Innovation diffuse through a population

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Why one innovation does diffuse throughout the whole population, and why do others stop in its interim process? Although practitioners can provide a wide array of anecdotes, systematic research on this issue has been rather sparse.

Researchers came up with seven elements which are associated with the diffusion process: the innovation itself, adopters of the innovation, innovation channels, time and space, change agents, and the social system.

Within this literature review the focus will

Somewhat like new viruses, the introduction of innovative ideas, techniques, technologies or products into new segments of social networks can trigger the partial diffusion of these innovations throughout parts of these networks.

The diffusion of an innovation is the process by which an innovation spreads among members of a social system (Rogers and Shoemaker [22]). Seven elements have come to be associated with this process: the innovation itself, adopters of the innovation, innovation channels, time and space, change agents, and the social system. (Bron: INNOVATION DIFFUSION IN A DYNAMIC POTENTIAL ADOPTER POPULATION)

the introduction of innovations into new segments of social networks does not guarantee these innovations' diffusions in these segments. This is the case even for highly beneficial innovations (Rogers 1995). Incontrovertible evidence that lime juice cured scurvy, for example, was presented first in 1601 by James Lancaster, an English sea captain, and again in 1747 by James Lind, a British Navy physician. This cure was ignored until 1795, however, at which point its diffusion virtually eradicated scurvy from the British Navy. Neither Lancaster nor Lind were prominent in British Navy social networks, which may explain why their radical cure was ignored for almost two centuries (Mosteller 1981).

These vignettes highlight four points. First, many innovations, whether they be new diseases, new cures, or new techniques and technologies, diffuse through social networks linking individuals or organizations. Second, these networks are segmented by internal boundaries which can form at geographic, status, cultural, or industry lines. Third, these boundaries can limit the diffusion of innovations, so that innovations frequently do not diffuse to all potential adopters.

Fourth, when and how extensively an innovation diffuses through social networks can be greatly affected by apparently insignificant events occurring at these networks' internal boundaries.

(Bron: Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation)

First, we consider a market with network effects, where the benefits of adopting the innovation grow as the number of adopters increases (e.g., Katz & Shapiro, 1985). Adoption dynamics of such network products or services are quite distinct from those of conventional ones. Network products and services are quite difficult to get started and often end up being under-adopted (Rohlfs, 1978, 2001). For example, consider an adopter of a communication service (e.g.email or instant messaging). The first adopter sees no benefit in adopting this service because there is no one to communicatewith. Customer benefits will be realized onlywhen other people begin to adopt an interoperable service. Because of such a lack of customer benefits at the early stage, under-adoption is natural for network products and services.

Bron: Role of network structure and network effects in diffusion of innovations

How, why, and at what rate a new idea, innovation, or technology spreads through the

society have been of interest to researchers in various fields. Attewell [1] classifies

diffusion studies into two groups-macro diffusion studies and adopter studies. The

former group deals with mathematical characterization of the rate and the pattern of

adoption of a technology among potential adopters, and the latter group deals with

the identification of factors that facilitate or hinder the adoption of the technology [8].

Examples of macro diffusion studies include Taga and Isii [31] in statistics, Mansfield

[19] and Williams [35] in economics, Coleman [6] in sociology, and Bass [2] in marketing.

These studies developed diffusion models measuring the adoption rate over

time, which is influenced by two forces at all times: the intrinsic tendency to adopt

(i.e., adopters' inner motivation) and social contagion (i.e., existing adopters' influence

on potential users). The former factor represents the time-invariant internal drive

that persuades the potential user to adopt an innovation (often called the innovation

factor), whereas the latter factor represents how a potential user considers the existing

adopters of the innovation in his or her adoption decision (often called the imitation

factor). The existing literature often assumed that innovation and imitation factors

ex ante affect all the potential users equally [2, 6, 19, 20, 31, 35].

Among macro diffusion studies, Bass [2], for example, proposed a growth model

for the timing of initial purchases of a new product. Assuming that consumers' initial

purchases are influenced by the number of previous buyers, Bass predicted that exponential growth of initial purchases peaks and then exponentially decays after the peak.

(Bron: Information Technology Diffusion with Influentials, Imitators, and Opponents)

Macro

All potential adopters of a new product do not adopt the new product at the same time. Consequently, on the basis of the degree to which an individual is relatively earlier in adopting the new product, adopters can be clas- sified into adopter categories (Rogers 1983). Develop- ment of adopter categories is important because they can assist in (1) targeting prospects for a new product (i.e., potential innovators and laggards; Kotler and Zaltman 1976), (2) developing marketing strategies for penetrat- ing various adopter categories (Engel, Blackwell, and Miniard 1986), and (3) predicting the continued accep- tance of a new product (Bass 1969; Mahajan and Muller 1979). Development of adopter categories requires determi- nation of (1) the number of adopter categories, (2) the percentage of adopters to include in each category, and (3) a method to define categories (Rogers 1983, p. 245). The most widely accepted method of adopter cate- gorization is that proposed by Rogers (1983).

