local neighborhoods as early predictors of innovation adoption

Marketing Science Institute Working Paper Series 2010 Report No. 10-104 Local Neighborhoods as Early Predictors of Innovation Adoption Jacob Goldenberg, Sangman Han, Donald Lehmann, Janghyuk Lee, and Kyung Young Ohk “Local Neighborhoods as Early Predictors of Innovation Adoption” © 2010 Jacob Goldenberg, Sangman Han, Donald Lehmann, Janghyuk Lee, and Kyung Young Ohk; Report Summary © 2010 Marketing Science Institute MSI working papers are distributed for the benefit of MSI corporate and academic members and the general public. Reports are not be to reproduced or published, in any form or by any means, electronic or mechanical, without written permission.

Report Summary Managers spend considerable effort trying to find ways to predict the behavior of large populations, particularly in marketing new products. In this report, Goldenberg, Han, Lehmann, Lee, and Ohk examine the role of predictive neighborhoods (groups of connected individuals whose adoption patterns evolve similarly to, yet earlier than, overall population behavior) in forecasting adoption of new products. With data drawn from Cyworld.com, a social network website in Korea, they create a network of 114 neighborhoods and track the diffusion process over the network of new items introduced by CyWorld to users. They begin by examining individual adoption within neighborhoods, specifically the influence of local characteristics on the timing and speed of adoptions. They find that an individual’s local neighborhood has a significant influence on his or her adoption behavior. Individuals who receive information from a greater number of sources (greater in-strength) become exposed to information, and hence tend to adopt, earlier. Individuals who share information with a greater number of sources (greater out-strength) also tend to adopt more products, consistent with the opinion leadership literature. Consistent with findings that hubs increase the speed of adoption, their study shows that density—how tightly connected members of a neighborhood are—affects the speed of adoption. The betweenness of the neighborhood (that is, how closely linked it is to other parts of the network) increases the number of adoptions. This is consistent with the neighborhood’s role as a broker of information to different neighborhoods, increasing the likelihood of exposure to products and consequently increasing the number of adoptions. In a second analysis, the authors examine whether some neighborhoods can be reliably used at an early stage of the product introduction process to predict overall network adoption behavior. Using the same set of 114 neighborhoods from the Cyworld.com data, they identify the top 10 items adopted (the “mega hits”). To determine how well particular neighborhoods predicted adoption in the total network, they used the correlation between (1) adoption of the mega-hit in a neighborhood at the time that 5%, 16%, and 50% of the eventual market had adopted it and (2) eventual total network adoptions. They then examined which characteristics were associated with the neighborhoods that were the best predictors of overall adoption. They found that large and central neighborhoods that adopted early were the best predictors and their predictions were more accurate than those of random sample of the same size. By contrast, small dense neighborhoods were relatively poor predictors of eventual adoption, possibly because of their closed and isolated nature. Overall their study demonstrates the potential of using identifiable clusters (predictive neighborhoods) to improve the accuracy of new product adoption predictions within a market.

Marketing Science Institute Working Paper Series 1

While the study focused on post-launch predictions, firms might use predictive neighborhoods as the basis for pre-launch tests as well. Jacob Goldenberg is Professor of Marketing, School of Business Administration, The Hebrew University of Jerusalem and a member in the research center at Ono. Sangman Han is Professor of Marketing at Sungkyunkwan University, Korea. Donald Lehmann is George E. Warren Professor of Business, Graduate School of Business, Columbia University. Janghyuk Lee is AssociateProfessor of Marketing, Korea University Business School, Seoul. Kyung Young Ohk is a postdoctoral fellow at the Massachusetts Institute of Technology. Acknowledgments The authors would like to thank Dominique Hanssens, Scott Neslin, Vithala Rao, Danny Shapira, and Olivier Toubia for their valuable comments.


Introduction

“As Maine goes, so goes the nation,” This proverb of U.S. politics suggests that what happens

in a particular neighborhood (in this case, the sparsely populated state of Maine) can be a good

predictor of a much larger population (in this case the United States, or more precisely who wins

the presidential election). More generally, both political scientists and managers spend

considerable effort trying to find ways to predict the behavior of large populations. This paper

focuses on predicting innovation adoption based on what we term predictive neighborhoods

(groups of connected individuals whose adoption patterns evolve similarly to, yet earlier than,

overall population behavior). We demonstrate that some neighborhoods can indeed be used to

predict overall adoption and that their predictions are superior to those of random samples.

Much of the research on innovation adoption has focused at the aggregate level, i.e., on

forecasting the rate of adoption in the overall population (for example the work on diffusion

models). Another major stream of research has concentrated on individual adoption and the role

of word of mouth in general and opinion leaders (influentials) in particular. More recently,

researchers have adopted a social network perspective on innovation diffusion (see Van den Bulte

and Wuyts 2007). This paper extends this later work by focusing on the role of (local) sub-

networks in the diffusion process. The main finding is that in a large network, some clusters (the

predictive neighborhoods) can be used as predictors of overall network adoption of new products

and the predictions are superior to those of random samples. These clusters have identifiable

characteristics, e.g., their size, centrality in the network and tendency to include early adopters.

