A Mixed-Method Design for Developing a Measure of Entrepreneurial Openness


Developing a measurement scale that would be valid and reliable is a challenging task in any research field. It takes time and reflection. This case study of mixed methods explains how we developed the concept and measurement scale of entrepreneurial openness—a personality characteristic that helps to understand the impact of an entrepreneur’s personality on a small firm’s performance. The case study describes several procedures that we applied in the scale development process: interdisciplinary literature review, interviews, focus group, pilot study, and two large-scale studies with questionnaires and data analysis for assessing measure’s reliability and validity. The reader gets to know the appropriate procedures and their sequence in the scale development process and the importance of the mixed-method approach in a scale development process. By applying the scale development guidelines presented in this case study, the reader will also be able to assess the appropriateness of scale development processes of existing measurement scales and develop a measurement scale on her own.

Learning Outcomes

By the end of this case, students should be able to

  • Describe the process of measurement scale development
  • Explain the importance of the mixed-method approach in the development of a new measurement scale
  • Evaluate the appropriateness of scale development processes for existing measurement scales
  • Assess various measures of reliability and validity of a measure
  • Develop a measurement scale on their own

Project Overview and Context

This case study reports on the entrepreneurial openness conceptualization and measurement development procedures. We were interested in the conceptualization and measurement of entrepreneurial openness because throughout the existing entrepreneurship literature (Brandstätter, 1997; Rauch & Frese, 2007; Zhao & Seibert, 2006), there was a strong position that openness is a good quality for entrepreneurs to have (among other personality determinants important in entrepreneurship), especially during the phase of firm performance, development, and growth. However, several authors have emphasized that to understand the mechanisms through which entrepreneur-related personal strengths such as openness contribute toward the achievement of important firm-related outcomes, we need a robust conceptualization of the construct (Luthans, 2002; McGee, Peterson, Mueller, & Sequeira, 2009). Although openness in entrepreneurship has been recognized as an important strength for entrepreneurs, a sound conceptualization and empirical validation of entrepreneurial openness was missing.

First, the conceptualization phase took part. We drew from the entrepreneurship and positive psychology literature to conceptualize entrepreneurial openness as an individual-level positive personal cognitive strength that has three subdimensions: engaging in learning, searching for novelty, and seeking feedback. Entrepreneurial openness helps to understand the impact of an entrepreneur’s personality on a small firm’s performance.

Then, the measurement part took part. We paid specific attention to the measurement part because effective measurement is a cornerstone of scientific research and recommends researchers to either use only those measurement scales that have been previously validated or when dealing with a new construct, for which a measurement scale has not yet been proposed, researchers must undertake the scale development process rigorously and accomplish all crucial steps.

The entire procedure for developing the measure of entrepreneurial openness incorporated a mixed-method approach (Creswell, 2015; Netemeyer, Bearden, & Sharma, 2003; Teddlie & Tashakkori, 2009), which can be summarized in the following steps:

  • Content domain specification: do a literature review, and interviews with relevant audience, and focus group
  • Item pool generation: generate a pool of potential items that sample the domain of the construct
  • Content validity evaluation: assess the adequacy of the proposed items from the item pool by the relevant audience
  • Questionnaire development and evaluation: develop the survey and test it
  • Translation and back-translation of the questionnaire: for cross-national studies where different languages are spoken, the scales have to be correctly translated
  • Pilot study performance: identify potential problems with the questionnaire and obtain pre-results of the reliability of the new measure and correlations among items
  • Sampling and data collection: generate a representative sample of respondents and collect data from these respondents
  • Statistical analyses: assess dimensionality (homogeneity of items), reliability (the extent to which a measurement procedure yields the same results on repeated trials), and construct validity (scale’s ability to measure what it is supposed to measure).

In defining the content domain of entrepreneurial openness, we did an in-depth literature search and carried out 28 interviews and a focus group with entrepreneurs and entrepreneurship-related interest groups. Statistical analysis of the measure consisted of descriptive statistics, correlation analyses, reliability and validity analyses, exploratory factor analyses, confirmatory factor analyses, and structural equation modeling. We evaluated the appropriateness of the entrepreneurial openness structure on samples of entrepreneurs from Canada, Slovenia, and the United States.

