The journal article readings for this unit discuss factors that can influence an individual’s perception of risk. Choose 1 of the articles attached and in a 3 page discussion paper: 1. Summarize the key points of each article. 2. Compare and contrast the articles’ approaches to risk perception.

The journal article readings for this unit discuss factors that can influence an individual’s perception of risk. Choose 1 of the articles attached and in a 3 page discussion paper:

1. Summarize the key points of each article.

2. Compare and contrast the articles’ approaches to risk perception.

3. Provide your conclusions on the importance of risk perception in the risk management process.

4. Suggest some practical measures that could be used to improve risk perception in a hazardous workplace with which you are familiar, and explain why you think these measures would work.

The assignment must be a minimum of 3 pages in length, not counting cover page and references. Follow APA style for the paper format as well as for all references and in-text citations.

Does expert trust and factual knowledge shape individual’s perception of science?ijcs_1044 668..677 Montserrat Costa-Font and José M. Gil CREDA-UPC-IRTA, Edifici ESAB, Parc Mediterrani de la Tecnologia, Castelldefels, Spain Keywords Risk perception, benefit perception, knowledge of science, expert trust, seemingly unrelated probit, Spain. Correspondence Montserrat Costa-Font, CREDA-UPC-IRTA, Edifici ESAB, Parc Mediterrani de la Tecnologia, C/Esteve Terrades, 8, 08860 Castelldefels, Spain. E-mail: montserrat.costa-font@upc.edu doi: 10.1111/j.1470-6431.2011.01044.x Abstract Explaining how individuals form their risks and benefit perceptions with regard to new technologies is a key issue in order to understand how new information disseminates. This paper examines the effect of knowledge, social values and trust in experts as shaping perceptions of risks and benefits of new technologies. Given that individual’s perceptions of a technology is affected by unobserved heterogeneity, we use a methodology to disentangle the effect of a joint estimation of risks and benefit perceptions, namely seemingly unrelated probit, and we draw upon evidence from a representative survey carried out in Spain. Our findings suggest that factual knowledge and trust in experts increase perceptions of benefit of new technology developments and jointly reduce the perceptions of risk. Furthermore, reliance on traditional social values only appears to affect perceptions of benefits but does not influence risk perceptions. Introduction Science and technology, understood as biotechnology, nanotechnology, robotics, etc., heavily rely on effective communication strategies for its development. This implies disseminating new and understandable information on the potential risks and benefits of new technologies. One key issue in supporting new scientific development is public acceptance that results from balancing out benefits and risks of new products. By risks we refer to the fact that, even though new products enter the supply chain by improving some attribute quality of existing ones, they also cause some concern due to their potential side effects to human health and the environment (i.e. crop contamination with pollen from genetically modified plants, potential diseases derived from the use of mobile phones, microwave, etc.). However, the determinants of behavioural reactions, learning and interpretation of new technology risks are still a black box. While some people may welcome such changes and regard them as benefits, others perceive them as being harmful and hazardous. Some competing explanations persist as to how to interpret individual’s perceptions of new technologies (Lowenstein, 2003). One of the initial models within the domain of public understanding of science has been the so-called ‘deficit model’, suggesting that when people lack information, this will preclude low perception of benefits (Miller, 1983, 2004). This model has been criticized by many studies that refer to it as simplistic (Sturgis and Allum, 2004). For example, Vandermoere et al. (2011) have observed that for the case of nanotechnology food packing, knowledge on risks is not enough to overcome consumers’ resistance to these technological innovations. One step forward is the so-called ‘contextual model’ (Fessenden-Raden et al., 1987; Bransford et al., 2000) that suggests that people can learn easily despite their own knowledge deficiencies drawing upon a context where to understand the meaning of the knowledge in their personal life. That is, individuals process information according to social and cognitive models shaped by their previous experiences and their social environment, namely seeing other people acting in a similar way. This approach introduces three main social psychological approaches: the trust, confidence and cooperation model (Earle and Siegrist, 2008), the diffusion effect (Ho and Leung, 1998), and the recreancy theorem (Freudenburg, 1993). The first approach shows the importance of trust on generating individual knowledge and risk perceptions. The second approach proves social acceptability of risks. That is, individuals perceive lower risks within a group than alone (Ho and Leung, 1998). These approaches belong to the so-called ‘psychrometric paradigm’ (Stebbing, 2009). Finally, the third theory relates risk perception and technology acceptant to individual trust in societal institutions. This theorem has been recently validated by Sapp and Downing-Matibag (2009) for the food irradiation technology case. Interestingly, this last approach introduces social, cultural and political factors that are explicitly addressed in the so-called ‘risk society perspectives’ and the ‘sociocultural approach’ (Stebbing, 2009). That is, elements such as individual values, cultural beliefs and political views do have some effect on constructing knowledge and risk perception towards new bs_bs_banner International Journal of Consumer Studies ISSN 1470-6423 International Journal of Consumer Studies 36 (2012) 668–677 © 2011 Blackwell Publishing Ltd 668 technological developments. The role of values and trust affecting risk perception has been highlighted by Siegrist et al. (2005) and Allum (2007), among others. The role of ideological and political concerns, as well as the importance of public participation, has been stressed by Jenkins-Smith et al. (2011) for the case of nuclear waste disposals. Furthermore, a number of other factors, such as ‘situation or emplacement’ of the population under analysis, ‘time’ and ‘technological specificity’, have been identified in contemporary studies as important determinants of risk perception (Masuda and Garvin, 2006; Henson et al., 2008; Stebbing, 2009) among others. In testing these approaches, it appears important to find reliable measures of local knowledge (Wachelder, 2003) and social trust (Joss, 1999) in order to identify the impact of personal values and information on shaping individual’s behaviour. Incomplete or incorrect information on the benefits and risks of new products often arouses an emotional response in individuals, who exercise their ‘exit voice’ by disapproving the commercialization of new technology. All together seem to indicate that trust, along with knowledge and social and political context, is a key determinant of individual’s appreciation of possible risks and benefits of new scientific developments (Franks, 1999). Recently, some other elements such as ‘societal inequality’ have been considered signifi- cant for risk perception analysis (Olofsson and Rashid, 2011). An adequate understanding of how risk perceptions are formed appears important for both private and public bodies designing communication strategies. Some studies have shown that, although consumers have little information, they perceive new science developments such as genetic engineering as hazardous (Wohl, 1998), often involving environmental and safety risks. Nonetheless, how to interpret these findings is open to debate. One explanation would be that due to insufficient information and knowledge, risks are often categorized qualitatively rather than being measured objectively. Given that such perceived risks do not solely apply to individuals alone but to society as a whole, there might well be certain dimensions of such risks that are important to individuals that experts could not ever attain to quantify. On the other hand, by adopting a wide definition of risks and benefits then, the decision to consume certain goods can be thought of being the result of perceived – both individual and social – benefits of such technology outweighed perceived risks. Although high-technology manufacturing occupies an increasing share of production in front-runner countries, such as Germany, the UK, Italy and France, countries such as Spain and Poland have only recently start promoting their high-tech industry and are allocated within the 10 countries with major number of enterprises devoted to high-technology manufacturing and knowledge-intensive high-technology services.1 This paper draws upon survey evidence from Spa
in on perception of science and technology developments. It defines two probit models2 to clarify which are the most relevant elements in shaping individual risk and benefit perception of science and technology. It emphasizes on the influence of factual knowledge and trust, along with what shape acceptance of new technologies. In doing so, the paper uses a seemingly unrelated probit approach,3 an empirical methodology that controls the existence of unobserved heterogeneity and endogeneity that affects both perception of risks and benefits. Finally, a third probit model has been defined to test the acceptance by means of valuing the net utility of science and technology developments. More specifically, the research questions addressed in this paper are: (a) Are risks and benefits an expression of a common feature (such as fear of progress), and thus act in response to a common scepticism towards science –, or they should be treated as independent behavioural elements? (RQ1); (b) What is the role of trust in experts and ‘factual knowledge’ as learning determinants in forming perceptions of risks and benefits associated to new scientific developments? (RQ2); and (c) Are values, especially cultural ones (Wildavsky and Dake, 1990), affecting the formation of risk perceptions of new technological developments? (RQ3). The latter question is of particular interest, as Spain is a country that has only recently adapted to European core values (Inglehardt and Baker, 2000). Therefore, Spain represents an interesting case study to examine the influence of elements such as religious values and political affiliation in shaping perceptions of risks and acceptance of new science developments. The structure of the paper is as follows: Sections 2 and 3 provide repressively a theoretical and methodological discussion on the extent to which risk and benefit perceptions are independent of each other, and on issues concerning the empirical specifications of the study; moreover, Section 3 describes the data and the preliminary evidence while Section 4 deals with the results; and finally, Section 5 contains the conclusions. Background In a world of perfect information, no difference between expert and public risk perceptions would be identified. However, given that this is not the case, significant research findings point towards systematic discrepancies between ‘objective’ and ‘subjective’ risk evaluations (Fischhoff et al., 1978; Slovic et al., 1981, 2000), so that perceptions of risk are a function of objective risk information though affected by a set of ‘biases’ (Kasperson et al., 1988). The extent to which risk perceptions would be more ‘accurate’ if people were provided with more complete information about a new technology attributes is subject to scrutiny. Even when some information is available, it often implies that individuals have to take time and effort to bring themselves up to date, and therefore these costs of information acquisition should be taken into account. As the level of knowledge acquisition varies according to the difficulties involved, it is interesting to consider to what extent information costs can be expected to have a positive effect on individual capacity to foresee the potential benefits and risks of technological developments (Koopmans, 1964; Day, 1986). However, besides information updating costs, it is important to acknowledge that human beings are not always able to update their knowledge and, even when they do so, may collect information selectively based on trust. 1 http://epp.eurostat.ec.europa.eu/portal/page/portal/science_technology_ innovation/data/database. 2 A model in which the dependent variable y is binary (0/1). 