(bron: Determination of Adopter Categories by Using Innovation Diffusion Models)

Adapter focus

We know that, for many products, a person's adoption probability increases with the number of that person's friends or contacts that have adopted the same product [3][6].

(Bron: Product adoption networks and their growth in a large mobile phone network)

Opposition of innovation

Hence, before adopting an innovation, decision makers have to understand how the innovation spreads through its target population to assess the potential effects of the innovation. Prospective adopters who value the innovation and will benefit from it are key actors and have been the focus of the extant literature. However, many innovations face resistance. Opposition to new technologies is not new [23]. More often than not, certain stakeholders aim to stop or slow down the diffusion of an innovation. These opponents do not perceive the innovation in the same way as the adopters. They are concerned about the risks associated with innovation. For example, some people are concerned about the health risk of wireless phones, privacy issues.

At the individual level, opponents can be people who totally reject the innovation, environmental or health organizations, or public interest groups. Opponents in the context of almost all innovations, particularly in the context of IT, are significant actors. They primarily have two objectives. First, they want to increase

the number of actors concerned about the innovation (i.e., increase the size of the position group). Second, they want to influence the diffusion process (i.e., restrain the adoption from reaching a certain level). They do not adopt the innovation.

The three-segment diffusion mode considers two groups of heterogeneous adopters: influentials are more in touch with the new innovation and are able to affect the other group of adopters, namely, imitators, whose own adoptions do not affect the influentials. The third segment of actors is opponents, who will not adopt the innovation and negatively influence the diffusion of innovation.

An opponent group can affect the diffusion process in three ways: (1) delaying peak adoption time, (2) reducing the peak adoption rate, or (3) changing the shape of the diffusion curve.

(Bron: Information Technology Diffusion with Influentials, Imitators, and Opponents)

Adapt or not..

Six sets of actor variables appear to modulate the adoption of innovations:

(a) societal entity of innovators, (b) familiarity with the innovation, (c) status characteristics, (d) socioeconomic characteristics, (e) relative position in social networks, and ( f ) personal characteristics that are associated with cultural variables that modify personality characteristics of actors at a population level.

FAMILIARITY WITH THE INNOVATION

The familiarity associated with an innovation relates to how radical it is (Dewar & Dutton 1986, Rogers 1995). Because people are naturally cautious in approaching novelty, the rate of adoption of an innovation-all other factors being equal-increases as its novelty decreases (Greve 1998). When the apparent familiarity of a new idea is increased, for instance by media information and the opinion of experts (Meyer & Rowan 1977, Mizruchi 1993, Newel & Swan 1995, Weimann & Brosius 1994), the perception of risk by an adopter is substantially reduced, facilitating adoptive behavior.

A number of factors reduce novelty and increase familiarity with innovations. For instance, as Rogers (1995) demonstrated, information obtained from close peers located in social and organizational networks has more weight than information obtained from objective sources, such as from the media or from scientific evaluations of an innovation. Moreover, familiarity with innovations having public consequences is increased by nonrelational sources of information (institutionalization and media) (Meyer & Rowan 1977),

STATUS CHARACTERISTICS

Status characteristics of adopters refer to the prominence of an actor's relative position within a population of actors. In the most general terms, variance of these characteristics is a function of an actor's social entity and the homogeneity of an actor's networks. Collective actors with high status, i.e., those that control either political power or economic resources, such as governments, large corporations, or world economic organizations, usually adopt an innovation first and then impose adoption of the innovation on lower status actors, An actor's high social position significantly modulates the likelihood of adoption within culturally homogenous groups, such as when adoption of innovations by high-status firms generates adoption of technological innovations in similar firms (Herbig & Palumbo 1994).

(Bron: INTEGRATINGMODELS OF DIFFUSION OF INNOVATIONS: A Conceptual Framework)

More recently, scholars have begun to ask a very different type of question: why, at particular points in time, do certain innovations diffuse fully and become the de facto standard or dominant design, whereas other innovations diffuse partially or not at all? (Granovetter 1978; Arthur 1983; David 1985, 1991; Granovetter and Soong 1986, 1988; Abrahamson and Rosenkopf 1990, 1993a).