Research Design

First, we reviewed the mainstream scale-development literature (e.g., DeVellis, 2003; Netemeyer et al., 2003; Nunnally & Bernstein, 1994) to define all crucial steps that we had to undertake to develop the measurement scale of entrepreneurial openness. After that we began the work on entrepreneurial openness. Each step is discussed in the following.

Content Domain Specification

In the first step, we specified the domain of the construct and provided a definition of it (Bygrave & Hofer, 1991; DeVellis, 2003). An in-depth literature review in different fields (e.g., entrepreneurship, management, organization, psychology, sociology, and measurement) served to set the boundaries of the entrepreneurial openness construct (Churchill, 1979; Netemeyer et al., 2003). In addition, we reviewed practitioners’ outlets, web pages, statements, interviews, and lectures of entrepreneurs, venture capitalists, business angels, and entrepreneurship scholars to gather additional insights into entrepreneurial openness. We searched the web using several key words (e.g., entrepreneurial openness, entrepreneurship openness, open to learning, open to feedback, personality openness, and others).

After assessing the initial content domain of entrepreneurial openness based on the literature review, we explored the existence of the new construct in practice. We conducted two rounds of semi-structured interviews (Miles & Huberman, 1994) with entrepreneurs and entrepreneurship experts. The latter category comprised entrepreneurship professors, representatives of business incubators and technology parks, representatives of financial institutions that deal with entrepreneurs—bank underwriters, and representatives of chamber of trade. The majority of the questions were the same for the interviewees, but some questions and subquestions were specific for some respondents based on the flow of the conversation and their background. Two rounds of interviews were necessary to define and refine the content domain of entrepreneurial openness. For the second round, we refined the interview protocol and shortened the number of questions and we were more focused in the three dimensions of entrepreneurial openness that were evidenced from the first round. We conducted 11 interviews in the first round and 17 interviews in the second round. All interviews were held face-to-face. Interviews were taped, transcribed, and analyzed as suggested by several scholars (Flynn, Schroeder, & Sakakibara, 1994; Hill & Birkinshaw, 2010).

In the final stage of content domain specification, we conducted a focus group (Miles & Huberman, 1994). The seven participants of the debate were from different backgrounds (an entrepreneur, an employee in a small company, a representative of a business incubator, a representative of a technology park, and a bank underwriter). Two participants were engineers. The focus group followed an established protocol and was taped and transcribed. While confronting their opinions, participant agreed that entrepreneurial openness is a factor that is important in entrepreneurship and has an impact on entrepreneurship-related outcomes.

At the end of content domain specification, the researcher should be able to clearly state why the new measure is needed given that several entrepreneurial constructs exist. The explanation should contain evidence that the older measures, if they exist, do not satisfy the research needs or that inadequate measures have been used. A well-expressed theoretical base that clearly specifies the content domain of the construct is crucial for all subsequent steps (Netemeyer et al., 2003).

Item Pool Generation

An initial list of items that potentially capture entrepreneurial openness was generated from three main sources in multiple disciplines (Cheng, Henisz, Roth, & Swaminathan, 2009). First, we drew from existing evidence in the entrepreneurship literature and existing openness questionnaires and scales in the broader management and psychological literature (Churchill, 1979). Second, coded information from web pages and lectures of famous entrepreneurial scholars and successful entrepreneurs, venture capitalists, and business angels provided further inputs. Finally, interviews with entrepreneurs and entrepreneurship experts and focus group provided practitioners-based evidence. A total of 78 items for sampling the domain of entrepreneurial openness were compiled in the first step. At this stage, over-inclusiveness was preferred to under-inclusiveness (Carmines & Zeller, 1979; DeVellis, 2003). Items were written according to suggested guidelines regarding the reading level ease, clarity and length, variability of responding, avoidance of double-barreled sentences, multiple negatives, and use of jargon (Di Stefano, Gino, Pisano, & Staats, 2014; Nunnally & Bernstein, 1994).

Content Validity Evaluation

The appropriateness and representativeness of items and instructions were assessed by 38 judges—entrepreneurial practitioners and academics (Haynes, Richard, & Kubany, 1995; Nunnally & Bernstein, 1994). In all, 12 judges in the first round and 26 judges in the second round were provided with a rating scale based on Zaichkowsky’s (1985) method, whereby each item was rated as “clearly representative,” “somewhat representative,” or “not representative” of entrepreneurial openness; to be retained, items had to be evaluated at least as “clearly representative” or “somewhat representative.” Judges were also asked to suggest additional items (Netemeyer et al., 2003).