3 A model that implies that two binary response variables vary jointly in which each of the equations has different predictors but computed on the same set of subjects. M. Costa-Font and J.M. Gil Individual’s perception of science International Journal of Consumer Studies 36 (2012) 668–677 © 2011 Blackwell Publishing Ltd 669 Indeed, perceptions of both risks and benefits could be simplified due to the evaluation of the information from a set of (weighted) information channels along with initial or baseline perceptions. The role of information channels as trusted sources is therefore crucial in explaining how individual attitudes and perceptions of new science applications are formed.4 Given that individuals lack information regarding known risks, consumption decisions are likely to be influenced by external, perceivable elements.5 Therefore, knowledge is expected to play a key role in determining the extent to which individuals perceive the risks and benefits of certain technologies, and consequently on how they react to them. Consequently, it is important to investigate the extent to which individual acceptance of new technology depends on individual knowledge. Certain types of information may affect risk more intensely than benefit perceptions. For instance, Thaler (1980) finds that individuals are systematically more sensitive to information signals conveying risks than to signals conveying benefits. Hence, it becomes important on empirical grounds to further explore the extent to which the general perception of risks is independent of benefit perception. In theory, risks and benefits should be distinct concepts, and no specific association should be found between them. However, in an environment given that both expected benefits and risks are subject to the perception of joint probability, activities that convey high benefits might bring low risks and the other way around. If risks and benefit perceptions are not independent, then one could argue that they are the expression of a common feature behind, arguably culturally dependent, expressing how people perceive their individual and social life challenged by the introduction of a new technology. One hypothesis in the literature is based on Alhakami and Slovic’s (1994) findings that show the relationship between perceived risk and perceived benefit linked to an individual’s general affective evaluation of a hazard. In understanding the underlying meanings of risk and benefit perceptions, one might argue that risks may be perceived as more intense due to the absence of benefits of the object under analysis. Conversely, benefits may be perceived as lower due to high risks. Another theoretical explanation according to Kanheman and Tversky (1986) states that perceptions of risks and benefits are linked, because people are more risk averse when the outcome of a decision is perceived as a benefit rather than the reduction of a loss. Another possible interpretation is that risks and benefits could be the expression of a common feature (Costa-Font and Mossialos, 2007), so that individuals when expressing risks end up conveying an expression of ‘new risks’ ‘net benefits’. Finally, it is assumed that any examination of the determinants of risk or benefit perception will establish the channels through which individuals obtain information on technological risk. Individual perception of technological developments is likely to depend on the type and level of information individuals can handle. This in turn determines the extent to which individuals are susceptible to alarms and likely to exhibit some kind of outrage reaction (Stadman, 1992). Prior research already provides certain determinants of individual risk perception (Slovic, 1987). For example, high publicity of low probability risks leads to overestimation of risk (Fischhoff et al., 1981). Some determinants are linked to observable socioeconomic variables that can be obtained from general survey data. It is important to establish empirically whether the findings on determinants of risks and benefits differ when risks and benefits are examined simultaneously. Methods Individuals are expected to resolve the perception of uncertainty or risk by means of learning processes. It is assumed that independent information channels produce signals about the qualities of the product that are weighted either as positive (beneficial) or negative (hazardous) in the information acqu
isition process (Viscusi, 1998). Prior literature on risk perceptions (Slovic, 1987) suggests that perceived risks are often inconsistent with objective risk information provided to individuals. Several explanations have been given for this, for example, the presence of some irrationality in individuals’ behaviour and a lack of perfect information (Viscusi, 1990). However, these findings apply to those areas where subjective risk perceptions can be estimated and compared with ‘objective’ risk information (e.g. due to the existence of epidemiological evidence). One of the areas where it is not possible to undertake straightforward comparisons between objective and perceived risks is the area of new technologies. In this case, the communication of the risks and benefits is hardly ever complete and is normally affected by ‘scientific uncertainty’ (objective information is therefore missing) on the expected future effects. Therefore, it is assumed that independent information channels produce signals on the product qualities that are weighted either as positive (or beneficial) or negative (or risky) in the information acquisition process (Viscusi, 1992; Benjamin and Dougan, 1997; Hakes and Viscusi, 1997). Thus, the final perception of risks and benefits is the result of some aggregation of such information. According to the process outlined, the decision to consume a certain new product results from individual risk acceptance, in other words, the perception that benefits overcome risks. However, risk and benefit perception is not observable unless individuals respond to a survey question on whether they perceive the risks (RISK*) or benefit (BP*) of scientific innovation. Consequently, variables based on individual responses can only be observed. This study measures risk and benefit perceptions from the response to the following question: ‘Do you expect technology developments to bring many/some/few/no risks (benefits) to society in the next 20 years?’. This question provides information on the two kinds of perception that are assumed to guide individual acceptance of new science and technology (RISK*) and (BP*). Furthermore, the survey includes risk and benefit question frames, such as ‘Do you perceive (risks/ benefits) in the next 20 years as a result of technological and scientific advances?’. Finally, the survey includes the following question: ‘Do you think that the benefits of new technology overcome the risks?’ (Yes/No). This 4 In the biotechnology example, some scholars such as Hoban (1998) argue that ‘the full benefits of biotechnology will only be achieved if consumers and the food industry accept the use of this new technology as safe and beneficial’. 5 It has been widely demonstrated that individuals exhibit some aversion to ‘the unknown’ (Ellsberg, 1961). If this is applied to the area of biotechnology, people can be expected to value prospects involving known risks higher than prospects involving unknown risks, even when the potential benefits associated to the unknown risks are greater. Individual’s perception of science M. Costa-Font and J.M. Gil International Journal of Consumer Studies 36 (2012) 668–677 © 2011 Blackwell Publishing Ltd 670 question provides information on the net utility that science and technology was perceived by Spanish consumers (NU*). When the responses were of discrete nature, a probit model was used that assumes a normal distribution of the data (Green, 1997). However, when the responses were on a Likert scale, ordered probit models were used (Green, 1997) insofar as the explanatory variables expressed a specific order (many, some, few or none). The probit model is defined as Pr(y = 1/) = F(b), where F is the standard cumulative normal probability distribution, and b is called the probit score or index. As b has a normal distribution, the interpretation of a probit coefficient, b, is that a one-unit increase in the predictor leads to an increase in the probit score by b standard deviations. In the ordered probit model, the explanatory variable may take the value of i = 1, 2, . . . . n, and the probability of observing a specific outcome is Pr(y = i) = Pr(ki-j < b1ij + … + bkkj + uj ki); that is, the probability of observing an outcome j = i is the probability of the estimated linear function plus a random error to be within the range of the cut points estimated, where mj is assumed to be normally distributed. Therefore, the perception of risks can be specified as: RISK* RISK if RISK* RISK otherwise i ii i i i i = + Xu = > = β 1 0 0 (1) And the perception of benefit as: BP* BP if BP* BP otherwise i ii i i i i = + Z = > = δ ε 1 0 0 (2) As already stated before, it is assumed that errors are distributed N(0, 1), and that the errors of the two models are independent of one another, so that Cov(ui, ei) = 0. Some studies indicate that risks and benefits are positively correlated as a result of the influence of the affective responses underpinning risk judgement (Slovic et al., 1991). Furthermore, Finucane et al. (2000) argue that risks and benefits tend to be positively correlated because high-benefit activities are unlikely to be high risk and vice versa. However, there does not seem to be any previous empirical study model of risk and benefit perception that takes into account the possibility of correlation of the error terms. It is probable that ui = hi + ni and ei = hi + wi, so that the errors in each model consist of a part ni, wi that is unique to that model, and a second part hi that is common to both. If this is the case, the error terms are likely to be dependent. As the study focuses on joint probability, a bivariate normal distribution is used, (ui, ei) ~ BiN(0,0,1,1,r), whereby r is a correlation parameter denoting the extent to which the two error terms covary. Finally, risk acceptance is crucial in risk policy making. Again, net utility is unobservable (NU*); all that can be observed is whether individuals perceive that the benefits of scientific innovation overcome the risks as follows: NU* NU if NU* NU otherwise i ii i i i i = + H = > = ω ϑ 1 0 0 (3) The relevant covariates The most obvious determinant of individual perception of risks and benefits of new technology is individual knowledge of science. The covariate referring to knowledge is labelled ‘Know’ and represents individual knowledge of science. However, its effect is not clear cut, and one of the objects of this study is to shed more light on the question. Another relevant variable is the extent to which individuals believe expert opinion and are appreciably affected by it. This variable expresses some of the possible effects of bias between public and expert perceptions (Slovic, 1987). It is labelled ‘Experts’ and indicates individual lack of belief in expert opinion. Although this bias may be negative or positive depending on the risks being considered, it is assumed here that experts tend to have a relatively positive view of technological development compared with the general public. Therefore, lack of belief in expert opinion is expected to increase risk perception and reduce both benefit perception and risk acceptance. It is also true that perception of science-related risks is likely to be sensitive to individual optimism regarding risks (Purim and Robinson, 2005), and the survey contains a question that is expected to enhance this effect. It is labelled ‘Techno’ and represents the individual belief that science and technology improves people’s way of life. Information channels are another important variable. Trust in public information channels, or government, is labelled ‘State’. Trust in private channels is defined by political ideology, age, gender, religiosity and family responsibilities. Perceptions of risks and benefits are likely to differ between private information channels (such as personal experience, knowledge, inputs from religious groups and information that results were derived through discussion with other people), and public information sources (e.g. environmental and consumer organizations, although mostly conveyed by the media, as we
ll as government sources). Other relevant determinants that approximate information channels are gender and age. This is explained by the fact that information processing is age dependent given the prevalence of certain values at different times, as well as the effect of previous experience on the cognition of potential risks. Age influences the capacity to acquire new information, as it tends to shape prior experience as well as access to new information at a school or university. Unlike younger cohorts, older people have less access to new technologies, and thus are expected to show a higher dread of technology in response to a specific lack of knowledge. On the other hand, several studies identify a gender effect to be as an important element in shaping risk attitudes (Dwyer et al., 2002). Being the household’s head is found to be associated with the probability of being aware of food-related risks (Dosman et al., 2001). In our empirical specification, we have included ‘family head’ as a variable to measure whether individuals who are responsible for others are likely to perceive risks in the same way. The definitions of the variables used in the models and its descriptive statistics are explained in Table 1. Data and preliminary evidence Data description The data used in this study are gathered from a public survey commissioned by the Spanish Centre for Sociological Research M. Costa-Font and J.M. Gil Individual’s perception of science International Journal of Consumer Studies 36 (2012) 668–677 © 2011 Blackwell Publishing Ltd 671 (CIS) in 1996 on ‘Attitudes towards Scientific and Technological Innovation’. This survey is typically face to face and representative of the Spanish population between 18 and 64 years of age. The initial sample was made up of 2552 respondents, and the survey consisted of personal interviews to individuals from 91 municipalities and 43 provinces. The sample was made up equally of men and women. Their mean age was 41, and 57% were married. Regarding other variables, we included in the model that it is worth commenting that roughly 23% regarded themselves as being right wing (political affiliation), 2% revealed they attend to church regularly, and, finally, 37% were head of the family. Results This section reports the results obtained from the different speci- fications on the determinants of risk and benefit perceptions of new scientific innovation in Spain. Risk and benefit perception As Table 2 shows, the preliminary evidence indicated that most respondents identified both significant benefits and risks of scientific and technological innovation, although the overall percentage that perceived benefits was greater than the percentage that perceived risks. This result is to be expected given that the question is a general one. However, a large percentage of the population perceived some risk. This could be due to a certain resistance to new technology, or, alternatively, to ignorance of the effects of new technology leading to what is known as ‘risk, ignorance, or ambiguity aversion’. Furthermore, 57% of the respondents agreed with the assertion that the benefits of new technology overcome the risks,6 indicating that although some individuals perceived technology-related risks, the potential benefits seemed to be greater. In order to find some explanation for this evidence, further data were examined. Interestingly, only 57% of the population trusted new technology, and 13% argued that new technology could not solve the problems posed by previous technology, 36% could be considered ‘techno-sceptics’, as they responded that life would be better without technology. Sixty-five percent believed that decisions related to technology could not be exclusively based on consumer knowledge and required expert intervention. However, although 92% agreed that science and technology would, on the whole, improve people’s quality of life, there was certainly some ambivalence in the individual responses on ‘positive’ and ‘negative’ dimensions of scientific developments. The most obvious explanation for this is that science and technology produces both positive and negative effects, and that questions on risks and benefits may stress specific effects. Accordingly, on the basis of these results, individuals thinking about new technology might suffer from a ‘perceived aggregation effect’. Another possible explanation is that individuals may be sensitive to the way the information is provided and exhibit specific framing effects.7 The estimated coefficients on risk perception (see Table 3) show that, as expected, individuals who were less likely to trust experts perceived greater risks. The same applied to the individuals who were pessimistic about science and technology, and those who perceived that technology would not improve people’s way of life. 6 ‘Do you think that the benefits of new technology overcome the risks?’ (Response: yes/no). 7 Given the general nature of the question and the fact that other survey questions referred to a vast range of technological developments (computers, cloning, space exploration, solar energy, etc.), there is no reason to suggest that responses were biased in some specific way. Table 1 Variables and descriptive statistics Variable Definition Variable type Mean Significant effect Experts Belief that experts are not trustworthy D 0.79 0.01 Techno Belief that technology will not improve way of life O 0.12 0.01 Know Knowledge levela C 6.96 0.05 State Trust in government = 1 D 0.45 0.01 Gender Female = 1 D 0.50 0.01 Age Age in years C 41.1 0.03 Married Married = 1 D 0.57 0.01 Politic Left wing = 1 C 0.23 0.74 Practice Practices a religion D 0.02 0.01 Headfam Head of household D 0.37 0.01 a Derived from 12 answers to questions on science and technology (information provided upon request). C, continuous variable; D, dummy variable; O, ordered variable. Table 2 Perception of risks and benefits in the next 20 years (%) as a result of technological and scientific advances Question: ‘Do you perceive (risks/benefits) in the next 20 years as a result of technological and scientific advances?’ Perception of Risks Benefits Many 16.69 21.69 Some 40.24 52.24 Few 28.21 13.06 None 4.11 2.98 Do not know 10.70 9.57 Did not reply 0.04 0.47 Individual’s perception of science M. Costa-Font and J.M. Gil International Journal of Consumer Studies 36 (2012) 668–677 © 2011 Blackwell Publishing Ltd 672 Thus, consistent with previous literature on risk perceptions (Slovic, 1987), it seems that biases between the public and experts are important. Moreover, this evidence suggests that it is worth noting that trust in the government did not have an effect on risk perception.8 On the other hand, optimism about technology was significantly associated with lower risk perception and had the highest effect of all the coefficients (that, however, are only relatively comparable). Interestingly, knowledge of science was significantly associated with lower risk perceptions. This lends support to the ‘ignorance aversion’ hypothesis, whereby individuals with low levels of knowledge are more likely to perceive high science-related risks. Some variables that affect private information sources, such as age, did not have a significant effect. This indicates that individuals of different ages, who were potentially exposed to different intensities of information acquisition, did not perceive risks in a systematically different way. On the other hand, women were more likely to perceive risks than men, which is consistent with previous literature (Gustafson, 1998). Finally, although political orientation and religious beliefs were not significant predictors of technological risk perceptions, being married had a negative effect, while heads of families did not have higher risk perceptions. The finding that married people perceived fewer risks in technological developments might be linked to having children that could benefit from future technology developments. Table 3 reveals that benefit perception was influenced by similar variables to risk perception but displayed opposite coefficients
. For instance, ‘belief that experts are not trustworthy’ was found to reduce the probability of perceiving benefits from science developments. As expected, both an optimistic belief in the capacity of science to improve quality of life and the possession of scientific knowledge increased benefit perceptions. Again, an optimistic belief in technology was the variable that exhibited the highest value. However, in the case of benefit perception, age did have some positive effects, which could indicate in the first place that older cohorts, who had witnessed significant innovation, may tend to perceive higher benefits than younger cohorts. However, age effects might well be the case, though younger cohorts have benefited more intensively from science developments, which might lead in turn to the view that they might give current status quo as given, or as some research quotes individuals of different ages might well have heterogeneous reference points (Kanheman and Tversky, 1986). Indeed, how a person assesses an economic outcome is often influenced by how it compares with a reference level, so that younger cohorts might perceive new developments only as being the results of science. Gender had the opposite effect that it had in risk perception; women were less likely to perceive positive effects of new scientific developments. Gender has been found to be linked with differential attitudes to risks (Gustafson, 1998). Finally, political affiliation can measure ‘reformist attitudes’ that arguably might be more prone to expect benefits from the future. As expected, left-wing political affiliation was positively associated with higher benefit perception, while those practicing a religion perceived lesser benefits. Figure 1 shows the predicted probabilities of each response conditioned by the knowledge effect. Interestingly, knowledge of science reduced the probability of a risk perception response and increased the probability of a benefit perception response. An old 8 The study was tested for the existence of multi-colinearity among regressions by performing cross-correlations, but all of them were lower than 0.30. Table 3 Risk perception (ordered probit), benefit perception (ordered probit) and risk acceptance (probit model)a Risk perceptionb Benefit perceptionb Risk acceptancec Coefficient Significant effect Coefficient Significant effect Coefficient Significant effect Experts 0.262** 0.057 -0.189** 0.059 -0.284** 0.067 Techno -0.702** 0.072 0.547** 0.082 0.547** 0.083 Know -0.019* 0.01 0.076* 0.012 0.077** 0.013 State -0.031 0.046 0.022 0.047 0.012* 0.055 Gender 0.123** 0.052 -0.112 0.053 -0.132* 0.063 Age 0.003 0.022 0.069* 0.023 0.054* 0.026 Married -0.117* 0.054 0.028 0.056 -0.105 0.066 Politic 0.001 0.001 0.051 0.001 -0.001 0.001 Practice 0.122 0.191 -0.267 0.224 -0.118* 0.060 Headfam -0.071 0.057 0.106** 0.057 0.026 0.069 Intercept 0.393 0.136 RV chi (2,11) 140.59 164.62 137.41 Likelihood ratio test -2603.45 -2313 -1442 Pseudo-R2 0.08 0.07 0.1 % Corr 67% 71% 89% a The pseudo-R2 for the ordered probit cannot be interpreted like the R2 value of a logistic regression. It has to be adjusted to be compared with an R2 . In the present case, the variability explained is of 84%, and therefore predictions are acceptable. b ‘Do you perceive (risks/benefits) in the next 20 years as a result of technological and scientific advances?’ (Response: Many, Some, Few, None). c ‘Do you think that the benefits of new technology overcome the risks?’ (Response: Yes/No). *Significance at 1%, **significance at 5%. M. Costa-Font and J.M. Gil Individual’s perception of science International Journal of Consumer Studies 36 (2012) 668–677 © 2011 Blackwell Publishing Ltd 673 interpretation would suggest that if knowledge represents individual capacity for updating information on new technology, the results indicate that proactive information policies would have a strong impact on risk acceptance of new technology. However, that might not be the case (Fischhoff, 1995), as knowledge might measure ability along with exposure to information sources, which would then call for a reinterpretation of a deficit model. Risk acceptance According to the theoretical model used above, individuals making decisions on issues that involve risks have to weigh potential risks against increasing benefits. In Table 3, a probit model is used to examine the determinants of individual perceptions that the benefits of science developments are greater than the risks. Interestingly, trust in experts, belief in the possibility of improvements in quality of life, gender and age were key risk acceptance drivers. Older individuals and especially men were more likely to accept technology-related risks. Another relevant variable was religion; individuals who practiced a religion were less likely to accept technology-related risks. In predicting risk acceptance, an important result is the significance of the intercept term, which, according to prospect reference theory (Viscusi, 1990), conveys information on prior assessment of risks and benefits, which has a positive effect. Figure 2 shows that predicted risk acceptance increases with individual knowledge, indicating that individual attitudinal reactions are sensitive to knowledge of science. The independence of risk and benefit perceptions Previous results might well be criticized in that they assume that people examined risks and benefit perception in isolation. By estimating a joint model, it is possible to take into unobserved heterogeneity resulting from a joint determination process. Table 4 provides evidence on the hypothetical determinants of risk and benefit perceptions (risk learning process factors assuming a Bayesian approach). Interestingly, the correlation coefficient of the error term was significantly different from zero, and negative, suggesting that both perceptions should be jointly estimated, and that there is unobserved heterogeneity that conveys opposite influence in risks and benefit perceptions. More importantly, experts’ mistrust still has, as expected, an effect in increasing risk perceptions. Optimistic beliefs in technology produced higher benefit perception and interestingly reduced risk perceptions. However, the effect of knowledge of science was sensitive to the joint estimation and took a positive coefficient for both risk and benefit perception. Interestingly, the effect over benefits comes up far larger than the effect on risks, indicating that an aggregate communication is likely to have a significant effect on the acceptance of new technologies. The gender effect was only significant for risk perceptions, once again increasing risk perception but not benefit perception consistently with other studies on risk perceptions (Gustafson, 1998). These results indicate that gender that arguably influences differences in risk attitudes (Dwyer et al., 2002) and affects potential perceived risks rather than benefits. This result is consistent with previous findings that indicate that people weigh risks and benefits differently (Thaler, 1980). The specific effects of religion disappeared once risk and benefit-learning equations were jointly estimated, while the political-affiliation effect remained significant only for benefit perception, indicating that left-wing sympathizers are more likely to show higher benefit perception of new technology. Overall, the results suggest that the processes of risk and benefit learning are 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Risk perceptions Total High Knowledge 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Benefit perceptions Total High Knowledge Pr (many risks) Pr (some risks) Pr (few risks) Pr (no risks) Pr (many benefits) Pr (some benefits) Pr (few benefits) Pr (no benefits) Figure 1 Risk and benefit perception of new technology. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Risk acceptance Total K<6 6>k> 9 K>9 Figure 2 Risk acceptance and knowledge. Individual’s perception of science M. Costa-Font and J.M. Gil International Journal of Consumer Studies 36 (2012) 668–677 © 2011 Blackwell Publishing Ltd 674 not independent, and that certain information channels in
