The Behavior Analyst and the Individual Behavior Change Program (Guideline 4)

By using graphical representations of simple portfolio choice problems, we generate a very rich dataset to study behavior under uncertainty at the level of the individual subject. We test the data for consistency with the maximization hypothesis, and we estimate preferences using a two-parameter utility function based on Faruk Gul (1991). This specification provides a good interpretation of the data at the individual level and can account for the highly heterogeneous behaviors observed in the laboratory. The parameter estimates jointly describe attitudes toward risk and allow us to characterize the distribution of risk preferences in the population. (JEL D11, D14, D81, G11)

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Scientific Discovery, Social Change, and Individual Behavior Change

Behavior and Social Issues ◽

10.5210/bsi.v16i2.1951 ◽

2007 ◽

Vol 16 (2) ◽

pp. 190-196 ◽

Cited By ~ 3

Author(s):

Stephen E. Wong

Keyword(s):

Social Change ◽

Behavior Change ◽

Scientific Discovery ◽

Individual Behavior

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Aiding Users in Green IS Adoption with Persuasive Systems Design

Urban Science ◽

10.3390/urbansci4040052 ◽

2020 ◽

Vol 4 (4) ◽

pp. 52

Author(s):

Nataliya Shevchuk ◽

Harri Oinas-Kukkonen

Keyword(s):

Behavior Change ◽

Systems Design ◽

Theoretical Background ◽

Green Is ◽

Persuasive Systems Design ◽

Sustainable Behavior ◽

The Individual ◽

Green Information Systems ◽

Persuasive Systems ◽

Adoption Intention

Green information systems (IS) is a research domain that contributes to finding solutions for fostering environmental behavior in individuals, organizations, and communities. So far, researching Green IS for individual users has been less abundant and requires more insight. Users’ engagement with technologies start from adoption. Green IS challenges users to modify their lifestyles in order to achieve sustainable behavior patterns. This article is focused on persuasive Green IS, which have in-built features to convince users to modify their lifestyles and to improve technology adoption intention. In the theoretical background, main concepts, especially sustainable behavior, Green IS, IS adoption, persuasive systems, and persuasive systems design (PSD) model are presented. In this article, we analyzed three studies that focused on individual sustainable behavior change with persuasive Green IS. Overviews of these studies are presented and the studies were analyzed as a whole. The reviewed studies suggest that the PSD model has a high potential for becoming a tool for Green IS enhancement. The key themes identified from the studies bring value to both academics and practitioners, as well as suggest directions for researching the individual behavior change with persuasive Green IS in the future.

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Identifying Social Network Conditions that Facilitate Sedentary Behavior Change: The Benefit of Being a “Bridge” in a Group-based Intervention

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17124197 ◽

2020 ◽

Vol 17 (12) ◽

pp. 4197 ◽

Cited By ~ 1

Author(s):

Sabina B. Gesell ◽

Kayla de la Haye ◽

Evan C. Sommer ◽

Santiago J. Saldana ◽

Shari L. Barkin ◽

...

Keyword(s):

Social Networks ◽

Social Network ◽

Behavior Change ◽

Sedentary Behavior ◽

Small Group ◽

Markov Models ◽

Healthy Lifestyle ◽

Multilevel Regression ◽

Using Data ◽

The Individual

Using data from one of the first trials to try to leverage social networks as a mechanism for obesity intervention, we examined which social network conditions amplified behavior change. Data were collected as part of a community-based healthy lifestyle intervention in Nashville, USA, between June 2014 and July 2017. Adults randomized to the intervention arm were assigned to a small group of 10 participants that met in person for 12 weekly sessions. Intervention small group social networks were measured three times; sedentary behavior was measured by accelerometry at baseline and 12 months. Multivariate hidden Markov models classified people into distinct social network trajectories over time, based on the structure of the emergent network and where the individual was embedded. A multilevel regression analysis assessed the relationship between network trajectory and sedentary behavior (N = 261). Being a person that connected clusters of intervention participants at any point during the intervention predicted an average reduction of 31.3 min/day of sedentary behavior at 12 months, versus being isolated [95% CI: (−61.4, −1.07), p = 0.04]. Certain social network conditions may make it easier to reduce adult sedentary behavior in group-based interventions. While further research will be necessary to establish causality, the implications for intervention design are discussed.

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A new behavior change program using psilocybin.

Psychotherapy ◽

10.1037/h0088612 ◽

1965 ◽

Vol 2 (2) ◽

pp. 61-72 ◽

Cited By ~ 11

Author(s):

Leary Timothy ◽

Metzner Ralph ◽

Presnell Madison ◽

Weil Gunther ◽

Schwitzgebel Ralph ◽

...

Keyword(s):

Behavior Change ◽

Behavior Change Program

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Rejoinder to Dykstra and Kousser

Social Science History ◽

10.1017/s0145553200015297 ◽

1986 ◽

Vol 10 (1) ◽

pp. 87-90 ◽

Cited By ~ 1

Author(s):

William H. Flanigan ◽

Nancy H. Zingale

Keyword(s):

Individual Behavior ◽

Ecological Data ◽

Ecological Analysis ◽

Ecological Patterns ◽

Way Of Knowing ◽

The Many ◽

The Individual ◽

Inherent Ambiguity

In our article “Alchemist’s Gold”, we tried to make three main points. First, a large number of models of individual behavior can fit observed ecological patterns, and these models entail different assumptions about the nature of the individual relationship within and across the ecological units. This topic has been most impressively developed by Gudmund Iversen (1981). Second, there is nothing in the ecological data or the various types of ecological analysis that informs the researcher about how to choose among the many dissimilar models. All the models are compatible with the ecological data, and we have no way of knowing whether a particular inference about individual behavior is quite accurate or extremely misleading. Because of the inherent ambiguity over the unobserved individual behavior within ecological units, we made a third point of recommending techniques for reducing the amount of uncertainty surrounding estimates.

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