Design Thinking Working Style (DTWS) Measurement

3.4 Definitions and Measures of Variables

3.4.2 Design Thinking Working Style (DTWS) Measurement

The 16 items from Table 2-6, Integrated Design Thinking Working Styles from Literature, were reviewed and compared to the measures being used to test the employee creativity (IEC). There was some overlap or similarities between the IEC and the 16 design thinking working styles listed. This is not surprising because design is a creative process and requires creative traits along with additional design thinking working styles.

Table 3-1 shows how the 19 design thinking working style (DTWS) items were developed. Each of the 16 styles has two items created to ask the respondent their self-perception of the item. This was done to ensure the respondent understood the statements; some of the working style concepts are complicated, such as abductive thinking. To assist in writing the measure in a clear and simple fashion that reflected the trait or style the definitions of each item were referred to (See Chapter 2 for details).

Three of the design thinking working style items were deleted for having similarity to the IEC measures: ability to work well with others, commitment and

46 organization. There were five items deleted; however, one question still remained to measure the style in which they were associated.

Table 3-3

Development of Design Thinking Working Style Items

# Design Thinking Working Styles

IEC Survey Item(s) DTWS Survey Item(s)

1 Ability to work with others

I am more creative…

2 Integrative thinking

In my work I tend to avoid choices when I can (+)

When solving problems I try to use intuition, reason, and imagination (+)

3 Inductive and deductive reasoning

I am comfortable with open-ended and exploratory research (+)

I am comfortable using different methods to solve or understand a problem. (+) ** DELETED

4 Abductive reasoning

I am comfortable inferring the cause or reason for something (+)

I often understand how others are feeling and want to help them when possible (+)

When problem solving I try to put myself in the stakeholders shoes (+) 8 Analytic thinking I analyze problems in depth (+)

# Design Thinking Working Styles

IEC Survey Item(s) DTWS Survey Item(s)

I am practical in my work (+)

9 Using Jungian ways of knowing

I learn and understand best through experience (+)

I use all my senses and intuition when trying to understand or learn (+) 10 Preference for

complex problems

I like to be challenged in my work (+) I prefer complex problems to solve (+)

11 Working iteratively

I don‘t often work in a linear fashion (+)

I am always trying to improve my work (+)

12 Ability to visualize

I use sketching to convey my ideas (+) I am comfortable visualizing my ideas (+)

13 Systemic vision or holistic thinking

I often think of solutions holistically, as systems (+) thinking working style. The employees will use their self-perception to measure their design thinking potential. All items were measured using a five point Likert-type scale ranging from Strongly Agree to Strongly Disagree.

The final 19 items are as follows:

1. When solving problems I try to use intuition, reason, and imagination 2. In my work I tend to avoid choices when I can

3. I am comfortable with open-ended and exploratory research

4. I often think of solutions holistically, as systems

5. I often understand how others are feeling and want to help them when possible

6. I analyze problems in depth

7. I learn and understand best through experience

8. I use all my senses and intuition when trying to understand or learn 9. I like to be challenged in my work

10. When problem solving I try to put myself in the stakeholders shoes 11. I prefer complex problems to solve

12. I am always trying to improve my work 13. I don‘t often work in a linear fashion 14. I am comfortable visualizing my ideas

15. I have a predisposition toward multi-functionality 16. I often work in a playful way

17. I am comfortable inferring the cause or reason for something 18. I am practical in my work

19. I use sketching to convey my ideas 3.4.3 Practiced Design Thinking (PDT) Measurement.

The dependent variable in the research model is the practiced design thinking by the employee. This means the design thinking that is currently performed in their jobs.

The five questions ask the employee to rate how much they are using design thinking in their work and how positively they feel about it. The sample for this study is employees who have undergone design thinking training and encouraged to use design thinking in their jobs. With the assistance of design experts including design thinking trainers from the organization five items were developed. However, one item

was for the benefit of the organization and will not be used in this study. The deleted item is ‗I would like to take more training in design thinking‘.

All items were measured using a five point Likert-type scale ranging from Strongly Agree to Strongly Disagree.

