With the rise of new media, customers have assumed a more active role as market players and the ways in which they collect and distribute or exchange information are constantly changing (Hennig-Thurau et al. 2010:331). These media platforms are a perceived threat to long-standing business models and corporate strategies, but simultaneously provide brands with growth opportunities and the potential to maintain their relationship with the public through online communication (Hennig-Thurau et al. 2010:331, Rybalko & Seltzer 2010:336). As a result, social networking sites (SNSs) serve significantly in environments where negotiation between individuals and business organisations may occur (Kent & Taylor 1998:332). It has not only attracted the attention of “youngsters,” but also adults aged 35-44 (Kaplan & Haenlein 2010:59). The free micro- blogging platform Twitter, in particular, is praised for having altered the realm of marketing in business and stipulation of dialogue on a global level (Bruns & Stieglitz 2013:100, Sevin 2013). However, communication on Twitter can become problematic for a brand since users are able to generate and share content instantaneously to public audiences without the supervision of traditional gatekeepers (Jansen et al. 2009:2169). This study attempts to understand ever-changing interactions between brands and consumers.
The purpose of this project is to gain insight into the use of social media platforms, particularly Twitter, by two different business brands to communicate. It will do so through a comparative analysis of two food ordering and delivery platforms, of which one is American-based, and the other Australian-based. The comparative analysis will cover the ways in which both businesses manage their own brand perception and engage with its users online in public relations and customer relation management. Compared to products, services are characterised as intangible and the quality provided varies (Hornikx & Hendriks 2015:179), therefore the information surrounding the industry is more unpredictable and interesting to study.
Menulog is Australia’s largest online food ordering platform and has been operating for over ten years. By contrast, the American-based UberEats is relatively new as it was launched by Uber in 2014 and partners with restaurants in many cities around the world. Through critical analyses of the types of messages these brands send out, it is revealed that both brands employ different promotional methods, as well as strategies to suppress the voice of users who share complaints or
question their quality of service. User voice is further interpreted through public sentiment !3
expressed about the brands. Although Twitter provides a platform for connectivity and discussion, it is ultimately up to the brand to improve their online presences by facilitating a space of safe and constructive communication (Rybalko & Seltzer 2010:337).
This report argues that Menulog is more susceptible to negative sentiments on Twitter than UberEats because it operates nationally instead of internationally. It also offers the possibility that UberEats manages negative sentiments more effectively, and therefore has a stronger customer service position when interacting with customers. This hypothesis will be tested on a randomly selected sample of tweets.
This section describes and justifies the methods used in the report, which are content analysis and sentiment analysis. Any apparent ethical concerns will also be raised.
1. Content Analysis (CA)
CA is a research technique for objective, systematic and qualitative analysis of communication content (Berelson 1952:18). The structure adopted for this study is similar to that of Hornikx and Hendrik’s (2015), where the goods and services of 24 brands were analysed on a smaller scale. The tweets were collected by building a scraper, using an Application Program Interface (API). Random sampling was used to reduce the sample size fairly, and then two rounds of coding was applied manually by two individuals. A reliability test was conducted to determine what percentage the two results matched.
This study uses NodeXL, which is a Microsoft Excel open-source template. Through the import tool, 300 tweets were collected “From Twitter User’s Network,” specific to the Twitter handles @Menulog and @UberEats. This ensured the tweets collected were only ones authorised by the brands’ accounts. The sample size was reduced by half through the RAND (=rand()) function in excel, adjacent to the tweets column. The RAND numbers were then copied and pasted as real
1. What types of messages do brands send out on Twitter? How do they compare?
2. How does sentiment expressed about the brands compare?
3. How do the social network maps for the chosen brands compare?
values in a separate column, and the initial RAND column deleted. After sorting the values from A to Z, smallest to largest, the first 150 tweets were selected as the final sample.
The coding frame above was then applied manually (see Appendix A for full manual). According to Krippendorff (2007:330), coding frames are used to interpret observable data with minimised judgement. Still, subjective categories can cause discrepancies hence a second observer re-codes the sample tweets before it is tested for reliability using ReCal2 by dfreelon.org (see Appendix B).
2. Sentiment Analysis (SA)
If brands are to protect public perception of their online image, it is crucial to monitor consumers’ opinions on Twitter. This allows them to respond to attacks directly and also foresee future harm (Hornikx & Hendriks 2015:177).
SA was collected using a lexicon-based program called SentiStrength, which estimates sentiment using a five-point scale. It reports the number “5” as being the most positive and “-5” as the most negative. It is important to note that the numbers “1” and “-1” are considered to be neutral sentiments.
300 tweets were collected using the import tool “From Twitter Search Network” on NodeXL. The results were cleaned to eliminate tweets by the brands themselves, as well tweets in another language besides English. The .txt file was uploaded onto SentiStrength and analysed. Using functions on excel like =AVERAGE and = COUNTIF, the averages of positive and negative sentiments were calculated, alongside the amount scored on each number of the five-point scale.
This method was adapted by the SA conducted in Jansen et al. (2009). Because not all nuances can be accounted for, SentiStrength must be combined with other methods.
3. Ethical Considerations
There are ethical debates surrounding the appropriateness of online research and anonymously collecting data without gaining formal approval to do so (Kadushin, 2005). However, the topic of this study does not touch on sensitive topics or threaten the safety of Twitter users therefore the process of gaining informed consent was deemed impractical.
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