The influence of ambient noise on the relationship between cognitive style and innovative performance

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The influence of ambient noise on the relationship

between cognitive style and innovative performance

Thesis MSc Business Administration, Strategic Innovation Management

Student:

Derk Venema

Student number:

S2400669

First supervisor:

G. Bălău

Second supervisor:

D.L.M. Faems

Date:

June 23, 2014

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1. Introduction

The importance of innovation for the survival of firms has been long recognized (Brown & Eisenhardt, 1995; Christensen & Brown, 1996). Needless to say, employees play an essential part in the innovation process. Many even argue that individuals are the primary objects that facilitate and drive learning and thus innovation (Sadler-Smith & Badger, 1998). Because of this, individual innovativeness is seen as a way to achieve organizational success (Van de Ven, 1986). In addition, Mirion, Erez and Naveh (2004) found that individual innovativeness does not conflict with behavior that promotes quality and efficiency of other work. This makes the concept of individual innovative performance and variables such as personality traits that influence this performance interesting to study, especially since the individual as the level of analysis seems to be underrepresented in innovation literature as compared to the firm as level of analysis (Crossan & Apaydin, 2010).

There are several studies that indicate the importance of personality traits on individual innovation. One way of these studies is performed by Hammond et al. (2011) who looked at the influence of the Big Five factors. According to the authors, the factor „openness to experience‟ most clearly contributes to innovative performance. Another viewpoint is that of Oldham and Cummings (1996), who looked at creativity-relevant personal characteristics such as insightfulness and unconventionalism. A different way of studying this relationship is by looking at the influence of cognitive styles on innovative performance. Scott and Bruce (1998) studied this relationship between cognitive styles and innovative performance and found that an analytical cognitive style has a negative effect on innovative performance (innovative performance decreases when the analytical cognitive style increases) and that an intuitive cognitive style has a positive effect on innovative performance (innovative performance increases when the cognitive style increases). The classification of the different cognitive styles into the analytical and intuitive style is a common one (e.g. Epstein et al., 1996).

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4 those with an analytical cognitive style. Rietzschel, De Dreu and Nijstad (2007) studied the moderating influence of fear of invalidity on the relationship between personal need for structure (related to the analytical cognitive style) and creative performance, which is closely related to innovative performance. They found that under high levels of fear for invalidity, personal need for structure had a negative influence on creativity. However, under low levels of fear for invalidity, this influence was positive.

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5 cognitive style. Depending on the findings, a different office layout might be considered to optimize innovation processes.

This study seeks to advance innovation literature by including the moderating influence of ambient noise on the relationship between cognitive style and innovative performance, helping to understand why individuals deliver different levels of innovative performance. The research question that follows from this literature gap is: “To what extent does ambient noise impact the relationship between cognitive style and innovative performance?”. The structure of this article is the following, first a theoretical background is provided and hypotheses are developed. Then the methodology is explained and the results are given. This thesis is completed by the discussion.

2. Theoretical background

In order to create an overview of current literature and to clarify the relevant variables, earlier research is explained and their definitions of the variables are given. Hereafter, the relationships between the different variables are explained and hypotheses are developed.

Innovative performance

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6 creativity, which is about coming up with novel and useful ideas (Amabile, 1997) and which might explain why according to West and Farr (in Scott & Bruce, 1994) creativity and innovativeness are often used interchangeably and the differences appear to be subtle.

Cognitive style

Cognitive style is a concept from cognitive psychology that entails individual consistencies in perception, memory, thinking and judgment (Messick, 1994). Previous research has shown or proposed that cognitive styles are related to different performance factors such as differences in learning at both the individual and organizational level (Hayes & Allinson, 1998; Riding & Douglas, 1993), team performance, team satisfaction (Basadur & Head, 2001), self-assessment (Kickul et al., 2009) and of course individual innovative performance (Scott & Bruce, 1994; Scott & Bruce, 1998; Sadler-Smith & Badger, 1998; Payne, Lane & Jabri, 1990). These studies indicate that cognitive styles play an important role in the functioning of employees and thus organizational behavior, making it an interesting concept to study. Even though cognitive styles are relatively stable over time, they are not unchangeable (Witkin et al., 1997). Task related context might cause variation in the strength of the presence of the respective styles (Guastello, Shissler, Driscoll & Hyde, 1998), making priming of a cognitive style possible, which can be useful in an experiment. This study aims to help obtain a greater understanding of the influence that (primed) cognitive styles can have on innovative performance.

