Our first hypothesis was that scientists with higher risk perceptions would be more supportive of nanotechnology regulation. We tested this hypothesis in two ways using our dataset. The first way was by comparing the nanotechnology application areas where scientists had the highest risk perceptions with the areas where they believed that new regulations were needed. The results in the second half of Table 2 present the mean response for the scientists’ risk perceptions across eight areas: overall risk, human health, privacy, terrorists, environmental, arms race, loss of jobs, and tiny robots. These results illustrate that the specific nanotechnology application areas for which scientists have the highest perceptions of risk are human health, privacy, terrorists, and the environment.
Table 2 Summary statistics for regulation and risk/benefit perception variables
Next, we compared these areas of high risk perceptions with the areas that the scientists felt had insufficient nanotechnology regulations. In order to capture this, we asked the respondents to report whether they believed that “current regulations were sufficient” or “new regulations were needed” (on a 5-point Likert-type scale) for eight different areas of nanotechnology applications: cosmetics, military, medicine, bioengineering, environment, computers, privacy, and other consumer products. The participants’ mean responses to these eight questions are presented in Table 3. Out of the eight areas of nanotechnology applications, the scientists believed that the four areas that most needed new regulations (i.e., for which current regulations were not sufficient) were privacy, human enhancement, the medical field, and the environment. Interestingly, these are also the areas for which the scientists had the highest risk perceptions. On the other hand, military/defense and machines/computers were the two areas where the scientists were most likely to think that current regulations were sufficient.
Table 3 Summary statistics for adequacy of existing nanotechnology regulations
We further tested this hypothesis (as well as our second and third hypotheses) using a multivariate analysis for the relationship between the support for regulation and risk perceptions, benefit perceptions and political/social values. In order to do this, we ran a hierarchical OLS regression model with three blocks of variables. The first block was a set of demographic variables that included gender, disciplinary field, and political/social values. The second block included a general measure for nanotechnology risk perceptions and a general measure for nanotechnology benefit perceptions. The third block included specific application areas for nanotechnology risk and benefit perceptions. The dependent variable for our analysis was the summative index of two statements: (1) “Academic nanotechnology research should be regulated” (1 = Strongly disagree; 5 = Strongly agree) and (2) “Commercial nanotechnology research should be regulated” (1 = Strongly disagree; 5 = Strongly agree).
The results for our OLS model are presented in Table 4. The before-entry Betas are presented in Table 4 for each block. The results demonstrate that the demographic variables account for 15.7% of the variance in the nanoscientists’ support for regulation of nanotechnology. We also included control variables for gender and scientific discipline based on their importance in previous research on nanotechnology. Powell (2007) found that the way scientists view nanotechnology risks can depend on the type of research they conduct and their disciplinary fields. Siegrist et al. (2008) found gender differences in the perceived benefits about nanotechnology. The negative coefficient on gender in our model indicates that male scientists were less supportive of regulation than their female peers. We also observed some interesting disciplinary differences for the support of regulation. The materials scientists in the sample were more supportive of regulation than any other discipline in the sample.
Table 4 Hierarchical OLS regression analysis for regulation of nanotechnologya
An analysis of the political and social ideology variables confirms our third hypothesis. Higher levels of societal allocation of risk were associated with less support for regulation. Therefore, as nanoscientists think that the public should be willing to accept more risks for potential societal benefits, they are less supportive of nanotechnology regulation. We further examined social values by including economic and social conservatism variables in our model. As previously mentioned, several scholars have found that scientists often use normative values when making decisions about risk, just as the public does (Douglas and Wildavsky 1982; Plutzer et al. 1998; Slovic et al. 1995). As Kunreuther and Slovic (1996) and others (Kahan et al. 2008; Renn and Roco 2006) have argued, value choices are unavoidable in the process of forming risk and regulation perspectives. Also, research has shown that people consider economic development opportunities when they think about regulating nanotechnology (Bowman and Hodge 2006). While social conservatism was not significant, economic conservatism was negatively correlated with support for regulation. More economically conservative nanoscientists were less supportive of nanotechnology regulation. Even though some scientists might argue that they make objective decisions about their areas of expertise, clearly the leading U.S. nanoscientists make policy suggestions about their fields by viewing the issue through their individual economic value frames. Since one of the main reasons for governmental regulation is the existence of market failure, if scientists perceive that the market is the most efficient mechanism for the development of technology, they might be less supportive of regulation.
