This Is AuburnElectronic Theses and Dissertations

Individualization and Information Quality of Location-Based Mobile Messages: An Application of Elaboration Likelihood Model




Jinhee, Han

Type of Degree

PhD Dissertation


Consumer and Design Sciences

Restriction Status


Restriction Type

Auburn University Users

Date Available



Location-based mobile technologies fulfilling consumers’ personalized needs during in-store shopping have gained significant attention in the retail world, as many brick-and-mortar stores and retailers had to adapt to the omnichannel presence in recent years. However, without sufficient knowledge about how to tailor location-based mobile messages (LBMM), retailers and marketers have been challenged by consumers’ perceptions of intrusiveness of receiving a LBMM and a lack of personal relevance of the LBMM, both of which can lead to suboptimal advertising effectiveness. This study empirically examined how varying levels of consumers’ task involvement in the generation of an LBMM, which is referred to in this study as LBMM individualization strategies, affect consumers’ perceptions of relevance and intrusiveness of an LBMM, which in turn impact the consumers’ elaboration (careful cognitive processing) of the LBMM content and attitude toward the LBMM. Data were collected employing a 3 (LBMM individualization strategies: randomization vs. personalization vs. customization) × 2 (information quality: strong vs. weak) experimental design. A U.S. national sample of 455 consumers participated in the online experiment. Results of the study indicate that consumers perceive an LBMM more relevant and less intrusive when it is more highly individualized or when they are more involved in the message generation process (i.e., customization > personalized > randomized). This finding is alarming in that a rushed LBMM sent based on only the consumer’s locational data (i.e., a randomization strategy) can lead to consumers’ perceiving it to be intrusive and irrelevant and thus may result in their avoidance behavior. In addition, this study revealed that a more individualized LBMM promotes consumers’ greater amount of cognitive processing (i.e., elaboration) of the message, which in turn promotes their positive attitude toward the LBMM, and this effect was found stable regardless of the quality of information (strong vs. weak) contained in the LBMM. These findings imply that consumers do not consider the content of a LBMM as a critical determinant to evaluate an LBMM when they receive a highly individualized LBMM. Due to the heightened personal relevance, a highly individualized LBMM motivates consumers to invest their time in processing the message more carefully and like the LBMM more. The findings of the study provide valuable theoretical and managerial implications.