Task
- Provide feedback to your colleague below with additional ideas about the examples they have posted, adding further insights or expanding on their recommendations for alternative ways of displaying or summarising the data in their example.
- Comment on ways of summarising and displaying research results in the clearest and least biased manner possible.
Making Sense of Data
The article by Switala (2016) "ENTERPRISES' READINESS TO ESTABLISH AND DEVELOP COLLABORATION IN THE AREA OF LOGISTICS" aim to investigate a cross-organizational collaboration within logistics organisation.
Qualitative and quantitative methods were used in the research paper.
The research was done on the primary and secondary data using companies' surveys. The authors used samples of the 50 business entities and sent 850 emails. Additionally, there were used two parts of the questionaries' both on 5-grade scales (5=very high and 1=very low)
As a result of the data collection, there was used a software SPSS to visualised research results. And there was a specific question asked such as "Do you agree with the opinion that while working together with other companies you can accomplish more than by acting alone?" (Switala2016).
Consequently, the results were presented in different tables using description and theirs scale proportions, and they used content analyses.
Moreover, authors in their research used Whitney U test to prove that identified differences are statistically significant within the role played within the supply chain (Switala 2016).
From the other hand, there were also methods used to compare of different groups that were used in the research to show different preferences. Consequently making analyses of the variation of their results to draw a study conclusion.
When looking for the alternative strategies, I would advise using additional qualitative methods apart from the surveys because there was the only respond of 6% to the all the email that were sent. Collecting more data would give a greater benefit of the research.
Furthermore, the benefit could be using more methods for data collection such as short interviews with the specific questions.
For instance when looking at the paper some other analyse method we would have done in a different manner when using CAQDAS to support in generating greater outputs of the collected data such as reports, models and charts to benefit in a more excellent visualisation of the examined concept relationship (Switala 2016). And also NVivo if we would choose other qualitative methods of data collection through the interviews. With attention to the interviews, we would expect a high degree of interaction with data for that reason; NVivo would benefit us to enable the refinement of codes as knowledge and ideas developed through- out the interview analyses (Easterby-Smith et al., 2012).
As a rule using more alternatives of the sample in order to present the results would be beneficial instead of focusing only on the very narrow group of the presentation such as geographical elements, supplier plants and consumer demands.
Finally, there is a limitation of data collection because there were not many responders to the surveys. I also believe that it shows the result that is not unyielding to benefit the research, and therefore we would require further investigation on that element. I found that the research did not fully justify primarily because of the research interpretation, sampling limitation and degree of understanding.
References:
- Easterby-Smith, M., R. Thorpe, & P. Jackson, (2012) Management Research, 4th edition, London: SAGE Publications
- Switala, M. (2016) 'ENTERPRISES' READINESS TO ESTABLISH AND DEVELOP COLLABORATION IN THE AREA OF LOGISTICS',Logforum, 12, (3) pp. 215-224. Available at: https://web.ebscohost.com (Accessed: 22/06/16).