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This paper aims to establish if quantified self-data positively impact motivation in a goal pursuit across a broad cross-section of consumers and in multiple contexts; and to understand the underlying causal mechanism and identify boundary conditions.

Exploratory qualitative research helped direct the hypotheses development. Two quantitative experiments were then conducted via MTURK, involving 331 respondents, to test the hypotheses in two different personal goal areas (fitness and carbon footprint reduction).

Self-quantification has a significant and positive impact on anticipated motivation in both contexts studied. The mediated model provides insight into the psychological process underlying self-quantification’s motivational impact, which involves strengthening user perceptions regarding feedback meaningfulness, self-empowerment and goal focus. Age (>50) was found to be a boundary condition; however, distance to goal was not.

This paper focuses on initial (anticipated) motivation, which is the vital first step in behavior change. However, more work is needed to understand quantification’s long-term impact over the course of a behavior change process.

This research encourages firms to incorporate self-quantification features into products/services aimed at behavior change and helps firms better understand consumer-perceived benefits. It alerts firms regarding the extra effort needed to convince older consumers of these benefits.

This is the first study to confirm the “quantification effect” on motivation in multiple life areas and provide a causal model to explain how it works. It is also the first to highlight age as a boundary condition.

By the end of this chapter, you should be able to demonstrate an understanding of:

The importance of big data in the information revolution.

The resource-based view of the firm and dynamic capabilities as they relate to big data.

The use of big data in marketing decisions.

Consumer security concerns over the storage and processing of big data.