crease risk perception and may affect benefit perception. Conclusion This study has attempted to examine the formation of individual’s perceptions of risks and benefits of new science developments using survey data from Spain that, compared with their European counterparts, fall behind in innovation records. We have used a specific methodology that accounts for unobserved heterogeneity, and we focus on three specific features affecting the learning processes, namely the role of factual knowledge, trust in experts, as well as that of values, such as religiosity and politics. Regarding our first research question (RQ1) of whether risks and benefits are an expression of a common feature or not, our results lead us to conclude that perceptions of both risks and benefits of science and technology appear to be the expression of common feature. Namely, fear to the unknown effects of technology developments or confidence on science and technology. However, investigating the role of trust in experts and ‘factual knowledge’ as determinants in forming perceptions of risks and benefits (RQ2), we find that the most important contribution refers to the effect of knowledge of science along with expert trust on individuals’ risk and benefit perception of science. We find that factual knowledge systematically reduced perceptions of risk and appears to boost perception of benefit and, ultimately, acceptance of new science developments. This suggests that by improving factual knowledge would pay off with higher acceptance of new technologies. Indeed, knowledge matters as the traditional ‘deficit model’ would predict. However, the effect of knowledge signifi- cantly changed when the determinants of risk and benefit learning were estimated jointly and, accordingly, some unobserved effects are captured. Indeed, the joint estimation suggests now that improved people’s knowledge of science would increase both perceived risks and perceived benefits. In the context of these theories on how does the public understand scientific developments, this evidence suggests that the ‘context matters too’, as the model seems to capture (at least in part) the influence of unobservable variables. Finally, our results have revealed a number of interesting issues related to the third research question, namely, do cultural and religious values, as well as political affiliation, affect the formation of risk perceptions of new technological developments? First, the significance of the variable ‘trust in experts’ suggests that cognitive biases between the public and experts are potentially important, and that possibly trust might well be a competing explanation in explaining the influence of knowledge on risks and benefit perceptions. Our findings suggest that the more the population trusts experts, the more (less) likely it is to perceive the benefits (risks) of new technology. This is coherent with the already stated role of factual or objective knowledge in determining perceptions of risks and benefits. This is because experts are the ones communicating science and technology’s objective knowledge. Second, in understanding the updating process, it appears to be important to take into account the potential dependence of similar information channels. Some information channel variables affected only perception of benefits, namely age, and besides values – both religious practice and political affiliation, while being married affected only risk perception. We believe that this is an important finding that indicates that the adherence to traditional values does not necessarily lead to higher perceptions of risks, but instead it might explain lack of perceived benefits. Only individuals who are used to be agents of their children, and therefore more likely to be used to ‘think for others’, are more likely to perceive higher risks of new science developments. Finally, the study confirms previous findings that private information channel and the effects of certain variables had a differential impact on perceived risks and benefits. Indicating that, learning the risks and benefits of new technology differs depending on the individual’s age group (accordingly capturing different Table 4 Joint estimation of risk and benefit perception (bivariate probit) Risk perception Benefit perception Coefficient Significant effect t-value Coefficient Significant effect t-value Experts 0.181** 0.063 -2.878 -0.287** 0.067 4.282 Techno -0.733** 0.087 -8.416 0.539** 0.080 6.717 Know 0.032** 0.011 2.886 0.126** 0.012 10.652 State 0.091 0.051 1.786 0.030 0.056 0.535 Gender 0.131* 0.058 2.242 -0.071 0.065 1.087 Age -0.025 0.024 -1.051 0.079** 0.026 3.031 Married 0.133* 0.060 2.204 -0.087 0.066 -1.317 Politic 0.000 0.001 -0.410 0.001* 0.001 1.958 Practice -0.238 0.205 -1.161 0.046 0.219 0.212 Headfam 0.082 0.064 1.290 -0.018 0.071 -0.256 Intercept 0.066 0.122 0.540 -0.094 0.130 -0.722 r -0.20 0.01 Log likelihood -2.999.77 Wald test (χ22 2 ) 345.31 Likelihood ratio test (r = 0) 32.45 *Significance at 1%, **Significance at 5%. M. Costa-Font and J.M. Gil Individual’s perception of science International Journal of Consumer Studies 36 (2012) 668–677 © 2011 Blackwell Publishing Ltd 675 life experiences with technology use), gender that captures gender-specific effects (e.g. higher risk aversion), along with family and personal affiliations that might have an influence on people’s values. The finding that left-wing political affiliation increased benefit perception may be the result of the development of postmodernist values in the discourse of left-wing parties (Inglehardt and Baker, 2000) rather than a purely ideological effect. Some concerns might be that we examine new technologies in general, not about a specific technology or a set of specific technologies. Hence, individuals respond from a rather abstract, macro framework that is socially determined and reflects risks and benefits as a continuum. However, even when the stimulus here is general, by asking specific questions individuals might be biased by specific information sources. The results of this study provide some issues for the implementation of risk communication in practice, especially when it comes to new technology developments. On the one hand, they raise the issue that perception of the benefits of new technology, although widespread, is largely dependent on individual knowledge, yet new technological developments might well be communicated without affecting individual’s knowledge, which in turn create counter-productive effects as some individuals might develop – at times arguably unfounded – risk or benefit perceptions. When risk and benefit perception were examined in isolation, it was found that knowledge of science increased the perception of benefits and reduced risk perception and risk acceptance. However, risk perception of new scientific technology cannot be entirely separated from benefit perception, given that new information signals conveying risky (meaning negative) information simultaneously lower the perception of benefits and raise the perception of risks. On the other hand, we find that a more scientifically informed public would not necessarily perceive fewer technology-related risks; one possibility is the influence of ‘affect’ in the interplay of such risk and benefit association. References Alhakami, A.S. & Slovic, P. (1994) A psychological study of the inverse relationship between perceived risk and perceived benefit. Risk Analysis, 14, 1085–1096. Allum, N. (2007) An empirical test of competing theories of hazardrelated trust: the case of GM food. Risk Analysis, 27, 935–946. Benjamin, D.K. & Dougan, W.R. (1997) Individual estimates of the risks of death: part I-A reassessment of the previous evidence. Journal of Risk and Uncertainty, 15, 2. Bransford, J.D., Brown, A.L. & Cocking, R.R. (2000) How People Learn: Brain, Mind, Experience, and School. Expanded edn., National Academy Press, Washington, DC. Costa-Font, J. & Mossialos, E. (2007) Are perceptions of risk and benefits of genetically modified food independent? Food Quality and Preference, 18, 173–18
2. Day, R.H. (1986) On endogenous preference and adaptative economising: a note. In The Dynamics of Market Economics (ed. by R.H. Day & H. Eliason), pp. 153–170. North-Holland, Amsterdam. Dosman, D.M., Adamowicz, W.L. & Hrudey, S.E. (2001) Socioeconomic determinants of health- and food safety-related risk perceptions. Risk Analysis, 21, 307–317. Dwyer, P.D., Gilkeson, J.H. & List, J.A. (2002) Gender differences in revealed risk taking: evidence from mutual fund investors. Economics Letters, 75, 151–158. Earle, T. & Siegrist, M. (2008) Trust, confidence and cooperation model: a framework for understanding the relation between trust and risk perception. International Journal of Global Environmental Issues, 8, 17–29. Ellsberg, D. (1961) Risk, ambiguity and the savage axioms. Quarterly Journal of Economics, 75, 643–669. Fessenden-Raden, J., Fitchen, J. & Heath, J. (1987) Providing risk information in communities: factors influencing what is heard and accepted. Science, Technology and Human Values, 12, 94–101. Finucane, M., Alhakami, A., Slovic, P. & Johnson, S. (2000) The affect heuristic in judgements of risks and benefits. Journal of Behavioural Decision Making, 13, 1–17. Fischhoff, B. (1995) Risk perception and communication unplugged: twenty years of process. Risk Analysis, 15, 137–145. Fischhoff, B., Lichtenstein, S., Slovic, P., Derby, S.L. & Keeny, R.L. (1981) Acceptable Risk. Cambridge University Press, New York. Fischhoff, B., Slovic, P., Lichtenstein, S., Read, S. & Combs, B. (1978) How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits. Policiy Sciences, 9, 127–152. Franks, J. (1999) The status and prospects for genetically modified crops in Europe. Food Policy, 24, 565–584. Freudenburg, W.R. (1993) Risk and recreancy: weber, the division of labor, and the rationality of risk perceptions. Social Forces, 71, 909–932. Green, W. (1997) Econometric Analysis. Prentice Hall, Upper Saddle, NJ. Gustafson, P. (1998) Gender differences in risk perception: theoretical and methodological perspectives. Risk Analysis, 18, 805–811. Hakes, J.K. & Viscusi, W.K. (1997) Mortality risks perceptions: a bayesian reassessment. Journal of Risk and Uncertainty, 15, 135–150. Henson, S., Annou, M., Cranfield, J. & Ryks, J. (2008) Understanding consumer attitudes toward food technologies in Canada. Risk Analysis, 28, 1601–1617. Ho, A. & Leung, K. (1998) Group size effects on risk perception: a test of several hypotheses. Asian Journal of Social Psychology, 1, 133–145. Hoban, T. (1998) Trends in consumer attitudes about biotechnology. AgBioforum, 1, 3–7. Inglehardt, R. & Baker, W.E. (2000) Modernization, cultural change and the persistence of traditional values. American Sociological Review, 65, 19–51. Jenkins-Smith, H.C., Silva, C.L., Nowlin, M. & deLozier, G. (2011) Reversing nuclear opposition: evolving public acceptance of a permanent nuclear waste disposal facility. Risk Analysis, 31, 629–644. Joss, S.E. (1999) Public participation in science and technology. Science and Public Policy, 26, 290–373. Kanheman, D. & Tversky, A. (1986) Prospect theory and evaluation. Journal of Cognitive Psychology, 73, 34–48. Kasperson, R.E., Renn, O., Slovic, P., Brown, H.S., Emel, J., Goble, R., Kasperson, J.X. & Ratick, S. (1988) The social amplification of risk: a conceptual framework. Risk Analysis, 8, 177–187. Koopmans, T.C. (1964) On flexibility of future preference. In Human Judgements and Optimality (ed. by S. Maynard & B. Glenn), pp. 243–254. Wiley, New York. Lowenstein, B.V. (2003) Models of public communication of science and technology. Working Paper, Departments of Communication and of Science & Technology Studies. Masuda, J.R. & Garvin, T. (2006) Place, culture, and the social amplifi- cation of risk. Risk Analysis, 26, 437–454. Miller, J. (2004) Public understanding of and attitudes toward scientific research: what we know and what we need to know. Public Understanding of Science, 13, 273–294. Miller, J.D. (1983) Scientific literacy: a conceptual and empirical review. Daedalus, 112, 29–48. Individual’s perception of science M. Costa-Font and J.M. Gil International Journal of Consumer Studies 36 (2012) 668–677 © 2011 Blackwell Publishing Ltd 676 Olofsson, A. & Rashid, S. (2011) The white (male) effect and risk perception: can equality make a difference? Risk Analysis, 31, 1016– 1032. Purim, M. & Robinson, D. (2005) Optimism and Economic Choice. NBER Working Paper Series, 11361. Sapp, S. & Downing-Matibag, T. (2009) Consumer acceptance of food irradiation: a test of the recreancy theorem. International Journal of Consumer Studies, 33, 417–424. Siegrist, M., Cvetkovich, G. & Roth, C. (2005) Salient value similarity, social trust, and risk/benefit perception. Risk Analysis, 20, 353–362. Slovic, P. (1987) The perception of risk. Science, 236, 280–285. Slovic, P. (2000) The Perception of Risk. Earthscan, London. Slovic, P., Fischhoff, B. & Lichtenstein, S. (1981) Perceived risk: psychological factors and social implications. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 376, 17–34. Slovic, P., Kraus, N., Lappe, H. & Major, M. (1991) Risk perceptions of prescription drugs: report on a survey in Canada. Canadian Journal of Public Health, 82, 15–20. Stadman, P.M. (1992) Hazard vs outrage. IOMA Broadcaster, 6. Stebbing, M. (2009) Avoiding the trust deficit: public engagement, values, the precautionary principle and the future of nanotechnology. Bioethical Inquiry, 6, 37–48. Sturgis, P. & Allum, N. (2004) Science in society: re-evaluating the deficit model of public attitudes. Public Understanding of Science, 13, 55–75. Thaler, R. (1980) Toward a positive theory of consumer choice. Journal of Economic Behavior and Organization, 1, 39–60. Vandermoere, F., Blanchemanche, S., Bieberstein, A., Marette, S. & Roosen, J. (2011) The public understanding of nanotechnology in the food domain: the hidden role of views on science, technology, and nature. Public Understanding of Science, 20, 195–206. Viscusi, K. (1998) Rational Risk Policy, pp. 17–21. Clarendon Press, Oxford. Viscusi, W.K. (1990) Do smokers underestimate risks? Journal of Political Economy, 98, 1253–1269. Viscusi, W.K. (1992) Fatal Tradeoffs: Public and Private Responsibilities for Risk. Oxford University Press, New York. Wachelder, J. (2003) Democratizing science: various routes and visions of Dutch science shops. Science, Technology and Human Values, 28, 244–273. Wildavsky, A. & Dake, K. (1990) Theories of risk perception: who fears what and why? Daedalus, 119, 41–60. Wohl, J.B. (1998) Consumers’ decision-making and risk perceptions regarding foods produced with biotechnology. Journal of Consumer Policy, 21, 387–404. M. Costa-Font and J.M. Gil Individual’s perception of science International Journal of Consumer Studies 36 (2012) 668–677 © 2011 Blackwell Publishing Ltd 677 Copyright of International Journal of Consumer Studies is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use.

