Rumpf and Breuer are with the Institute of Sport Economics and Sport ..... took place in a purpose-built laboratory equipped with a comfortable armchair, a table-.
Journal of Sport Management, 2012, 26, 521-531 © 2012 Human Kinetics, Inc.
Official Journal of NASSM www.JSM-Journal.com ARTICLE
The Viewer’s Reception and Processing of Sponsorship Information in Sport Telecasts Christoph Breuer and Christopher Rumpf German Sport University Cologne Although enormous sums are spent on sport sponsorships, knowledge of sponsorship information processing is still limited. For a continuing growth of sponsorship as a field significant improvements in our understanding of sponsoring effectiveness are required. Whereas the direct effect of sponsor signage exposure on sponsor recall has been identified in several studies, attention to sponsor signage as the mediator of sponsorship information has not been investigated thoroughly. Based on spotlight theory and the associative network model of memory, the present paper addresses this research gap and investigates the viewer’s visual attention to sponsorship information by applying eye tracking methodology. Regression models have been estimated to analyze information reception and processing in sport telecasts. The results reveal that the capture of attention is determined by the placement of sponsor signage and by exposure variables. Furthermore, sponsor recall is found to be a function of attention and brand-related variables. Corporate investments in sponsorships have increased enormously during the last decades (IEG, 2012; IFM Sports Marketing Surveys, 2011). However, both academics and managers are skeptical about the marketing effectiveness of sponsorships compared with alternative communication instruments (Lardinoit & Derbaix, 2001; Olson & Thjømøe, 2009). Since sponsorship investments fall into a broad set of marketing communication options, it is likely that marketing managers will only invest in a sponsorship program if it serves the overall marketing objectives more effectively and efficiently than its alternatives. Therefore, a valid measurement of effectiveness and efficiency is essential to the future of this communication discipline (O’Reilly & Madill, 2009). Even though the literature has realized the need for a more thorough measurement (Cornwell & Maignan, 1998; Harvey, Gray, & Despain, 2006; Meenaghan, 1983), research still lacks a substantial understanding of how sponsorship works, especially in a sport media context (Cornwell, 2008; Crompton, 2004). As a consequence, most attempts to measure the effectiveness of sponsorships in relation to other communication strategies have provided limited insights. To date, the measurement of sponsorships has been based on the simple assessment of sponsor signage exposure time within sport telecasts (Cornwell, 2008; Olson, 2010). However, there is no evidence that television viewers actually absorb the exposed sponsorship information because they are primarily focused on sport action. Thus, the exposurebased approach (counting seconds of visibility) seems Rumpf and Breuer are with the Institute of Sport Economics and Sport Management, German Sport University Cologne, Cologne, Germany.
highly problematic and might lead to invalid sponsorship evaluations. In this context, Shilbury, Westerbeek, Quick and Funk (2009) state that “exposure is one thing, but what really matters is the impact of exposure” (p. 270). Another shortcoming of the current approach is that it rates each sponsor’s signage exposure equally. No distinction is made as to whether the signage is exposed exclusively, or together with ten other sponsor signages. Further, the size of sponsor signage exposure is not considered, even though we know from cognitive psychology that the relative size of an object (in the field of view) plays an important role in our visual perception (Goldstein, 2002). Sponsor consultancies try to overcome these shortcomings by inventing “rules of thumb” for weighting the quality of sponsor signage exposure. However, these “rules of thumb” lack empirical verification (Crompton, 2004; Olson & Thjømøe, 2009). Against this background, our paper aims to achieve the following objectives: 1. To investigate the sponsorship impact on the viewer’s attention while watching sport telecasts as a function of sponsor signage exposure. 2. To model the process by which attention to sponsorship information leads to recall for this sponsoring brand.