Our paper begins by examining the influence of local neighborhood characteristics on

individual adoption within the neighborhood. We then show that the behavior of some local


neighborhoods closely mirrors that of the entire population and can be used as early predictors of

the success or failure of the overall adoption process.

Background

Social networks

The social network literature can be classified into two principle research themes: (1) network

formation and depiction (e.g., Stephen and Toubia, 2009a) and (2) information dissemination and

diffusion in networks (see among others: Brown 1981; Goldenberg et al. 2001a; Kocsis and Kun

2008; Rogers et al. 1970; Valente 1995; Young 2006). The focus here is on diffusion in

networks.

Previous research has shown that product adoption decisions are influenced by social peers

and relationships (see Godes et al. 2005; Goldenberg et al. 2009; Goldenberg et al. 2006; Hogan

et al. 2005; Libai 2005; Libai et al. 2009; Libai et al. 2005; Rogers 2003; Trusov et al. 2008b;

Valente 1995; Van den Bulte and Joshi 2007; Van den Bulte and Wuyts 2007; Watts and Dodds

2007b)). Research also provides evidence of the benefits garnered from an advantageous network

position and the structural properties of one's local network. These properties include the

resources of one's direct network (Burt 1997; Lin 2001), the number and strength of ties (e.g.,

Granovetter 1973) and closure or local clustering (Coleman 1990; Lin 2001).

A few studies have found that the individual adoption process is driven by group adoption

(Jones and Ritz 1991; Kim and Srivastava 1998). However, these studies focus on cases where

group adoption precedes individual adoption within that group. Here we consider the case of

social networks characterized by a high clustering coefficient (Watts and Strogatz 1998). This

paper first investigates how the individual adoption decisions within a neighborhood are


influenced by the properties of the neighborhoods to which they belong, and second, whether

certain local neighborhoods can be reliable predictors of overall network adoption (i.e., are

predictive neighborhoods).

Predictive Neighborhoods

Rapid post-launch evaluation of new product performance is critical because of the large

expenditures involved and the short window of opportunity available to make decisions about

improvements or pulling the plug (Goldenberg et al. 2001b; Golder and Tellis 1997). However,

extant diffusion models are hard to calibrate at early stages of the diffusion process based on

aggregate adoption data only (see for example Hauser et al. 2006; Mahajan et al. 1990; Van Den

Bulte and Lilien 1997). Early forecasts based on aggregate adoption data are also sometimes

quite far off. Some of the reasons for this are that 1) the early (pre take-off) stages of product

introduction do not necessarily fit easily into existing “diffusion of innovation” frameworks; 2)

only a small number of data points exist before takeoff; and 3) in the presence of the volatility

which is quite common during introduction stages, the search for explicit solutions for nonlinear

differential equations through linear approximations can lead to multiple (dis)equilibria (e.g.,

Nijkamp and Reggiani 1998) and fail to account for discontinuities. Indeed, reviews of diffusion

models (e.g., Mahajan et al. 2000; Parker 1994) find little utilization of growth models around

take-off. Several studies have proposed generalized models of new product growth that capture

the effect of sales volatility by adding a stochastic term to the sales equation (see for example

Boswijk and Franses 2005; Goldenberg, Lowengart and Shapira, 2009).

There is growing consensus on the fundamental role that the structure of social networks plays

in how information reaches consumers, channel members, and suppliers. This is particularly

important in the marketing of new products and the creation of marketing collaborations (Achrol


and Kotler 1999; Iacobucci 1996; Rosen 2002). Attempts have been made to directly link social

network properties to the success of marketing activities such as pricing or promotion (Mayzlin

2002; Shi 2003) and sales (Stephen and Toubia, 2009b). Yet much of the research in this area has

focused on relatively small networks, for example intra- or inter-organizational networks (see

Houston et al. 2004 for a review), tie-strength (Brown and Reingen 1987; Rindfleisch and

Moorman 2001), or social capital (Ronchetto et al. 1989).

Largely because networks and other more complex structures make modeling and estimation

procedures much more complex, most of the diffusion literature has implicitly assumed that the

market is homogeneous. Nonetheless, in order to better capture the mechanism through which

new products penetrate the marketplace, several attempts have been made to divide the total

market into submarkets and model the interactions among them. For instance, market structures

for new product introduction in international markets (i.e., “international diffusion”) have been

examined to determine which market should be penetrated first (e.g., countries with a higher

connectivity level, etc.). For example, Putsis, Balasubramanian, Kaplan, and Sen (1997)

examined how adoption in one country affects adoption in others by modeling a mixing pattern

(interaction between countries) that is grounded in communications within and across countries.

While this is an important direction to pursue, the size of the network used (i.e., only several

dozen nodes) and the resolution are limited (i.e., the nodes are countries rather than individual

consumers).

