Analysis of comments to the first posting on their Malaysia Facebook

For the main study, I replicated the analysis process I used for the pilot study by following Mayring (2014) guideline.

I used the data collected from the comments posted in response to the first post made by MAS their M17 crisis and followed the same process I used for the pilot study. This process enables me to test the replicability of the process I used for the M370 crisis.

Using my knowledge of the theoretical background and the sub-research question that I intend to answer, which is to know to know how the public reacted to MAS CRM. I applied the selection criterion for the analysis process which I did by first capturing the comments that were made in response to the first posting made by MAS when the crisis broke out.

I read through the collected data so as to familiarise myself with it and then proceeded to data cleaning by separating comments that were written in English, non-English, comments that were spam or unrelated to the crisis or MAS post into different nodes (themes) (Figure 26).

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I then got myself familiar with the clean data (selected comments) as I did for the pilot study and read them line by line individually again and ran a word frequency query of the 100 most frequently repeated words, this is assumed to help in identifying themes and concepts (Bergin, 2011). Themes emerged along the process with a category label. As done with the pilot study, subsequent reading of comments were either subsumed into an already identified theme or give a new label. The process was repeated for until I got to the level of saturation where a new theme emerged i.e blame theme.

A total of 610 comments were posted in response to the first posting on their Facebook page which was used to notify the public that the organisation is in crisis with the loss of contact with the M17 plane over Ukrainian airspace. Of these comments, 528 comments were included for data extraction and analysis as they meet the inclusion criteria, 82 comments were deemed uncodeable as they either were not related to the post or non-English comments.

The flow diagram below illustrates the data extraction and analysis of the comments included for this analysis.

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Figure 27: Flow diagram for the data extraction and analysis of the comments for M17 Of the total 528 comments that met the inclusion criteria, some comments however, fell into more than one category.

5.4.1 Themes of the comments to first posting on Malaysia Airlines Facebook page In the first stage of coding and theme identification, I identified some recurring and similar

patterns and themes that were similar to that of the comments of the M370 analysis however, a new theme emerged in this analysis as some comments were apportioning crisis responsibility as to who should be held accountable for the cause of the crisis.

The main themes and codes that emerged after the coding process were the perspective related theme, Information related theme, emotion related theme with the same coding measures as used for the M370 crisis.

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The emotion related theme represents comments that included more of prayers to God/ Allah, blessings for the passengers and crew members and their families, sadness, surprise, and concern for those on board.

Figure 28 below gives a visual representation of the word frequency query using NVIVO software. Pray, Mh17 and sad represents the three most recurring words in this theme.

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Figure 29: Word frequency cloud for most recurring words

The perspective related: As explained in chapter four, the perspective related theme represented comments gives a personal view of the author of the comment on their action or the action of other e.g. like explaining the essence of clicking on the “like” icon on the Facebook page, educating other commenter on their actions or questions, judgment based on their past experience of using the airline or the route and personal experience with MAS, giving their opinion on the aircraft, flight route or even Malaysia as a country.

Figure 30 below gives a visual representation of the word frequency query using NVIVO software.

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Figure 30: Word frequency query for perspective related theme

Figure 31: Word frequency cloud for perspective related theme

The Information related theme were contexts where the comments were educating on the cause of the crisis or providing information to the public about the crisis.

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Figure 32 below gives a visual representation of the word frequency query using NVIVO software.

Figure 32: Word frequency query for information related theme

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However, a new theme emerged in the data for the M17 crisis which was categorised as the Blametheme.

The blame theme represents comments that attributed crisis responsibility for M17 as MAS had a prior history of a missing plane that occurred some months back and the fact that the plane was travelling over a crisis zone even made it more so easier for their stakeholders to see the need to apportion blame for who was responsible for the crash. This type of sense-making has great impact on the reputation of the organisation and their business continuity.

The blame theme included comments that absolved MAS as being responsible for the crisis, some comments blamed MAS, the terrorist and some attributed it to fate as MAS was not the only airline that used the route but just unfortunate to be the one that was targeted and crashed. Figure 34 below gives a visual representation of the word frequency query using NVIVO software.

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Figure 35: Word frequency cloud for the blame theme

The perspective, information and emotion themes retained the same definition as was used for the pilot study.

Table 14 gives an outline of the different themes that were generated from the comments in response to the CRS posted by MAS.

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Table 14: Themes from the comments to the first post of MAS

Theme Percentage Blame 9% Emotion related 65% Information related 17% Perspective related 5% Others 4% Total 100%

Figure 36: Graph showing the no of comments in each theme