Abstract
The phenomenon of information stickiness is widely seen by a declined information vitality and is hypothesised with multiple causes, but research on it is scant. Therefore, it is necessary to unmask the myth and concretise the concept. This research adopts an agent-based simulation model incorporating information accumulation, attitudinal proposition, interest, and preference for searching and matching information to determine how identified antecedents affect four defined symptoms of information stickiness in a simulated virtual community. This paper demystifies these symptoms, which are embedded in various personal, relational, and environmental settings. Some aggregate-level outcomes of consumers’ message-triggered responses emerge as an endogenous outcome of information stickiness. Analysis of data is visualised and quantified for its longitudinal changes in information acquisition, group affiliation, information growth, and the system's vitality.
Highlights
The technology-mediated community extends social space and social networks from face-to-face to online communication (Treiblmaier and Chong, 2011; Wellman, 1996), where consumers share and exchange content of interests (Hagel, 1999); attach/detach to/from groups (Granitz and Ward, 1996; Ebner et al, 2009); shape delight, engagement, trust, and loyalty toward products; and impose compound influences on others via reviews, comments, and online communication
The results show that information stickiness exists as a phenomenon with different symptoms and is embedded in and traceable back to online customers’ micro-level motives and restriction of information exchange and belief-framing
Hypothesis 8: Aggregately, information stickiness exists in the form of a declining information exchange tendency and displays a different evolution pattern in terms of information flow (H8a), vitality (H8b), volume (H8c), and disengagement (H8d) across promotional levels imposed on online community participants
Summary
The technology-mediated community extends social space and social networks from face-to-face to online communication (Treiblmaier and Chong, 2011; Wellman, 1996), where consumers share and exchange content of interests (Hagel, 1999); attach/detach to/from groups (Granitz and Ward, 1996; Ebner et al, 2009); shape delight, engagement, trust, and loyalty toward products; and impose compound influences on others via reviews, comments, and online communication. Et al, 2012), have proved that group features (Olmos-Peñuela et al, 2014), the intervention of an opinion leader such as a consultant (Olmos-Peñuela et al, 2014), group learning support (Krishnaveni and Sujatha, 2012), group characteristics such as network structure (Reagans and Mcevily, 2003), individual heterogeneity (Bhagat et al, 2002), and trust (Zhao and Lavin, 2012) can alleviate the symptoms of information stickiness to allow a flowing transmission between destinations These identified factors have been spotted for causing information stickiness from various aspects, a systematic framework is required to integrate all the considerations, which range from personal to interpersonal societal influences. The theoretical base of testing is reviewed as follows
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