Risk, proactivity and uncertainties as determinants of the decision to imitate or to innovate

Int. J. Technology Intelligence and Planning, Vol. 3, No. 4, 2007 343

Copyright © 2007 Inderscience Enterprises Ltd.

Risk, proactivity and uncertainties as determinants of the decision to imitate or to innovate

Ana Pérez-Luño* and Ramón Valle-Cabrera The University Pablo de Olavide, CR Utrera Km, 1. 41013 Sevilla, Spain E-mail: [email protected] E-mail: [email protected] *Corresponding author

Johan Wiklund The Jönköping International Business School, SE-511 11 Jönköping, Sweden E-mail: [email protected]

Abstract: This paper empirically tests the impact of uncertainties, proactivity and risk taking on the decision to innovate or to imitate and on an objective and a subjective measure of the company’s performance. Based on a survey of 399 companies, and using the company’s size and age as control variables, our empirical results support the view that proactivity is the most important determinant of the decision of whether to innovate or imitate. Also, we find that the company’s performance is not conditioned by the decision of innovating or imitating, but is rather determined by the company’s size, proactivity and risk taking.

Keywords: innovation; imitation; risk taking; proactivity and environmental uncertainties.

Reference to this paper should be made as follows: Pérez-Luño, A., Valle-Cabrera, R. and Wiklund, J. (2007) ‘Risk, proactivity and uncertainties as determinants of the decision to imitate or to innovate’, Int. J. Technology Intelligence and Planning, Vol. 3, No. 4, pp.343–354.

Biographical notes: Ana Pérez-Luño is an Assistant Professor at Pablo de Olavide University, Spain. She received her PhD in Organization and Strategic Management as ‘Doctor Europeus’. Her interests are in the fields of innovation, knowledge and entrepreneurship. She is currently working on two research projects financed by the Spanish Minister: “Effects of the capabilities, new organizational forms and architecture of human resources in the innovation of products and processes” and “Companies and industrial establishments with foreign capital and activities in Andalusia”. Her interest has led her to work in Jönköping International Business School (Sweden) where she has established long-term projects.

Ramón Valle Cabrera is a Professor at the Business Administration Department of the Pablo de Olavide University, Spain. He has published widely. His papers have appeared in Human Resource Management, Creativity and Innovation Management, Journal of Management Studies, International Journal of Technology Management, The Journal of Management Development, Revue Science de Gestion, International Journal of Human Resource Management, Journal of European Industrial Training, Journal of Business Research, etc.

344 A. Pérez-Luño et al.

His main research areas are the strategic human resource management and innovation management. He is responsible researcher of a National Research Project on organisational innovation.

Johan Wiklund is a Professor of Entrepreneurship at the Jönköping International Business School, Sweden. His research interests include entrepreneurship, growth and competitiveness. His papers have appeared in Strategic Management Journal, Journal of Business Venturing, Journal of Management, Journal of Economic Psychology, Entrepreneurship Theory and Practice, Journal of Management Studies, Swedish Economic Policy Review, etc.

1 Introduction

There is a wealth of studies that claim to demonstrate the positive effect of innovations on a company’s competitiveness. However, the management literature has forgotten that imitation is an organisational behaviour that can also generate sustainable competitive advantages and, apart from very recent exceptions (Lieberman and Asaba, 2006; Zhou, 2006), imitation has only been analysed from the point of view of a company that wishes to avoid being imitated (Barney, 1991), or of a company that may wish to encourage others to imitate it (McEvily et al., 2000). This situation, together with the lack of consensus on the conceptual delineation of the terms ‘innovation’ and ‘imitation’, has led us to raise several research questions such as: What are the differences between innovating and imitating? How do risk taking, proactivity and uncertainties influence the firm’s decision about imitating or innovating? Does the decision of innovating or imitating influences the company’s performance? Our answers to these questions contribute to the literature by giving a clear concept of the terms ‘innovation’ and ‘imitation’. Our second contribution is to empirically demonstrate which factors have the greatest impact on the decision to innovate or to imitate, and on the firm’s performance (measured both in an objective and a subjective way).