The ‘Black Box’ of Sponsorship Information Processing Despite the breadth of research in this field, only few studies have examined the sensory information reception and cognitive information processing in the consumer’s ‘black box’. Cornwell, Weeks and Roy (2005) were among the first authors providing a general consumer-focused model 521
522 Breuer and Rumpf
of sponsorship information processing. Therein, they bring together variables on the individual, market and management level that are assumed to affect sponsorship outcomes. By analyzing relevant theoretical approaches and empirical findings, Cornwell and colleagues set the basis for a theory-based research on sponsorship effectiveness. However, their review clearly indicates that mediating elements in the information flow, such as the sport viewer’s attention, are neglected in empirical sponsorship research to date. In the experimental studies by Lardinoit and Derbaix (2001) and Lardinoit and Quester (2001), attention is regarded as a driver for the processing of sponsorship information. The authors analyzed the effect of field and telecast sponsorship on the participant’s recall. As a result they argue that television sponsorships receive more attention than field sponsorships since both aided and unaided recall is higher. However, memory scores should be considered as limited indicators of attention, since an object can be attended without being stored in long term memory (Rosbergen & Pieters, 1997). To our knowledge, there are only very few studies that use eye-tracking to measure attention in a sponsoring context. Important to note are the contributions by d’Ydewalle, Abeele, Rensbergen, & Coucke (1988) and d’Ydewalle and Tasmin (1993). Their results imply that sponsorship information only receive a very small share of the viewer’s attention (about 3% of exposure time). Beyond this, they did not find strong interrelations between visual attention and memory. However, these findings must be treated with caution since the study did not use multivariate statistics to control for, for example, the prominence of the sponsoring brands. In a number of studies, brand prominence has been identified as a moderating variable for the cognitive retrieval of event-sponsor relationships (e.g., Johar & Pham, 1999, Pham & Johar, 2001; Wakefield, BeckerOlsen, & Cornwell, 2007). Based on an experimental design, Pham and Johar (2001) provided empirical evidence that sponsor identification is biased toward wellknown brands. As an explanation, the authors argue that consumers tend to rely on heuristics when they are unable to retrieve the sponsor’s name from memory. In general, most experimental designs arrange the conditions so participants are instructed to focus all their attention on a sponsorship which is often presented in form of a fictional press release (e.g., Humphreys, Cornwell, McAlister, Kelly, Quinn, & Murray, 2010; Johar & Pham, 1999; Pham & Johar, 2001). In a real-life situation the viewer’s attention is mostly dedicated to game or race action and only a very small share of attention is directed to sponsorship information as a sideline (d’Ydewalle et al., 1988; d’Ydewalle & Tasmin, 1993; Le Roy & Vivier, 2008). Given this fact, research designs need to be deployed that (1) allow of an ordinary allocation of attention and (2) measure the share of attention devoted to sponsorship information.
The Flow of Sponsorship Information Most approaches to the evaluation of sponsorship neglect the complex information processing in the minds of sport television viewers (Cornwell, 2008). Accordingly, effectiveness is often analyzed as a simple input-outputmodel: A stimulus (sponsorship information) is given to a sport television viewer, and causes a certain reaction, which can be cognitive, affective or behavioral in nature. This present study seeks to extend the understanding of sponsoring effectiveness by focusing on the sensory and cognitive processes which occur in between the input and output stage. In this context, we analyze sponsoring effectiveness as a sequential flow of information: first, the sponsorship information is selected by the viewer’s visual system from the tremendous amount of visible sponsorship information on the television screen. In this throughput stage, the chance for cognitive processing increases but it still does not mean that the sponsorship information has been processed. Only when the viewer’s attention reaches a certain threshold and the information content is relevant enough, does the information get processed and finally stored in long term memory (KroeberRiel, Weinberg, & Gröppel-Klein, 2009). The ‘pyramid of sponsorship information’ in Figure 1 illustrates this model of information flow. However, most sponsorship information fails to gain attention (d’Ydewalle & Tasmin, 1993) or is not correctly stored in mind (Pham & Johar, 2001). The resultant question — why sponsorship X is perceived and sponsorship Y is not — is highly relevant for both academics and marketers. The analysis of consumerrelated variables like involvement with the sport event could hardly give an answer to this question (Alexandris, Tsaousi, & James, 2007; Dees, Bennet, & Villegas, 2008; Pham, 1992). Therefore, we will focus on sponsor-related variables, such as the different placements of sponsor signage at the sports venue (e.g., sponsor signage on the jersey or signage on the perimeter board) and variable exposure conditions in a television context. Olson and Thjømøe (2009) found that the placement of the sponsor signage and the duration of exposure affect sponsoring effectiveness, among other variables. Like most research to date Olson and Thjømøe (2009) directly assessed the influence of variable exposure conditions on cognitive and affective outcomes. However, the effects on memory are more subtle, and therefore the analysis of information processing needs to include mediating concepts like (visual) attention. When sport viewers move their eyes across the television they extract visual information exposed on the screen. In this context, attention functions as both a selective and executive mechanism (Pieters & Wedel, 2008). This means that the role of attention is central in sponsorship information processing: firstly, attention selects sponsorship information from the enormous amount of alternative information on screen, and secondly, attention
Processing Sponsorship Information in Sport Telecasts 523
Figure 1 — Pyramid of sponsorship information.
regulates the flow of sponsorship information. Therefore we propose that the measurement of attention should be prefixed to the measurement of cognitive, affective or behavioral reactions because only sensory-received information can be processed, stored and later retrieved (Kroeber-Riel et al., 2009).