Item purification yielded 34 items for the pilot study. We refined the items based on suggestion by DeVellis (2003). We checked item-scale correlations, item variances, and item means. Items that had low variance were eliminated from further analysis, as these items did not discriminate substantially among individual respondents (DeVellis, 2003).

Based on Zaichkowsky’s procedures, we excluded items that were evaluated by most judges as not representative of entrepreneurial openness. We deleted some items because they had low variances and did not discriminate among respondents. There were some items that correlated poorly with other potential items of entrepreneurial openness and were excluded from further analysis. Moreover, judges were asked to evaluate the wording of items and problematic items were reviewed and slightly modified. We added some items, because rounds of interviews and item judgments revealed that some parts of entrepreneurial openness domain were not covered sufficiently by existent items.

Questionnaire Development and Evaluation

We collected self-reported data on entrepreneur’s openness via a questionnaire, given that questionnaires are a common method of data collection in entrepreneurship research (Chandler & Lyon, 2001; Crook, Shook, Morris, & Madden, 2010). Following Dillman’s et al. (2009) tailored design method, we developed a questionnaire containing 34 items of the pilot version of entrepreneurial openness. As suggested by several scholars (e.g., DeVellis, 2003), we included some widely validated scales in our questionnaire to detect flaws or problems in the scale. In so doing, we also wanted to demonstrate that entrepreneurial openness is a distinct construct from other openness constructs. The questionnaire included the following measures: entrepreneurial openness, entrepreneurial self-efficacy (Chen, Greene, & Crick, 1998; Prodan & Drnovsek, 2010), entrepreneurial alertness (Tang, Kacmar, & Busenitz, 2012), openness to experience (Costa & McCrae, 1992), emotional openness (Kahn & Hessling, 2001), and optimism (Scheier, Carver, & Bridges, 1994). We also included demographic questions. As pretesting is generally agreed to be an indispensable stage in questionnaire development (Presser & Blair, 1994), the questionnaire was pretested on four respondents, who were asked to fill in the questionnaire and to check the understanding of instructions, rating options, wording, visual design, and navigation issues.

Translation and Back-Translation

To establish the cross-national equivalence of items in the questionnaire and effective comparison across nations (Hui & Triandis, 1985), two approaches were used. For a Slovene version of the survey instrument, we used existing translations of the openness to experience and entrepreneurial self-efficacy. On the contrary, Brislin’s (1970) method of translation and back-translation was used on entrepreneurial openness, entrepreneurial alertness, emotional openness, and optimism. The back-translated versions were compared with original versions and discrepancies were discusses and solved.

Pilot Study

We performed a pilot study to pretest the scale and identify potential problems unique to the survey method. The sample consisted of 40 Slovenian entrepreneurs who answered the survey containing the following measures: entrepreneurial openness, openness to experience, emotional openness, optimism, entrepreneurial self-efficacy, entrepreneurial alertness, and demographic questions. The proposed seven-point scale for entrepreneurial openness asked respondents to indicate how often they engage in activities related to engaging in learning, searching for novelty, and seeking feedback ranging from “Never” to “Always.”

The analysis of the pilot study included calculations of descriptive statistics and correlations and replacement of missing values. Several considerations were applied when deciding on retention or exclusion of an item: variability in response values, correlations with other entrepreneurial openness items and items of other included measures, and assuring to preserve the content domain of entrepreneurial openness. Regarding the frequencies and distributions, the following rule was followed. If an item did not have high variability in responding (Clark & Watson, 1995; DeVellis, 2003) and did not discriminate substantially among individual respondents, it was a candidate for exclusion from the scale of entrepreneurial openness. Based on the correlation matrix, a candidate for exclusion was an entrepreneurial openness item that correlated poorly with other entrepreneurial openness items and/or correlated highly with items of other scales. Finally, when deciding on whether to retain or eliminate an item we considered the content domain of entrepreneurial openness; we paid attention not to exclude some facet of entrepreneurial openness by eliminating an item.