The burgeoning literature exploring the diffusionextent question has yielded a host of counterintuitive propositions. Extremely small differences in the initial distribution of preferences about an innovation can

have extremely large effects on the extent of its diffusion (Granovetter 1978). One variant of an innovation may prevail completely over another due to small, random factors prompting a few more adoptions early in the diffusion of the innovation that prevailed (Arthur 1983, David 1991).

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To address this question, we develop a simple numerical model of adoption dynamics with two key components: network effects and network structure. First, we consider a market with network effects, where the benefits of adopting the innovation grow as the number of adopters increases (e.g., Katz & Shapiro, 1985). Adoption dynamics of such network products or services are quite distinct from those of conventional ones. Network products and services are quite difficult to get started and often end up being under-adopted (Rohlfs, 1978, 2001). For example, consider an adopter of a communication service (e.g.email or instant messaging). The first adopter sees no benefit in adopting this service because there is no one to communicatewith. Customer benefits will be realized onlywhen other people begin to adopt an interoperable service. Because of such a lack of customer benefits at the early stage, under-adoption is natural for network products and services.

In particular, Watts and Strogatz (1998) developed the small-world graph model, which is a formal representation of Granovetter's (1973) conceptualization of the architecture of social networks. In his view, a social network consists of two essential elements: (1) cliquish sub-networks and (2) bridges. A cliquish sub-network consists of individuals who are interacting extensively with one another. Consider, for example, a friends and family network. Granovetter (1973, 1974) found that this sort of sub-network is not very helpful when people look for jobs. The main reason is that information traversing through cliquish subnetworks is more likely to be limited to a few cliques, which tend to share redundant ties. Instead, people get more useful job information from random contacts, or people who are not in extensive relationships. Such connections are called bridges, which serve to connect diverse members from different, or often socially distant, subnetworks.

In social worlds, people often create bridge-building mechanisms, such as conferences, parties, or Internet chat rooms, to facilitate interactions among random contacts or strangers. Onnela et al. (2007) empirically confirmed that the above architecture indeed

represents the structure of real-world communication networks for mobile phone users. Key questions are: should marketers focus on a few cliquish subnetworks for launching network products? Or should they exploit random bridges from the outset? Which network strategy is more likely to lead to full diffusion? To explore this question, we use Watts and Strogatz's (1998) small-world graph to represent consumer networks. The main benefit of using this model is that we can address the above questions by generating diverse kinds of networks that lie between a network of purely cliquish sub-networks and that of purely random bridges. Our numerical analysis shows that the network structure does play a moderator role for the link between network effects and innovation diffusion. More specifically, we found that a new product is less likely to reach full diffusion in random networks than in cliquish networks. Unlike information diffusion, the spread of network effects reveals that randomness in connection topology makes it harder for an

innovation to build up network effects or network benefits at the initial stage, and that insufficient network effects in turn result in a

lack of momentum for the diffusion to reach the whole customer base. However, once the diffusion process reaches a critical mass, abundant bridges in a random network accelerate the process. A marketing implication is that the network structure of the target

consumer groups at the early market stage may be a critical factor affecting whether a new product makes full or partial penetration.

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Since then, other researchers have examined network effects in relation to various aspects of marketing practices, such as pricing, promotion policy, distribution, and firstmover strategies (e.g., Sun, Xie, & Cao, 2004; Srinivasan, Lilien, & Rangaswamy, 2004; Li, 2005). Recently, some research has begun to examine network effects in the context of new product adoption and diffusion, which is more related to our work. Based on a survey of consumers' patronage of Microsoft's Windows, Pae and Hyun (2002) showed that network effects play a key role in buyers' adoption and repurchase decisions. They also showed that compatibility, upgradability, and preannouncements are the key sources of network effects. Lee and O'Connor (2003), on the other hand, set up an empirical model that provides a link between launch strategies and the success of network products. One of the key conclusions of their study is that the size and development speed of the installed base plays a critical role in a product's long-term performance, which is consistent with the current study's finding that building up network effects at the early stage is the key to the success of innovation diffusion. Specifically, they tested the impacts of various launch strategies such as order of entry, product advantage, penetration pricing, bundling, mass targeting, and pre-announcing strategies upon the development of an installed base. Although their model includes many factors that affect installed base development, they did not take into account the structure of social networks, which we believe is another crucial variable for the long-term performance of an innovation. From a slightly different angle, Padmanabhan, Rajiv, and Srinivasan (1997) studied how network effects influence the success or failure of a new product. This work deserves some attention since its analytical structure looks similar to that of our model. They compared one-time release vs. sequential release (or upgrades) strategies in the context of new product introduction. They proved that a company can use a sequential release strategy to convey a credible signal of a future installed base, and that consumer expectations about a future installed base positively affect their product adoption decisions.

Bron: Role of network structure and network effects in diffusion of innovations

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