RESEARCH PAPER Relationship among cognitive biases, risk perceptions and individual’s decision to start a venture M. Kannadhasan • S. Aramvalarthan • B. Pavan Kumar Published online: 29 March 2014 Indian Institute of Management Calcutta 2014 Abstract The study examines the relationship among the cognitive biases (viz., overconfidence, illusion of control, optimism and planning fallacy), risk perception and individual’s decision to start a venture. To understand the relationship, this study developed and tested a model by partial least square— structural equation modelling. The study collected responses from 136 post graduate students after teaching discussion of a Harvard Business School case titled ‘Optical Distortion, Inc (Clarke 1988)’.This study found that planning fallacy and illusion of control have direct as well as indirect influence on new venture formation. Conversely, overconfidence and optimism have influenced new venture formation through risk perception. The study also indicates the overall preparation of management graduates for being an entrepreneur. It would act as an indicator of entrepreneurial orientation. All these understandings would be used as a base for the teaching of business skills as well as increasing the understanding the potential Indian entrepreneur’s minds towards the entrepreneurship and risk perception in particular. Keywords Cognitive biases Risk perception New venture formation Decision-making Entrepreneurs Introduction Entrepreneurship is a complex and multifaceted phenomenon, and it is gaining importance in many countries around the world. Despite the high risk involved in becoming an entrepreneur, many individuals decide to pursue entrepreneurship (Simon et al. 2000). This behaviour has prompted many researchers to explore why some individuals choose to become an entrepreneur while others do not. Researchers have tried to explore how an entrepreneur differs from others by exhibiting certain traits to a greater extent than others (for example, Das and Teng 1997) based on the trait approach. A number of psychological traits viz. need for achievement, locus of control and risk propensity has been studied in an attempt to differentiate entrepreneurs from non-entrepreneurs. Early efforts met modest success in identifying consistent differences between entrepreneurs and non-entrepreneurs (for M. Kannadhasan (&) Accounting & Finance Group, Indian Institute of Management Raipur, GEC Campus, Old Dhamtari Road, Sejbhar, Raipur, Chhattisgarh, India e-mail: kannadhasan76@gmail.com; mkdhasan@iimraipur.ac.in S. Aramvalarthan Periyar Management and Computer College, Periyar Centre, FC-33, Plot No 1 and 2, Institutional area, Jasola, New Delhi 110025, India B. Pavan Kumar Institute of Management Technology Hyderabad, Survey No. 38, Cherlaguda Village, Shamshabad Mandal, RR District, Hyderabad, Andhra Pradesh 501218, India e-mail: pavans5@gmail.com 123 Decision (March 2014) 41(1):87–98 DOI 10.1007/s40622-014-0029-1 example, Shaver and Scott 1991; Hatten and Coulter 1997). Therefore, researchers have turned to studying how entrepreneur think and the role played by cognition in the process. (Nigel Wadeson 2008). The researchers believed that the entrepreneur would think in a different manner (Baron 1998). If the cognitive process of entrepreneurs is different from those of others, the manner of assessment of opportunities, process of information gathering and the perceptions of risks would also vary from others. Building on the cognitive theory, this study proposes that various cognitive mechanisms may be associated with identifying the opportunities and assessing the creation of a new venture. As discussed above, risk is a central element in decisions to enter new ventures, whether by an established firm or an entrepreneur establishing a new firm (Mullins et al. 2002). As the creation of new venture or decision to start a venture involves risk, an individual who has a tendency to take risk would form a new venture as compared to someone who is averse to taking risk (Shaver and Scott 1991 and Kannadhasan and Nandagopal 2010a, b and Kannadhasan 2012). The extant literature suggests that risk propensity is a multifaceted personality trait. However, it fails to differentiate entrepreneurs from others (for example, Brockhaus, 1980). Even an individual who does not have high-risk propensity might unknowingly involve in risky ventures if he perceives less risk than others (Simon et al. 2000). This leads to a question, would this risk taking behaviour be different if all of them evaluate the same situation? The answer is no. Even when different individuals evaluate identical situations, some individuals may not perceive the situation very risky; others may perceive it very risky (Nutt 1993). If the perception influences risk taking behaviour of an individual, it is indispensable for us to understand what leads to variations in risk perception. This is due to the individual differences in knowledge management style or cognitive process. For example, an individual collects, organises and categorises the information that supports previously conceived ideas/ experience. This selectivity process creates cognitive frameworks with regard to how individuals think and act in their domain. This is due to the different cognitive schemata and approaches towards information management (Baron and Markman 1999). Therefore, the actual understanding new venture formation would require examination of how various cognitive biases influence human perception towards risk and thereby their decision to form new ventures. Cognitive biases, risk perceptions and new venture formation Entrepreneurship is contributes to any nation’s economic growth and wealth creation. For economies of developing countries, like India, entrepreneurship is seen as an engine of economic progress, job creation and social adjustment. Change is pertinent in today’s world, and change creates opportunities for the entrepreneurial class. The exploding industry sector has opened up exciting opportunities in India. The increased prevalence of outsourcing by many business operations is creating new opportunities for entrepreneurs. The blurring of national borders, the encouragement to world trade and the increasing availability of information has opened up international opportunities to all sections of the society. It is believed that an entrepreneur is the person who discovers, creates and recognises opportunities, and translates these into added value to society (Baron and Markman 1999) by assuming the risk of starting a business (Hatten and Coulter 1997). Although risk propensity of an individual does not differ between entrepreneur and others, it differs in terms of how they think about the business situations in terms of strengths, opportunities and potential gain (Palich and Bagby 1995). It is evident that an individual’s decision to start a venture depends on one’s perception about the risk involved in a venture. Palich and Bagby (1995) and Simon et al. (2000) opined that individuals who perceive less risk than others will start a new venture. This line of thought is consistent with several studies (for example, Brockman et al. 2006; Keh et al. 2002; Forlani and Mullins, 2000; Simon et al. 2000; Chen and Dong 2007; Panzano and Billings 1997; Sitkin and Pablo, 1992). Therefore, it is expected that H1 Individuals who perceive less risk than others will start a new venture Cognitive biases and risk perception As discussed above, if different individuals think and perceive differently, then it is essential for us to understand the reasons for such difference in 88 Decision (March 2014) 41(1):87–98 123 behaviour. Cognition aspects differentiate entrepreneurs from non-entrepreneurs, which include their beliefs, values, cognitive styles and mental processes (Sa´nchez Garcı´a et al. 2011). Cognitive literature speaks of different cognitive styles viz. knowledge structures that are used to make assessment, judgement and decisions; and what an entrepreneur thinks, says or does in acquiring, using and processing information. This study inves
tigates the participants’ responses in the second perspective i.e. entrepreneurs think differently and process information in a different way and such differences would help to differentiate between the entrepreneurs and non-entrepreneurs (Sa´nchez Garcı´a et al. 2011). In particular, this study observes how cognitive biases affect individual’s decision making under risky conditions (Laibson and Zeckhauser, 1998). This section discusses how cognitive process affects the new venture formation, mediated by risk perception. In this process, the study included four cognitive biases viz., over confidence (‘a failure to recognise the limits of our knowledge’) (Baron and Markman 1999), optimism (a tendency to believe things will turnout well), illusion of control (a tendency to believe that one can control outcomes over which in fact he/she has no control) and planning fallacy (‘a tendency to assume that one can achieve more in a given period of time than that is warranted in reality’) (Baron and Markman 1999) that are studied widely and relevant to entrepreneurship. Overconfidence: Generally, entrepreneurs have higher level of self-confidence compared to others (Levander and Raccuia 2001). This leads to over selfesteem. A person with high self-esteem is highly prone to make decisions with uncalculated risks (Ivanova and Gibcus 2003). This study is not interested to know whether entrepreneurs are overconfident per se. It is, however, interested to know how well they know what they do not know (Baron and Markman 1999). This is because what they know about themselves (i.e. metaknowledge and information) is very much important to the success or failure of their new venture formation (Baron and Markman, 2000). According to Baron and Markman (2000), the overconfidence bias refers to ‘the tendency to underestimate our lack of knowledge or to think that we know more than we really do i.e., poor Meta-knowledge)’. Knowledge has been bifurcated into primary and secondary. Primary knowledge consists of facts and principles that one believes are true. On the other hand, the secondary knowledge refers to ‘the extent our primary knowledge is reliable’ (Russo and Schoemaker 1992). They also pointed out that this bias is the result of the availability heuristic, adjustment and anchoring heuristic, hindsight bias and confirmatory bias. Due to these biases, overconfident individuals attach higher probabilities to particular outcomes than are warranted by what they know (Zacharakis and Shepherd 2001) by remembering the evidence that supports their view and their knowledge instead of taking into account disconfirming evidence (Russo and Schoemaker, 1992). However, this reasoning may not improve the accuracy of the available information (Schwenk 1986). In addition, they fail to revise the initial estimation after receiving new information. As a result, there is a possibility that their estimates may be wrong (Tversky and Khaneman 1973). Moreover, they treat their assumptions as facts. This outlook results in inadequate information search (Zacharakis and Shepherd 2001). As a consequence, they do not consider the uncertainty closely associated with the decisions stemming from those assumptions. This bias leads them to conclude that the decision is not risky and hence enter risky ventures unknowingly (Tversky and Khaneman 1973). Though this bias is very common in unstructured decision situation like introduction of new product (Simon and Houghton 2003), the confidence level is not justified as the entrepreneurs will fail in the collection of relevant information thereby affecting the quality of their decisions (Sa´nchez Garcı´a et al. 2011). The extant literature exhibits that this bias diminishes an individual’s perception towards the level of risk