The Behavior Shift Score®

Summary

The Behavior Shift Score® (BSS) is a research-informed signal of how strongly your initiative is designed to support behavior change. It helps you assess plan quality before launch — not predict or guarantee outcomes.

The score is developed by Shiftic in collaboration with RISE (Research Institutes of Sweden). It builds on behavior change research, including the 2024 review by Albarracín, Fayaz-Farkhad, and Granados Samayoa, and has been extended for organizational change contexts such as leadership, culture, support structures, access, systems, and implementation design.

This is an early-stage model and should be understood as a directional design signal.

The BSS is provided “as is,” may contain errors or inconsistencies, and is intended to support — not replace — professional judgment. The BSS does not process personal data.

Why Does the Score Matter?

Nearly 90% of learning and change initiatives fail — often because they miss the steps and methods needed to make learning stick and translate into actual behavior change.

The BSS addresses this by giving you a measurable, research-backed signal of how strong your initiative’s design is from a behavioral science perspective. It helps you see what’s missing, what’s working, and where to improve — before you invest in implementation.

The Science Behind It

The Behavior Shift Score® is grounded in behavior change research, with one of its core foundations being:

“Determinants of Behaviour and Their Efficacy as Targets of Behavioural Change Interventions” Albarracín, Fayaz-Farkhad & Granados Samayoa (2024, Nature Reviews Psychology)

This review is one of the most comprehensive syntheses to date on behavior change. It brings together multidisciplinary meta-analyses across a wide range of individual and social-structural determinants of behavior, and compares how effective different intervention targets tend to be across domains. Key insights:

Determinants of behavior — individual and social-structural factors — vary substantially in how useful they are as targets for behavior change interventions.
Interventions that enable action, reduce friction, strengthen support, build habits, or improve access often outperform those focused mainly on awareness, knowledge, or beliefs.
Different intervention targets can be compared using typical effect sizes, often expressed as Odds Ratios (OR), though these should be understood as relative effects rather than literal probabilities of success.

The BSS is developed by Shiftic in collaboration with RISE (Research Institutes of Sweden), Sweden’s leading research institute. The Albarracín et al. review is a core scientific foundation for the model, but it is not the whole model. Building on that research, Shiftic and RISE have developed and adapted the model for organizational change contexts — especially in relation to workplace realities such as leadership, culture, support structures, access, systems, and implementation design.

The partnership focuses on building a rigorous, evidence-based scoring model — starting from a strong correlational foundation and developing it over time toward more causally informed and context-sensitive modeling.

How the Score Works

The scoring process follows five steps:

Shiftic scans your initiative and identifies which behavioral determinants each activity is most likely to influence — factors like Social Support, Active Practice, or Access to Resources.
Each determinant is linked to a typical effect size based on its Odds Ratio range: Minimal, Small, Medium, or Large.
Activities are ranked by impact and adjusted using a diminishing returns factor (0.75 decay), reflecting that each additional activity contributes somewhat less than the one before.
A duration multiplier is applied based on how long the initiative runs (short, medium, or long).
The result is a relative Behavior Shift Score® — a consistent indicator of how behaviorally sound your design is on paper, and where there’s room to improve.

How to Read the Score

The score is best understood as a relative design strength indicator, not a literal success rate or probability.

A score in the 30–50 range signals a reasonably strong behavioral design.
Higher scores reflect more powerful combinations of interventions.
Lower scores may indicate missing ingredients or an over-reliance on low-impact activities.

Note: The numbers may look like percentages, but they are not tied to real-world baselines yet. The current model uses a placeholder starting assumption to simulate a relative scale — this will be replaced with empirically grounded baselines in future versions.

What the Score Makes Possible

Makes behavioral quality visible — see at a glance whether your plan covers the right determinants and with sufficient impact.
Gives concrete design feedback — want to raise your score? Add higher-impact activities or rework weaker ones.
Builds shared language — move teams beyond gut feeling toward structured, research-backed planning.
Enables consistent comparison — benchmark different program ideas, drafts, or iterations against each other using the same logic.

Current Limitations

This is an Alpha model — useful, but intentionally simplified. Known limitations we’re actively working to address:

Prototype stage: Score calculations are not final and will improve as the model evolves.
No causation: The model identifies correlations between determinants and behavior change, not direct causes.
No dose modeling: A 10-minute e-learning and a two-day workshop are treated equally if they target the same determinant.
No timing or sequencing: Activities are scored statically, without accounting for when or how they're delivered.
No interaction effects: Interventions are assumed to add up independently; in reality, they may amplify or dampen each other.
No audience variation: The model doesn't yet adjust for differences across participant groups.

A Note on Correlations vs. Causation

At this stage, the score focuses on identifying correlations between determinants and behaviors — not establishing direct causation. Correlations indicate a relationship between variables but do not confirm that one causes the other.

This is a deliberate and necessary first step. By mapping these connections now, Shiftic is building the foundation for deeper causal studies and more actionable predictions in the future.

What’s Coming Next

Shiftic is working with RISE to evolve the BSS into a fully predictive model. The roadmap includes:

Structural Causal Modeling (SCM) — a more rigorous simulation of how interventions lead to outcomes.
Bayesian uncertainty modeling — future scores will express confidence ranges, not just single numbers.
Dose-response logic — a 10-minute nudge will be scored differently from a 3-week coaching cycle.
Context-aware scoring — adjustments for audience, setting, and baseline behavior.
Empirical grounding — scores progressively tied to real-world outcomes, replacing placeholder assumptions.

Important: This is an early version of Shiftic’s approach to helping you design and refine initiatives for real impact. A key part of this is the Behavior Shift Score, which gives you measurable insights based on a research-backed score. We’re excited to give you early access!

Disclaimer: The BSS is a directional design indicator, not a guarantee of real-world outcomes. As it is continuously developed and applied using AI, occasional inconsistencies or errors may occur. The BSS does not process personal data and is provided “as is” — the user remains solely responsible for decisions made based on its outputs.

© Shiftic AB, 2026. All rights reserved. The Behavior Shift Score® is a registered trademark and intellectual property of Shiftic AB and may not be copied, shared, or used without prior written permission.

Overall References

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Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268.
Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331–362.
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.
Salas, E., Tannenbaum, S. I., Kraiger, K., & Smith-Jentsch, K. A. (2012). The science of training and development in organizations: What matters in practice. Psychological Science in the Public Interest, 13(2), 74–101.
Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71(3), 500–507.
Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55, 591–621.
Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254–284.
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.
Judge, T. A., & Piccolo, R. F. (2004). Transformational and transactional leadership: A meta-analytic test of their relative validity. Journal of Applied Psychology, 89(5), 755–768.
Denison, D. R. (1990). Corporate Culture and Organizational Effectiveness. Wiley.
Schein, E. H. (2010). Organizational Culture and Leadership. Jossey-Bass.
American Society for Training and Development. (2010). The Accountability Study on Goal Setting and Follow-up. Retrieved from Google Books.
Fredrickson, B. L. (2001).
Baumeister, R. F., Vohs, K. D., DeWall, C. N., & Zhang, L. (2007).
Kok, B. E., & Fredrickson, B. L. (2010).
Kahneman, D., & Tversky, A. (1979).
Resources on intrinsic motivation: Self-Determination Theory website; Digital Promise.
McGowan, J. (2016).
McCormack, H. M. et al. (2015).
Cummings, C. A., & Worley, C. G. (2014).
Wandersman, D. P. et al. (2016).
Noe, K. A. (2017).
Valente, M. A. (2012).
Smith, R. J. (2018).
Nonaka, I., & Takeuchi, H. (1995).