The role of stimulating employees’ creativity and idea generation in encouraging innovation behaviour in Irish firms

Since the concepts of idea generation, creativity and innovation are used interchangeably in the literature (Ford, 1996; Shalley et al., 2004), it is important to analyse them in the context of this research. An idea is classified as being new if it is new to the firm. New ideas can either be novel ideas or they can be copied from other places. The term creativity, on the other hand, refers only to useful, novel and relevant ideas (Amabile, 1996; Heinze, 2013; Rank et al., 2004). Mumford (2012) defines creativity as the ‘production of high quality, original and elegant solutions to problems’. For an idea to be considered as creative, it must be useful, relevant and novel at the same time. Beheshtifar and Kamani-Fard (2013) argue that an individual’s level of creativity is driven by their expertise, their creative-thinking skills and their level of motivation. Amabile (2013) argues that managers can stimulate and facilitate organisational creativity, where organisational creativity is defined by Woodman et al. (1993) as the ‘creation of a valuable, useful new product, service, idea, procedure or process by individuals working together in a complex social system’. Cirella and Shani (2012) argue that creativity within organisations happens when people work together to trigger ideas through dialogue, debate and conflict. Similarly, Baer et al. (2010) argue that organisations rely on both team-based structures and internal competition between these teams to elicit creativity.

Innovation is the successful application of creativity and as a result creatively is said to ignite innovation (Amabile, 1996; Çokpekin and Knudsen, 2012; Mumford, 2000; Shalley et al., 2004). Innovation is not the same as creativity (Amabile, 1996; Lewis and Wright, 2012). When a company decides to introduce a new good or service, it creates lots of ideas, and then picks the best of these for development (Girotra et al., 2010). In other words, innovation involves the crafting of creative ideas into new products, processes or services (Mumford, 2012; Nyström, 1979). Given the importance of idea generation and creatively for innovation, extensive research has studied factors stimulating employee creativity (Beheshtifar and Kamani-Fard, 2013; Ford, 1996; Woodman et al., 1993) and has acknowledged the importance of a supportive work environment (Amabile, 1996). Despite this Çokpekin and Knudsen (2012) and Puccio and Cabra (2010) argue that there is still little evidence to show whether boosting creativity results in innovation, and Ettlie and Reza (1992) question whether different types of stimuli are required to motivate the different types of innovation. In this paper, we address both these questions.

Since new ideas and creativity are prerequisites for innovation, and innovation is essential for survival and growth in the modern economy, it is important that organisations manage and develop these attributes. One primary model of creativity is the Componential Model of Organisational Creativity (developed by Amabile (1988) and updated by Amabile (1996) and Amabile and Mueller (2008)). This model argues that creativity arises through the coming together of four elements: three relate to the individual – knowledge (all the relevant understanding an individual brings to bear on a creative effort), creative thinking (how people approach problems) and motivation (the passion and interest the individual has for their work), and one relates to the external environment in which the individual works. The model identifies a number of ways creativity can be stimulated by adapting these four elements. In this section, we begin by briefly examining knowledge, creative thinking and motivation before turning to how the collective introduction of these elements can create a supportive and stimulating environment which fosters creativity and innovation.

Gardner (1993) argues that two types of knowledge are required for creativity. Firstly, employees need to build their technical expertise over time. This knowledge then acts as a solid foundation from which creativity can emerge. Without this knowledge base, Simonton (1980) suggests that individuals cannot be creative. Secondly, employees need to be able to recognise opportunities and combine previously disparate elements in new ways. Johansson (2004) argues that a balance is needed between these elements and Adams (2006) proposes that one way to achieve this balance is by building multi-disciplinary teams. This mirrors the general innovation literature, which suggests that team processes and behaviours such as reflexivity and knowledge sharing are important predictors of innovation (DeDreu, 2002).

Knowledge is a unique asset to a firm; in the right hands, it can create immense value, however, people can leave the business at any moment in time, taking all their knowledge with them. It is not easy to create or share knowledge. While it is the ultimate economic renewable, and its value comes from sharing it with others, it is very difficult to encourage and facilitate this sharing. Knowledge sharing is an important dimension of innovation, particularly the sharing of new, diverse knowledge. Nahapiet and Ghoshal (1998) model learning and argue that it takes place through the combination and exchange of knowledge. Knowledge can be combined by merging knowledge that was previously unconnected or by finding novel ways of blending pre-existing knowledge. This process is often dependent on the exchange of information, especially where resources are held by different parties. Moran and Ghoshal (1996) contend that for knowledge sharing to be effective, there must be an opportunity for employees to share information. This can be done through the structure of the firm where a flat structure with autonomy and work teams has been found to promote innovation, while specialisation, formalisation, standardisation and centralisation inhibit innovation (Martins and Terblanche, 2003).

