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Putting Theory Into Practice Part 10: Testing the Key Assumptions with VoC

Our fictitious company Teknovantage has identified the core business hypothesis (losing tools on a job site is a big pain worth solving) and key assumptions that define the business model and opportunity. A key assumption team Teknovantage might make is:

Location aware tools success will depend on the wide spread adoption of wireless sensor technologies in industrial settings (i.e. construction sites).  “Once the market is comfortable with wireless sensor technology new products like ours stand a better chance of success.”  (i.e. a major technology adoption hurdle has been overcome). 

The team’s assumption is that WSN adoption is critical for market acceptance for the concept and provides a good proxy for us to gain deeper insights to what the market wants.  We want a better understanding of what’s driving the adoption rate and discover under what circumstances companies are most likely to adopt WSN technology.

To narrow the field of applications and markets we would investigate, we relied on prior market research that pointed us to investigate industrial instrumentation and monitoring – especially for facilities and platforms that had a lot assets and processes worth checking (i.e., critical equipment like pumps and conveyors – things we might find on big construction sites).

FYI this was actual research iNPD did for a client with a promising product concept that depended on the adoption of wireless sensors to be successful. A copy of our research findings “Wireless Sensor Adoption Study Key Findings” is available at

Research Methodology and Design Actual Client Case Study

In the above WSN adoption case study, we were able to mine significant information from a small but targeted sample by using a research methodology  that combined qualitative and quantitative approaches.

The qualitative component was based on in-depth customer interviews structured around specific questions meant to uncover the jobs-to-be-done and job satisfaction levels, and provide a clearer understanding of the current attitudes towards the adoption of WSN technology for industrial applications.  With explicit permission of course from the interviewee, interviews were recorded and transcribed.

Through referrals from the client, personal networking, and cross-referrals from interviewees, we were able to have in-depth discussions with 15 participants who represented 12 different companies. All participants were experts in their given fields. Job roles ranged from Lead User to R&D technical expert to consultant to industry service provider. There were several published authors and industry speakers in the group.

The quantitative component of the research was based on rating scales associated with key questions. The rating scale was a modified Likert scale with a numerical range of 1-10 and descriptive anchors at each end, for example:

On a scale of 1 to 10 where 1 = not aware, 5 = experimenting, and 10 = mass deployment where would you rank the adoption of wireless sensor networks in your industry for monitoring and collecting data? 

Not attractive   1   2   3   4   5   6   7   8   9   10   Very attractive

The key to this kind of research is that the real value of the numerical rating lies not in the number itself but in the explanation of why the number was chosen. When the rating scale is combined with a follow-up question asking “What would it take to make it a 10?” the discussion yields in-depth information about the specific components of the score, what elements the respondent used to judge the item in the question, and the missing “wow’s.”

Additional analysis of the combined scores of all respondents helped to create an understanding of the range of answers, high and low scores, and trends. In this study scores of 8 – 10 were considered “high” ratings. Scores of 1 – 7 were considered “low” ratings. Again, the real value of the numbers was as a focal point for analyzing the verbatim transcripts of the interviews. Opinions (and quotes) associated with high scores were compared to those associated with low scores. Definite themes were identified and reported in the research findings.

In the next  blog I’ll talk about how to decide who to interview, how to find and approach them.

See you next week,


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