Amsterdam University of Applied Sciences

How location-based message characteristics lead to message value and store visitattitudes: An empirical studyVerhagen, Tibert; Meents, Selmar; Merikivi, Jani; Weltevreden, Jesse; Moes, Anne

Published in:Proceedings of the Oxford Retail Future Conference 2019, University of Oxford, Saïd Business School

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Citation for published version (APA):Verhagen, T., Meents, S., Merikivi, J., Weltevreden, J., & Moes, A. (2019). How location-based messagecharacteristics lead to message value and store visit attitudes: An empirical study. In Proceedings of the OxfordRetail Future Conference 2019, University of Oxford, Saïd Business School

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Download date: 24 01 2021

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How location-based message characteristics lead to message value and

store visit attitudes: An empirical study (EXTENDED ABSTRACT)

Tibert Verhagen (Amsterdam University of Applied Sciences, corresponding author)

Selmar Meents (Amsterdam University of Applied Sciences)

Jani Merikivi (Grenoble Ecole de Management)

Jesse Weltevreden (Amsterdam University of Applied Sciences)

Anne Moes (Amsterdam University of Applied Sciences)

Keywords: location-based message, message value, personalization, location congruency,

privacy concern, intrusiveness

Introduction

Having customers decide to enter a store is an important prerequisite for retail success and

seems to demand even more attention nowadays given the transformation of retail into a

highly competitive and complex multi-channel industry (cf. Pantano, Priporas, Sorace, and

Iazzolino, 2017). Surprisingly, except for some pioneering exploratory research into how

interactive technology in storefronts can be used to communicate with passers-by (see

Pantano, 2016), the role of advanced technology in store entry decision-making has largely

been left unaddressed. This has motivated us to setup this study into the effects of location-

based messaging (LBM), that is, the sending of marketer-controlled information tailored to

customers’ geographic position (Bruner and Kumar, 2007, p.4). In particular, this study aims

to answer the question of how and to what extent location-based message characteristics

generate location-based message value and influence customers’ attitudes to visit a physical

store.

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Research model

To answer our research question, we draw upon perceived value research (Zeithaml, 1988),

the theory on net valence (see e.g., Peter and Tarpey Sr., 1975), and mental accounting theory

(Thaler, 1985; Thaler, 1999) to propose and test a model. Next to location-based message

value and the store visit attitude, the model contains location-based message characteristics

that represent two benefits (personalization, location congruency) and two sacrifices (privacy

concern, intrusiveness). The inclusion of these particular benefits and sacrifices follows calls

for adding contextual richness to research models when studying IT-mediated settings (e.g.,

see Breward, Hassanein, and Head, 2017; Burton-Jones and Gallivan, 2007; Burton-Jones

and Volkoff, 2017; Hong, Chan, Thong, Chasalow, and Dhillon, 2014). Accordingly,

personalization, location congruency, privacy concern and intrusiveness were selected as they

specifically pertain to the typical nature of LBM; it is a marketing application that offers

recipients the opportunity to receive rather personal messages that are tailored to their

location, yet at the same time sending location-based messages appropriately is challenging

for retailers since such messages may invade customers’ privacy and may interrupt and

therefore irritate recipients (Gazley, Hunt, and McLaren, 2015; Hühn et al., 2017; Lee and

Rha, 2016). Figure 1 shows our research model and the corresponding hypotheses1.

1 Extensive support the model structure and each of the hypotheses is excluded from this extended abstract due to page limit considerations, but is available from the authors upon request.