1. I have successfully integrated design thinking into my job 2. I feel that design thinking has improved my work

3. I have used design thinking in my project and/or research work 4. I did not find design thinking to be useful to my work

3.4.4 Workplace Atmosphere (WA) Measurement.

For this study there is a focus on employees‘ perceptions of the atmosphere of their organization. Rice (2006) and Wongtada and Rice (2008) compiled questions to assess the workplace atmosphere for creativity. All 7 items were measured using a five point Likert-type scale ranging from Strongly Agree to Strongly Disagree.

1. I enjoy doing my work so much that I forget other things 2. I feel a sense of time pressure in my work

3. There is truly an atmosphere of fun and playfulness at my workplace 4. There is free and open communication in my organization

5. People are quite concerned about negative criticism of their work in my organization

6. In my organization, there is an atmosphere of caring about building up employees‘ skills and expertise

7. The members of my workgroup feel a strong sense of commitment to working for our organization

3.4.5 Workplace Innovative Activity (WIA) Measurement.

The workplace innovative activity measures were adapted from a study by Wongtada and Rice (2008). All items were measured using a five point Likert-type scale ranging from Strongly Agree to Strongly Disagree.

1. New ideas are always being tried out in my organization 2. In my organization, a lot of ideas are generated

3. New workplace processes are often implemented in my organization 4. Compared to other organizations in Singapore, my organization is one of

the most innovative

5. My organization can respond quickly to changes in the external environment

6. My organization regularly introduces new products/services into the marketplace

3.4.6 Workplace Innovation Opportunity (WIO) Measurement.

Five survey items that measure workplace innovation opportunity are originally from items identified by Hinton (Hinton, 1968, 1970) and then the construct validity was tested by DiLiello and Houghton (2008). In these items the employee‘s perception of the perceived opportunities to use their creative potential is measured.

The five survey items were developed that describe a variety of opportunities in the workplace to use one‘s expertise, creativity skills and abilities. All items were measured using a five-point Likert (1932) scale ranging from Strongly Agree to Strongly Disagree.

1. I have opportunities to use my design thinking skills and abilities at work 2. I am invited to submit ideas for improvements to the use of design

thinking in the workplace

3. I have the opportunity to participate in team work involving design thinking

4. I have the freedom to decide how to integrate design thinking into my work

5. My design thinking abilities are used to my full potential at work 3.4.7 Qualifying Questions.

To obtain ethics review committee approval the survey had to have the first question in the survey to gain the respondents consent to take part in the study and have the results shared. The first question also further ensures the respondent that their answers will stay anonymous. The second qualifier was that the respondent had to have undergone training in design thinking at Singapore Polytechnic. Over 900 staff had been trained at the time of this survey.

The two qualifying questions are as follows:

1. I consent for the data that I ANONYMOUSLY contributes to the results of this survey to be shared with management and for the purposes of publication.

2. I have participated in a Design Thinking or Business Design course and/or workshop at Singapore Polytechnic.

3.5 Control Variables.

Consistent with previous research (George & Zhou, 2007) the variables gender, approximate age, job tenure, education level and field education were controlled. The first control variables to discuss are gender and age. It has been shown in previous research that the role of gender and age has often been overlooked in the investigation of organizational creativity (Binnewies et al., 2008). When age and gender have been examined in previous research it has shown that these factors may account for

differences in practiced creativity to potential creativity (Amabile et al., 2005).

Furthermore is has been shown that age can make it more difficult for people to adapt and learn new skills. This can hinder the creative process.

Job tenure was measured in approximate number of years at the organization.

This item was controlled because in previous studies creative potential is describe to consist of domain specific knowledge. This knowledge takes time to build and may come with a longer time in an organization (Oldham & Cummings, 1996). However, the longer an employee has been doing the same job, there is an assumption that the more resistant to change they may become. Therefore, the longer tenure may reduce practiced design thinking. However, Kunze, Boehm, and Bruch (2010) found that sometimes it is younger employees who are more resistant to change. Either way, using age as a control variable will give us more details on the subject.

The educational level was controlled because this may influence a person‘s practiced creativity. Employees with higher education levels are often the ones in positions to innovate and develop strategies. This is not commonly a position that a junior staff would be given to do the high importance and risk to the organization.

