The phantom steering effect in Q&A websites

Virtual rewards, such as badges, are commonly used in online platforms as incentives for promoting contributions from a userbase. It is widely accepted that such rewards “steer” people’s behaviour towards increasing their rate of contributions before obtaining the reward. This paper provides a new probabilistic model of user behaviour in the presence of threshold rewards, such a badges. We find, surprisingly, that while steering does affect a minority of the population, the majority of users do not change their behaviour around the achievement of these virtual rewards. In particular, we find that only approximately 5–30% of Stack Overflow users who achieve the rewards appear to respond to the incentives. This result is based on the analysis of thousands of users’ activity patterns before and after they achieve the reward. Our conclusion is that the phenomenon of steering is less common than has previously been claimed. We identify a statistical phenomenon, termed “Phantom Steering”, that can account for the interaction data of the users who do not respond to the reward. The presence of phantom steering may have contributed to some previous conclusions about the ubiquity of steering. We conduct a qualitative survey of the users on Stack Overflow which supports our results, suggesting that the motivating factors behind user behaviour are complex, and that some of the online incentives used in Stack Overflow may not be solely responsible for changes in users’ contribution rates.

A Social Network Analysis–based approach to investigate user behaviour during a cryptocurrency speculative bubble

In this article, we present a Social Network Analysis–based approach to investigate user behaviour during a cryptocurrency speculative bubble in order to extract knowledge patterns about it. Our approach is general and can be applied to any past, present and future cryptocurrency speculative bubble. To verify its potential, we apply it to investigate the Ethereum speculative bubble happened in the years 2017 and 2018. We also describe several interesting knowledge patterns about the behaviour of specific categories of users that we obtained from this investigation. Furthermore, we describe how our approach can support the construction of an identikit of the speculators who maneuvered behind the Ethereum bubble analysed. Finally, we show that this capability of supporting the hunting for speculators is intrinsic of our approach and can cover past, present and future bubbles.

THE EXTENDED UTAUT MODEL AND LEARNING MANAGEMENT SYSTEM DURING COVID-19: EVIDENCE FROM PLS-SEM AND CONDITIONAL PROCESS MODELING

The undertaken research investigates the extended unified theory of acceptance and use of technology (UTAUT) model from the perspective of online education in the deadliest period of COVID-19. This research investigates the extended dimensions, for instance, mobile self-efficacy and perceived enjoyment besides traditional elements of the UTAUT model with the relationship of behavioural intention and user behaviour of LMS. Since the COVID-19 led to social isolation (SIS), thus, this study has incorporated SIS as mediating factor and fear of COVID-19 (FOC) as the moderating factor for the considered extended model of UTAUT. The data of 1875 respondents was collected from five different Asian countries. For the data analysis, this study employed structural equation modeling through PLS-SEM and condition process modeling. This research demonstrates that the extended dimensions such as mobile self-efficacy, besides the traditional elements of the UTAUT model, exerted a cogent impact on behavioural intention except for the perceived enjoyment. Similarly, the behavioural intention demonstrated a substantial effect on the user behaviour of LMS. Additionally, social isolation as a mediating factor and FOC has a significant effect between dimensions of extended UTAUT model and behavioural intention of LMS. The outcomes of this research demonstrate significant theoretical and practical implications during the COVID-19 pandemic.

Characterizing user behavior in online social networks: Analysis of the regular use of Facebook

The analysis of user behaviour in online social networks (OSNs) is one of the important research interests related to human-computer interactions. OSNs gives a large space to share news with no limits around the world and allows user to benefit from properties of this interactive and dynamic system. The study of user behaviour on a social and popular platform characterized by the use of new technologies requires to understand and the analysis of collective behaviour on Facebook. This paper aims to analyse the usage patterns in OSNs using the visible interactions of Facebook, by studying the time of activity and the evolution of human behaviour through a process of detection of visible and non-volatile interactions. In the first step, we perform a data collection process based on breadth first search algorithm (BFS) and semi-supervised crawler agent. In the second step, we build an interaction quantification process to measure users’ activities and analysis related time series. The study of the frequency of periodic use has shown that the communities monitored follow a weekly rhythm that decreases over time to reach a frequency of daily use, which reflects a stability of activities and a case of dependency of use.