When innovation is mandated professionals such as researchers, engineers, designers and programmers often collaborate on assigned or original projects (Sundstrom et al., 1990). Mohrman et al. (1995: 6) define a team as ‘a group of individuals who work together to produce products or deliver services for which they are mutually accountable’. They go on to propose that ‘team members share goals and are mutually held accountable for meeting them, they are interdependent in their accomplishment, and they affect the results through their interactions with one another’. Groups composed of people with differing professional backgrounds, knowledge, skills and abilities will be more innovative than those whose members are similar, because they bring differing perspectives on issues to the group (Paulus, 2000; West, 2002). Their divergence of views can create multiple perspectives, which if managed correctly can lead to more innovative actions (Paulus, 2000). The ability to rotate to different projects and positions within a firm increases the level of flexibility within a firm and it is values like flexibility, freedom and cooperative teamwork which promote creativity and innovation (Martins and Terblanche, 2003). On this basis we hypothesise that:

H1: Knowledge generation stimuli, such as work teams and job rotation, are positively related to innovation output.

Since creativity involves the production of high-quality, original, and elegant solutions to complex, novel, ill-defined or poorly structured problems (Mumford and Gustafson, 1988), it is essential that individuals are able to combine existing elements of knowledge or understanding in new ways. This calls for creative thinking. Mumford et al. (2012) argue that creative thinking involves multiple, complex processing operations, and the execution of these processes depends on the knowledge available to the individual at the time and the procedures he/she uses when executing the processes. They argue that the creative-thinking process begins with problem definition. This is followed by information gathering, information organisation, conceptual combination, idea generation, idea evaluation, implementation planning and solution monitoring. These processes operate in a dynamic fashion and failure to adequately complete any one stage will lead individuals to step-back to early processing activities. Adams (2006) argues that the creative mind can be enhanced by environments or efforts that encourage individuals to generate new variations and new combinations of ideas.

Brainstorming is one of the most popular techniques used to induce creativity (Adams, 2006). Its purpose is to generate a limited number of good ideas which can be developed further with a view to implementing them (Nijstad and De Dreu, 2002). Johansson (2004), amongst others, argues that group brainstorming can be particularly effective when individuals are allowed 15–20 minutes to think individually and write their ideas on an anonymous piece of paper which is then handed to a facilitator. All ideas can then be discussed openly with a view to considering whether each one could be feasible rather than seeking to criticise or find the reasons why it wouldn’t work. Rietzschel et al. (2006) and West (2002) claim that this type of group brainstorming can outperform individuals working alone, particularly on intellective tasks whilst Paulus (2000) reports that sharing ideas with others in a team can increase the chances of producing novel ideas. We hypothesise that

H2: Creative thinking stimuli, such as brainstorming, are positively related to innovation output.

While creative thinking depends a lot on an individual’s characteristics (e.g. independence, self-discipline, risk-taking attitude, willingness to deal with failure), Amabile (1996) argues that these skills can be increased with education and training. The acquisition of information about the job enables employees to broaden and enrich their knowledge of the job task, task problems and the job context. Weisberg (1999) presents theoretical and empirical evidence which suggests that work-based learning strategies promote knowledge acquisition and that knowledge acquired in this way boosts the potential to create and generate new and useful ideas. Since the innovation process is knowledge intensive and since employees may need to acquire new knowledge to participate in the development and implementation of ideas, we anticipate that job-specific-training is positively related to innovation output. Using this theoretical and empirical evidence we hypothesise:

H3: Creative thinking stimuli, such as job-specific training, are positively related to innovation output.

The third element in the Componential Theory of Creativity is motivation. Mitchell (1982) defines motivation as ‘the psychological processes that cause the arousal, direction and persistence of behaviour’. Ames (1992) argues that motivation is the reason individuals behave in a particular manner in a certain situation. Motivation exists as part of one’s goal structures, one’s beliefs about what is important, and it determines whether or not one will engage in any given pursuit. Deci (1975) separates motivation into extrinsic and intrinsic motivation. While extrinsically motivated people do the work because of some threat (e.g. evaluation, surveillance, competition with peers) or because there is some promise of reward (e.g. money, promotion), intrinsically motivated people do the work because they find it interesting, involving, exciting, satisfying or personally challenging. Lindenberg (2001) further sub-divides intrinsic motivation into normative and hedonic motivation. The former group act because they want to comply with personal, social or organisational norms, while the latter group act because they find the task challenging, exciting and enjoyable. Amabile (1996) argues that people are more creative when they are hedonically intrinsically motivated as they are more likely to explore various pathways and alternatives.