The educational field was measured to understand the spread of respondents throughout the organization. It was also measured because the type of expertise or field a person is employed in may affect their ability or desire to be creative. For example, there are ubiquitous thoughts that people in finance would probably be less creative than someone in the arts, or design field. Roger Martin describes traditional business executives to be very different from designers and often less creative (2007a). This is a common discussion on ‗Suits‘ versus ‗Creatives‘. For this study the position or department at the organization was not asked to keep the respondents privacy and ensure no repercussions for honest answers.

3.6 Questionnaire Design and Testing.

3.6.1 Questionnaire.

A survey questionnaire includes 51 main questions, 2 qualifying questions and 5 control variables used for this study. The questionnaire has four main parts. The first part is testing the individuals design thinking potential. Second, is to test the moderating effects of workplace context. The third measurement is of the dependent variable, practiced design thinking. The last part includes basic descriptive statistics in order to directly compare differences in individuals, including their age and tenure.

The questions for the survey use self-perception to measure the items. Each respondent will use a 5-point Likert scale to adequately answer each item. The measure will be self-administered over the internet using Survey Monkey.

3.6.2 Data Collection.

A preliminary version of the questionnaire was designed by the author and tested with a small group, mostly in management, at the sample organization. Questionnaire items were revised based upon the feedback of the test sample of employees. The survey was assessed and approved by the internal Ethics Review Committee.

To administer the survey the introduction was carefully constructed to ensure the respondents felt safe being honest in their replies. The introduction is also used to help motivate the staff to take the time to respond to the survey. The person is thanked for taking part in the organizations design thinking journey and prompted to respond to the survey to give their feedback. The hope is that employee will view this survey as a safe way to voice their opinions about their work and the use of design thinking in their organization. The letter is displayed in Appendix 1.

The first notice was sent out to over 400 staff by the Director of the Educational Department at the organization. This department has a goal to implement and support

design thinking at SP. Colleagues in management positions at the organization later helped to send the survey out to their networks. The author also sent out two more reminders and request for the staff to complete the survey. The response rate was quite slow; it is predicted this is because of the very busy schedules of staff. It may also be because staffs do not feel comfortable sharing their feedback.

3.7 Methods of Analysis.

After collecting the data several methods of analysis will be employed to test the research framework and hypotheses. This study will use comprehensive statistical analyses including factor analysis, multiple regression analysis, and SEM. These tests will help to explore the relationships between the constructs and to evaluate the overall model. Chapter four will give more details on the methods of statistical analysis.

3.7.1 Descriptive Analysis.

To understand a summary of the sample and data descriptive analysis will be conducted. Descriptive statistical analysis used in this study includes mean, median, standard deviation, range and variance. The results from this data will help to understand the control variables used in the study; age, tenure, sex, education level and education field.

3.7.2 Correlation Coefficient Analysis.

The Pearson correlation coefficient analysis is a preliminary analysis used to explore the relationship between variables on a one-by-one basis. It will be used to obtain a general measure of the strength of linear dependence between two separate variables (Hair, Black, Babin, & Anderson, 2006). Pearson correlation coefficient analysis, or Pearson‘s product-moment correlation, assumes that two variables are measured at least on interval scales. The coefficients measure how variables are

proportionally correlated to each other and whether proportional means there is linear relationship. The test will show if there is a high correlation, if it can be estimated by a regression line (straight line). The correlation coefficient also measures the fit of predicted value with actual data.

3.7.3 Reliability Test.

Reliability test is the extent to which a variable or set of variables is consistent in the intended measures. Reliability is an assessment of the degree of consistency between multiple measurements of a variable. If several measurements are taken, the reliable measures will all be consistent in their values. Internal consistency is one of the commonly used measures of reliability for each of the research factors. The individual items of the summated scale should all be measuring the same construct and therefore be inter-correlated. In the research of Hair et al. (2006) they set the average figures for coefficient correlation in term of coefficient alpha or Cronbach‘s alpha value. If the alpha value is higher than 0.7 means this means there is high reliability and lower than 0.3 means that there is low reliability.

3.7.4 Confirmatory Factor Analysis (CFA).

The confirmatory factor analysis test, or CFA, explains how well the theoretical specification of the factors matches reality (the actual data) and provides a confirmatory test of measurement theory. For this study CFA is used to confirm the construct validity of the model and to confirm the factor loadings (correlations between factor and variables). It will also reflect group effects on factors.

