The spread of technological innovations: effects of psychology, culture and policy interventions

Abstract

Technological innovations drive the evolution of human societies. The success of innovations depends not only on their actual benefits but also on how potential adopters perceive them and how their beliefs are affected by their social and cultural environment. To deepen our understanding of socio-psychological processes affecting the new technology spread, we model the joint dynamics of three interlinked processes: individual learning and mastering the new technology, changes in individual attitudes towards it, and changes in individual adoption decisions. We assume that the new technology can potentially lead to a higher benefit but achieving it requires learning. We posit that individual decision-making process as well as their attitudes are affected by cognitive dissonance and conformity with peers and an external authority. Individuals vary in different psychological characteristics and in their attitudes. We investigate both transient dynamics and long-term equilibria observed in our model. We show that early adopters are usually individuals who are characterized by low cognitive dissonance and low conformity with peers but are sensitive to the effort of an external authority promoting the innovation. We examine the effectiveness of five different intervention strategies aiming to promote the diffusion of a new technology: training individuals, providing subsidies for early adopters, increasing the visibility of peer actions, simplifying the exchange of opinions between people, and increasing the effort of an external authority. We also discuss the effects of culture on the spread of innovations. Finally, we demonstrate that neglecting the cognitive forces and the dynamic nature of individual attitudes can lead to wrong conclusions about adoption of innovations. Our results can be useful in developing more efficient policies aiming to promote the spread of new technologies in different societies, cultures and countries.

Keywords: cognitive dissonance; conformity; diffusion of technological innovations; dynamics of attitudes and beliefs; social norms.

Figure 1.

Model structure. The model integrates three interlinked processes: individual decision-making process regarding technology use, the dynamics of individual attitudes towards a new technology, and individual learning process in mastering the new technology. An individual chooses a technology by maximizing their utility function that integrates an expected material pay-off and a normative value. The expected material pay-off depends on the benefit of using a new technology, which is formed as a result of a learning process. The learning process encompasses two effects: (i) individuals can get higher benefits through repetition of an activity (i.e. learning by doing) and (ii) individuals can learn from others who use the same technology. The normative component of the utility function depends on individual attitudes (through cognitive dissonance), on the actions and attitudes of others (as a result of conformity with peers), and on the message of an external authority (as a result of conformity with the authority). The attitude of an individual, in turn, changes as a result of cognitive dissonance, conformity with peers’ actions and attitudes, and conformity with the authority.

Figure 2.

Convergence to equilibria in four independent runs with no between-individual variation and no errors in decision-making. Shown are: the frequency of adopters p (top row) individual attitudes y (middle row), and individual benefits b (bottom row). Different individuals are shown by different colours. The values of parameter ω are shown below the graphs. Other parameters: b_min = 0.5, b₀ = 1, b_max = 2.4, a₀ = a₁ = 0.05, c = 0.1, v = k₁ = k₂ = k₂ = α = β₁ = β₂ = β₃ = 0.25, ɛ =0.5, s = 0.1, N = 1000, ν = 0.1, λ = ∞. Initial values of y are drawn randomly and independently from a beta distribution with mean 0.5 and standard deviation 0.2.

Figure 3.

The dependence of the frequency of adopters p and attitudes y on: (a) the maximum benefit parameter b_max, (b) the minimum benefit parameter b_min, (c) the foresight parameter $\overline{\omega }$. Each point corresponds to an outcome of a particular run. Characteristics were calculated at T = 100. (The graphs corresponding to T = 50 and 200 are very similar to the ones shown.) Attitudes of adopters and non-adopters are marked in red and blue colours, respectively. Curves show the average values of corresponding characteristics among all runs. Baseline parameters: $b_{\min }=0.5,b_{\max }=1.5,{\overline{a}}_1=0.05,\overline{\omega }=0.25,\overline{\epsilon }=0.5,{\sigma }_{a_1}=0.005,{\sigma }_{\omega }=0.025,{\sigma }_{\epsilon }=0.05$.

Figure 4.