associated with new venture formation (for example, Russo and Schoemaker 1992; Simon et al. 2000; Zacharakis and Shepherd 2001; Keh et al. 2002). Therefore, it is expected that H2 Overconfidence decreases one’s perception of the level of risk associated with new venture formation Optimistic bias: The literature shows optimism as a stimulator of persistence and commitment to new venture creation (for example, Seligman & Schulman 1986). As entrepreneurs are likely to be optimistic, they frequently make judgements and decisions based on subjective factors (Cooper et al. 1988; McCarthy et al. 1993). Optimistic bias refers to the tendency in believing that things will turnout well. It has three forms namely over positive self-evaluation, over optimism about future plans and events and over optimism Decision (March 2014) 41(1):87–98 89 123 due to the illusion of control bias (Taylor and Brown 1988). A study by Cooper et al. (1988) found that 81 % of entrepreneurs believed that their chances of success were at least 70 and 33 % claimed that they were certain of success. However, reality showed that only 25 % of new businesses survive for more than 5 years. The results require some attention. For instance, such positive statements partially reflect a need for selfjustification and thereby reduce the perception of the level of risk. In addition, they have a normal predisposition to talk positively about their efforts with a view to encouraging others, such as financiers, employees and customers into believing that they will be successful. Further entrepreneurs operate by a unique set of cognitive process and thereby support their optimism (Palich and Bagby 1995). However, this kind of positive outlook about their future very often enables entrepreneurs to downplay on uncertainty and thereby decreases the risk perception about the new venture formation (Cheng and Dong 2007; Simon et al. 2000). Therefore, it is expected that H3 Optimism decreases one’s perception of the level of risk associated with new venture formation Illusion of control: Illusion of control refers to the tendency of the entrepreneurs to believe that they can control the outcomes over which they have no control actually or overemphasise the level of control that they do have (Nigel Wadeson 2008). This bias is the result of two factors viz. difficulty in distinguishing the relative importance of skill and chance elements, and motivation to control their environments. There is a difference between overconfidence and illusion of control. As discussed above, overconfidence refers to ‘an overestimation of one’s certainty regarding the current information’, whereas illusion of control refers to ‘an over-estimation of one’s skills and consequently one’s ability to cope with and predict future events’ (Simon et al. 2000). Typically, the entrepreneurs overemphasise their skills that would increase the performance in situations where chance plays a larger role as a deciding factor (Langer 1975). This bias makes oneself to believe that she or he can control and predict the outcome of uncertain events precisely (Duhaime and Schwenk 1985; Shaver and Scott, 1991). This leads one to underestimate the risk associated with an event as they believe that their skills can overcome negative occurrences. This may generate overly optimistic estimates leading to risky decisions, such as acquiring poorly performing firms (Duhaime and Schwenk 1985). Not only this, but also it affects the decision to form a new venture (Boyd and Vozikis, 1994). However, this belief is based on perceptions of an individual (Shaver and Scott 1991) and decreases one’s perception of the level of risk associated with a new venture formation (Simon et al. 2000 and Keh et al. 2002).Therefore, it is expected that H4 An illusion of control bias decreases one’s perception of the level of risk associated with a new venture formation. Planning fallacy: It refers to the tendency of most individuals to underestimate the amount of time that it will take to complete a task or overestimate the extent of accomplishment in a given period of time (Baron and Markman 2000). This bias is due to the fact that entrepreneurs have a relentless tendency to step into new experiences and they do not have adequate reference of how much resources or personal efforts are required fo
r a new venture. (Kahneman and Lovallo 1993). In addition, they are failing to break down their multifaceted mental tasks into different components (Kruger and Evans 2004). If one is able to do so, the planning fallacy becomes reduced. Baron (1998) pointed out that ‘the idea that entrepreneurs tend to be more susceptible to the planning fallacy than others, because they operate in a dynamic and uncertain environment, under the severe pressure of time and substantial amounts of information’. Therefore, they treat the current situation or decisions as unique and therefore are isolated from past experience (Kahneman and Lovallo 1993). Research indicates that this bias leads to underestimation of risks and overestimation of the success possibilities. As new venture formation is future oriented and highly uncertain, one may be more prone to planning fallacy and hence perceive less risk associated with a new venture (Keh et al. 2002). Therefore, it is expected that: H5 Planning fallacy decreases one’s perception of the level of risk associated with a new venture formation. The mediating role of risk perception Sitkin and Pablo (1992) and Sitkin and Weingart (1995) tested the proposition that risk perception mediated the relationship between determinants and 90 Decision (March 2014) 41(1):87–98 123 risky behaviour. Similarly, the extant literature shows that risk perception mediated the relationship between cognitive biases and new venture formation (Simon et al 2000) and opportunity evaluation (Keh et al. 2002). In addition, the proposed hypotheses (1–5) show that cognitive biases directly influence risk perception which, in turn, influences new venture formation (Keh et al. 2002). Thus, cognitive biases indirectly influence the new venture formation decision. The above discussion leads to the following hypothesis and the model (refer Fig. 1): H6 The relationship between cognitive biases and new venture formation is mediated by risk perception. Methodology Procedure The purpose of the study was to explore the decision making process of individuals to start a new venture. As suggested by Krueger and Brazeal (1994) and others, this study avoided asking an existing entrepreneur looking backwards to explore how they decided to start a venture. Instead, this study has explored the decision making process of individuals who have not started the business yet. This, in turn, would not influence the individuals’ decisions relating to the demands of running a new venture (Busenitz and Barney 1997a, b).The study has collected the responses from the students after teaching a Harvard Business School case titled ‘Optical Distortion, Inc(Clarke 1988)’. The survey instrument was used to capture the students’ cognitive biases, risk perception and decision to start the venture after a week from the date of discussing the case. This method is in consistent with the method used in past research conducted by Mark Simon et al. (2000). This method ensured that all the participants analysed the same venture. It also minimises the variances among the participants in terms of types of ventures being studied, demand of running the venture, risk assessment and environmental differences in relation to new venture formation (Krueger and Brazeal 1994). Sample 142 out of 168 students of Post Graduate students at a reputed B-School volunteered to participate in this research. The survey yielded a response rate of 84.50 %. Therefore, all analyses were conducted with a sample size of 142. Since the researcher is working in the same institute, it was informed to the participants that the responses would not to be used for evaluation. The mean age of the participant was 24.53 years (SD = 2.02) and average experience of the participant was 27.18 months (17.41). Eighty-two per cent of responses were from males and the rest were from females. Measurement In order to measure the risk perception, new venture formation, illusion of control and optimism, the study used the scales which were developed by Simon et al. (2000). Overconfidence and planning fallacy were measured using the scales developed by Henry Friedman (2007) and Keh et al. (2002), respectively. In order to verify the properties of the measurement scales, this study tested reliability, convergent validity and discriminant validity of the scales. New venture Over Confidence Illusion of Control Planning Fallacy Risk Perception New Venture Formation Optimism Fig. 1 Research model Decision (March 2014) 41(1):87–98 91 123 formation and planning fallacy were measured with a 2-item scale and have a reliability of 0.926 and 0.789, respectively. Overconfidence, illusion of control and optimism were measured with a 3-item scale and have a reliability of 0.907, 0.800 and 0.884, respectively. Risk perception was measured with an 8-item scale and has a reliability of 0.807. The reliability of the constructs is above the minimum threshold level for a construct (Nunnally 1978) and hence all the constructs have good reliability (see Table 1). Note that all the items in the constructs have a minimum loading of 0.503 which is greater than the threshold level of 0.40 (Hulland 1999). After verifying the reliability, it is important to examine the convergent and discriminant validity of the constructs. All the variables have convergent validity (see Table 1) which was tested by calculating ‘Average Variance Extracted (AVE)’ value. The AVE value describes the amount of shared variances among the indicators for a construct (Cohen 2001). Generally, constructs which have AVE [0.50 (Hair et al. 2006) or have AVE close enough to 0.50 are considered to have a good convergent validity (Cohen 2001). The discriminant validity was tested by examining the squared root of the AVE that exceeds the intercorrelation of the construct with the other constructs or squared correlation between the constructs which should be less than the AVE (see Fornell and Larcker 1981; Hair et al. 2006). Table 2 shows that all the constructs have good discriminant validity. Therefore, the measurement model was considered satisfactory with the evidence of adequate reliability and validity and could be used for testing hypotheses and proving the research model. Power analysis Power analysis test was used to examine the stability of the model’s parameters with the sample size used in the analysis (Chin 1998). The effect size was computed using R2 (Cohen et al. 2003). All inputs were entered in G*Power software and output is shown in Fig. 2 (Faul et al. 2009). Figure 2 indicates that power of the overall model increases as number of sample increases and is achieved 100 % with sample size of 75. The sample size of this study is 136 which is adequate for achieving substantial explanatory power of the model. Structural model results The study used Partial Least Squares approach to structural equation modelling (PLS-SEM) which is a variance-based approach to assess their interrelations of all the constructs simultaneously (Chin 1998). The PLS model estimation was carried out using Smart PLS 2.0- M3 software. Fig. 3 shows PLS with its path coefficient value of the measurement model. The cognitive biases are negatively associated with risk perception. Further, Paths (i.e. cognitive biases) linking to risk perception were significant at 1 per cent level (refer Table 3). This result reveals that cognitive biases decrease one’s perception level of risk associated with Table 1 Quality review of the latent variables Variable Alpha Composite reliability AVE New venture formation (NVF) 0.926 0.964 0.930 Risk perception (RP) 0.807 0.851 0.430 Overconfidence (OC) 0.907 0.942 0.844 Planning fallacy (PF) 0.789 0.902 0.822 Illusion of control (IC) 0.800 0.878 0.706 Optimism (Opt) 0.884 0.928 0.811 Table 2 Latent variable correlations Variable NVF RP OC PF IC Opt HAVE New Venture Formation (NVF) 1.000 0.964 Risk Perception (RP) -0.268 1.00 0.656 Overconfidence (OC) 0.075 -0.386 1.00 0.919 Planning Fallacy (PF) 0.180 -0.362 0.318 1.00 0.907 Illusion of Control (IC) 0.053 -0.349 0.336 0.443 1.00 0.840 Optimism (Opt) 0.080 -0.418 0.518 0.411 0.416 1.00 0.901 92 Decision (M