When new products are introduced into a market, they diffuse over time and space (Allaway et

al. 1991; Bell and Song 2007; Bronnenberg and Mahajan 2001; Mahajan et al. 1979). The spatial

aspect of diffusion is strongly related to interactions between adopters and potential adopters

(normally termed word of mouth or internal influence). For communication to take place,


adopters and potential adopters typically must be in proximity to each other. Indeed, the diffusion

literature reports a clear correlation between geographic proximity and the strength and speed of

word-of-mouth spread, sometimes labeled the “neighborhood effect” (Baptista 2000; Case 1991;

Mahajan et al. 1979). Garber, Goldenberg, Libai and Muller (2004) showed how such spatial

clusters of communication can be used as a strong predictor of take-off by calculating the cross-

entropy of the spatial distribution: When a product is successful, geographic clusters of adopters

emerge. Even when a product is a failure, some consumers adopt, mainly as a result of external

effects (marketing efforts). However, because the effect of their adoption on other consumers will

be negligible or negative, adopters become randomly distributed in space in close to a uniform

geographical distribution. Thus, the lack of contiguous units adopting a product may be a strong

signal of likely product failure.

The interest in spatial distribution is mainly a result of the availability of spatial data. Yet

spatial distribution is only a proxy for the underlying social networks that account for the growth

curve. If, for example, we know the structure of the social network, we can calculate predictors

such the cross–entropy of the distribution, using more accurate propagation measures. While the

improvement of communication technologies in general, and the Internet in particular, has

reduced the dependence of word of mouth communication on geographical proximity, it is still

useful to examine communities (local neighborhoods) defined based on communication among

members.

Importantly, it is possible that certain local neighborhoods consistently behave like the entire

network (“as Maine goes…”). If these neighborhoods also adopt early, they can be used to

predict product success or failure. In this paper we examine web-based social networks and


communications among potential adopters, and demonstrate the existence of local neighborhoods

that provide early and accurate predictions of eventual overall population adoption.

Factors That Influence Adoption

Individual decisions are strongly influenced by the others to whom the individual is linked.

Although there is a debate over the exact mechanism behind this effect (e.g. cohesion vs.

contagion), there is agreement that network properties influence adoption. We examined several

common network properties in this work. Below is a brief glossary of some key terms. More

precise definitions appear in the Appendix.

Term Description

Centrality The importance of a node within the network (i.e. are they in the center or on the periphery?)

Betweenness centrality

Nodes that lie on the shortest paths between other nodes have higher betweenness centrality.

Neighborhood

A group of connected nodes (people) with more connections inside the group then outside it.

Degree

The numbers of ties (links) a node (person) has.

Density Number of ties divided by the number of possible ties in a cluster, i.e. how tightly connected members of a neighborhood are.

Path length Smallest number of links between two nodes (i.e., the shortest distance between them.)

Tie strength Frequency and strength of communication between the two nodes (people).

Social Capital

The value (e.g. productivity knowledge, information accessibility) a person has due to his/her position in the social network.

These properties can be classified into individual based properties (how an individual is

connected to peers in the network) and overall properties of the network to which the individual


belongs. We next describe each of the properties, and present the variable definitions and

operationaliztion used here which are summarized in the appendix.

Individual-based properties

The way individuals are connected to the network can influence their own adoption as well as

the resulting cascade of adoption by others (Watts, 2002). Several properties exist which describe

how individuals relate to their neighborhoods. Here we focus on the two most prominent

properties: overall tie-strength to neighbors and individual relationships with local hubs.

Tie-strength. The strength of the ties is known to influence the adoption process (Goldenberg

et al. 2007; Granovetter 1973; Reingen and Kernan 1986). Granoveter (1973) showed that weak

ties in some cases may be more influential in influencing others than strong ties (coined “the

strength of weak ties”). Specifically, in the case of a successful product with mainly positive

word-of-mouth, weak ties accelerate the diffusion process at least as much as strong ties if there

are many sub-networks (cliques/local neighborhoods) in the market. However, in the case of a

less successful product when negative w-o-m appears, strong ties become more important and

have more influence on adoption (Goldenberg et al. 2007). We utilize two measures of tie-

strength here: InStrength and OutStrength (the number of visits a person receives from others and

the number of visits a person makes to others).

Relationship with hubs. Social hubs are often referred to as influentials. One key characteristic

of influentials (along with knowledge and persuasiveness) is their number of ties; social hubs

have an exceptionally large number of ties (but they are not necessarily experts). Although some

work argues that the influence of social hubs is not large (Watts and Dodds 2007a; Watts and

Dodds 2007b), there is evidence that social hubs adopt early in the process, even if they are not

innovative, because they are exposed to the new product early in the process (Goldenberg et al.


2009). A more generalized positive correlation between degree and adoption time was recently

found by Katona, Zubcsek and Sarvary (2009). Due to their many ties, hubs accelerate the

diffusion process and increase the market size. In cases where social hubs are relevant (i.e., when

w-o-m activity exists), individual adoption may be influenced by how many direct links to hubs

an individual has, as well as the strength of those ties.

Neighborhood-level properties

To function as a useful predictor, a neighborhood must: 1) be similar to the entire network

in its eventual adoption behavior, and 2) adopt early in the diffusion process, hopefully at a rapid

rate. Assuming neighborhoods in a network “function” as entities that influence individual

adoption, standard network measures that differentiate between them should be relevant. Here we

focus on four general properties: density, centrality, the number and strength of connections

among hubs in a neighborhood, and neighborhood size.