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Fig. 1. Research model and hypotheses

Method

To test our research model we conducted a quantitative vignette study, consisting of a

vignette and a follow-up online survey which was used to measure the research constructs

and basic socio-demographics (Atzmüller and Steiner, 2010). The sample consisted of 1225

customers of a fashion retailer in the Netherlands. In the vignette, the respondents were asked

to imagine themselves walking in a shopping area that they are familiar with and that

contains a store of the fashion retailer. Then, the respondents were confronted with the

hypothetical situation that they would receive a location-based message on the retailer’ app

while being within walking distance from the store. Given that message content plays an

important role in shaping recipients’ message perceptions (Chang, Yu, and Lu, 2015; Chou et

al., 2014; Lin, Zhou, and Chen, 2014; Schindler and Bickart, 2012), we decided to make use

of multiple, disparate messages and distribute these at random to the respondents (a between

subjects design, see Atzmüller and Steiner, 2010). In collaboration with a team of marketing

professionals from the retailer, who indicated to prefer messages tapping into the persuasion

principles authority, scarcity, social proof and reciprocity as these were part of their current

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marketing communication practices, and drawing upon previous literature in the field of

persuasion (e.g., Cialdini, 2007, 2009; Griskevicius et al., 2009; Kaptein and Eckles, 2012;

Orji, Mandryk, and Vassileva, 2015), the following four messages were selected: “Visit our

store now and experience the expertise of our trained stylists” (authority-related), “Visit our

store now and benefit only today from a 20% discount on a product of your choice” (scarcity-

related), “Visit our store now and experience the customer service that other customers rate

as excellent” (social proof-related), ”Visit our store now and get a free cup of coffee or tea”

(reciprocity-related).

Results

We used Partial Least Squares (PLS) modeling using the software SmartPLS 3.0 (Ringle,

Wende, and Becker, 2015) with the consistent-PLS algorithm (500 iterations) to estimate the

model. After having established the validity and reliability of the multi-item measures and

absence of common method bias, we computed the standardized path coefficients and

amounts of variance explained. Together, personalization (b = 0.21, p < .001), location

concruency (b = 0.38, p < .001) and intrusiveness (b = - 0.28, p < .001) accounted for 53.9

% of the location-based message value variance. The influence of privacy concern on

location-based message value was non-significant. Furthermore, a total of 66.8 % of store

visit attitude was explained by personalization (b = 0.06, p < .05), location congruency (b =

0.34, p < .001), privacy concern (b = - 0.16, p < .001), intrusiveness (b = - 0.15, p < .001) and

location-based message value (b = 0.29, p < .001). Post-hoc tests were run to evaluate the

specified partial mediation model structure and test for possible effects of app usage and

differences in message type. Overall, the empirical testing confirms the predictive validity

and robustness of the model and reveal that location congruency and intrusiveness are the

location-based message facets with the strongest effect on both message value and store visit

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attitude. Together, the results lead to the support of eight hypotheses (hypotheses 1, 2, 3, 4, 6,

7, 8, 9) and rejection of one hypothesis (hypothesis 5).

Conclusions and implications

This study examined the influence of four location-based message characteristics

(personalization, location congruency, privacy concern, and intrusiveness) on location-based

message value and the attitude towards visiting the physical store. Except for the insignificant

influence of privacy concern on location-based message value, all tested relationships were

significant. As such, our findings add to the scarce literature (e.g., Pantano, 2016) on the

effect of advanced mobile technology on consumer store entry decision-making. When

centering on the relative relevance of the location-based message characteristics, our study

especially highlights the key role of location congruency as a benefit and intrusiveness as a

sacrifice. Location congruency was the main determinant of the perceived value of location-

based messages as well as of the customers’ attitude towards visiting the physical store,

thereby adding to previous studies demonstrating the importance of contextual relevance of

location-based messages (e.g., Luo, Andrews, Fang, and Phang, 2014). The found negative

effect of intrusiveness is in keeping with previous marketing literature on intrusive

advertising messages (e.g., Edwards, Li, and Lee, 2002; Wang and Calder, 2006). For

retailers, the fact that location congruency seems the strongest predictor of the dependents in

our model implies that creating and sending locational congruent messages should have

priority when engaging in LBM-activities but not without taking into account that the specific

message may come across as intrusive. One way to accomplish well-balanced location-based

messages could be, for instance, by ensuring that the sent messages are of high interest given

the proximity of the store (e.g., by making use of special, time-limited discounts and offers),

instead of reflecting generic store promotions (cf. Unni and Harmon, 2007), while at the same

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time making use of a friendly, more social communication style (cf. Alhakami and Slovic,

1994; Keeling, McGoldrick, and Beatty, 2010).

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