Deciding not to decide: Antecedents of consumer decision delegation

Behavioral researchers and managers have long been interested in the processes consumers use to choose between several product alternatives. Traditionally, researchers have assumed that consumers acquire attribute-level information from various personal and impersonal sources and process that information on their own to make a decision. This study examines a different kind of choice strategy wherein consumers delegate the decision-making to someone else, usually an expert. Using this strategy, a consumer seeks a recommendation instead of attribute-level information and uses that recommendation to make a purchase decision. In other words, the consumer subcontracts the decision-making to an expert (surrogate). In this study, a theoretical framework is developed to understand the phenomenon of decision delegation and the factors that influence delegation. The impact of three sets of factors--consumer, surrogate, and task characteristics--on decision delegation is examined. Empirical verification of the model is conducted in the context of new computer purchases. Structural equation modeling is used to estimate the model. Empirical results strongly support the hypothesized relationships between the three sets of antecedent variables and the decision to delegate. For example, there is strong empirical evidence to suggest that consumers delegate more when a surrogate customizes his/her recommendation and when consumers can hold the surrogate accountable for the recommendation given. Similarly, task characteristics such as the evaluability of a product\u27s attributes and the expected return on search also significantly influence the decision to delegate. Consumers who are less uncertain about a product or have a higher need for control over their decisions tend to delegate less. Contributions, implications for managers, surrogates, and public-policy makers are discussed. The study concludes with a section on limitations and suggestions for future research