Density. One distinctive property of neighborhoods is their density. When the number of links

among nodes is larger, each individual node is exposed to more influence, and hence information

travels faster and is shared sooner by more nodes in the network. This means that the individuals

in a dense neighborhood may share similar preferences, and a product that successfully matches

this preference profile will be adopted by most of this neighborhood. This type of clustering

effect was explored by Katona, Zubcsek and Sarvary (2009) who found that the density of

connections in a group of adopters has a positive effect on the adoption of individuals connected

to this cluster. Two common measures of cluster density are path length (the distance between

pairs of nodes in the network, measured by the number of nodes that separate a pair of nodes;

Newman 2001), and the clustering coefficient (the number of links among one’s neighbors

divided by the total possible links between all pairs of these neighbors; Watts and Strogatz 1998).


Neighborhood centrality. A neighborhood whose nodes are connected to more other social

worlds (neighborhoods) is likely to have access to a wider range of information. The correlation

of neighborhood and overall market adoption is expected to be stronger when the neighborhood

is connected to several different neighborhoods rather than when the majority of its connections

are within that single neighborhood. For example, Burt (1992) relates the information advantage

of an actor in a network to the extent to which the actor spans structural holes, that is uniquely

links separate parts of the network. We follow this logic, with the difference being that we use the

centrality measure at the neighborhood (and not the node) level, in effect assuming

neighborhoods can act as brokers in a network by bridging to other neighborhoods.

Here we concentrate on betweenness centrality (Girvan and Newman 2002), which measures

which parts of the network are connected through a node (or a group of nodes, in our case).

Neighborhoods with high betweenness are more closely linked to other parts of the network,

exposing their members to more, and possibly varied, information earlier in the product adoption

process. Other centrality measures, e.g., degree centrality, are less relevant to the neighborhood

level. Since the different centrality measures are highly correlated (in a common scale-free

network, the correlations are typically around .8), and because we obtained similar results when

we used other centrality measures, we focus on betweenness centrality here.

Size. We define size as the number of nodes (individuals) in a given neighborhood. Being a

member of a large neighborhood means that an individual is exposed (directly or indirectly) to a

greater number of peer adoptions, and therefore has a better chance of being informed on a

variety of products.

Hub-related characteristics. The greater the number of hubs in each neighborhood, the faster

we expect dissemination to be (as demonstrated in Goldenberg et al. 2009). In addition, the


greater the tie-strength among the hubs, and the more densely the hubs in the cluster are

connected, the greater their influence on adoption. We use two measures of hubs here – the

number of hubs/size (number of hubs in the same cluster divided by cluster size) and the tie-

strength among hubs (the sum of tie-strength among hubs divided by the number of hubs in a

given neighborhood). Of course if no hubs are linked, this measure is zero.

Data

To examine the role of neighborhoods, data is needed to map a large network, and information

about the timing of individual adoption for multiple diffusion processes must be available. One

such data set that was available for this research comes from Cyworld.com, a social network

website in Korea.

Cyworld was founded in 1999. In the period of this study, the number of members in the

Cyworld database grew from 2,492,036 in December 2003 to 12,685,214 in July 2005. In

October 2006, there were about 22 million registered members (compared to about 100 million

for Myspace), and an average of 20 million monthly unique visitors (compared to 24.2 million

unique daily visitors in MySpace, according to wikipedia.com, 2006, and Businessweek, 2006).

Many people considered Cyworld a part of their everyday life and as a tool for building

relationships and sharing information about their lives on their homepages.

A key aspect of the service, for our purpose, allows people to customize their homepages by

including documents, photos, and other "goodies" at no charge. Members can also decorate their

minihompy (personal homepage) with paid items such as virtual household items—furniture,

electronics, wallpaper (Cyworld generates money from sales of these items and from

advertising). People can also adopt items such as pictures or video clips directly from the


minihompies they visit (called “scrapping” in Cyworld.). This study focuses on this latter type of

adoption, using data from December 2003 to July 2005.

The data contain information on “scrapped” items – the item number, time of scrapping, and

creator ID of each item. By combining network information with information on “scrapped”

items, we track the diffusion process over the network of new items introduced by CyWorld.

There are multiple ways to define links between people, in particular based on the level of

activity or on the existence of a “pointer” between them (as in Facebook). Here we define links

between nodes based on the direct activity between them (e.g., visits) rather than by pointers such

as membership in address books. (Trusov, Bodapati and Bucklin 2008a). Importantly, a pointer

between two individuals in a social networking site, such as LinkedIn or Face Book, does not

necessarily imply influence.

Once a link is established, all the variables defined above can be measured (see appendix). For

example, the degree of each node (person) is the number of links (connections) to other nodes

(people) in the network. We measure out-degree in our data set as the number of other nodes ever

visited by the hub and in-degree as the number of other nodes that have visited the hub.

Consistent with Goldenberg, Han, Lehmann, and Hong (2009), we define hubs as people with

both in- and out- degrees more than three standard deviations above the mean. In this dataset,

hubs comprised between 1.28% and 3.30% of the dataset over time, averaging 2.63%.

The Role of Local Neighborhoods in Individual Adoption

We first explore adoption speed and scope across products at the individual level, and

how they are influenced by the properties of the individual and the individual’s neighborhood.

