Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information

Whose Advantage? Measuring Attention Dynamics across YouTube and Twitter on Controversial Topics

The ideological asymmetries have been recently observed in contested online spaces, where conservative voices seem to be relatively more pronounced even though liberals are known to have the population advantage on digital platforms. Most prior research, however, focused on either one single platform or one single political topic. Whether an ideological group garners more attention across platforms and/or topics, and how the attention dynamics evolve over time, have not been explored. In this work, we present a quantitative study that links collective attention across two social platforms -- YouTube and Twitter, centered on online activities surrounding popular videos of three controversial political topics including Abortion, Gun control, and Black Lives Matter over 16 months. We propose several sets of video-centric metrics to characterize how online attention is accumulated for different ideological groups. We find that neither side is on a winning streak: left-leaning videos are overall more viewed, more engaging, but less tweeted than right-leaning videos. The attention time series unfold quicker for left-leaning videos, but span a longer time for right-leaning videos. Network analysis on the early adopters and tweet cascades show that the information diffusion for left-leaning videos tends to involve centralized actors; while that for right-leaning videos starts earlier in the attention lifecycle. In sum, our findings go beyond the static picture of ideological asymmetries in digital spaces and provide a set of methods to quantify attention dynamics across different social platforms.Comment: Accepted into ICWSM 2022. 11-page main paper and 11-page appendi

Review of value and lean in complex product development

Approaches are being developed to improve complex product development from the perspective of value generation. However, the ideas and their relationships are still not fully articulated. We provide a structured literature review, with a primary but not exclusive focus on value ideas relating to lean in complex system product development. A framework organizes the concepts, methods, and their relationships. It clarifies the value delivery mechanism and could help to understand and thus improve value systems. Areas deserving further research attention are identified.We wish to thank the reviewers and editor for their valuable feedback, which helped to substantially improve early ver-sions of this articleThis is the accepted manuscript for a paper published in Systems Engineering Volume 18, Issue 2, pages 192–207, March 2015, DOI: 10.1002/sys.2129

Organizational energy: A behavioral analysis of human and organizational factors in manufacturing

This paper seeks to explore the behavior and embodied energy involved in the decision-making of information technology/information systems (IT/IS) investments using a case within a small- to medium-sized manufacturing firm. By analyzing decision making within a given case context, this paper describes the nature of the investment through the lens of behavioral economics, causality, input-output (IO) equilibrium, and the general notion of depletion of executive energy function. To explore the interplay between these elements, the authors structure the case context via a morphological field in order to construct a fuzzy cognitive map of decision-making relationships relating to the multidimensional and nonquantifiable problems of IT/IS investment evaluation. Noting the significance of inputs and outputs relating to the investment decision within the case, the authors assess these cognitive interrelationships through the lens of the Leontief IO energy equilibrium model. Subsequently, the authors suggest, through an embodied energy audit, that all such management decisions are susceptible to decision fatigue (so-called 'ego depletion'). The findings of this paper highlight pertinent cognitive and IO paths of the investment decision-making process that will allow others making similar types of investments to learn from and draw parallels from such processes