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The objective of this pilot study was to apply the hard laddering technique [1], [2], embedded in meansend chain (MEC) theory [3], to understand why users utilize the various features and functionalities of social networking sites (SNSs). A convenience sample of 72 SNSs users in Brazil took part in the study. The study focused on Facebook as it is one of the primary means of social networking in this developing country. MEC theory has been developed in order to understand how consumers link attributes (A) of products with particular consequences (C), and how these consequences satisfy their personal values (V). The associations in the mind of the consumer between A’s, C’s, and V’s are labeled means-end chains. They are often seen as a representation of the basic drive that motivates consumer behaviour, for they link attributes of a product (such as Facebook), through the consequences (e.g., to Facebook users) stemming from these attributes, and, ultimately, to the personal values (e.g., of Facebook users) that underlie these consequences. Respondents were initially asked to write down up to three features (the attributes A) of Facebook that they consider the most important. For this purpose, respondents were presented with three text boxes to type in the attributes, which then were referred to in the subsequent questions. Next, respondents were asked why the first attribute they have just identified was important to them (the consequence C). Respondents subsequently were asked to give a reason (the personal value V) why they indicated that this consequence was important to them. After completing the above process for the first attribute, respondents were then prompted to fill in text boxes for the second and third most important attributes as well. Two researchers familiar with the topic coded the data. The first step of the data coding consisted of the content analysis of the attribute and consequence levels. Then the values were coded using the Schwartz’s list of values [4]. Cases where there were disagreements were resolved by the third, independent, researcher. In the end, four key attributes (Information search, Wider availability, User friendliness, and Social engagement), six consequences (Information access, Perceived usefulness, Socialisation, Ease of navigation, Perceived risk, and Satisfaction/ Entertainment), and five personal values (Intellectual/ Broadminded, Selfcontrolled/ Responsible, True friendship, Social recognition/ Sense of Accomplishment, and Comfortable life/ Happiness) were elicited. The most meaningful links between the attributes (A), consequences (C) and personal values (V) were presented in the form of a so-called hierarchical value map (HVM) [5]. The HVM constitutes the most popular approach for presenting MEC data [6]. The HVM is a graphical representation of the most meaningful relationships (meansend chains) between the A, C, and V categories. In the resulting HVM map, the users’ knowledge about Facebook’s functional attributes (features or physical characteristics) are linked with their knowledge about consequences (tangible benefits or risks) as well as personal values (high level reasons such as social recognition, self-control or happiness). The chains that are of most importance can be identified easily by looking for the largest numbers of respondents who indicated specific chain categories and the links between them. Such an investigation revealed, for example, the following chains: Information search Information access Intellectual/ Broadminded Wider availabilityPerceived usefulnessSelf-control/ Responsible User friendlinessSocialisationTrue friendship Social engagement Satisfaction/Entertainment Comfortable life/Happiness. A cursory scan of these selected results reveals immediate potential for using the study to understand which Facebook features are the most important to its users and why. This, in turn, may be used by, for example, advertising companies to better target specific market segments of SNSs users.

The Relationships between Cognitive Motivational Factors of Users of Social Networking Sites SNSs