Twitter Usage Across Two Geographic Markets
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Twitter Usage Across Two Geographic Markets
Section:
Since the research aimed to examine and compare Twitter usage across two geographic markets, a variety of organizations were considered for analysis. To be eligible for study, an organisation was required to be:
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publicly owned;
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have a corporate Twitter account based in both the US and in Australia; and
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to have a minimum level of use of the account, equating to a minimum of one tweet per day (that is a minimum of 182 tweets from each account over the data collection period from December 2009 to May 2010).
Since the Twitter strategy of an organisation is likely to vary with the customer’s involvement with the product (and thus with customers’ potential interest in related tweets), six companies which fulfilled these conditions were chosen for analysis: three consumer goods companies (Dominos Pizza, Billabong – a surf and leisure clothing company – and Cosmopolitan magazine – also known as Cosmo, a monthly magazine targeted at females) and three primarily service organisations (Microsoft, Qantas and Virgin Mobile). The companies reflect different involvement categories, ranging from low involvement products (Dominos Pizza) to Billabong (assumed to be medium or higher involvement for its target segment) and service companies assumed to be moderate (or higher) involvement.
The brands also represent different corporate structures; Cosmopolitan Australia and Microsoft Australia are subsidiaries of US parent companies; Billabong US and Qantas US both support the US operations of Australian companies; Dominos Australia is the master franchise holder for the US public company, and Virgin Mobile is operated by separate telecommunications companies in different markets (i.e. Sprint in the US, and Optus Singtel in Australia). The companies chosen thus allow cross‐country comparisons of organisations selling the same product, including those operated by the same company (Billabong, Cosmopolitan, Microsoft and Qantas) and those operated by different owners (Dominos and Virgin Mobile).
Since there can be multiple accounts tweeting on behalf of one organisation, we chose for analysis the Twitter account which appeared to be the most visible and/or central for each organisation, in order to best reflect any organisational policy or practice on the use of Twitter. For each company, this identified one US and one Australian account: “dominos”, “billabongusa”, “CosmoOnline”, “qantasusa”, “Microsoft” and “virginmobileus” for the US accounts, and “pizza_dominos”, “billabong1973”, “CosmopolitanAU”, qftravelinsider”, “MSAU “and “virginmobileaus” for the Australian accounts. All tweets from these accounts were downloaded for the period from 6 December 2009 to 27 May 2010 1 . A random selection of 200 tweets from ten of the corporate accounts was selected for coding and analysis. Two accounts (Billabong US and Qantas US) sent only 194 and 196 tweets respectively during the data collection period, so all of their tweets were used for analysis.
Tweets were coded to reflect the contrasting interpretations of interactivity discussed in the literature review section, thus reflecting both interpersonal and machine interactivity. First, tweets were classified relative to their interpersonal interactivity, based on levels of interactivity developed by Sundar et al. (2003) , and Rafaeli and Sudweeks (1997) . Tweets were classified as high‐interactive, if they contained a hashtag (#) – a Twitter convention allowing users to create and/or follow a thread of discussion by prefixing a tweet with a “#” character ( Kwak et al. , 2010 ); as medium interactive when the tweet contained retweets (forwarded tweets) or “mentions” (thus referring to another user, but not replying to a particular message), and as reactive/low interactive if the tweet consisted of a reply to another tweet. Tweets, which reflected more than one level of interactivity, were coded for the highest level; that is, a tweet which contained a reply, but which also contained a hashtag, was coded as fully interactive.
To reflect the concept of machine interactivity ( Hoffman and Novak, 1996 ), tweets were separately coded for whether they included hyperlinks, thus allowing recipients to choose to access additional material. From an organisation’s point of view, a link to an organisational web site (here termed an “internal” link) serves a very different purpose from a link to an external web site; the first continues organisational communication with the recipient, whereas an external link takes an individual to an external site, which may lead them to material which negates or compromises the organisation’s message ( Trammell et al. , 2006 ). As a result, tweets were coded separately for internal and external links.
Coding was separately performed by two coders, and agreement reviewed. There was very high agreement (over 95 per cent) and inconsistencies were resolved by identifying errors in coding, resulting in 100 per cent agreement. Comparisons between the percentages of tweets of different types were made using Mood’s median test (a non‐parametric test which is appropriate for comparing distributions which are not normally distributed and which contain outliers, such as these).
- Results
Section:
Descriptive statistics for the number of followers of each account, and the number of tweets sent by each account, are shown in Table I . Since the number of tweets sent may be a function of the length of operation of the account, the registration date for each account is also shown. Perhaps surprisingly, given the lower number of Twitter users in Australia, there were no significant differences across countries in the median number of tweets, followers, or ratio of tweets to followers (p > 0.1). However Table I shows very large differences across companies in the number of tweets and in the number of followers. Differences in the number of followers are not surprising, since a service like Microsoft (whose US account had the largest number of followers) might be expected to have many more followers than a low involvement product like Dominos pizzas. However, differences in the number of followers, are not explained by the product type, since Microsoft Australia had the lowest number of followers, among all the accounts.
Table I also reveals large intra‐company differences in the efficiency of organisational Twitter communication, as assessed by the ratio of followers to total tweets sent; the site with the largest ratio of followers to tweets (and thus with the highest efficiency of communication) was Microsoft US, with 93.8 followers for every tweet ever sent. In contrast VirginMobile Australia and Microsoft Australia had the lowest efficiency, with more tweets sent than followers as of May 2010. Because some people will follow for a short time, drop out and be replaced by others, the ratio of followers at one date to total tweets ever sent will under‐estimate efficiency of communication at any one point of time, but the low number of followers of some organisations, relative to the number of tweets sent, suggests that a significant amount of corporate time may be being invested in communicating with a relatively small number of followers.
3.1 Interpersonal interactivity: use of replies, retweets and mentions
The interactivity of tweets, based on Rafaeli’s (1988) levels of interactivity, is shown in Table II . There were again no significant differences in the median percentage of tweets in any of the three interactivity levels between US, and Australian companies (p > 0.1). More surprisingly, there was little evidence of consistent practices across US and Australian accounts from the same companies: for example Qantas Australia was the highest user of hashtags, with 60.5 per cent of its tweets containing hashtags, but only 5 per cent of the tweets from Qantas US contained hashtags. There were similar stark differences in the level of reactivity, judged by the percentage of tweets which were replies to others’ tweets: for example only 3 per cent of tweets by Microsoft US were replies, but 77.5 per cent of tweets sent by Microsoft Australia were replies. Microsoft and Qantas showed similar inter‐country differences in the use of retweets and mentions, with Microsoft US being the largest retweeter, and Microsoft Australia one of the lowest, and Qantas Australia being the second highest retweeter, and Qantas US equal lowest.
This apparent inconsistency in practices within organisations across the two market areas is shown in Figure 1 , which summarises organisational use of hashtags and tweets with replies. The figure shows a median split to differentiate organisations, which are low and high on replies, and (due to the large number of organisations which were low on hashtags) an upper quartile split to show the three highest users of hashtags. This categorisation shows one group of organisations whose tweets were largely reactive, being dominated by replies, another group (Qantas Australia, Microsoft US and Cosmo US) which were highly interactive, being the largest users of hashtags, and another group which was low on both hashtags and replies. It is notable that the two country accounts from only two organizations (Billabong and Virgin) fell into the same group using this categorization, and of those, Billabong’s consistent categorization was the result of low use of either interactivity feature.
Twitter Usage Across Two Geographic Markets
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