Based on the Resources Based view of the firm and on the Contingency Theory, the aim of this article is to provide answers to these questions. Firstly, we delineate the concepts of innovation and imitation. Secondly, we identify and try to empirically demonstrate that proactivity, risk taking and uncertainties are important factors that influence a firm’s decision to innovate or to imitate. Thirdly, we empirically analyse how this decision mediates the relation between these three factors and the company’s performance. All these aims are pursued using firm size and firm age, as control variables.

2 Delimitation of the terms innovation and imitation

A review of the literature demonstrates that the results in the field of innovation have been inconclusive, inconsistent and characterised by limited explanatory power (Wolfe, 1994; Zmud, 1982). One possible explanation for the lack of similarity in the conclusions of researchers is the diverse range of concepts, contexts, characteristics, types, and stages used by different authors to study innovation. As a consequence, the

Risk, proactivity and uncertainties as determinants of the decision 345

current state of the organisational innovation literature offers little guidance to those interested in this concept (Wolfe, 1994). Because of the different value judgements attached to the term, there are many problems in establishing a complete and precise definition of innovation. The only feature common to all the definitions is that innovation implies novelty (Damanpour, 1991; Grossman and Helpman, 1991; Mahmood and Rufin, 2005). With regard to the term imitation, there does seem to be a consensus in the literature that to imitate is to copy (Grossman and Helpman, 1991; Lieberman and Asaba, 2006; Mahmood and Rufin, 2005; Zhou, 2006), although this statement does not clarify what is understood or implied by copying activity or behaviour.

As stated in the Introduction, the first objective of this paper is to conceptually delineate the terms ‘innovation’ and ‘imitation’. The reason for this objective is that there are papers that, in our view, speak of imitation when they are really referring to incremental innovations, or of innovations when they are really explaining imitation behaviour. As an example of the first case, Zhou (2006) speaks of ‘creative followers’ to refer to companies that make incremental innovations based on the radical innovations of others. When defining authors who utilise the term ‘innovation’ to refer to imitations, we would include all those who consider that innovation covers “the adoption of an idea that is only new for the organisation adopting it” (Damanpour, 1991). Arguments supporting our position are developed next.

We have mentioned that the only common element among all the definitions of innovation is that they imply novelty. However, there is no consensus on the remainder of the parameters that define this term (Wolfe, 1994). For example, some authors consider that innovations should represent a positive benefit, especially in the form of an economic improvement, should be of value to the organisation that adopts it and should be internally generated (Mahmood and Rufin, 2005), while others do not mention any of these considerations. Some authors combine what we consider to be the two necessary factors, the generation of new ideas and their resulting commercial success, when referring to innovation. Grossman and Helpman (1991) and Mahmood and Rufin (2005), define innovation as a form of technological development that not only expands a firm’s existing knowledge set but also the existing world knowledge set, whereas imitation is defined as the form of technological development that expands only the firm’s existing knowledge set but not the existing world knowledge set.

The first notable aspect of these definitions is that they state that whereas innovation expands the knowledge existing in the world, imitation expands only the knowledge existing in the company that adopts something new. This is a key determining factor that differentiates between the two concepts, as only the company that innovates actually generates the idea, whereas the rest (imitators) apply knowledge that already exists without making any new combination of such knowledge (Mahmood and Rufin, 2005).

3 Factors determining the decision to innovate

In this section we want to identify the factors that influence the firm’s decision to innovate or to imitate. It has been mentioned in the previous epigraph that an innovator generates new ideas to achieve success. This statement leads us to consider the proactive behaviour of the innovator and the risk-taking necessary to undertake this expansion of knowledge. On the other side, it is reasonable to think that market uncertainties could


also positively influence the decision to innovate. In the following lines, we are going to analyse how these three factors could influence the decision of innovating or imitating.