We measure the adoption timing of persons as the average percent of other people who


eventually adopt a product before person j adopted it, and adoption extent (size) as the total

number of scraps adopted by the individual.

In order to allow for random errors at both the individual and neighborhood levels, we

utilize hierarchical linear models (Bryk amd Raudenbush 1992). The hierarchical linear model

estimates the individual and neighborhood levels simultaneously.

Level 1: individual

ij

Q

qqijqjjij rX ++= ∑

=10 ββη

Level 2: neighborhood

qjsj

S

sqsqqj uW

q

++= ∑=1

0 γγβ

where the Xs are individual-level variables (in strength, out strength, closeness to hubs, and tie-

strength to hubs), and the Ws are neighborhood characteristics (path length, clustering

coefficient, cluster betweenness centrality, tie-strength among hubs, and number of

hubs/neighborhood size).

Basic Analyses

To create a manageable size database to analyze, 24,368 minihompies were sampled using a

“snowballing” method. The corresponding individuals were then classified into 114

clusters/neighborhoods according to Newman and Girvan’s modularity method (2003) based on

declared links.1 Basic statistics (see Table 1, following References). Essentially, the sample is

made up of young people (average age of 21) who made an average of about 10 visits to other

members and received an average of 10 visits from other members in the network. Individuals


adopted an average of 3 items in the time period studied. The average cluster/neighborhood size

is 214, with a range between 102 and 1452. Each neighborhood contains an average of 4.8 hubs.

Correlations among the individual-based variables (see Table 2, following References). The

largest individual-level correlation is between tie-strength to hubs and in strength. At the cluster

level, the strongest correlations are among betweenness centrality, the number of hubs, and

cluster size (see Table 3, following References).

Model Results

Number of adoptions

Table 4 (following References) presents the results for predicting the number of

individual adoptions based on the individual’s position in his or her cluster and the characteristics

of the cluster. Two of the four individual variables (out-strength, tie-strength to hubs) are

significant in Model 1. We therefore dropped the insignificant variables (in-strength, closeness to

hub) from the analysis. Model 2 adds the neighborhood-level variables (path length, clustering

coefficient, betweenness centrality, tie-strength among hubs, and number of hubs/size.) Only one

neighborhood variable is significant: the betweenness centrality of the neighborhood has a

positive impact. Model 3 includes only the significant individual-level and neighborhood-level

variables. Finally, in Model 4 we added demographic variables (gender and age) to the model to

control for their effect. (Basically the number of adoptions was greater for males and for younger

people).

At the individual level, out-strength has a significant positive relationship to adoption size in

Model 4. This implies that influential members (individuals with high out-strength) tend to adopt

more actively than non-influential members. This gives them more to talk about as well as

credibility as information sources given their greater use of the product category. Interestingly,


tie-strength to hubs also has a significant positive influence on adoption size, consistent with

previous findings (Goldenberg et al. 2009) that demonstrated that hubs influence individual

adoption. At the neighborhood level, only betweenness centrality significantly influences

adoption size. This implies that individuals in central neighborhoods tend to adopt more.

Neighborhoods with high betweenness are more connected to other parts of the network,

allowing their members to be exposed to diverse information earlier. Because individuals in

central neighborhoods have more information earlier, they tend to adopt more.

Because of the high correlation between neighborhood size and betweenness centrality (.88)

we substituted size for centrality and re-ran all the analyses. It was not surprising to find that size

was significant and the coefficients of the other variables remained essentially unchanged.

Because the log likelihoods were slightly smaller when betweenness centrality was used, we only

report these latter results (see Table 4). One interpretation of these results is that neighborhood

size is a major driver of centrality, or at least that central neighborhoods are larger.

Adoption speed. Adoption speed was modeled in a manner parallel to adoption size (see

Table 5, following References). In the initial model, in-strength was significant at the 5% level

while out-strength was not. In Model 2, we added the cluster-level variables—path length,

clustering coefficient, betweenness centrality, tie-strength among hubs, and number of hubs/size.

Of these, only the clustering coefficient was significant at the .01 level. In Model 3, we estimated

a parsimonious model with individual in-strength and the clustering coefficient, which improves

the likelihood from -1451.12 to -1439.86. In Model 4, we added the demographic variables

gender and age, which improves the likelihood from -1439.86 to -1410.42.

At the individual level, in-strength has a significant negative coefficient, while out-strength

does not. This means that individuals who are visited more often by others tend to adopt new

products faster. Interestingly, the central position of a neighborhood does not affect the adoption


speed of its members even though it has significant influence on the number of items adopted of

its members. Further, in denser neighborhoods, individuals tend to adopt new products faster.

Key findings

Overall the results lead to at least three key findings. First, at the individual level, out-

strength affects the number of adoptions while in-strength affects adoption speed. Individuals

who receive information from a greater number of sources (in-strength) become exposed to

information, and hence tend to adopt, earlier. Furthermore, those people who adopt more

products tend to speak to others more, consistent with the opinion leadership literature. Finally, at

the neighborhood level, density (the clustering coefficient) affects adoption speed, while

centrality affects the number of adoptions. The density effect on speed is consistent with findings

that hubs increase the speed of adoption by influencing a larger “area” of their network. The

effect of the position of the neighborhood (its betweenness) on the number of adoptions is

consistent with the neighborhood’s role as a broker of information to different neighborhoods,

thereby increasing the likelihood of exposure to products and consequently increasing the number

of adoptions. More generally, the results demonstrate that an individual’s local neighborhood has

a significant influence on his or her adoption behavior.

