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In summary, based on the results of this food processor survey, the comparative pairs analysis method was successful in highlighting that views of downstream customers were significantly different from views about upstream suppliers. Despite the difficulties in making close comparisons to previous studies, one major finding was that purchasing and sales staff had differences in perceptions. These inherent differences between purchasing and sales staff have implications for conducting chain research and comparing results of different studies. Many studies have only looked one way in the chain by examining organizations’ views of either suppliers (upstream) or customers (downstream). When comparing the results of chain studies, it would be necessary to compare upstream studies with upstream studies and vice versa. In addition, it is suggested that when conducting chain research, a better view of each counterpart in the chain can be achieved by using comparative pairs data collection methods, by getting the views of both purchasing and sales staff.
In terms of validity testing, individual respondents were able to differentiate about the IOIS, relationships, and environment for one organization where they dealt in different food categories. The consequences are that when collecting data about IOIS and relationships with other third parties, it is important to specify which product category is being examined. This would be especially important when multiple respondents are being surveyed within the one organization. In addition, individual respondents were able to differentiate about the IOIS, relationships, and the environment for pairs of organizations. This means that asking about pairs of organizations is a valid means by which to get rich qualitative data during the surveying process to explain differences in responses.
In terms of conducting research, collecting information about pairs of organizations simultaneously from each respondent is very powerful. Primarily, it allows respondents to clarify how their differences in perception were based on a variable by variable basis. This gives insights into how respondents think and aids in explaining differences overall. In addition, it allows the researcher to under- stand what is going on at each data collection instance. It also allows the researcher to revise the data collection instruments if the explanations given are not covered by structured questions asked of all the respondents. Often, these insights are not gained until the data is being analyzed, and the research is left to hypothesis what may have happened if data were collected about other issues.
In the food processor research project, it became clear after 10 interviews that differences in information satisfaction may relate to the added knowledge that the information exchanges generated as well as if it had improved the business. As a result, additional questions were included to ask all respondents about this. Not only were these additional questions asked, but also their explanatory power was explored with each interview. Further insights were gained about why they explained some respondents’ perceptions but were not very useful for others. Subsequent data analysis will be conducted to test the explanatory power of these questions.
The main advantage of comparative pairs data collection and analysis is that explanations can be gained before the data is analyzed. For many industry research projects, the population of interest is small (e.g., sales managers or computer software users in an organization), and each group being analyzed may have less than 50 potential respondents. As a result, detailed statistics such as t-tests, discriminant analysis, and regression analysis are not appropriate. This data collection method allows for an investigation of explanation of reasons for differences in opinions without detailed statistics. Data can be collected until a general consensus of views is reached to get a qualitative assessment of an explanation.
While many may see comparative pairs data collection and analysis as only of interest to researchers, managers can also use it. When looking for an explanation about why a process is not working well, stakeholders can be asked to make a comparison to another process they think is working well and explain the differences. Similarly, the performance of two customers, two suppliers, two products, two services, two managers, etc., can be compared.
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