3.1 Proactivity

Proactivity is considered to be one of the main dimensions of the Entrepreneurial Orientation (Lumpkin and Dess, 1996; Miller, 1983) and refers to companies that are oriented towards action. A proactive attitude or stance is identified with technological leadership and with the desire to be first, or to be a pioneer (Ansoff, 1965), whereas a reactive attitude or stance better describes those companies that are always second, or the imitator (Ansoff, 1965; Porter, 1980). Expanding knowledge on a worldwide scale needs a proactive attitude, whereas expanding it within an organisation (imitation) can be a symptom of a mere reaction to the changes that are taking place in the environment of an organisation. These assumptions lead us to propose the following hypothesis:

H1: The higher the levels of proactivity, the higher the disposition to innovate rather than to imitate.

3.2 Risk-taking

The risk factor has been analysed in the literature from several perspectives (Lumpkin and Dess, 1996; Wiseman and Bromiley, 1996) and it is considered to be one of the main dimensions of the Entrepreneurial Orientation (Miller, 1983; Lumpkin and Dess, 1996). The degree of risk incurred by the innovator is understood to be much greater than that accepted by the imitator. This is because the innovator confronts a change in the knowledge that exists at the global level, and must be able to assume the commercial risk and the technological risk inherent in true innovation (Zhou, 2006). The case of the imitator is different in that imitation assumes only an expansion of internal or local knowledge of an idea that is already functioning in the market; hence, the technological risk is much less and the commercial risk should be lower when the market of the imitator is similar to that being successfully supplied by the innovator. These assumptions lead us to propose the following hypothesis:

H2: The higher the levels of risk-taking, the higher the disposition to innovate rather than to imitate.

3.3 Uncertainty

The nature of the environment that organisations compete in is known to influence their innovative behaviour (Subramaniam and Youndt, 2005). Much of the theoretical and empirical research on organisational environments fails to clearly define the elements involved (Thompson, 1967). There are many dimensions to the environment that require analysis, and different ways of considering each dimension. However, we found that most discussions on innovation begin with a description of the environmental dynamics, sophistication, and hostility faced by a firm (Khandwalla, 1977; Zhou, 2006); or with a description of the environmental uncertainty (Duncan, 1972).

Based on Duncan (1968, 1972) and using two of the dimensions proposed by Khandwalla (1977), we define environmental uncertainty as the interaction between complexity and dynamism. In this sense, the simple-complex dimension is defined as the


number of factors taken into consideration in decision making; and the static-dynamic dimension is viewed as the degree to which these factors in the decision unit’s environment remain basically the same over time or are in a continual process of change (Duncan, 1972). Therefore, the interaction between complexity and dynamism, called uncertainty, could be defined as the lack of information regarding the environmental factors associated with a given decision making situation (for example, deciding between imitating or innovating), not knowing the outcome of a specific decision and inability to assign probabilities with any degree of confidence with regard to how environmental factors are going to affect the success or failure of the decision unit in performing its function. This definition leads us to think that environmental uncertainty will hinder imitating and the only decision that the firm will be able to make is to innovate. Thus:

H3: The higher the levels of uncertainty, the higher the disposition to innovate rather than to imitate.

4 Relation between the decision to innovate and performance

A review of the literature leads us to believe that innovation is the main source of competitive advantage (Barney, 1991). The development of an innovation is usually projected as a contribution to the performance of a company (Damanpour, 1991). Although there is a debate in the literature about whether both innovators and imitators can achieve profits with their strategies (Zhou, 2006), in this research, we propose that innovators will be more profitable than imitators. The reason is that we consider that the proactive focus on satisfying customers’ needs and the risk assumed with this way of acting is rewarded with higher benefits. These assumptions lead us to propose our last hypothesis:

H4: The higher the disposition to innovate rather than to imitate, the higher the performance will be.

