Purpose The purpose of this paper is to focus on understanding how social influence and personality of individuals differentiate between users’ social network fatigue and discontinuance behavior. Furthermore, the most common discontinuance behavior among users was investigated. Design/methodology/approach The research model was tested with the data from 163 Instagram users based on online and offline surveys. The partial least squares method was used to test the proposed hypotheses of this study. Findings The results indicate that social influence affects users’ discontinuance behavior and social network fatigue. Social network fatigue is greater in users with higher reported social influence compared to those with a lower one. Moreover, in response to social network fatigue, users prefer to keep their activities under control instead of switching to alternative social network sites (SNSs) or a short break in social network activities. Practical implications By achieving a better understanding of users’ feeling and behaviors, social network providers may codify their strategies more efficiently. Originality/value The study is novel in exploring users’ SNS fatigue and their discontinuance behavior by integrating social influence and personality. The authors defined a new concept of effect of social influence on social network fatigue. Additionally, the authors examined which discontinuance behaviors in individuals were more prevalent.

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Web Service adaptation and evolution is receiving huge interest in the service oriented architecture community due to dynamic and volatile web service environment. Regarding quality of service changes, Web Services need to be able to adapt dynamically to respond to such changes. However, formulating quality of service parameters and their relationship with adaptation behaviour of a service based system is a difficult task. In this paper, a Fuzzy Inference System (FIS) is adopted for capturing overall QoS and selecting adaptation strategies using fuzzy rules. The overall QoS is inferred by QoS parameters, while selection of adaptation strategies is inferred by the overall QoS, importance of QoS and cost of service substitution. In particular, hierarchical fuzzy systems were used to reduce the number of rules. Our approach is able to efficiently select adaptation strategies with respect to QoS changes. We test and compare our fuzzy adaptation with a naive adaptation approach that works based on precise measurement of QoS in order to show the performance of the approach in reducing the number of service substitutions and adaptation cost

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