Incentives are commonly used in business to motivate employees and to align their wants with the needs of the employer (Laffont and Martimort, 2002). The purpose of an incentive is to provide the decision maker with a reason to follow a particular course of action. Brynjolfsson and Mendelson (1997) argue that the best way for employers to induce the optimal level of effort from their employees is to base their salary directly on the effort they exert. However, paying according to effort requires that the employer can monitor the employee perfectly and cheaply. In many cases, it is not possible to do this and therefore carefully designed incentive mechanisms are needed. The behavioural literature on compensation systems warns that using incentives often leads to unintentional and dysfunctional consequences. Asymmetric information and unintended consequences can make incentives much more complex than the people offering them originally expected and can lead either to unexpected windfalls or to disasters. The problem with incentive contracts is not that they don’t work but that they work too well. Agents do exactly as the incentive desires. For example, contracts that promote quantity often result in poor quality products or if bonuses are more dependent on timely project completion than on discovered failures, the employee may over invest in expedience at the expense of quality (Wash and MacKie-Mason, 2006).

Intrinsically motivated people and extrinsically motivated people need to be incentivised in different ways. While hedonically intrinsically motivated employees may be more likely to be creative, they can be demotivated by certain types of extrinsic rewards. Amabile (1996) identifies two types of extrinsic motivators: non-synergistic motivators and synergistic motivators. The former are controlling and they are likely to have negative impacts on creativity. For example, when monetary rewards are given for meeting specific targets, then employees only do what is necessary to meet that target. Synergistic motivators, on the other hand, support creativity and can be informational or enabling motivators. These motivators include things like frequent constructive feedback on the work, reward and recognition for creative ideas and clearly defined overall project goals. This idea is supported by recent survey findings which show that non-financial incentives are more effective and valued by employees than financial incentives. A survey, conducted by MacKinsey Quarterly, found that praise is the best incentive as it makes employees feel like the firm appreciates them (Dewhurst et al., 2009). Other incentives valued by employees are leadership attention (for example, one-on-one conversations), and a chance to lead projects or task forces. We hypothesise that

H4: Non-financial incentives support hedonically intrinsically motivated employees and hence increase creativity and innovation whilst financial incentives have less of a positive impact.

The work environment is important when motivating idea generation, creativity and innovation. Mumford (2000) argues that organisations should consider multiple interventions that take into account the individual, the group, the organisation and the strategic environment when selecting interventions intended to enhance creativity. This view is supported by the Computational Model of Creativity (Amabile, 1988, 1996; Amabile and Mueller, 2008) which argues that creativity comes from the bringing together of knowledge, creative effort, creative thinking and motivation. Mauzy et al. (2003) argue that effective information flow within an organisation is critical and according to Johansson (2004) creative success is most likely to occur where widely different ideas bump into each other. Cummings and Oldham (1997) find that organisations, which provide a supportive innovation context for creativity, tend to reap greater benefits from employees who are innately creative while Deci and Ryan (1985) find that management can motivate employees’ creativity. They note that support that pays attention to the employees’ needs enhances curiosity and work effort while simultaneously reducing their fear of making a mistake thereby encouraging risk-taking (Madjar and Ortiz-Walters, 2008; Shin and Zhou, 2003) and facilitating creativity. Work colleagues also play a role in creating a supportive work environment. Zhou and George (2001) show that the information and skills of co-workers generates feedback, new information and the elaboration of unusual ideas thus enhancing creativity. Mauzy et al. (2003) suggest that job rotation can help create this stimulating work environment. Adams (2006) argues that less-structured and less-bureaucratic firms are more likely to facilitate such information flows as there is likely to be more focus on idea generation and sharing rather than on career progression. Amabile (2013) affirms that many techniques can be used to stimulate a creative work environment such as the creation of work teams that are collaborative, diversely skilled and idea-focused; the creation of incentives that recognise creative work; and through creating norms for actively sharing ideas across the organisation. On the other hand, political problems within the firm, excessive time pressures and harshly criticising new ideas can block creativity. We hypothesis that

H5: Supportive work environments (i.e. those introducing more than one idea generation and creativity stimuli) enhance creativity and hence result in higher innovation output.

In the next section, we specify a model which allows us to test whether work teams and job rotation by stimulating knowledge generation increase the level of innovation within firms (H1), whether brainstorming and job training by stimulating creative thinking increase the level of innovation within firms (H2 and H3), and whether financial or non-financial incentives motivate employees to become more creative and hence increase the innovation within firms (H4). We also test whether a combination of these stimulating factors create a more supportive work environment which facilitates greater levels of innovation (H5).