As a summary of the mentioned relations, we introduce Figure 1:

Figure 1 Framework


5 Method

5.1 Sample

In order to examine the extent to which firms innovate and imitate, we needed a sample of firms that were actually involved in these activities to some extent. We, therefore, started out with a sampling frame covering the most innovative companies in Spain. The National Statistical Institute of Spain identified five industries of the economy as containing the most innovative firms. These industries are: NACE1 24, Chemical; NACE 32, Radio TV and communication equipment; NACE 33, Medical, precision, and optical instruments; NACE 34, Manufacture of motor vehicles, trailers, and semitrailers; and NACE 35, Manufacture of other transport equipment (Survey of the National Statistical Institute of Spain, 2004). We then used the SABI database (the most comprehensive database of company information in Spain) to identify all companies in these sectors. There were a total of 2,942 firms with more than ten workers of our targets sectors. We included organisations with more than ten employees because they are more likely to have knowledge sharing than smaller companies. We called all of these firms and asked to speak to the R&D manager. In cases where none existed, we instead spoke to the CEO (typically very small firms). In total, 2,854 firms responded (response rate 97%). During the interview, we first ensured that they, indeed, belonged to one of the target sectors as specified in the database and that they had more than ten employees. If this was the case, we then posed the following question:

“Has your company during the past five years introduced any new product to the market? It does not matter if the products are new to the world, to your industry or only new to the company.”

A total of 1,784 firms either did not belong to the sectors identified, did not have more than ten employees or had not introduced any new product in the past five years. These firms were excluded from further enquiry. Among the remaining 1,070 firms, we asked if we could send them our questionnaire via e-mal. Most of the companies agreed to receive the questionnaire by this channel, and we sent it by fax to the few companies that did not have internet yet. In total, 399 firms responded to this questionnaire. This corresponds to a response rate of close to 40% of the firms in our target population (firms with more than ten employees, in certain industries introducing new products over the past five years). An analysis of respondents and non-respondents showed no differences in industry membership, number of employees, and revenues.

5.2 Measures

With the exception of the size and age of the organisation, we measured all constructs in the model with multi-item scales (using the seven-point Likert format) to enhance the content coverage of each one. We took several steps to ensure data validity and reliability. First, we pretested all measures in 25 interviews with R&D managers and asked them to closely review the survey, to ensure the clarity of the questions and to ascertain whether or not the scales were capturing the desired information. We then revised any potentially confusing items before submitting the questionnaire. Finally, we used confirmatory factor analysis to ensure discriminant and convergent validity.


Independent variables. Proactivity and risk-taking were measured using Covin and Slevin’s (1989) scales, while and uncertainty, following Duncan (1972) was measured by the interaction between dynamism and complexity (Khandwalla, 1977). To develop such interaction, we conducted Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) for dynamism and complexity and later, we developed an interacting factor that we called ‘uncertainty’.

Dependent variables. We measured subjective performance with Deshpandé et al. (1993) items and objective performance with the ratio of total revenues divided by total assets. Lastly, we developed a scale to measure the decision to innovate or imitate, based on core conceptual attributes developed by prior research. We first drafted a set of five items from various theoretical works, such as Lieberman and Montgomery (1988), and Zhou (2006). Then, on the basis of in-depth interviews with 25 Research & Development (R&D) managers, we modified the measures and reduced the number of items to four. When an item had to be modified or created, we used Churchill’s (1991) multiple-step and multi-validation methods.

Control variables. This study has kept in mind the effect of two control variables. These have been the size and age of the organisation.