To understand what the concept of a cognitive style exactly is, existing literature will be described. Sadler-Smith and Badger (1998) regard cognitive styles as “qualitatively different ways of organizing and processing information”. Witkin et al. (1997) refer to it as “individual differences in how we perceive, think, solve problems, learn, relate to others etc.” and make the important notion that cognitive styles are not concerned with the content of cognitive activity, but rather with its form.

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intuition-7 analysis (Allinson & Hayes, 1996), field-dependence and field-independence (Witkin, 1949; Witkin, 1997) and adaption-innovation (Kirton, 1984) theories. That dual processing is a more contemporary approach is evident from the fact that recent factor analysis of the Cognitive Style Index which is used to measure the intuition-analysis unifactoral bipolar scale, shows that it might be used more appropriately as two separate unipolar scales (Hodgkinson & Sadler-Smith, 2003a; Hodgkinson & Sadler-Smith, 2003b).

Dual processing theories explain that there are two different modes of processing (Evans, 2008), for which in this thesis the terms intuitive and analytical will be used. This means that no individual is only analytical, or only intuitive. Every individual has a balance of these two styles, while one is mostly dominant. Evans describes that almost all scholars agree that intuitive processes are unconscious, rapid and automatic whereas analytical processes are conscious, slow and deliberate. This difference is caused by the fact that analytical processes require access to a person‟s working memory and is because of this capacity limited. Intuitive processes on the contrary do not require working memory access, which explains the higher speed of the processes.

Ambient noise

In their search of ways to improve innovation, open office designs have become more popular with firms. Companies expect that this type of office design leads to more communication, collaboration and thus innovation (Lawrence, 2007). An obvious consequence of this type of office design is the emergence of ambient noise. Ambient noise in offices can manifest itself in the form of for example conversations, ringing phones and printers.

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8 distracted by these stimuli because of the fact that ambient noise requires employees to process the ambient information in addition to the information that is required for the task employees are working on (Loewen & Suedfeld, 1992). This cannot be done simultaneously due to the fact that cognitive capacity is limited. Consequences of prolonged exposure of employees to ambient noise include dissatisfaction with their working environment and job (Sundstrom, Town, Rice, Osborn & Brill, 1994) and it even has a modest negative effect on psychological stress and leads to motivational deficits (Evans & Johnson, 2000). There does not appear to be a consensus in the literature whether a change from a traditional to an open office design is favorable. Oldham and Brass (1979) found that this change harms employee satisfaction and motivation, while Zalesny and Farace (1987) found mixed results for different categories of employees and Meijer et al. (2009) found no long-term productivity differences. It is thus unclear whether the advantages of easier communication and collaboration outweigh the disadvantages that ambient noise brings in an environment that aims for innovation. This study can contribute to answering this question by researching the moderating influence that ambient noise has on the relationship between cognitive style and innovative performance.

Cognitive style and innovative performance

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9 innovative performance. This means that the more an individual uses the intuitive cognitive style, the higher his innovative performance will be.

The analytical cognitive style on the other hand is expected to be negatively related to innovative performance, meaning that the more an individual uses the analytical cognitive style, the lower his innovative performance will be. This is expected, because the style it is more abstract, domain specific and rule based (Evans, 2008), working within established methods or procedures (Scott & Bruce, 1994). Individuals with this cognitive style process information according to established methodology (Scott & Bruce, 1998), meaning they do not search out novel solutions. Instead of doing things differently, individuals with the analytical cognitive style focus on doing things better (Kirton, 1984). Because of this, it is likely that the analytical style leads to more incremental, conventional solutions instead of innovative ones.

Hypothesis 1a: The intuitive cognitive style is positively related to innovative performance.

Hypothesis 1b: The analytical cognitive style is negatively related to innovative performance.

Ambient noise, cognitive style and innovative performance

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10 information. The processing of ambient noise that is the result of open office designs can be that concurrent memory load. As a result, it is expected that ambient noise will affect the relationship between the analytical cognitive style and individual innovative performance. Because the analytical cognitive style is dependent on working memory, it is expected that ambient noise will have a moderating influence on the relationship between the analytical cognitive style and innovative performance. Ambient noise will influence the capacity limited working memory, taking up available memory, which will affect the relationship between the analytical cognitive style and innovative performance in such a way that the negative relationship gets more negative. In other words, because of ambient noise individuals with the analytical cognitive style will have a lower innovative performance as compared to without ambient noise. In addition, it is expected that ambient noise will not affect the relationship between the intuitive cognitive style and innovative performance, since that cognitive style is not dependent on working memory. Even though ambient noise will also take up working memory from individuals with the intuitive cognitive style, since the cognitive processes are unconscious, rapid and automatic (Evans, 2008) and thus independent of working memory, ambient noise will not hamper innovative performance.