Based on all these criteria we eliminated eight items. To double-check for the problematic items, we analyzed the results of reliability testing if problematic items were deleted. SPSS version 17.0 suggested the same eight items that were candidates for exclusion. Results of the pilot study indicated good initial reliability and showed high correlations among the remaining 26 entrepreneurial openness items.

Sampling and Data Collection

We conducted a web-based survey among entrepreneurs in three different countries: Canada, Slovenia, and the United States. To compile a sample population, we randomly sampled official national databases: ReferenceUSA for Canada and the United States samples and PIRS for the Slovenian sample. The following inclusion criteria were used: number of employees between 5 and 249, all NAICS sectors except sector 92—public administration, only privately owned companies. During the 2-month data-collection period, we sent two reminders to entrepreneurs from Canada and the United States and one reminder to Slovenian entrepreneurs. We received the following responses rates: 7.2% for Canada, 23.2% for Slovenia, and 4.0% for the United States.

Statistical Analyses

To identify those items which are the most central to the domain of entrepreneurial openness and to explore which items are the most closely related to each other, we performed the internal consistency analysis (item-total analysis, inter-item analysis, and Cronbach’s alpha calculations) and exploratory factor analysis of entrepreneurial openness using SPSS version 17.0 on all three samples—it is argued that multiple samples are the best means of demonstrating generalizability (DeVellis, 2003). To confirm the structure of entrepreneurial openness we run a confirmatory factor analysis, a multigroup analysis, convergent validity analysis, and discriminant validity analysis in AMOS version 20.

Reliability Analysis and Exploratory Factor Analysis

Based on inter-item and item-total correlations, we deleted six items because they all had low correlations to other items. The 20 remaining items were subjected to exploratory factor analysis by means of maximum likelihood extraction method with oblimin rotation with Kaiser Normalization. Out of the 20 items, 11 items loaded correctly and significantly on three factors, whereas 9 items were deleted because they either had too low correlations with their underlying factors (correlations under 0.300) or cross-loaded or had inconsistent loadings on different factors in different samples (Hair, Black, Babin, & Anderson, 2010). We reran exploratory factor analysis and the 11 items exhibited good factor loadings, reliabilities, and variance explained. The analysis yielded consistent results in all the three samples with items loading on the same factors and exposing remarkably similar factor loadings. Factors with eigenvalues larger than one were extracted. We checked the values for variance explained and Kaiser–Meyer–Olkin measure of sampling adequacy. These results evidence the appropriateness of the data for factor analysis. The Bartlett’s test of sphericity was significant in all three samples indicating an overall significance of correlations within the correlation matrix (Hair et al., 2010). The reliability of entrepreneurial openness was assessed with Cronbach’s alpha coefficient for all three factors in all three samples. Cronbach’s alpha coefficients were above the 0.700 threshold (Hair et al., 2010). The final scale of entrepreneurial openness consisted of three items capturing learning dimension, four items capturing novelty dimension, and four items capturing feedback dimension.

Confirmatory Factor Analysis and Multigroup Analysis

In the next step, we performed a confirmatory factor analysis of the 11 entrepreneurial openness items. First, we determined the best-fitting model for the calibration sample which in our case was the Slovenian sample. Then we tested the final model on two validation samples—the Canadian and American samples. The purpose of testing across calibration and validation samples was to determine the extent to which this final model replicates across independent samples of entrepreneurs and to assess validity generalization (Byrne, 2006; Diamantopoulos & Siguaw, 2009). These analyses allowed us to confirm the second-order three-factor structure of entrepreneurial openness. After conducting the confirmatory factor analysis on single samples of Canada, Slovenia, and the United States, we conducted a multigroup confirmatory factor analysis of the second-order three-factor model. We performed the multigroup analysis to test for configural and metric invariance of entrepreneurial openness across different nations. Configural invariance requires the number of factors and the factor-loading pattern to be the same across the groups of entrepreneurs, whereas metric invariance requires the equality of factor loadings.