arch 2014) 41(1):87–98 123 Fig. 2 Power analysis Fig. 3 Measurement model Table 3 Bootstrap summary of research model and hypotheses results Hypothesis Path Path coefficients Standard error T-statistic Results 1 RP -[NVF -0.273255 0.031850 8.579475 Significant 2 IC -[RP -0.131663 0.035558 3.702749 Significant 3 OC -[RP -0.186587 0.036787 5.072029 Significant 4 Opti -[RP -0.200004 0.046622 4.289885 Significant 5 PF -[RP -0.162307 0.032869 4.937948 Significant Decision (March 2014) 41(1):87–98 93 123 a new venture formation. Therefore, this study concludes that hypotheses 2–5 were accepted. In addition, hypothesis 1 is also accepted (refer Table 3). It indicates that perceiving a lower level of risk is associated with the new venture decision. To understand the mediating effect of risk perception between cognitive biases and new venture formation, this study applied Iacobucci and Duhachek (2003) simultaneous assessment of mediation effect, which ensures superior results to those given by other existing methods (Helm et al. 2010). To apply this method, the analysis has to meet the criteria for mediation analysis viz. predictors have significant influence on the mediator, mediator has significant influence on criterion variable and predictor has significant influence on the criterion variable. Even though, paths (overconfidence and optimism) linking to new venture formation are not significant, the path coefficient is different from zero. Therefore, in order to test the significance of indirect effect (a*b) of cognitive biases on new venture formation through risk perception, the Z-test (Sobel 1982) is applied. If the Z-value exceeds 2.58 at 1 % significance level, there is an indirect effect. Table 4 shows that there is an indirect effect at 1 % significant level. Therefore, planning fallacy and illusion of control biases have direct effect as well as indirect effect through risk perception on new venture formation. However, overconfidence and optimism have only indirect effect through risk perception on new venture formation. The study also conducted Global Fit Index (GoF) for path modelling (Tenenhaus et al. 2005) as it may serve as a cut-off value for global validation (Wetzels et al. 2009). GoF is defined as the geometric mean of the AVE and average R2 . The GoF value of this study is 0.3635 i.e. 36.35 % for the complete model that exceeds the cut-off value. As compared to the base line values of power, this model has exceeded the required level (i.e. GoF = 0.36). Therefore, this study concludes that the model has adequate support to validate the PLS model globally (Wetzels et al. 2009). Discussion and implications Nowadays, all businesses face an unstable business environment with high levels of uncertainty. This uncertainty makes decision-making more complex than ever before. In a rapidly changing environment, it is a challenging task to use available opportunities and make decisions by utilising all available information for being a rational decision maker. By the time decisions are made, there is a possibility that the opportunity would not exist. In such complex circumstances, cognitive biases play an important role in decision making (Kannadhasan and Nandagopal 2010a, b). This study has extended a cognitive theory in the context of new venture formation by capturing the students’ perceptions regarding their overconfidence, illusion of control, optimism, planning fallacy, risk perception and decision to start a new venture. The study developed and tested a model with the help of PLS-SEM using Smart PLS software. The tested model of this study contributes empirical support to the studies by Simon et al. (2000) and Keh et al. (2002) in the Indian context. Although this study could not find any surprising results, it supports the existing literature. Obviously, the perception towards risk associated with new venture plays an important role in decision making. If one perceives higher level of risk associated with a new venture formation, she or he does not start the venture. The study found that there is a significant negative relationship between risk perception and new venture formation. This finding is similar to the findings of Keh et al. (2002); Simon et al. (2000); Forlani and Mullins (2000); Sitkin and Weingart (1995) and Sitkin and Pablo (1992). Further, individuals do not need a greater risk propensity to start a venture as long as they perceive less risk associated with a new venture. The study also found differences among the individuals in starting the venture even though they evaluated the same venture. It is due to the influence of cognitive biases on risk perception as well as new venture formation. Out of the four biases, planning fallacy and illusion of control have direct effect as well indirect effect on new venture formation. Table 4 The results of indirect effect Hypothesis Path Sobel’s Z-value Results 6 IC – > RP- > NVF 4.26 Significant OC – > RP- > NVF 4.34 Significant Opti – > RP- > NVF 3.82 Significant PF – > RP- > NVF 3.38 Significant 94 Decision (March 2014) 41(1):87–98 123 On the other hand, overconfidence and optimism have indirect effect through risk perception. As discussed above, respondents perceive that they are able to control the outcomes of the venture, over which actually they have no control. The reasons might be that they fail to consider the competitors’ response or they may think that competitors are beyond their control (Kerin et al. 1992). Moreover, their cognitive biases lead to the belief that competitors’ responses will not affect their chances of success. With regard to planning fallacy, respondents think that they are able to break down their tasks in their mind into their different parts and thereby they can complete their task on time. This belief leads them to underestimate the level of risk. This perception, in turn, influences their decision to start the venture. These findings are similar to those of Simon et al. (2000) and contrast to those of Keh et al. (2002). The contrasting result is due to the differences in study groups. The present researchers and Simon et al. (2000) studied a group of MBA students, whereas Keh et al. 2002 studied actual entrepreneurs from Singapore. Another reason could be the key difference in the way in which entrepreneurs collect and process information from others (Baron 1998). Entrepreneurs are more realistic in terms of viability of new venture formation rather than students (Keh et al. 2002). Overconfidence influences new venture formation indirectly. Overconfident individuals have a greater faith in the correctness of their assumptions. These assumptions may lead to two directions: estimates either being too pessimistic or too optimistic depending on how their estimates are positively or negatively biased (Sa´nchez Garcı´a et al. 2011). These assumptions may lead to optimistic conclusions. Therefore, they may be certain regarding their assumptions which lead them to perceive the level of risk associated with the venture to be low and start the venture. This finding is in contrast with those of Simon et al. (2000) and Keh et al. (2002). Also, optimism does not influence venture formation directly. In addition to the above stated reasons, the survey was done in a specific context rather than more generally. Although this study could not produce any conclusive results towards individual’s cognitive aspects, the study attempted to understand the influences of cognitive biases on decision making process and focused on four variables in a specific context. The results of this study suggest that cognitive biases would produce superior results when the information and time is limited for decision making (Gigerenzer and Todd 2000). However, one should minimise his or her biases, because the incomprehensive decisions will reduce the performance of the ventures (Smith et al. 1988). If individuals are too optimistic in their estimates, it leads to incorrect estimates of risk that they have to face in their venture. Sometimes it may lead to lower performance or failure in their venture. To minimise or avoid
biases in decisions, one needs to do systematic research. For instance, they may collect the information like success rate, industry position, range of profits, size of industry, strength of existing products and so on. Moreover, it will still be extremely difficult for individuals to minimise biases in their decision processes, because they are often unaware that they exhibit biases (Hogart 1980). This study also indicates that it is essential to include risk perception as a mediator when analysing the influences of cognitive biases on risky decisions (Sitkin and Weingart 1995) and distinguish between risk perception and risk propensity (Sitkin and Pablo 1992). The reason for emphasising about the risk perception is that it promotes entrepreneurship as entrepreneurs perceive less risk in the venture. Another understanding from the study is that the quality and process of decision making is also an important determinant of the success of the venture. As noted above, less comprehensive decision-making lowers a venture’s performance (Smith et al. 1988). For example, those who exhibit a greater level of biases when deciding to start a venture may not cope with the risks while venture is in progress. This, in turn, decreases the performance of the venture. It also demotivates individuals who wish to start the new venture. As pointed out by Simon et al. (2000), a further study could explore the relationship among cognitive biases, risk perception and venture performance with the objective of exploring why many startups fall short of the entrepreneurs’ expectations. Most individuals perceive less risk while comparing the opportunity costs of alternative employment. It is due to their education and their past successes (Keh et al. 2002). To overcome this kind of issues, in addition to the systematic research, one has to seek views and advice from the experts in the respective field. In addition, one may take group decisions instead of taking decisions on his or her own (Russo and Schoemaker 1992). Else, they have also to prepare Decision (March 2014) 41(1):87–98 95 123 themselves to face risk that may arise from uncontrollable external factors and remember those experiences while making next decisions. Even though current scenario is considered a knowledge era, one should pay more attention and care to the reliability and validity of any information before making important decisions based on such information. Schwenk (1986) and Busenitz and Barney 1997a, b) suggested that cognitive biases should not be minimised, since they are the motivational factors to start a venture. However, if they are not minimised, one may enter a risky venture unknowingly. Therefore, it is suggested that one should pay careful attention to cognitive biases and their level of influences on one’s decision for successful decision making. Directions for future research As stated above, this study investigated only four cognitive biases in a specific context. The authors believe that the findings of this study can be complemented by further investigation on the following areas: Research could be taken up on entrepreneurs and also by adding some more variables like self-efficacy, affect infusion, escalation of