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11 cognitive capacity due to ambient noise) has a similar effect on innovative performance. The following are two opposing hypotheses that explain the workings of the concept according to Kruglanski and Webster (1996). The first hypothesis states that due to situational factors (in this case ambient noise), individuals‟ cognitive capacity is depleted which forces them to result to easy solutions, meaning following their existing train of thought. The second hypothesis states that because processing work related and ambient noise information is costly, an individual is motivated to choose the easy solution.

Hypothesis 2a: Ambient noise has no impact on the relationship between the intuitive cognitive style and innovative performance.

Hypothesis 2b: Ambient noise has a moderating influence on the relationship between the analytical cognitive style and innovative performance in such a way that the relationship gets more negative.

The dependent variable innovative performance, the independent variable cognitive style and the moderating variable ambient noise from the research question are shown in the conceptual model below.

Figure 1.

Conceptual model

Cog Cognitive style Innovative performance

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3. Methodology

This study has a theory-testing approach with an experimental setup. This approach is chosen because of the fact that a gap in the literature in the form of the unknown influence of ambient noise on the relationship between cognitive style and innovative performance exists. In addition, following the reasoning of Scott and Bruce (1994) that people might employ different cognitive styles in different situations, this study relies on priming of a cognitive style in order to ensure that the participants are using a specific cognitive style. This has an advantage over measuring cognitive style, since then you are not sure whether participants are actually using the previously measured style during the experimental task. A changed mindset such as a primed cognitive style is sticky and remains active for a longer period than for the initial task they need to perform, allowing this primed cognitive style to also affect unrelated tasks (Hamilton et al., 2011). Since the moderating effect of ambient noise is also included in this research, an experimental setup is necessary to replicate identical conditions for every participant. The experiment includes four conditions (analytical priming and noise, analytical priming and no-noise, intuitive priming and noise, intuitive priming and no-noise) ensuring a minimum of 80 participants is required. Taking possible non-response or invalid results into account, at least 100 participants was the aim.

Procedure

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13 purpose of distracting the participant with ambient noise (in the no-noise conditions no noise is heard) that will affect the participant‟s working memory load and will consequently affect the participants with a primed intuitive analytical cognitive style‟s ability to show innovative performance.

Manipulations

In order to prime the participants in the first stage of the experiment with a certain cognitive style, two different short tasks were used. One with feelings related questions for the intuitive-priming condition, one with mathematical questions for the analytical-priming condition. This method is based on an experiment by Hsee and Rottenstreich (2004). For the intuitive-priming condition the participants were asked five questions with the goal of them examining and reporting their feelings and consequently priming them with an intuitive cognitive style. Example questions are “When you hear the word baby, what do you feel?” and “When you hear the word music, what do you feel?”. The questionnaire that was used for the analytical-priming condition, also consists of five questions requiring the participants to make conscious and deliberate calculations, with the aim of priming them with an analytical cognitive style. Example questions for this condition read: “If an object travels at five feet per minute, then by you calculations how many feet will it travel in 360 seconds?” and “If a number is divided by 4 and then 3 is subtracted, the results is 0. By your calculations, what is the number?”.

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14 a printer and ringing phones could be heard. The length of the manipulation is 12 minutes and the average peak volume is predetermined at around 70 dB which closely resembles the volume of other studies that used a noise manipulation (e.g. Collins-Eiland, Dansereau, Brooks & Holley, 1986; Evans & Johnson, 2000; Banbury & Berry, 2005) and is typical for office noise (Mackenzie, 1975 in Veitch 1990). The content of noise manipulation was chosen because Hedge (1982) found that other staff talking, staff holding meetings nearby and telephones were the office disturbances that most bothered them. Banbury and Berry (2005) found that other people‟s (phone) conversations and telephones (left) ringing were found the most distracting sources of noise. They also found that employees do not habituate to these types of ambient noise. Besides Banbury and Berry, Sundstrom, Town, Rice, Osborn and Brill (1994) also found that talking people and ringing phones are bothersome. In addition, Leather et al. (2003) also used office noise as a moderating variable.

Measurement instrument

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Control variables

Previous experience with organizing events, total time spent on task and age were used as control variables. Ruth and Birren (1985) found that people become less creative with increasing age. They explain this by pointing out that information processing becomes slower and willingness to risk unconventional situations decreases. It is assumed that the same effect applies to innovativeness.