Convergent and Discriminant Validity

We assessed convergent and discriminant validity of entrepreneurial openness based on recommendations by several authors (Fornell & Larcker, 1981; Hair et al., 2010). Convergent validity of entrepreneurial openness was first demonstrated by high factor loadings (above 0.700) of the three entrepreneurial openness dimensions on the overall construct. Second, convergent validity was demonstrated also by the average variance extracted of entrepreneurial openness dimensions, which should be higher than 0.500. Discriminant validity was assessed between entrepreneurial openness and the following constructs: openness to experience, emotional openness, optimism, the three factors that compose entrepreneurial alertness—scanning and search, association and connection, and evaluation and judgment, and the three factors that compose entrepreneurial self-efficacy—management, innovation, and marketing. We compared the squared root of average variance-extracted values for any two constructs (e.g., entrepreneurial openness and optimism) with the correlation estimate between these two constructs. The squared root of average variance-extracted values should be larger than correlations with other constructs to prove discriminant validity. This assessment provided evidence of discriminant validity of entrepreneurial openness to other constructs.

Cumulative statistical evidence provided enough empirical support to our proposition that entrepreneurial openness is a second-order construct and has discriminant validity from other investigated constructs.

Research Practicalities

In doing this research, we had a primary ethical consideration to consider—the confidentiality of the data, as we dealt with sensible data about one’s personality. Yet, we still wanted to know how entrepreneurs’ personality relates to their small-firm performance. For this reason, every participant was given a token with 15 alphanumeric signs, which were related to a firm’s tax number. Despite erasing the identifiable information of the owner and the firm, we were still able to link the entrepreneurs to their firms while maintaining the anonymity of the data.

We also had to plan to have multiple samples of entrepreneurs to triangulate the data and effectuate data analysis on distinct samples of entrepreneurs for generalizability purposes. For these reasons, we exposed the entrepreneurial openness measure to international contexts to evidence its validity in different environments—large countries versus small country; Anglo-Saxon versus Continental Europe; traditionally market economy versus recently transitioned economy; multicultural country versus culturally more homogeneous country. In so doing, we provided common grounds for future research on the role of entrepreneurial openness in the entrepreneurship process. We were also aware that we had to have big data samples for structural equation modeling and therefore we generated big datasets for mailing and e-mailing and we sent several reminders to entrepreneurs.

Method in Action

A mixed-method approach to develop a new measurement scale proved to be the appropriate one, as no single method either qualitative (interviews, focus group) or quantitative (survey) could provide enough support for the richness, soundness, reliability, and validity of the measure. But first and foremost, the in-depth literature review was crucial to understand properly the nature of the new concept. Yet, looking back and based on reviewers’ comments we realized that greater attention should have been paid for this first step.

Interviews and focus groups with the relevant audience are also crucial. Without interviews, focus groups or some other method for confronting the new concept in practice, a researcher would lack important insights into the concept. Semi-structured interviews and focus groups proved appropriate, as a researcher should know what to ask, but should also let the conversation go into some untapped fields. Such qualitative data gave us substantial input and confirmation of the entrepreneurial openness domain, but also highlighted some new areas that we had to research more in-depth. Researchers should talk to the relevant audience and should not use convenient samples of audience, such as students or friends.

The pilot study with a survey instrument turned out to be very useful and we strongly recommend carrying it out before doing a large-scale survey study. But attention should be paid not to carry it out on a convenient sample of students. Rather, researchers should strive to do the pilot study on the target audience. Preliminary statistical analysis on the pilot sample was very useful for item evaluation. Looking back, we think that a bigger sample for the pilot study would give us even more confidence when evaluating an item’s performance.

The large-scale survey study as a data collection method was appropriate for our field of research. Attention should be paid when doing the research in different countries, cultures, and languages, because the survey instrument should be altered appropriately. The right wording and meaning should be verified. We also realized that the paper-version of the survey brought us higher response rates than the online version. Therefore, if researchers have enough funds for mailing paper-versions, we advise them to do so and combine it with online remainders and online surveys for those respondents who would prefer filling in online versions.

We also applied several analytical techniques for quantitative data analysis: different reliability analyses, exploratory factory analysis, confirmatory factor analysis, convergent and discriminant validity, and structural equation modeling. We strongly advise researchers to apply all these empirical procedures when developing a multi-item measurement scale of a latent construct.