Uncovering Predictive Neighborhoods

The previous analyses examined adoption within the neighborhood. In the following analysis,

we examine whether some neighborhoods can be reliably used at an early stage of the product

introduction process to predict overall network adoption behavior.


Method

We examine the same set of 114 neighborhoods analyzed earlier. We identified the number of

scraps (adoptions) for each item and eliminated those with fewer than 20 adoptions (i.e., niche

products and abject failures which had very sparse data on which to base analyses). Of the

remaining products, the top 10 items (in effect the “mega hits”) were adopted by between 104

and 7952 people while the bottom 30 were adopted by 20-25 individuals, meaning highly

successful items were adopted at a rate of at least four times greater, and in many cases at an

order of magnitude greater, than less successful ones.

As a measure of how well a local neighborhood predicts adoption in the total network, we

used the correlation between adoption in a neighborhood at the time 5%, 16%, and 50% of the

eventual market had adopted it (i.e., early adoption in the neighborhood), and eventual total

network adoptions after the adoption process is completed. Using these 40 items, (the top 10 plus

the bottom 30), we then compared the ability to predict total adoption in the overall market based

on a) a random sample (the “gold standard” of market research) of size 200, similar to the

average cluster size of 214, b) the average of all the neighborhoods in the network, and c) the 20

clusters with the highest correlation between their adoption at the time 16% of the market had

adopted and eventual market adoption.

We also examined which characteristics were associated with the neighborhoods that were the

best predictors of overall adoption. More specifically, for each neighborhood, we analyzed the

following local neighborhood-level variables: path length, clustering coefficient, betweenness

centrality, cluster size, tie-strength among hubs, and number of hubs/size.


Results

The correlation between early neighborhood adoption and total population adoption

ranged between -.07 and + .99. (see Table 6, following References). At the time when 5%, l6%,

or 50% of the overall market has adopted a product, an average neighborhood predicts total

market adoption slightly, but not significantly, better than the average of 20 random samples of

200 individuals. More importantly, in all three cases, the top 20 (of the 114) clusters substantially

and significantly outperformed the random samples with correlations of .96, .96 and .98 vs. .62,

.72, and .84 respectively. Even more impressively, when the sample of products is split in half,

the holdout sample performance of the top 20 clusters remains strong (average correlations of

.79, .95, and .96 respectively), whereas the results for the 20 random samples degrades noticeably

(to .21, .40, and .53 respectively).

A key question is how to identify a predictive neighborhood. Obviously if a database with

multiple adoptions over a mapped network is available, managers can use the data directly.

Absent such data, one option is to use characteristics of the network itself. To examine which

types of local neighborhoods predict best, we regressed the correlation between local

neighborhood and overall market adoption against the characteristics previously analyzed (cluster

size, path-length, clustering coefficient, betweenness centrality, tie-strength among hubs, number

of hubs/size, time to first adoption, time to 16% adoption.) We use neighborhood size and

betweenness centrality separately because they are highly correlated (.88).

Table 7 (following References) shows the results of a regression predicting the correlation

between neighborhood and overall adoption across the products studied. Notably, only

neighborhood size and centrality (which is highly correlated with size) showed any significant

predictive power (except for the regression on the correlation of neighborhood adoption at 50%


of eventual market adoption and total network adoption) for identifying which local

neighborhoods were the most effective at predicting overall adoption. Basically, large and central

neighborhoods that adopt early predict overall adoption better than average neighborhoods or

random samples. By contrast, smaller dense neighborhoods are relatively poor predictors,

possibly because of their relatively closed and isolated nature. However, the average size of the

20 best predictive neighborhoods is not particularly large. the average size of them is 214, 193, or

233 depending on whether you select them at the time of 5%, 16%, or 50% adoption respectively

(see Table 6). Further, 12 of the 20 best predictive neighborhoods have fewer than 200 members

at the time 5% or 16% have adopted. This suggests that small (e.g., Maine), but not isolated, test

markets may be useful for prediction purposes.

Discussion

The literature generally recommends using either random samples to represent the overall

population or some kind of stratified sample with representation of all strata. When there is no

information about the population or about the network structure, this strategy is probably the

most reliable. However, when the social structure is known, specific groups of people can better

represent the entire population than randomly selected individuals. In this paper we argue that it

is possible to identify such predictive groups and use one such group (similar to a single stratum)

to predict overall adoption. The following are the key findings of this study:

First, local neighborhoods have an influence on individual adoption within the neighborhood.

In particular, the number of individual new product adoptions is influenced by the centrality of

the neighborhood to which the individual belongs. We also found that individual adoption speed

is influenced by the density of the individual’s neighborhood.


Second, certain local neighborhoods tend to perform quite well in predicting overall adoption.

The range of correlation of early adoption within the top 20 neighborhoods with overall adoption

was between .81 –.99. By tracing the adoption pattern of these predictive neighborhoods we can

predict the success/failure of the new products better than when we use random samples.