It goes without saying that having experience with a certain task or spending more time on a task might lead to a higher performance, which explains why previous experience with organizing events and total time spent on task are also included as control variable. The gender of participants is not included as a control variable since research shows that there often is a lack of difference between gender groups what creativity is concerned (Baer & Kaufman, 2008).

Data analysis

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16 presence of missing values. Finally, idea quality could be calculated by multiplying individuals‟ average novelty and attractiveness score and those values were then entered into an SPSS database. Next, correlations were calculated between the relevant variables in order to get a preliminary view of the relationships between the different variables. Finally, multiple regression analysis was then used to test hypothesis 1a and 1b. To test the moderating effect in hypotheses 2a and 2b, interaction variables were computed and also included in a multiple regression analysis.

4. Results

Descriptive statistics and correlations among the variables used in the conceptual model are shown in table 1. Before the relevant results for the hypotheses are discussed, first descriptive statistics and manipulation checks will be discussed.

Table 1.

Correlations and descriptive statistics

Variable Mean SD 1 2 3 4 1. Intuitive priming - - 2. Analytical Priming - - 1.00** 3. Ambient noise - - .01 -.01 4. Activities count 8.08 3.26 .04 -.04 .00 5. Idea quality 20.91 5.21 .09 -.09 -.13 .14 Note. ** p < .01, n=145

Descriptive statistics

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17 was Dutch (n = 93). The mean score on the question “How much experience do you have organizing events?” of which the answers differed from 1 (none) to 5 (a lot) was 2.8 (SD = 0.9).

Manipulation checks

In order to determine if the manipulations worked, checks were included in the experiment. To see whether the participants were influenced by the priming task, “How much of your answers were based on rational thought?” and “How much of your answers were based on emotional consideration?” was asked. The results of an Independent-Samples T Test shows a significant difference in the answers on both questions, showing that participants that had the analytical task answered the questions more using rational thought, where the participants with the intuitive task answered more using emotional consideration. This indicates that the appropriate cognitive styles were induced. For the first question the results were M=2.75 (SD = .98) for the intuitive cognitive style and M=4.51 (SD = .92) for the analytical cognitive style conditions; t(142) = -11.41, p = .00. For the second question the results were M=4.05 (SD = .88) for the intuitive cognitive style and M=1.22 (SD = .59) for the analytical cognitive style conditions; t(125) = 22.83, p = .00.

To check whether the noise manipulation had worked, again two questions were asked: “How difficult was it for you to concentrate on the second study?” and “How noisy was the setting in which you performed the second study?”. For the first question the results were M=2.93 (SD = .98) for the noise group and M=3.42 (SD = 1.01) for the no-noise group conditions; t(143) = -2.98, p = .003 where a higher mean indicates less difficulty concentrating. For the second question the results were M=3.86 (SD = .84) for the noise group and M=1.30 (SD = .62) for the no-noise group conditions; t(130) = 20.83, p = .00 where a higher score indicates a noisier setting. These results show that the noise manipulation had a significant influence on the participants.

Regression assumptions

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18 that correctly display the strength of the relationships. First, the variables need to have the correct measurement level. Nominal or ordinal variables need to be converted into dummy variables in order to be included in the analysis. Innovative performance, both number of activities and idea quality have the ratio measurement level. The dichotomous cognitive style and ambient noise dummy variables are also allowed in the analysis (Baron & Kenny, 1986). The assumption of normality assumes that all metric variables are metrically distributed. A Shapiro-Wilk test, which is a powerful test of departure from normality for sample sizes between 50 and 2000 (Royston, 1992), shows that the variable idea quality is and number of activities is not normally distributed. In order to satisfy the assumption of linearity, a linear relationship must exist between the metric independent variable and the dependent variable. Since the primed cognitive style is a dichotomous independent variable, this relationship cannot be found. A test of homogeneity of variance shows that for both idea quality and number of activities the assumption of homogeneity of variance is met. Due to the dichotomous nature of both the independent and moderating variable and the setup of the experiment, unexpected multicollinearity is not an issue. Since most regression assumptions have been met, regression analysis can be performed.

