Justin wants to buy a new smartphone. He did a lot of research over the Internet and identified four attributes that he would rate his smartphone options on. He considered low price, sensitivity to touch, low weight, and compatibility with apps, as the attributes. He used a ten-point scale to rate how he thought each of his options would perform or how likely was it that the phones had those attributes. Next, he used a -3 to +3 scale to rate how he felt about these attributes. Finally, he opted for one particular model based on the highest overall attitude score. Which model did Justin implement?

Question

BlackTom AI · Accepted Answer

We start by restating the scenario to anchor the analysis: Justin evaluates four smartphone attributes—low price, sensitivity to touch, low weight, and compatibility with apps—on a ten-point scale for performance, and then rates his feelings about these attributes on a -3 to +3 scale. He then selects the model with the highest overall attitude score. Now, let's examine each answer choice in turn.

Option 1: The balance model
This option suggests a general balancing approach where attitudes are formed by weighing positive and negative judgments to reach a compromise. However, the description in the stem emphasizes a structured evaluation of attributes on a performance scale combined with a separate affective rating and then selecting the highest attitude score. This aligns more with a target model of attribute-based attitude formation rather than a generic balance concept. Therefore, this choice does not best fit the described process.

Option 2: The elaboration likelihood model
The elaboration likelihood model (ELM) explains how attitudes are formed and changed through central or peripheral routes depending on motivation and ability to process information. While ELM is a foundational attitude theory, the stem’s emphasis on rating specific attributes and summing to an overall attitude score points to a more narrow, attribute-based approach rather than a dual-route processing framework. Hence, this option is unlikely to be the model Justin implemented.

Option 3: The behavioral intention model
The behavioral intention model (often tied to theories like the Theory of Planned Behavior) focuses on intention to perform a behavior as a predictor of actually performing it, incorporating attitudes, subjective norms, and perceived behavioral control. The scenario describes forming an overall attitude toward options and choosing a model based on attitude strength, not a calculation of behavioral intention to buy. Therefore, this option does not match the described method.

Option 4: The changing schema-based affect model
This model would involve attitudes shifting with changes in core schemas and affective responses over time, typically in response to broader cognitive restructuring. The stem’s procedure is a straightforward attribute-by-attribute evaluation and a resulting attitude score, lacking the emphasis on schema-driven affect change. Thus, this option does not fit well with the described process.

Option 5: The attitude-toward-the-object model
This option directly aligns with the described approach. The attitude-toward-the-object (AtoO) framework involves evaluating multiple salient attributes of an object (here, a smartphone) on cognitive scales (ten-point performance ratings) and affective responses (-3 to +3), then combining these to form an overall attitude toward the object and choosing the option with the highest attitude score. The sequence in the stem—rating attributes, assessing feelings about those attributes, and selecting the model with the highest overall attitude score—maps precisely onto AtoO methodology.

In summary, the reasoning for each option centers on matching the described process to a theory of attitude formation. The balance model’s broad compromising idea, ELM’s dual-route processing, the behavioral intention focus on purchase intentions, and the schema-based affect notion all diverge from the explicit attribute-by-attribute plus affective rating and summation described. The attitude-toward-the-object model uniquely captures this direct attribute-and-affect evaluation leading to a highest-attitude choice.

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