Third, we identified key variables that are significantly related to predictive neighborhoods.

Interestingly, of the neighborhood-level variables, only cluster size and centrality (which is

highly correlated with size) show any significant predictive power.

One limitation of this research is the focus on post launch predictions. However, once

predictive neighborhoods are identified, firms can, and probably should, use them as the basis for

pre-launch tests as well.

Clearly, this research is only a first step. Several additional areas for future research may be

identified. First, although our application provided support for our approach, other field

applications should be examined and the predictive ability of alternative models should be tested

further. Second, the models may be extended to capture effects such as negative word-of-mouth

(Mahajan et al. 1984), complex network structures (Shaikh et al. 2005), and different types of ties

or relationships (Ansari et al. 2008; Iyengar et al. 2008). Third, the analysis should be extended to

include covariates such as marketing mix variables (Bass et al. 1994; Horsky and Simon 1983;

Kalish and Sen 1986; Robinson and Lakhani 1975). Fourth, the proposed models use discrete

time intervals, making the parameters a function of the data frequency. Future research may

explore continuous-time versions. Finally, future research may develop new methods for

collecting the social interactions data both online and offline. Hopefully this research will spur

further exploration in these and other directions.


In practice, one rarely has full information on the networks that comprise the potential market.

Internet based social networks often provide information about linkages (ties) among individuals

so that the network can be identified using a clustering algorithm based on the number of contacts

between individuals. However, in order to identify clusters (neighborhoods) in other situations,

firms may need to develop creative ways to identify such clusters, perhaps by using observed

behavior and characteristics to infer links between members. Naturally, this requires efforts and

resources which small firms may be reluctant to invest. Market research firms may thus benefit

from building panels of people who are socially connected and offering these panels for use in

monitoring reactions to new products.


Appendix: Variables Definitions and Operationalizations

Variable Definition Operationalization Individual Level

Tie Strength

In-strength Total number of visits an individual receives in a given period

where visit(jik) is the number of times person j visits individual i.

Out-strength

Total number of visits an individual makes in a given period

where visit(ijk) is the number of times individual i visits person j .

Relationship to Hubs

Closeness to hubs

The number of hubs an individual is directly connected with

where d_hub(h) is 1 if hub h is connected with individual i, otherwise 0.

Tie-strength to hubs

The total number of hubs an individual is directly connected with

where in_strength_hub(h) is the total number of visits individual i receives from hub h, and out_strength_hub(h) is the total number of visits individual i makes to hub h. i.e., in strength plus out strength.

Neighborhood level

Density

Average path length

Average shortest distance between all the pairs of two members in the same neighborhood

where path-length(ij) is the shortest distance between member i and j in the same cluster, and n is the number of members in the cluster.

Clustering coefficient

Average clustering coefficient member in the same neighborhood

where the clustering coefficient is the number of triangles connected to member i divided by the number of triples centered on member i, and n stands for the number of members in the cluster

Centrality

Cluster betweenness

Betweenness centrality of the cluster in the network


centrality of neighborhood where we take each cluster (neighborhood) as a node, and d_cluster(ij) is the number of times the cluster is on the shortest path between all the pairs of clusters i and j, C stands for the total number of clusters.

Neighborhood size Size of the Neighborhood Total number of members in each neighborhood Hub characteristics

Number of hub/ size

Number of hubs in the same cluster divided by cluster size

(Number of hubs in the cluster) / cluster size

Tie-strength among hubs

The sum of tie-strength among hubs divided by number of hubs in the cluster

where tie strength hub(i,j) is the total in-strength and out-strength of the pair of hubs i and j in the same neighborhood, and Hc stands for the number of hubs in the neighborhood..


Note

1. In this algorithm the link betweenness centrality is calculated for all links. By removing each link (starting with the one with the highest centrality) and recalculating the centrality of all links, the links that span clusters are identified. After the process is completed, the set of separating links is used to define the clusters.


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Table 1: Descriptive Statistics

Individual level (n = 24,368)

Variable Mean S.D. Min Max In-strength 10.22 24.45 0 883 Out-strength 10.22 41.05 0 1579 Distance to hub .43 .87 0 24 Tie-strength to Hub 3.86 19.62 0 1070 Gender .66 .47 0 1 Age 21.39 1.68 19 24 Adoption Size 3.17 16.24 0 1276 Adoption Speed .53 .27 .01 1

Neighborhood level (n = 114)

Variable Mean S.D. Min Max Path Length .48 .05 .36 .68 Clustering coefficient .03 .02 0 .11

Centrality .01 .02 0 .17 Density of hubs .28 .38 0 1 Tie-strength of hubs 16.02 30.05 0 195.5 Number of hubs 4.77 6.66 0 65 Cluster size 213.75 153.15 102 1452 Number of Hubs/Size .02 .01 0 .04


Table 2: Individual-level Correlations

Pearson Correlation In-Strength Out-Strength

Distance to Hub

Tie-strength to Hub

Adoption Size Adoption Speed

In-Strength 1 .415** .260** .659** .045** -.027

Out-Strength .415** 1 .255** .333** .034** -.021*

Distance to Hub .260** .255** 1 .040** .026** -.009

Tie-strength to Hub .659** .333** .439** 1 .070** -.016

Adoption Size .045** .034** .026** .070** 1 -.011

Adoption Speed1 -.027 -.021* -.009 .-.016 .-.011 1

**. Correlation is significant at the .01 level (2-tailed).

*. Correlation is significant at the .05 level (2-tailed).