Hypothesis 1

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19 used, showing whether the addition of the independent variables explains unique variation in the variables that stand for innovative behavior. In the first model only the control variables are entered. In the second model the cognitive styles are added, revealing their influence. The third model concerns hypotheses 2a and 2b. Due to the fact that one cognitive style is primed per participant, the dummy variables (analytical/intuitive) have opposite values. Because of this, both dummy variables cannot be included in one regression analysis, requiring different analyses. The results in table 2 and 3 are based on the analyses using the intuitive cognitive style. The regression effects of the variables in model 2 other than the cognitive style dummy variable, are identical when using the analytical dummy variable.

First the influence of cognitive style on number of activities will be looked at. Multiple regression analysis showed that the different cognitive styles and the control variables explained 5% of the total variation of the number of activities that participants came up with (R2_{=.050, F(1,465), p=.21). It was}

found that the primed cognitive styles do not significantly predict number of activities. Because of the fact that per participant only one of the cognitive styles is primed and the thus dichotomous nature of the dummy variables, the β-values are opposite for both styles (β = .04, p=.632 for the intuitive cognitive style and β = -.04, p=.632 for the analytical cognitive style). Except for total time spent on task (β = .18, p=.029) the control variables also do not significantly predict number of activities. When the same multiple regression but with idea quality as dependent variable is performed, it also shows that cognitive style is not a significant predictor (β = .09, p=.277 for the intuitive cognitive style and β = -.09, p=.277 for the analytical cognitive style). None of the control variables significantly predict idea quality. The different cognitive styles and the control variables explained 4,2% of the total variation of the number of activities that participants came up with (R2=.042, F(1,219), p=.303)

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Hypothesis 2

To determine whether ambient noise has no impact on the relationship between the intuitive cognitive style (hypothesis 2a) and whether it does have a moderating influence on the relationship between the analytical cognitive style and innovative performance in such a way that it gets more negative (hypothesis 2b), again multiple regression analysis is performed. Hypothesis 2a and 2b are tested in the third model, where the interaction (moderator) variable is added as well. The interaction variable is computed by multiplying the ambient noise dummy variable with a primed cognitive style dummy variable. The influence of these variables on the dependent number of activities can be found in table 2 and the influences on idea quality can be found in table 3. Again, the results in table 2 and 3 are based on the analyses using the intuitive cognitive style. The regression effects of the variables in model 3 other than the cognitive style dummy and ambient noise dummy are identical when using the analytical dummy variable. These results show, contrary to the expectations, that the influence of the interaction variable (the ambient noise moderator) is not significant when looking at both the influence of the intuitive cognitive style and the analytical cognitive style on innovative performance (β = -.10, p=.71 for number of activities and β = .15, p=.591 for idea quality). Because of this, both hypothesis 2a and 2b are not supported. Also note the low R2 _{value for all models, indicating that only little}

variation in the independent variable data is explained by the models. Only 5,1% of the total variation in the number of activities that participants came up with (R2_{=.051, F(1,237), p=.291) and 4,4% of the}

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21 Table 2.

Multiple regression on number of activities

Independent variable Model 1 Model 2 Model 3

Age .01 .01 .00

Experience with organizing events -.11 -.12 -.12

Total time spent on task .19* _.18* _.18*

Ambient noise -.01 -.01 .02 Intuitive priming .04 .13 Interaction variable -.10 R2 _.048 _.050 _.051 Change in R2 _.002 _.001 Note. * p < .05, n=145 Table 3.

Multiple regression on idea quality

Independent variable Model 1 Model 2 Model 3

Age .10 .09 .09

Experience with organizing events .00 -.01 -.01

Total time spent on task -.08 -.09 -.09

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5. Discussion and conclusion

In this chapter first the findings will be discussed and the research question will be answered. Next, theoretical and managerial implications based on the findings will be discussed. In addition, limitations and further research will be mentioned.

Findings

In this study, the influence of ambient noise on the relationship between cognitive style and innovative performance was looked at. The research question which formed the basis for this study is as follows: “To what extent does ambient noise impact the relationship between cognitive style and innovative performance?”. No evidence for a relationship between cognitive style and innovative performance has been found. In addition, no moderating influence of ambient noise on this relationship has been found.

The missing relationship between cognitive style and innovative performance might be explained by research done on switching mindsets and the fact that it exhausts self-regulatory resources. Hamilton et al. (2011) found that when you switch mindsets, for instance by changing cognitive style due to priming as is the case in this experiment, it can have negative effects. They mention that switching mindsets is an executive function, which is a neurocognitive process that “maintains an appropriate problem solving set to attain a future goal” (Wilcut et al., 2005). Switching mindsets has a negative influence on different domains, including on persistence at difficult tasks (Hamilton et al., 2011). What might have happened in this experiment is that due to the cognitive style priming, participants were less persistent to successfully finish the innovative performance task, diminishing differences between the intuitive and analytical cognitive style.

