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Effects of Brand-Fit Music on Consumer Behavior: A Field Experiment


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Businesses might use music to align consumers with brand values, thereby inuencing consumers' choices and perceptions. However, previous studies have focused on the eects of various characteristics of the music choice (e.g., tempo and style) and not on the eect of the congruence between music and brand values. Our cooperation with Soundtrack Your Brand, the exclusive provider of Spotify Business, makes it possible for us to test the eect of congruence between music and brand values on consumers in a eld experiment using 16 chain restaurants within the Stockholm metropolitan area. Our results show that a playlist that only includes brand-t songs increases revenues by 9.1 percent in comparison to selecting music that does not t the brand. We also nd that brand-t music has a positive impact on consumers' emotions and that music seems to have an unconscious eect on consumers.
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lnSALESit =α+β2T2it +β3T3it +β4T4it +
γ1(W EE K) + γ2(W E EK DAY ) + γ3Ri+it
(W EE K) (W E EK DAY )
.036 .030 .038 .021 .094.079
(.032) (.032) (.035) (.026) (.050) (.042)
.091∗∗ .090∗∗ .094∗∗∗ .080∗∗ .114∗∗∗ .107∗∗∗
(.038) (.040) (.036) (.034) (.035) (.033)
.048 .048 .061 .041 .093.088
(.038) (.035) (.042) (.027) (.052) (.063)
100 ×[eβ1] eβ1 + β
.034 .040 .058 .019 .063.096 .057
(.036) (.035) (.038) (.033) (.037) (.072) (.058)
.086∗∗ .082 .076∗∗ .111∗∗ .067.150∗∗∗ .156∗∗
(.039) (.051) (.059) (.019) (.033) (.033) (.035)
.052 .051 .059 .019 .033 .033 .003
(.044) (.036) (.040) (.042) (.048) (.115) (.041)
(AN SW E Rcit) = α+β2T2it +β3T3it +β4T4it
+θ1(CO NT R OLScit) + cit
i t
1.196 1.012 1.109 1.317∗∗ 1.2301.248∗∗ .900 1.192 1.2211.319∗∗
(0.142) (0.122) (0.128) (0.152) (0.143) (0.139) (0.104) (0.132) (0.135) (0.149) (0.568)
†† .601∗∗∗
††† .755∗∗
†† .930 .828†† .889 .685∗∗∗
††† .695∗∗∗
††† .824
†† .834†† 1.966∗∗∗
(0.091) (0.072) (0.088) (0.107) (0.097) (0.100) (0.079) (0.078) (0.092) (0.094) (0.615)
1.014 .641∗∗∗
††† .828 .944 .848.865 .690∗∗∗
††† .721∗∗∗
††† .841.808
(0.121) (0.077) (0.096) (0.108) (0.098) (0.096) (0.080) (0.080) (0.094) (0.091) (0.600)
† † † ††
.052.038 .040 .014 .002 .009
(.030) (.040) (.033) (.035) (.029) (.072)
.038 .108∗∗ .112∗∗∗ .080.050 .043
(.030) (.054) (.037) (.044) (.035) (.119)
.027 .051 .030 .021 .014 .099
(.038) (.059) (.045) (.050) (.034) (.140)
... Music is a form of art that attracts the vast majority of global population and has a remarkable impact on people's emotions and behavior. In particular, in-store consumer purchase behavior has been related to background music in the research literature [9,21,22,31]. Also, the role of music popularity, liking and recognition levels in shopping intentions [4,35] and the perception of time [2] has been investigated. ...
... A study was then conducted in order to obtain the actual recognition percentages for each of these 100 songs among a test population of 1041 annotators in Sweden. 9 We divided the initial list of 100 songs in 10 groups of 10 songs (5 of low and 5 of high recognition level in a randomized order), then each participant listened to 30-second samples of all the songs of one group and for each song he/she indicated whether he/she recognized it or not. We had ∼100 respondents per song 10 so we got a score 0-100 based on the percentage of respondents who responded positively. ...
... least and most recognised songs) to be less noisy than in intermediate recognition levels. This motivated our choice to perform the initial song selection out of two distinct sets (high, low).9 The study was performed through the Cint survey platform (https: // ...
Conference Paper
Full-text available
Cultural products such as music tracks intend to be appreciated and recognized by a portion of the audience. However , no matter how highly recognized a song might be at the beginning of its life, its recognition will inevitably and progressively decay. The mechanism that governs this decreasing trajectory could be modelled as a forgetting curve or a collective memory decay process. Here, we propose a composite model, termed T-REC, that involves chart data, YouTube views, Spotify popularity of tracks and forgetting curve dynamics with the purpose of estimating song recognition levels. We also present a comparative study, involving state-of-the-art and baseline models based on ground truth data from a survey that we conducted regarding the recognition level of 100 songs in Sweden. Our method is found to perform best among this ensemble of models. A remarkable finding of our study pertains to the role of the number of weeks a song remains in the charts, which is found to be a major factor for the accurate estimation of the song recognition level.
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