Kratzer and Lettl 2009). Therefore, we measured self-reported opinion leadership using two items on persuasiveness from the scale by Flynn et al (1994), three items on innovativeness (Goldsmith and Hofacker 1991;Steenkamp and Gielens 2003), and two items on perceived knowledge (Flynn and Goldsmith 1999;Pritchard et al 1999).…”

Section: Surveymentioning

confidence: 99%

“…Multiple studies have shown that network characteristics are related to opinion leadership (Delre et al 2010;Katona et al 2011). There is less evidence for selfreported opinion leadership as an indicator (Eck Van et al 2011;Kratzer and Lettl 2009). In particular, it is unclear to what extent self-reported opinion leadership is an indicator of opinion leadership over and above the network characteristics.…”

mentioning

confidence: 99%

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Indicators of opinion leadership in customer networks: self-reports and degree centrality

2015

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In this paper, we assess two alternative indicators of opinion leadership, selfreported opinion leadership and degree centrality, on the same dataset. We also investigate the interaction effect of these two indicators and the social network environment on opinion leadership. We use social network and survey data from the mobile telecom industry to analyze opinion leadership in smartphone adoption. We find that degree centrality indicates opinion leadership, but that self-reported opinion leadership indicates opinion leadership only under the right social circumstances. In case of weak to moderate network ties, the effect of self-reported opinion leadership is not significant. However, self-reported opinion leaders more effectively influence their strong ties. This study sheds light on indicators of opinion leadership and provides insights for managers to improve their social marketing campaigns.

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Section: Surveymentioning

confidence: 99%

mentioning

confidence: 99%

Indicators of opinion leadership in customer networks: self-reports and degree centrality

2015

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“…Related research has also been conducted in children and adolescent networks. For instance roles of nodes within a network of school children have already been identified [10] and diffusion of social norms and harassment behavior in adolescents school networks have been empirically studied and evidenced [11]. It then has been reasonably shown that behavior, norms information and opinions flow within social networks of adults and children.…”

Section: Diffusion Processes In Social Networkmentioning

confidence: 99%

Modeling Contagion of Behavior in Friendship Networks as Coordination Games

Jordan

Wilde

Neto

2017

Advances in Intelligent Systems and Computing

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Abstract. It has been shown that humans are heavily influenced by peers when it comes to choice of behavior, norms or opinions. In order to better understand and help to predict society's behavior, it is therefore desirable to design social simulations that incorporate representations of those network aspects. We address this topic, by investigating the performance of a coordination game mechanism in representing the diffusion of behavior for distinct data sets and diverse behaviors in children and adolescent social networks. We introduce a set of quality measurements in order to assess the adequacy of our simulations and find evidence that a coordination game environment could underlie some of the diffusion processes, while other processes may not be modeled coherently as a coordination game.

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“…This literature treats the entire population of past adopters as the reference group for a representative agent's product adoption decision. With access to more disaggregate data on consumer's social networks, the recent empirical literature has used a more nuanced view of the reference group, leveraging social networks based on self-elicitation (Conley and Udry, 2008;Kratzer and Lettl, 2009;Iyengar, van den Bulte, and Valente, 2010;Nair, Manchanda, and Bhatia, 2010); dorm/work location (Sacerdote, 2001;Dufflo and Saez, 2003;Sorensen, 2006); ethnic/cultural proximity (Bertrand, Mullainathan, and Luttmer, 2000;Munshi and Myaux, 2006); or as in the current application, geographic location (Topa, 2001;Arzaghi and Henderson, 2007;Bell and Song, 2007;Manchanda, Xie, and Youn, 2008;Choi, Hui, and Bell, 2010;McShane, Bradlow, and Berger, 2010;Nam, Manchanda, and Chintagunta, 2010;Bollinger and Gillingham, 2011).…”

Section: Introductionmentioning

confidence: 99%

Estimating Causal Installed-Base Effects: A Bias-Correction Approach

Narayanan¹,

Nair

2011

SSRN Journal

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New empirical models of consumer demand that incorporate social preferences, observational learning, word-of-mouth or network effects have the feature that the adoption of others in the reference group -the "installed-base" -has a causal effect on current adoption behavior. Estimation of such causal installed-base effects is challenging due to the potential for spurious correlation between the adoption of agents, arising from endogenous assortive matching into social groups (or homophily) and from the existence of unobservables across agents that are correlated. In the absence of experimental variation, the preferred solution is to control for these using a rich specification of fixed-effects, which is feasible with panel data. We show that fixedeffects estimators of this sort are inconsistent in the presence of installed-base effects; in our simulations, random-effects specifications perform even worse. Our analysis reveals the tension faced by the applied empiricist in this area: a rich control for unobservables increases the credibility of the reported causal effects, but the incorporation of these controls introduces biases of a new kind in this class of models. We present two solutions: an instrumental variable approach, and a new bias-correction approach, both of which deliver consistent estimates of causal installed-base effects. The bias-correction approach is tractable in this context because we are able to exploit the structure of the problem to solve analytically for the asymptotic bias of the installed-base estimator, and to incorporate it into the estimation routine. Our approach has implications for the measurement of social effects using non-experimental data, and for measuring marketing-mix effects in the presence of state-dependence in demand, more generally. Our empirical application to the adoption of the Toyota Prius Hybrid in California reveals evidence for social influence in diffusion, and demonstrates the importance of incorporating proper controls for the biases we identify.

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Distinctive Roles of Lead Users and Opinion Leaders in the Social Networks of Schoolchildren

Cited by 114 publications

References 70 publications

Indicators of opinion leadership in customer networks: self-reports and degree centrality

Indicators of opinion leadership in customer networks: self-reports and degree centrality

Modeling Contagion of Behavior in Friendship Networks as Coordination Games

Estimating Causal Installed-Base Effects: A Bias-Correction Approach

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