The second block of variables in our OLS regression included overall risk and benefit perception measures. These two variables explained an additional 6.8% of the variance in the model. The results demonstrate that our first hypothesis was confirmed for the overall risk perceptions, but our second hypothesis was not confirmed for overall benefit perceptions. As nanoscientists had higher risk perceptions, they were more supportive of nanotechnology regulation. We were surprised to find, however, that benefit perceptions were not significantly correlated with support for nanotechnology. This finding demonstrates that the public and scientists use different heuristics when they are asked to make decisions about nanotechnology. As previously mentioned, Scheufele and Lewenstein (2005) found that the public uses cognitive shortcuts to make policy-related decisions about nanotechnology because they have little information about the scientific details. In addition, they argued that these cognitive shortcuts are often provided by the way the media portrays the issue of nanotechnology (i.e., media framing) since media coverage of nanotechnology in the USA has been largely focused on the potential benefits of the field rather than the potential risks (Gaskell et al. 2004). Thus, Scheufele and Lewenstein (2005) found that the public used benefit perceptions, but not risk perceptions, when making decisions about nanotechnology. Our results for the nanoscientists are in contrast to the earlier results for the public’s heuristics, with nanoscientists relying on their risk perceptions but not benefit perceptions. We speculate that the nanoscientists might view regulations as protections for the public (and therefore focus on the potential risks), while the public might think of regulations as restricting their access to nanotechnology benefits (and therefore focus on the potential benefits). Clearly, these are contrasting ways of thinking about nanotechnology and its potential benefits and risks.
The third block of our OLS regression model included a series of risk perception variables and benefit perception variables for specific nanotechnology application areas. These variables explained an additional 9.4% of the variance in the nanoscientists’ support for regulation. The results for the risk perceptions further confirmed our first hypothesis. Risk perceptions for all nanotechnology application areas except one (loss of U.S. jobs) were significantly—and positively—correlated with support for nanotechnology regulation. Thus, as perceptions of risk increased for privacy, defense, human health, terrorists, tiny robots, and environment, nanoscientists’ support for regulation increased. The standardized coefficients show that the two risk application areas that had the strongest relationship with support for regulation were defense and human health.
The results for the benefit perceptions were similar to those for the previous block with only two benefit perceptions being significantly correlated with support for regulation: treating diseases and improving human abilities. As the nanoscientists perceived higher benefits for treating diseases, they were less supportive of nanotechnology regulation. The relationship for the benefit of improving human abilities was in the opposite direction. As the nanoscientists perceived more nanotech benefits related to improving human abilities, they were more supportive of nanotechnology regulations. We speculate that this result, combined with the above result about risks related to human health, means that the nanoscientists are particularly concerned about how nanotechnology will impact human health. While they were concerned about risks related to human health and this led to more support for regulation, they are also concerned about benefits related to improving human abilities. We speculate that this is because the nanoscientists are not sure that the benefits related to improving human abilities will be held in check without governmental regulations. Therefore, while they see this as a potentially beneficial area, they are concerned about how those benefits might play out in the absence of stricter regulations. Thus, as they perceive higher potential benefits, they are more supportive of having regulations in place that could limit accompanying negative impacts on human health.
In addition to testing our three formal hypotheses, we found some other noteworthy results. First, the summary statistics for the regulation variables in Table 2 demonstrate that the scientists were slightly more supportive of regulating commercial nanotechnology than they were for regulating academic nanotechnology. Perhaps this is related to the fact that commercial nanotechnology research is often directly related to product development and the use of nanotechnology by consumers. Second, nanoscientists were most supportive of national level nanotechnology regulations, with the international level coming in second (see Table 2). This is another interesting finding because recent study by Scheufele et al. (2009) demonstrates that people in different countries have different perceptions about the moral acceptability of nanotechnology. Therefore, it is quite possible that national level policies could differ significantly across countries (Marchant and Sylvester 2006; Tyshenko and Krewski 2008). If a nanotechnology regulatory framework is adopted that is not internationally implemented, then companies might simply move their products and research to countries with less stringent regulations.