The dependence of the frequency of adopters p and attitudes y on: (a) the fraction of trained individuals p₀, (b) the subsidy to early adopters b_s, (c) increasing the visibility of peer actions f₁, (d) simplifying the exchange of opinions between people f₂, (e) increasing the effort of the external authority f₃. Each point corresponds to an outcome of a particular run. Characteristics were calculated at T = 100. Attitudes of adopters and non-adopters are shown in red and blue colours, respectively. Curves show the average values of corresponding characteristics across all runs. Baseline parameters: $b_{\min }=0.5,b_{\max }=1.5,{\overline{a}}_1=0.05,\overline{\omega }=0.25,\overline{\epsilon }=0.5,{\sigma }_{a_1}=0.005,{\sigma }_{\omega }=0.025,{\sigma }_{\epsilon }=0.05$.

Figure 5.

The dependence of the frequency of adopters p and attitudes y on: (a,b) the strength of normative factors ɛ, (c,d) the strength of cognitive dissonance f₀. Each point corresponds to an outcome of a particular run. Characteristics were calculated at T = 100. Attitudes of adopters and non-adopters are shown in red and blue colours, respectively. Curves show the average values of corresponding characteristics across all runs. Baseline parameters: $b_{\min }=0.5,b_{\max }=1.5,{\overline{a}}_1=0.05,{\sigma }_{a_1}=0.005,{\sigma }_{\omega }=0.025,{\sigma }_{\epsilon }=0.05$, (a,b) $f_0=1,\overline{\omega }=0.3$ and (c,d) $f_3=1,\overline{\omega }=0.25$.

Figure 6.

Dynamics of adoption. (a) Average characteristics of adopters and non-adopters at T = 10. The averages and the 95% confidence intervals are calculated among 1000 independent runs. (b) An example of a single run: the frequency of adopters p, individuals attitudes y and individuals benefits b. Different individuals are shown by different colours related to their value of parameter K₃. Individuals with the highest values of parameter K₃ measuring conformity with the authority are shown in red, while those with smallest K₃ are shown in blue. Other parameters: $b_{\min }=0.5,b_{\max }=1.5,\overline{\omega }=0.25,\overline{\epsilon }=0.5,{\sigma }_{a_1}=0.005,{\sigma }_{\omega }=0.025,{\sigma }_{\epsilon }=0.05$.

Figure 7.

Effect of attitudes on the adoption curves. Examples of a single run of the model with dynamically changing attitudes (the black curve), fixed attitudes (the blue line, s = 0) and the model that does not take into account attitudes (the cyan line, s = 0 and v = k₂ = α = β₂ = 0 for all individuals) are shown. Baseline parameters: $b_{\min }=0.5,{\sigma }_{a_1}=0.005,{\sigma }_{\omega }=0.025,{\sigma }_{\epsilon }=0.05$, (a) $b_{\max }=1.5,\overline{\omega }=0.25,\overline{\epsilon }=0.5,f_3=1$ and (b) $b_{\max }=1.45,\overline{\omega }=0.3,\overline{\epsilon }=0.45,f_3=0$.

Figure 8.

Effects of the President Obama’s announcement promoting the deployment of additive manufacturing. (a) Comparison of the normalized interest measured by Google Trends with reference to the technologies referred to as ‘rapid prototyping,’ ‘additive manufacturing,’ and '3D printing'. The normalized interest is measured as a search interest relative to the highest point on the chart for the given region and time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. A score of 0 means there was not enough data for this term. The vertical line corresponds to the month and year when President Obama announced the launch of the manufacturing institute mainly focused on additive manufacturing. (b) Comparison of normalized publications per year with key words additive manufacturing, and 3D printing shows rapid expansion after the launch date of the institute on additive manufacturing and overtaking the slowly evolving rapid prototyping technology. This is attributed to the slow realization of the benefits of this new technology by the engineering communities. The Web of Science data: the normalized publications per year is measured as the percentage of papers published that year that mention the corresponding term (3D printing, additive manufacturing, or rapid prototyping, respectively).