Adjusting Stated Intention Measures to Predict Trial Purchase of New Products: A Comparison of Models and Methods

Jamieson, Linda F.; Bass, Frank M.

doi:10.2307/3172905

Cited by 190 publications

(79 citation statements)

References 15 publications

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“…One example is voting: polls typically overestimate intention to vote (as do recollections) and sometimes misforecast the direction of voting (Crespi, 1989;Keeter and Samaranayake, 2007;Hopkins, 2009). In both commercial and academic marketing research, self-reported intentions to buy goods are widely used to plan the launch of new products and forecast sales (Silk and Urban, 1978;Urban et al, 1983;Infosino, 1986;Jamieson and Bass, 1989;Chandon et al, 2004), but these intentions are often upward-biased. Surveys are also routinely used to measure the value of nontraded public goods (e.g., for cost-benefit analyses as inputs to environmental protection, or to assess legal damages).…”

Section: Discussionmentioning

confidence: 99%

Hypothetical and Real Choice Differentially Activate Common Valuation Areas

Kang¹,

Rangel²,

Camus³

et al. 2011

J. Neurosci.

154

118

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Hypothetical reports of intended behavior are commonly used to draw conclusions about real choices. A fundamental question in decision neuroscience is whether the same type of valuation and choice computations are performed in hypothetical and real decisions. We investigated this question using functional magnetic resonance imaging while human subjects made real and hypothetical choices about purchases of consumer goods. We found that activity in common areas of the orbitofrontal cortex and the ventral striatum correlated with behavioral measures of the stimulus value of the goods in both types of decision. Furthermore, we found that activity in these regions was stronger in response to the stimulus value signals in the real choice condition. The findings suggest that the difference between real and hypothetical choice is primarily attributable to variations in the value computations of the medial orbitofrontal cortex and the ventral striatum, and not attributable to the use of different valuation systems, or to the computation of stronger stimulus value signals in the hypothetical condition.

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Section: Discussionmentioning

confidence: 99%

Hypothetical and Real Choice Differentially Activate Common Valuation Areas

Kang¹,

Rangel²,

Camus³

et al. 2011

J. Neurosci.

154

118

View full text Add to dashboard Cite

show abstract

“…Concept evaluations have proven accurate for new product variants and for new brands that can be described well with a written description. See Blattberg and Neslin (1990), Jamieson and Bass (1989), Mahajan and Wind (1988), Pessemier, et. al.…”

Section: Existing Premarket Forecasting Methodsmentioning

confidence: 99%

“…The judged probabilities are based on the IA. Intent scales are indicators of true probabilities (Juster 1966, McNeil 1974, Morrison 1979, Kalwani and Silk 1983, Jamieson and Bass 1989, but must be adjusted to provide specific predictions. Our first adjustment, shown in table 2, is made by normalizing the stated probabilities by the stated intent probabilities for all the cars in the respondent's consideration set.…”

Section: Measurement and Modelingmentioning

confidence: 99%

Premarket Forecasting of Really-New Products

Urban

Weinberg

Hauser

1996

Journal of Marketing

225

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“…When measuring adoption, scholars use both purchasing intentions and actual purchasing behaviour (Jamieson and Bass, 1989), although the two phenomena are quite different (Arts et al, 2011). The adoption intention, which is the specific interest of the present paper, is associated with the desire of consumers to purchase a new product: it refers to the consumer's state of mind before the actual purchase takes place and depends on the level of information and perceptions the consumer has at that time.…”

mentioning

confidence: 99%

Price or performance? A probabilistic choice analysis of the intention to buy electric vehicles in European countries

2018

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Price or performance? A probabilistic choice analysis of the intention to buy electric vehicles in European countries AbstractTraditional choice models perform poorly in understanding the determinants of the adoption of new products. First, data on pioneers and early adopters are biased towards specific performance characteristics of the product and the socio-demographic characteristics of the consumers. Second, surveys on the intention to buy underperform in detecting movements of those who do not intend to buy, who are the majority in the case of new products. Probabilistic choice models try to overcome this issue. By using survey data on electric vehicles, we theoretically contribute to this stream of literature and empirically estimate the impact of specific performance improvements and price reduction on the probability of consumers switching from non-intention to buy to intention to buy. Results show that price reduction is the most important triggering factor for the diffusion of electric vehicles, as it determines more than other factors the transition of consumers from the nonintention to the intention to buy an electric vehicle. The improvement in the driving range constitutes the second most important factor for low initial values of the stated intention to buy, while the possibility of recharging at home matters significantly more for consumers with high initial values of the stated intention to buy.

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Adjusting Stated Intention Measures to Predict Trial Purchase of New Products: A Comparison of Models and Methods

Cited by 190 publications

References 15 publications

Hypothetical and Real Choice Differentially Activate Common Valuation Areas

Hypothetical and Real Choice Differentially Activate Common Valuation Areas

Premarket Forecasting of Really-New Products

Price or performance? A probabilistic choice analysis of the intention to buy electric vehicles in European countries

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