Proven Research Methodology

MPI uses a five-step methodology to move a research survey, study or initiative from conception to insightful data:

1. Develop consensus on survey scope

To be successful, a research initiative must reach consensus on several questions:

• Should the research initiative look for broad-based information, or drill down into specific data points? [Note: High-level surveys usually pull a higher number of respondents.]
• What is the appropriate balance of depth and breadth of data available and obtainable (i.e., that can yield a significant number of responses)?
• What does the research sponsor hope to accomplish with the initiative?
• Develop industry benchmarks?
• Identify industry training/consulting needs?
• Grow industry recognition for research sponsor?
• Attract media attention?
• Other?

2. Identify research hypotheses

With research scope roughly defined, the next step is to determine what the research should discover or reveal (i.e., the three or four major hypotheses that research findings are likely to prove or disprove). For example, an initiative may try to determine:

• If a positive correlation exists between certain management practices and improved performance.
• If management practices and processes differ by industry, industry segment, or company, and whether this difference correlates with performance.

Successful surveys always focus around key hypotheses. Proceed without hypotheses will deliver a set of unconnected data points that severely limits the use of the survey results. For example, a litany of dollar or metric measures may provide benchmarking references, but will do little to generate industry or media excitement around the survey results.

3. Determine specific research methodology

What type of questions must the research put before respondents in order to generate data that supports or refutes the hypotheses? In MPI’s experience, a blend of practice and performance/metric questions provides a significant opportunity to correlate practices to performances.

While each industry and corporate function has its own unique language, metrics by which to assess processes almost always fall within the following categories.

• Costs (e.g., straight costs/headcounts or costs/headcounts as a percentage of a total)
• Quality (e.g., error rates, scrap rates, accuracy)
• Delivery (e.g., on-time delivery, speed of process)
• Productivity (e.g., costs/headcounts relative to output)

In addition to questions supporting or refuting hypotheses, the research must also capture information that enables the survey sponsor to clearly define the respondent base (e.g., size of company) and the general performance of the company (e.g., revenues, profits). Correlating functional practices and performances with corporate performances will likely be at the heart of various hypotheses, and are frequently the “hooks” that generate media interest.

4. Develop and test research instrument(s)

MPI focuses on three factors when designing research instruments:

• Make maximum use of limited survey real estate;
• Promote ease of understanding; and
• Drive optimum response rates.

MPI uses a variety of best practices to develop research projects:

• If possible, the depth of information (and degree of difficulty for respondent to obtain) sought should remain consistent at across the survey (e.g., keep question “spikes” to a minimum).
• Successful surveys combine multiple questions so that they appear, from a respondent’s perspective, as one larger question.
• Successful surveys emphasize questions that elicit metrics relative to a corporate metric:
• Less worrisome to respondents (i.e., “Do I want to reveal that absolute value, even if my response is anonymous?”)
• Provides a ready benchmark that can then be applied to companies of any size or description without using cross tabs of data.

5. Implement research mechanism

MPI implants a plan including:

• Identification of target distribution
• Distribution of research instrument
• Provision of incentive for respondent participation, if any
• Collection of data
• Tabulation of data
• Creation of Summary Report of data, including frequencies and statistics on all questions.

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