Research has demonstrated that a company’s size may be linked to a greater or lesser tendency for innovation (Cohen and Mowery, 1984; Bantel and Jackson, 1989). Some scholars have established that an increase in the size of the organisation implies a higher number of resources and higher innovative potential, while other scholars argue that small organisations can be more innovative because they have more flexibility, a higher ability to adapt and less difficulty in accepting and implementing changes (Damanpour, 1991). Following these arguments, we consider that the firm’s size will have an influence on the decision of innovating or imitating of the organisation. The organisation size variable was determined by the number of employees in the firm. The values of this variable range from 10 to 35,000 workers, therefore, because of its wide dispersion, a Napierian logarithm of the number of workers in the firm has been used to estimate them. This avoids the scale effect that could be produced if we considered the original variable.

Authors such as Sorensen and Stuart (2000), among others, have pointed out the importance of keeping in mind the organisational age as a variable that can impact on the innovative behaviour. The same here as with the variable ‘size’, there are authors that consider the age of an organisation as an element that may favour the decision of innovating and others that consider that it may harm it (Tushman and Anderson, 1986). For this reason, we have introduced the age of a company as the second control variable of this investigation. As we do with size, and for the same reasons, we use its Napierian logarithm.

Both size and age are also used to control the company’s performance.

5.3 Reliability and validity

We took several steps to ensure data validity and reliability. First, as we explained above, we pretested the survey with 25 interviews with R&D managers and asked them to closely review the survey. We then revised any potentially confusing items. Then, we sent the questionnaire to the R&D managers of all the companies in our sample.


Multiple-item measures were used for most constructs to enhance content coverage. All of our multiple-item constructs achieved Composites reliabilities of 0.76 or higher, indicating strong internal consistency.

The hypotheses were tested using the structural equation modelling method. We followed the two-stage procedure recommended by Anderson and Gerbing (1988). In the first stage, the measurement model was estimated using EFA and CFA in order to test whether the constructs exhibited sufficient reliability and validity. The second stage identified the structural model that best fit the data and tested the hypothesised relationships between the constructs.

The purpose of CFA was to test the unidimensionality of multi-item constructs and to eliminate unreliable items. Items that loaded on multiple constructs and had item-to-construct loadings that were too low were deleted. To ensure discriminant validity, a series of CFA were conducted with covariance matrix as inputs (See Table 1).

Table 1 Squared correlation matrix

F1 F2 F3 F4 F5 F6 F7 F8 F1 1 F2 0.003 0.700 F3 0.002 0.019 0.520 F4 0.002 0.003 0.000 1 F5 0.000 0.021 0.083 0.000 0.770 F6 0.001 0.220 0.297 0.003 0.330 0.590 F7 0.011 0.078 0.000 0.000 0.004 0.013 1 F8 0.009 0.009 0.000 0.000 0.000 0.000 0.104 1 CR 1 0.88 0.76 1 0.95 0.87 1 1

All correlations are significant at p < 0.05 (n = 399). AVE is represented in the Principal Diagonal; CR is Composite Reliability.

6 Results

As presented in Table 2, we conducted two structural models to test the hypothesised relationships between the constructs. In Model 1 we presented all the possible equations and in Model 2, we only presented those equations that best fit the data.

As we can see in Model 2 of Table 2, only proactivity has a significant positive influence on the decision to innovate. This means that from the first three hypotheses, only H1 is supported. There is no significant control variables for the decision of innovation or imitate.

Performance is not determined by the decision to innovate or imitate. Thus, we do not find support for H4. This is quite important because it supports the arguments that claim that imitation could be as important as innovation for competitiveness (Zhou, 2006). Both proactivity and risk taking have a positive and significant influence on the subjective measure of performance. On the side of the control variables, size influences both the objective and the subjective measure of performance but in opposite directions.


Therefore, while side positively influences the subjective measure of performance, it negatively influences the objective measure.