The empirical approach adopted by this paper is the estimation of an innovation production function, which is prevalent in the literature on the drivers of innovation in firms (Doran and O’Leary, 2011; Freel, 2003; Love and Mansury, 2007; Roper et al., 2008). We extend the standard innovation production function to include measures of creative stimuli. Our innovation production function follows the standard form and is displayed as equation (1): IOi=α<!-- a -->0+SFiα<!-- a -->+R&<!-- & -->Diβ+Niχ<!-- ? -->+Ziλ<!-- ? -->+ε<!-- e -->i. $$I{O_i} = {\alpha _0} + S{F_i}\alpha + R\& {D_i}\beta + {N_i}\chi + {Z_i}\lambda + {\varepsilon _i}.$$(1)

The dependent variable IO_i is a binary indicator of whether firm i innovated. We consider four types of innovation: (i) product, (ii) process, (iii) organisational and (iv) marketing. α₀ is a constant term. We include four sets of independent variables: the first set, SF, is the key set of interest and is used to test our first four hypotheses. The remaining three sets of variables (R&D, N and Z) are included as controls in the estimation.

SF_i is a N*6 matrix of variables indicating the type of stimulating factors utilised by firm i to produce innovation output. The six stimulating factors considered are (i) multidisciplinary or cross-functional work teams, (ii) job rotation of staff to different departments or other parts of their enterprise group, (iii) brainstorming sessions, (iv) training employees on how to develop new ideas or creativity, (v) financial incentives for employees to develop new ideas and (vi) non-financial incentives for employees to develop new ideas, such as free time, public recognition, more interesting work, etc. α is a 6*1 vector of coefficients showing the impact of these factors on the likelihood of a firm innovating. These six variables are used to test hypotheses H1 to H4 specified in Section 2.

Since it is widely established in the literature that R&D is an important driver of innovation activity (Feldman, 1999; Love and Mansury, 2007; Mansury and Love, 2008), we control for this variable in our model. R&D_i is an N*2 matrix of variables representing the R&D effort of the firm which includes intramural and extramural R&D performance. β is a vector of associated coefficients. The second control is N_i, an N*4 matrix of binary variables which indicate whether firm i engages in backwards, forwards, horizontal or public networking. Freel (2000, 2003) notes that external networking may assist firms in accessing knowledge pertinent to innovation. χ is the 4*1 vector of associated parameters. Our last control, Z_i, represents firm-specific factors that might explain heterogeneity in the innovation performance of firms. λ is the vector of associated coefficients. Z_i contains information on firm size, whether it is Irish owned or not and the sector in which firm i operates. These have all been previously shown to have an impact on the innovative performance of firms (Cohen and Klepper, 1996; Cohen et al., 1987; Pavitt, 1984; Roper, 2001).

As noted previously we consider four distinct types of innovation. The standard practice in the literature would be the estimation of four distinct probit models (Doran and O’Leary, 2011; Roper et al., 2008). However, it is likely that individual heterogeneity not captured by the independent variables could impact on the likelihood of firms engaging in numerous forms of innovation simultaneously. This upward bias (or indeed downward bias if the firm possesses unobserved characteristics which impede innovation performance) in innovation likelihood will manifest in the error terms, ε_i, being correlated across the four regression equations. This may result in biased estimates. Therefore, in order to take account of this potential bias, we estimate a multivariate probit model, which estimates the four equations taking account of potential correlation across the error terms (Cappellari and Jenkins, 2003, 2006).

In addition to these stimuli individually driving innovation, it is possible that engaging in a variety of activities may create a more supportive work environment and hence increase the likelihood of innovation (H5). In the context of geographical proximity and networking, Storper and Venables (2004) note that engaging with an increasing variety of individuals and ideas can stimulate the creative process within firms. McCann and Simonen (2005) test this in the context of the variety of external interaction agents firms engage in. Based upon this concept of variety stimulating innovation, we hypothesise that the variety of creative processes undertaken by the firm may stimulate the innovation process within the firm. In order to test this hypothesis, we modify equation 1 in line with McCann and Simonen (2005): IOi=α<!-- a -->0+NSFiα<!-- a -->+R&<!-- & -->Diβ+Niχ<!-- ? -->+Ziλ<!-- ? -->+ε<!-- e -->i. $$I{O_i} = {\alpha _0} + NS{F_i}\alpha + R\& {D_i}\beta + {N_i}\chi + {Z_i}\lambda + {\varepsilon _i}.$$(2)

Where all variables are defined as above with the exception that NSF_i is now a N*6 matrix of six dummy variables indicating the number of stimulation factors implemented by the firm. Where the first dummy variable takes a value of 1 if the firm engages in any one of the stimulation factors considered and 0 otherwise. The second dummy variable takes a value of 1 if the firm utilises two stimulation factors and 0 otherwise. This continues for the third, fourth and fifth dummy variables and the final dummy variable takes a value of 1 if the firm engages all forms of stimulation factors and 0 otherwise. The coefficients thus provided in α indicate whether increasing the variety of stimulation increases the likelihood of innovation. Again, equation (2) is estimated using a multivariate probit model.