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23 (Evans, 2008). A possible explanation for the missing moderating influence of ambient noise is that differences might have occurred due to the time of day of the experiments. Time of day appears to have an influence in the relationship between noise and performance and the effect of gender on this relationship. Loeb et al. (1982) looked at the relationship between ambient noise and problem solving performance and the role of time of day on that relationship. They found that in the morning, men perform worse when exposed to ambient noise as compared to when they are not exposed to ambient noise. In the afternoon however, men performed better when exposed to ambient noise as compared to not exposed to ambient noise. When Loeb et al. looked at the influence of ambient noise on problem solving performance including both male and female participants, they did not find a significant influence of ambient noise on problem solving performance. Even though white noise was used as ambient noise instead of office noise and ambient noise was treated as an independent variable instead of a moderating variable, a similar effect might have occurred in this study, rendering the moderating influence non-significant.

Since no hypotheses are supported, the answer to the research question is that ambient noise does not impact the relationship between cognitive style and innovative performance.

Theoretical and managerial implications

The results implicate that when firms strive for innovation, the side effect of ambient noise that an open office design brings, does not hamper innovation. Because of this, firms that have a traditional office layout should not feel the need to worry about the impact this side effect could have on innovation when they consider the open-office design. In addition, due to the missing relationship between cognitive style and innovative performance, they do not have to alter their hiring policy in order to only hire intuitive, and thus what were thought to be, innovative individuals.

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Limitations

There are however some limitations that need to be discussed which could possibly explain the unexpected outcomes of this study.

Sample limitations. The sample that was used consisted solely of students from Rijksuniversiteit

Groningen because of practical reasons. Using students as research participants is a common approach in university studies. However the downside of doing this, is that the generalizability of the results is limited to this specific population and not to all working people. Gordon, Slade and Schmitt (1986) have reviewed different studies and found that in 73% of those, at least one significant between-subject difference was found when comparing student to nonstudent samples.

One rater. Only one rater was used to assess the activities the participants came up with in the

innovative behavior task. Experimenter bias was however minimized due to the fact that all activities were rated by the author‟s supervisor. Multiple raters should however increase the reliability of the innovative performance task scores, as is for instance shown by Saner et al (1994).

Dual processing. Earlier in this thesis dual processing is discussed, which explains that

individuals use both analytical and intuitive processing. However in this study, a single cognitive style was primed and the influence of this style on innovative performance was looked at. By doing this, you disregard the fact that there might be an interplay of both cognitive styles which could explain innovative performance. Instead, you assume that only the primed cognitive styles are responsible for the variation in innovative performance. Further research should also take the interplay of cognitive styles into account.

Further research

In this chapter, both suggestions for improving this study and suggestions for closely related future research are given.

Improving this study. Improving generalizability should be a point of attention in when

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25 to the fact that this method is ingrained in present-day research according to Gordon et al. (1986), a few points should be taken into consideration in order to improve generalizability. The authors mention that experimental subjects should be trained in order to improve external validity. In addition, they refer to Adair (1984) when suggesting a thorough clinical debriefing that makes all perceptions, understandings and meanings associated with the experiment clear. They indicate that this might uncover differences between the perception of both students and nonstudents about the experiment and thus allows you to draw better conclusions. Finally, they mention that it is wise to select student participants that reflect the nonstudent population with regards to demographic and interest profiles. Rating the results on the innovative performance task should on its turn be improved by having multiple raters and thereby improving scoring reliability.

Another aspect that should be taken into account when designing a similar study with an ambient noise manipulation, is the time of day. In order to counteract the effect that individuals show a lower or higher performance in the morning or late afternoon, all experiments should take place between 13.30h and 15.30h, a method which was also used by Veitch (1990).

Future research. Since individuals might employ different cognitive styles for different tasks and

at different times, it is important to determine the cognitive style of the participants by measuring it using a short questionnaire both when enrolling for the experiment, before the start of the experiment and after the innovative performance task by using a questionnaire. This way it can be known for sure whether the cognitive style priming has worked as compared to when only manipulation checks are used. In addition, it can be seen whether participants actually switch cognitive styles, by comparing the measured style at the time of enrollment and before the start of the experiment.

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26 whether the participants switch cognitive style, and whether a combination of high intuitive and low analytical styles is best for innovation, or whether for example a medium score of both styles is best.

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