Practical Lessons Learned

The first thing to emphasize is the importance of an in-depth (multidisciplinary if applicable) literature review of the concepts under study. This forms the basis for all subsequent steps. It is also instructive to consult the literature after data collection through interviews and focus group if new insights emerge from these field studies. If these first steps are not done properly, all subsequent work might be worthless. Moreover, it is crucial to review guidelines for scale development to embrace this challenging and satisfying process properly. There are plenty of good books and papers on scale development (e.g., Bagozzi & Edwards, 1998; DeVellis, 2003; Netemeyer et al., 2003; Nunnally & Bernstein, 1994; Pedhazur & Pedhazur Schmelkin, 1991) and challenges of measurement design specific for entrepreneurship research (e.g., Crook, Shook, Madden, & Morris, 2009; Crook et al., 2010; Hinkin, 1995; Kuskova, Podsakoff, & Podsakoff, 2011; Slavec & Drnovšek, 2012). We would like to highlight that it is not appropriate to just write some items for a new construct, administer them in a survey, and do statistical analysis (usually only confirmatory, not also exploratory factor analysis) on such items. In such instance, several crucial steps in scale development are omitted and such measures are neither reliable nor valid.

Interviews and focus groups require a thoughtful preparation of semi-opened questions, their sequence, and data analysis afterward. They took a lot of time but brought very rich insights and powerful quotes for afterward usage in papers and presentations. We were confident that we had done enough interviews when we did not get any new information or insight about entrepreneurial openness. Two rounds of interviews and a total of 28 interviews were enough. Some of the interviewees became so interested in our research that they provided additional contacts of entrepreneurs with whom we could talk. Such snowball sampling was very useful. Reflecting on the research methods, we realized that we could also do some observation studies with entrepreneurs.

The quantitative method of survey data collection in the pilot study and large-scale study proved appropriate as the next step in measurement development. Researchers should compile big enough samples and follow guidelines for survey development and administration to get satisfactory response rates (e.g., Callegaro, Lozar Manfreda, & Vehovar, 2015; Dillman, Smyth, & Christian, 2009).

We also realized that a multicountry research poses some challenges. First, the difference in languages should be properly addressed by translation and back-translations techniques. Item meanings should be reviewed by native speakers. Interestingly, in the United States respondents wanted to know our background. At the time we were administering the survey, some of us did not have Facebook and LinkedIn accounts and American respondents were doubtful of our legitimacy. No such doubts were raised by Slovenian respondents. It might be that our Slovenian names sounded familiar enough and legit for Slovenians. We also realized that American respondents valued the fact that we were connected to a renowned American institution and were not some random people collecting sensitive data about them. When conducting a research, it is important to be available for respondents also through phone, not only e-mail.

Regarding the statistical analysis of quantitative data for a new measurement scale, it is important to also do the exploratory factor analysis and not only the confirmatory factor analysis. The former is largely omitted in scale development papers, or at least not reported.


We strongly recommend applying a mixed-method approach when researches want to conceptualize and develop a new measure. Interviews and focus group provided a deep understanding of the nature and context of entrepreneurial openness, whereas the survey instrument provided a dataset for empirical validation of entrepreneurial openness. The qualitative methods brought contextualized, in-depth insights, but also took a lot of time. The quantitative method was more efficient, but less rich in predicative power. Yet, no single method alone would suffice the complex research that a concept development and scale development process bring.

Exercises and Discussion Questions

  • Find a scientific paper with a newly developed measurement scale. What criteria would you use to assess the appropriateness of a scale development process of a selected measurement scale? Evaluate the scale development process of the selected measure.
  • What are the benefits of a mixed-method design in a scale development process?
  • What are the challenges of a mixed-method design in a scale development process?
  • Which other qualitative and quantitative methods would you use in a scale development process? Explain your decision.
  • Propose improvements for the scale development process for the measure of entrepreneurial openness.

Further Reading

Callegaro, M., Lozar Manfreda, K., & Vehovar, V. (2015). Web survey methodology. Los Angeles, CA: SAGE.
Creswell, J. W. (2015). A concise introduction to mixed methods research. Los Angeles, CA: SAGE.
Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, mail, and mixed-mode surveys: The tailored design method (
3rd ed.
). Hoboken, NJ: John Wiley & Sons.
Slavec, A., & Drnovšek, M. (2012). A perspective on scale development in entrepreneurship research. Economic and Business Review, 14, 3962.


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