Table 2 Estimated coefficients and model fit indices

Latent factors Dependents Independents Coeff1a (t-value) Coeff2a (t-value)

Proactivity 0.504 (5.811) 0.460 (6.189) Risk-taking 0.022 (0.037) – Uncertainty –0.035 (–0.550) – Size –0.044 (–1.483) –

Decision of innovating or imitating

Age 0.026 (0.479) – Decision I-I –0.112 (–1.313) – Proactivity 0.603 (5.956) 0.523 (6.274) Risk-taking –0.147 (–2.277) –0.139 (–2.420) Uncertainty 0.051 (0.746) – Size 0.155 (4.434) 0.168 (4.826)

Subjective performance

Age 0.038 (0.682) – Decision I-I –0.060 (–1.041) – Proactivity 0.061 (1.037) – Risk-taking –0.035 (–0.647) – Uncertainty 0.043 (0.765) – Size –0.045 (–1.749) –0.054 (–2.029)

Objective performance

Age –0.064 (–1.327) –

Overall fit index Model 1 Model 2 χ2 (df) 217.075 (0.00) 110.940 (0.012) Satorra–Bentler χ2 (df) 189.983 (0.00) 97.456 (0.089) GFI 0.942 0.965 AGFI 0.915 0.948 CFI 0.956 0.988 Robust CFI 0.960 0.992 RMSEA (90% CI) 0.052 (0.042, 0.061) 0.031 (0.015, 0.044) Robust RMSEA (90% CI) 0.045 (0.035, 0.055) 0.023 (0.000, 0.038)

acoeff1 = Model 1 parameters; coeff2 = model 2 parameters.

7 Conclusions

Although innovation has generated substantial attention in the literature, few studies have analysed the determinants of the decision of innovating or imitating. Also, the scarce number of studies is mainly theoretical. Furthermore, the few studies that empirically analyse whether to innovate or to imitate have not found much support for their


assumptions (Zhou, 2006). This study seeks to fill this void, examining the determinants of the decision to innovate, including the contingency of the environmental environment and the firm’s proactivity and risk taking.

Our first main empirical contribution, consistent with the leader and follower literature (Lieberman and Montgomery, 1988), is that we find that the decision to innovate or imitate is not the determinant of the firm’s performance. Rather, it is the company’s proactivity which best determines the decision to innovate.

Our second main empirical contribution comes from the analysis of the control variables. The size of the company has a double influence. The smaller the firm is, the greater its objective measure of performance. This result could be explained by the way in which we have measured the objective performance (of total revenues divided by total assets). However, when the dependent variable is a subjective measure of performance, the sing of its relation with size is positive. This may be explained by the fact that big firms think that they have the resources and capabilities to absorb all the benefits of the innovation process as they will be able to use production, distribution, and marketing synergies from other products or services to sell their new product. The company’s age does not influence the decision of innovate or imitate. Lastly, it is important to highlight the significant positive effect of proactivity on the company’s subjective measure of performance and the significant negative effect of the risk taking on the company’s subjective measure of performance. This issue could lead us to think that too risky projects are not as profitable as some people think.

8 Limitations and further research

Proactivity is the most important factors for a firm’s success. This statement could be useful for those firms that may not have the strength to innovate but are capable of acting in a proactive way by copying other firms’ innovations when they detect an unsatisfied need. It would be desirable to empirically analyse whether those who copy products from competitors in other countries or markets to satisfy their target market are more profitable that those that copy competitors already working in their target market. It could be determined whether those companies which have the proactive behaviour to look outside for new product opportunities to satisfy their customers’ needs are more profitable than those that wait in their market until another firm has launched the product.

As a limitation we could state that we have analysed firms from five different industries. We must indicate that, as we have only analysed five sectors which are innovative, we cannot generalise the results outside these industries. For future researchers, it could be interesting to control for the effect of the different industries.

Lastly, we want to reiterate that uncertainty does not appear to have a significant effect on either the decision to innovate or on performance. These findings should be analysed in more depth.

Acknowledgement

Financial support from the Ministry of Science and Technology of Spain, grant SEC2003-07741, is gratefully acknowledged.


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Risk, proactivity and uncertainties as determinants of the decision to imitate or to innovate

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