Construct validity is measured by factor loading estimates, average variance extracted (AVE – a summary measure of convergence among a set of items representing a latent construct), construct reliability (CR – measure of reliability and internal consistency to the measured variables representing a latent construct). The

average figures for the above criteria are standardized loading estimates that should be higher than 0.5, AVE should be 0.5 or higher, and construct reliability should be 0.7 or higher.

3.7.5 Hierarchical Multiple Regression Analysis.

For testing the hypotheses, this research will employ a moderated hierarchical regression analysis. In this study there are several variables that can interact, therefore several regression diagnostics will be used to assess if the modeling assumptions were satisfied. Hierarchical multiple regression analysis will be applied to examine hypothesis derived on the design thinking potential (IEC, DTWS) effect on practiced design thinking (PDT) with the moderating effect of the workplace context (WA, WIO, WIA). This analysis is used to find the interaction effects of the moderator variables or multi-collinearity setback that prevent independent variables to separate its effects in multiple regression analysis.

CHAPTER FOUR

RESEARCH RESULTS

4.1 Descriptive Statistic Analysis.

Characteristics of respondents are recorded in descriptive analysis. The results show that about 42 percent of respondents are female. Ages range from 20 – 69 years old and majority of respondents‘ ages is between 30 – 59 years old. Participants having master degree and majoring in engineering dominate the sample, which account for about 64 percent and 42 percent, respectively. Generally, about one-fourth of the sample respondents have been working in the industry for more than 20 years.

Table 4-1

Characteristics of Research Respondent (N=160)

Question Categories Frequency Percentage

Gender Blank 4

Male 91 58.3

Female 65 41.7

Age Blank 4

20-29 years old 3 1.9

30-39 years old 54 34.6

40-49 years old 49 31.4

50-59 years old 47 30.1

60-69 years old 3 1.9

Education Blank 4

Diploma 0 0

Bachelor Degree 38 24.4

Master Degree 100 64.1

Doctorate 16 10.3

Other 2 1.3

Question Categories Frequency Percentage

Major Blank 4

Engineering 65 41.7

Maths and Science 20 12.8

Design 10 6.4

Business 21 13.5

Information Technology 40 25.6

Other 0 0 consistency among multiple measurements of a variable. Coefficient alpha (Cronbach‘s α) is used as a valuable measurement of reliability since the individual scale items should be all measured in the same construct to achieve highly inter-correlated results. The threshold of coefficient alpha is generally set at .70, if it is higher this suggests good reliability for confirmatory analysis (Hair et al., 2006).

Internal consistency exists as there is high construct reliability, meaning that the measures all consistently represent the same latent construct. In contrast, low level of the Cronbach‘s Alpha (lower than 0.3) implies that there is low reliability. In addition, the second measurement scale is Kaiser-Meyer-Olkin‘s Measure of Sampling adequacy (KMO). The threshold of KMO to guarantee the high reliability level is 0.5.

Table 4-3 demonstrates the reliability analysis of each construct.

As shown in the Table 4-2, all the KMO and coefficient alpha surpass the threshold, showing high construct reliability and inter-correlated items to construct.

Table 4-2 Reliability Test

Construct KMO Cronbach's Alpha

Individual Employee Creativity 0.894 0.872

Design Thinking Working Style 0.893 0.881

Practiced Design Thinking 0.638 0.772

Workplace Atmosphere 0.743 0.738

Workplace Innovative Activity 0.811 0.884

Workplace Innovative Opportunity 0.831 0.818

For this study AMOS 18.0 was used to examine the viability of the research model. Confirmatory Factor Analysis (CFA) was conducted to analyze the validities of each individual construct and the full measurement model. Generally, items above the criterion (0.4) were employed in CFA. In order to achieve the best model fit, some items might need to be deleted. Moreover, modification indices were used to adjust the goodness of fit.

Seven indicators are used to determine the goodness of fit for the overall model and individual constructs. The first criterion is the Chi-Square value. It should be notes that a low value of Chi-Square is considered as goodness of fit to the data. If the Chi-Square value is close to zero this means that there is little difference between expected and observed covariance matrices. Furthermore, the value of Chi-Square fit index divided by degree of freedom should be less than 3. The resulting value of this

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