1. Observations of adoption speed = 11,625 vs. 24,368 for the other correlations


Table 3: Cluster-level Correlations

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Cluster Size

Path Length

Clustering Coefficient

Betweeness Centrality

Tie-Strength among Hubs

Number of Hubs

Time to First

adoption

Time to 16%

adoption (t16)

Correlation

Cluster Size 1.000 -.003 .098 .878** .**315 ** .924 **-.374

.431** .226**

Path Length -.003

1.000 .181 .066 -.117 .031 .100 .038 -.056

Clustering Coefficient

-.098 .181 1.000 -.088 .134 -.029 -.020 -.196* -.012

Betweeness Centrality

.878** .066 -.088 1.000 .279** .811** -.258** .366** .134

Tie-strength among Hubs

**.315 -.117 .134 .279** 1.000 .397** -.188*

.103 .023

Number of Hubs

**.924 .031 -.029 .811** .397** 1.000 -.243** .367** .157

Time to First Adoption

-.374

.100 -.020 -.258** -.188* -.243** 1.000 -.087 -.362**

Time to 16% adoption (t16)

.431** .038 -.196* .366** .103 .367** -.087

1.000 -.248**

Correlation with overall adoption

.226** -.056 -.012 .140 .024 .157 -.362** -.248** 1.000


Table 4: Predicting Individual Adoption Within Local Neighborhoods

Model 1 Model 2 Model 3 Model 4

Log Likelihood -102338.00 -102375.20 -102379.10 -102342.40

Individual Level Variables coef p-value coef p-value coef p-value coef p-value

Intercept 2.790 .00

In-Strength .009 0.33

Out-Strength .006 .00 .005 .01 .005 .01 .006 .01

Closeness to hubs .062 .56

Tie-strength to hubs .020 .06 .037 .01 .037 .01 .036 .02

Gender -.932 .00

Age .173 .01

Neighborhood Level Variables

Intercept 3.474 .00 2.661 .00 -.136 .92

Path length -1.444 .46

Clustering coefficient 1.889 .74

Betweenness Centrality 7.828 .03 9.333 .00 6.524 .00

Tie-strength among hubs .002 .58

Number of hubs/Size -6.196 .73


Table 5: Predicting Individual Adoption Speed

Model1 Model2 Model3 Model4

Log Likelihood -1460.53 -1451.12 -1439.86 -1410.42

Individual Level coef p-value coef p-value coef p-value coef p-value

Intercept .5310 .00

In-Strength -.0003 .02 -.0003 .00 -.0003 .00 -.0003 .00

Out-Strength -.0001 .10

Closeness to hub -.0011 .73

Tie-strength to hub .0000 .82

Gender -.0461 .00

Age -.0044 .00 Neighborhood Level

Intercept .5097 .00 .5367 .00 .6590 .00

Path length .0611 .21

Clustering coefficient -.2769 .01 -.2101 .01 -.2176 .01

Betweenness Centrality -.0887 .27

Tie-strength among hubs .0001 .53

Number of hubs/Size -.0318 .92


Table 6: Average Correlation Between Local Neighborhood and Overall Adoption

Correlations MAPE*

Sample Time Measured

Average Size of Top 20

Neighborhoods

All 114 Clusters

20 Random Samples

Top 20 Neighborhoods

20 Random Clusters

Top 20 Neighborhoods

5% 214 Calibration: Total .70 .62 .96 .62 .34 Calibration: Split-Half

Holdout .77 .70

.21 .99 .79

16% 193 Calibration: Total Calibration: Split-Half Holdout

.75

.81

.72

.76

.40

.96

.99

.95

.55 .18

50% 233 Calibration: Total Calibration: Split-Half Holdout

.90

.91 .84 .87 .53

.98

.99

.96

.50 .18

*The absolute value of the predicted percent adopting in the total population adoption minus actual total population adoption.


Table 7: Characteristics of Predictive Neighborhoods*

Network Characteristics

Time Period for Determining Predictions 5%

16%

50%

Path Length - .03 - .02 -.03 -.02 -.01 -.01 Clustering Coefficient .02 .03 -.09 .07 -.08 -.07 Betweeness Centrality .11 NA .17c NA .16c NA Cluster Size NA .19c NA .29 a NA .27a

Tie-strength among Hubs -.12 -.12 -.11 -.12 -.03 -.03 Number Hubs/Size .07 .04 -.13 .09 .25a .21b

Time to First Adoption -.38a -.34a -.38a -.32a -.22a -.16 Time to 16% Adoption -.19b -.22b -.37a -.42a -.57a -.61a

R2 .17 .19 .25 .28 .33 .35

*Standardized Regression Coefficients

a = significant at .01

b = significant at .05

c = significant at .10


local neighborhoods as early predictors of innovation adoption

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