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While social media sites have been successfully adopted and used in the B2C context, they are perceived to be irrelevant in B2B marketing. This is due to marketers’ perception of poor usability of these sites in the B2B sector. This study investigates the usability of social media sites when adopted for B2B marketing purposes in the one of world’s largest social media market: China. Specifically, by extending the Technology Acceptance Model with Nielsen’s Model of Attributes of System Acceptability, we assess the impact of usefulness, usability and utility on the adoption and use of these sites by B2B marketing professionals. The empirical investigation reveals that marketers’ perception of the usefulness, usability and utility of social media sites drive their adoption and use in the B2B sector. The usefulness is subject to the assessment of whether social media sites are suitable means through which marketing activities can be conducted. The ability to use social media sites for B2B marketing purposes, in turn, is due to those sites learnability and memorability attributes

This study extends literature on e-commerce trust and re-purchase intentions by exploring the role of swift guanxi and perceived effectiveness of institutional mechanisms (PEEIM) in the context of a Chinese e-marketplace-Taobao. We explore how Taobao's social media technologies (online reviews and instant messenger) can improve swift guanxi and PEEIM by increasing online interactivity and presence. We find that buyers' PEEIM negatively moderates trust in online sellers and repurchase intentions. We show that swift guanxi, created by social media's interactivity and presence, enhances trust, which further increases repurchase intentions. Theoretical and managerial implications and future research directions are discussed.

In the last decade, social media platforms have become important communication channels between businesses and consumers. As a result, a lot of consumer-generated data are available online.Unfortunately, they are not fully utilised, partly because of their nature: they are unstructured, subjective, and exist in massive databases. To make use of these data, more than one research method is needed. This study proposes a new, multiple approach to social media data analysis, which counteracts the aforementioned characteristics of social media data. In this new approach the data are first extracted systematically and coded following the principles of content analysis, after a comprehensive literature review has been conducted to guide the coding strategy. Next, the relationships between codes are identified by statistical cluster analysis. These relationships are used in the next step of the analysis, where evaluation criteria weights are derived on the basis of the social media data through probability weighting function. A case study is employed to test the proposed approach.

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