commitment, attributional styles, self-esteem and belief in the law of small numbers. This study could explore decision environment as a mediator or moderator of the relationship between cognitive biases and new venture formation. This study could also examine the influence of counterfactual thinking on new venture formation and when and why entrepreneurs think differently than others as done by Baron (1998). A study could be undertaken on opportunity evaluation under risky condition as done by Keh et al. (2002), continuation of a project as done by Keil et al. (2000), and introduction of new products as done by Simon and Houghton (2003). The results of the above studies could be compared with the results of studies on managers by Mullins et al. (2002) and Busenitz and Barney (1997a, b). It is a known fact that India as a country has a rich tradition and different cultures. This study could focus on how the cultural differences (Boris Urban, 2004), prior experience (Zhai, 2007), social capital (Carolis and Saparito 2006; Carolis et al 2009), alertness and social networks (Singh et al. 1999), impact on entrepreneurial intentions and decision making process. The area is very vast. The present research adds value to the existing literature and taken an initial step towards the understanding the research topic in the Indian context. The unexplored area motivates us to do more research on this area. References Baron RA (1998) Cognitive mechanisms in entrepreneurship: why and when entrepreneurs think differently than other people. J Bus Ventur 13(3):275–294 Baron RA, Markman GD (1999) Cognitive mechanisms: potential differences between entrepreneurs and nonentrepreneurs. Frontiers Entrepreneurship Res 123–137 Baron RA, Markman GD (2000) Beyond social capital: how social skills can enhance entrepreneurs’ success. Acad Manag Exec 14(1):106–116 Boyd NG, Vozikis GS (1994) The influence of self-efficacy on the development of entrepreneurial intentions and actions. Entrepreneurship Theory Pr 18:63 Brockhaus RH Sr (1980) Risk-taking propensity of entrepreneurs. Acad Manag J 23(3):509–520 Brockman BK, Becherer RC, Finch JH (2006) Influences on an entrepreneur’s perceived risk: the role of magnitude, Likelihood, and risk propensity. Acad Entrepreneurship J 12(2):103–121 Busenitz LW, Barney JB (1997a) Differences between entrepreneurs and managers in large organizations: biases and heuristics in strategic decision making. J Bus Ventur 12(1):9–30 Busenitz LW, Barney JB (1997b) Differences between entrepreneurs and managers in large organizations: biases and heuristics in strategic decision-making. J Bus Ventur 12:9–30 Chin WW (1998) The partial least squares approach for structural equation modelling. In: George A, Marcoulides (eds) Modern methods for business research. Lawrence Erlbaum Associates, Mahwah, p 100–116 Chen Z, Dong J (2007) Risk perception and entrepreneur’s decision to start a venture: An empirical study from Optical Valley of China (Wuhan). In: Wuhan (ed) The sixth wuhan international conference on E-Business-innovation management track Clarke CR (1988) Optical distortion. Harvard Business School Press, Boston Cohen JF (2001) Environmental uncertainty and managerial attitude: effects on strategic planning, non-strategic decision making and organizational performance. South African J Bus Manag 32(3):17–32 Cohen J, Cohen P, West SG, Aiken LS (2003) Applied multiple regression/correlation analysis for the behavioral sciences, 3rd edn. Lawrence Jersey, London Cooper AC, Woo CY, Dunkelberg WC (1988) Entrepreneurs’ perceived chance of success. J Bus Ventur 3(1):97–108 Das TK, Teng BS (1997) Time and entrepreneurial risk behavior. Entrepreneurship Theory Pr 22(2):69–88 De Carolis DM, Litzky BE, Eddleston KA (2009) Why networks enhance the progress of new venture creation: the influence of social capital and cognition. Entrepreneurship Theory Pr 33(2):527–545 96 Decision (March 2014) 41(1):87–98 123 De Carolis DM, Saparito P (2006) Social capital, cognition, and entrepreneurial opportunities: A theoretical framework. Entrepreneurship Theory Pr 30(1):41–56 Duhaime IM, Schwenk CR (1985) Conjectures on cognitive simplification in acquisition and divestment decision making. Acad Manag Rev 10:287–295 Faul F, Erdfelder E, Buchner A, Lang AG (2009) Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods 41(4):1149–1160 Forlani D, Mullins JW (2000) Perceived risks and choices in entrepreneurs’ new venture decisions. J Bus Ventur 15(3):305–322 Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39–50 Friedman H (2007) Does overconfidence affect entrepreneurial investment? Whart Res Sch J 42:2–3 Gigerenzer G, Todd PM (2000) Simple heuristics that make us smart. Oxford University Press, USA Hai
r JF Jr, Anderson RE, Tatham RL, Black WC (2006) Multivariate data analysis with readings. Prentice Hall, New Jersey Hatten TS, Coulter M (1997) Small business: entrepreneurship and beyond. Prentice Hall, New Jersey Helm S, Eggert A Garnefeld I (2010) Modeling the impact of corporate reputation on customer satisfaction and loyalty using partial least squares In: Esposito VV, Chin WW, Henseler J, Wang H (eds) Handbook of partial least squares. Springer, New York Hogart RM (1980) Judgement and choice: the psychology of decision. Wiley, New York Houghton SM, Simon M, Aquino K, Goldberg C (2000) No safety in numbers: persistence of biases and their effects on team perception and team decision making. Group Org Manag 25(4):325–353 Hulland J (1999) Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 46.Faul, F., E. Erdfelder, A. Buchner and A.G Lang. Strategic Management Journal 20:195–204 Iacobucci D and Duhachek A (2003) Mediation analysis-round table ACR 2003, presentation at the round table of the ACR conference, Toronto Ivanova E, Gibcus P (2003) The decision-making entrepreneur. Recuperadojunio, 23, 2006 Kahneman D, Lovallo D (1993) Timid choices and bold forecasts: a cognitive perspective on risktaking. Manage Sci 39(1):17–31 Kannadhasan M (2012) Risk analysis in strategic investment decisions: a contingency approach. LAP Lambert Acad Publ, Germany Kannadhasan M, Nandagopal R (2010a) Influence of decision makers’ characteristics on risk analysis in strategic investment decisions. J Mod Acc Audit 6(4):38–44 Kannadhasan M, Nandagopal R (2010b) Do company-specific factors influence the extent of usage of risk analysis techniques in strategic investment decisions? IUP J Financ Risk Manag 7(4):55–72 Keh HT, Foo MD, Lim BC (2002) Opportunity evaluation under risky conditions: the cognitive processes of entrepreneurs. Entrepreneurship theory pr 27(2):125–148 Keil M, Wallace L, Turk D, Dixon-Randall G, Nulden U (2000) An investigation of risk perception and risk propensity on the decision to continue a software development project. J Syst Softw 53(2):145–157 Kerin RA, Varadarajan PR, Peterson RA (1992) First-mover advantage: a synthesis, conceptual framework, and research propositions. J Mark 56(4):33–52 Krueger NF, Brazeal DV (1994) Entrepreneurial potential and potential entrepreneurs. Entrepreneurship Theory Pr 18:91 Kruger J, Evans M (2004) If you don’t want to be late, enumerate: unpacking reduces the planning fallacy. J Exp Soc Psychol 40(5):586–598 Laibson D, Zeckhauser R (1998) Amos tversky and the ascent of behavioral economics. J Risk Uncertain 16(1):7–47 Langer EJ (1975) The illusion of control. J Pers Soc Psychol 32(2):311–328 Levander A, Raccuia I (2001) Entrepreneurial profiling–a cognitive approach to entrepreneurship. In: Stockholm school of economics, Seminar presentation McCarthy A, Schoorman F, Cooper A (1993) Reinvestment decisions by entrepreneurs: rational decision-making or escalation of commitment? J Bus Ventur 8:9–24 Mullins JW, Forlani D, Cardozo RN (2002) Seeing differently, acting differently? New venture perceptions and decisions of managers and successful entrepreneurs. J Res Mark Entrepreneurship 4(3):63–190 Nunnally J (1978) Psychometric theory. McGraw Hill, New York Nutt PC (1993) Flexible decision styles and the choices of top executives. J Manage Stud 30(5):695–721 Palich LE, Bagby DR (1995) Using cognitive theory to explain entrepreneurial risk-taking: challenging conventional wisdom. J Bus Ventur 10(6):425–438 Panzano PC, Billings RS (1997) An organizational-level test of a partially mediated model of risky decision-making behavior. Academy of Management Best Paper Proceedings. Edited by Dosier LN, Keys B. Statesboro, Ga, Georgia Southern University, Office of Publications Russo JE, Schoemaker PJH (1992) Managing overconfidence. Sloan Manag Rev 33(2):7–17 Sa´nchez Garcı´a, J. C., Carballo, T., & Gutie´rrez, A. (2011).The entrepreneur from a cognitive approach. Psicothema, 23(3), 433-438. http://www.psicothema.es/pdf/3906.pdf Schwenk CR (1986) Information, cognitive biases, and commitment to a course of action. Acad Manag Rev 11(2):298–310 Seligman ME, Schulman P (1986) Explanatory style as a predictor of productivity and quitting among life insurance sales agents. J Pers Soc Psychol 50(4):832–838 Shaver KG, Scott LR (1991) Person, process, choice: the psychology of new venture creation. Entrepreneurship Theory Pr 16(2):23–45 Shrader RC, Simon M (1997) Corporate versus independent new ventures: resource, strategy, and performance differences. J Bus Ventur 12(1):47–66 Simon M, Houghton SM (2003) The relationship between overconfidence and theintroduction of risky products: Evidence from a field study. Acad Manag J 46(2):139–149 Simon M, Houghton SM, Aquino K (2000) Cognitive biases, risk perception, and venture formation: how individuals decide to start companies. J Bus Ventur 15(2):113–134 Decision (March 2014) 41(1):87–98 97 123 Singh RP, Hills GE, Lumpkin GT, Hybels RC (1999) The entrepreneurial opportunity recognition process: examining the role of self-perceived alertness and social networks. In Academy of Management Meeting, Chicago, IL Sitkin SB, Pablo AL (1992) Reconceptualizing the determinants of risk behavior. Acad Manag Rev 17(1):9–38 Sitkin SB, Weingart LR (1995) Determinants of risky of decisionmaking behavior: a test of the mediating role of risk perception and propensity. Acad Manag J 38(6):1573–1592 Smith KG, Gannon MJ, Grimm C, Mitchell TR (1988) Decision making behavior in smaller entrepreneurial and larger professionally managed firms. J Bus Ventur 3(3):223–232 Sobel M (1982) Asymptotic confidence intervals for indirect effects on structural equation models. In: Leinhardt S (ed) Sociological methodology. Jossy-Bass, San Francisco, pp 290–312 Taylor SE, Brown JD (1988) Illusion and well-being: a social psychological perspective on mental health. Psychol Bull 103:193–210 Tenenhaus M, Vinzi VE, Chatelin Y-M, Lauro C (2005) PLS Path Modeling. Comput Stat Data Anal 48(1):159–205 Tversky A, Khaneman D (1973) Availability: a heuristic for judging frequency and probability. Cogn Psychol 5:207–232 Urban B (2004). Understanding the moderating effect of culture and self-efficacy on entrepreneurial intentions (Doctoral dissertation, University of Pretoria) Wadeson N (2008) Cognitive aspects of entrepreneurship: decision-making and attitudes to risk. In: Casson M, Yeung B, Basu A, Wadeson N (eds) The oxford handbook of entrepreneurship. Oxford University Press, Oxford, pp 91–113 Wetzels M, Schroder GO, Oppen VC (2009) Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Q 33(1):177–195 Zacharakis AL, Shepherd DA (2001) The nature of information and overconfidence on venture capitalists’ decision making. J Bus Ventur 16(4):311–332 Zhai J (2007) Prior exposure to entrepreneurial experience and risk perception: a comparative study of potential entrepreneurs in Canada and China 98 Decision (March 2014) 41(1):87–98 123 Copyright of Decision (0304-0941) is the property of Indian Institute of Management Calcutta and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use.

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