Attention to Sponsorship Information on Television Even if the viewer’s eyes are directed to the television, it does not appear that the visual system receives all the information visible on screen. Rather, the sport television viewer attends to different positions on screen at specific points in time. According to spotlight theory, the screen position where the spotlight of attention is focused is “illuminated” so that it stands out and can be processed more efficiently than the less illuminated screen positions. Once the information from the screen position has been processed the attentional spotlight can be shifted (Palmer, 2002). Eye-tracking methodology plays an important role in the assessment of sponsoring since eye movements are eminent indicators of visual attention (Duchwoski, 2007; Holmqvist et al., 2011; Pieters & Wedel, 2004). By applying eye-tracking, how often sport television viewers devote their attention toward sponsorship information while watching sports becomes measureable. This is highly relevant, since sponsor signages which fail to
gain attention cannot be effective. Thus, attention has a selective function, filtering out irrelevant information (Palmer, 2002). However, the investigation of visual attention is not sufficient if we do not assess the drivers of attention, namely placement variables and sponsor signage exposure variables. By placement variables, we mean diverse locations of sponsor signage at the sports venue, for example, signage on the jersey or on the perimeter board. Sponsor signage exposure can be described as the visibility of sponsor signage on television screen and is usually assessed by counting the duration of signage appearances (e.g., Le Roy & Vivier, 2008; Olson & Thjømøe, 2009). However, one can easily conceive of two sponsor signage exposures which are equal in duration but variable in size as well as exclusiveness. Therefore, we assume that the size of exposure (in terms of screen share) and the degree of exclusiveness (in terms of amount or degree of screen clutter) are also significant variables influencing the viewer’s attention. The first assumption that the size of the sponsor signage influences the attention is backed by vision science, which found that the relative size of the object in the field of vision plays an important role for the information reception and processing since individuals assume at first sight that larger objects are more interesting and more meaningful (Palmer, 2002). The second assumption that more exclusive sponsor signage exposures lead to higher attention can be explained by ‘overload theory’. Overload theory suggests that, when a viewer is overloaded with
524 Breuer and Rumpf
too many objects at one time, the absorption of one piece of information will be at the expense of another piece of information (Ha & McCann, 2008). In a sponsoring context, the more sponsors are exposed on-screen simultaneously, the less attention will be paid toward one particular sponsorship information. Specifically, we propose the following hypotheses:
In an advertising context, Ehrenberg (2000) argued that consumers are more likely to perceive brands they have bought before. This causality fits comfortably into the theory of the associative network model since memory will be higher for those brands that are prior-associated in the viewer’s memory. Accordingly, the following hypotheses are formulated:
H1: The longer the sponsorship information is exposed on the television screen, the higher the viewer’s attention for this information. H2: The larger the sponsorship information is exposed on the television screen, the higher the viewer’s attention for this information. H3: The less other sponsorship information is simultaneously exposed on the television screen, the higher the viewer’s attention for this information. H4: The impact of sponsor signage exposure on the viewer’s attention varies depending on the different placements in each kind of sport.
H5: The higher the attention for a sponsorship information, the higher the probability of sponsor recall. H6: Being familiar with a sponsoring brand has a positive effect on sponsor recall. H7: Being a customer of a sponsoring brand has a positive effect on sponsor recall.
Memory for Sponsorship Information Obtaining attention is necessary to gain sponsorship effectiveness, but meritless if it does not lead to the development of cognitive associations between the event and the sponsoring brand (Quester & Farrelly, 1998; Wedel & Pieters, 2000). One popular approach in sponsorship literature to explain how sponsorship information is stored in memory is called the associative network model of memory (Anderson, 1996). It suggests that memory consists of nodes that are linked to other nodes with different strengths of association. An activated node stimulates those nodes that are most strongly associated with it. In a sponsorship context, associations are sponsorship informational nodes linked to brand nodes in the viewer’s memory (Keller, 1993). If a brand node is strongly associated to a sponsorship informational node it is more likely to be retrieved than less associated nodes. Christianson, Loftus, Hoffman and Loftus (1991) found that attention is not only central in selecting information for processing, but also reflects the building of associative memory. A theoretical explanation would be that during the time attention is allocated to the sponsor signage, the viewer abstracts sponsorship information and thus new associations with the brand node can be built. Based on this reasoning, we assume that the likelihood of accurate sponsor recall increases with attention allocated to sponsor signage. In other words, the strength of association with a sponsor brand is related to the degree of visual attention: the longer the gaze contacts gained, the higher the probability of sponsor recall (Rayner, 1998). However, some brands are inherently easier to retrieve because they are more familiar to the viewer (Pham & Johar, 2001), that is, the brand node already exists in the viewer’s memory network. Further, we assume that the ability to recall brands from memory also depends on the viewer’s prior purchase behavior.
Method To determine how far sponsoring-related variables — like sponsor signage exposure and placements, as well as brand-related variables such as brand familiarity and prior purchase behavior — drive attention and memory outcomes, a quasi-experimental design was conducted. Overall, 26 stimulus films covering four different types of sports were presented on television to a sport-interested sample. Raw video material was first recorded from television, edited according to the needs of our study and then imported into the eye-tracking system.
Sample and procedures A sample of 85 participants, 46% male and 54% female, was invited to the institute’s eye-tracking laboratory. To ensure a heterogeneous sample, we recruited the participants purposively based on their age and educational level. This sampling procedure resulted in two contrary test groups: The ‘young academics’ (aged £ 25 years with an academic degree) and the ‘old nonacademics’ (aged ³ 40 years without an academic degree). Accordingly, the participants’ age ranged between 20 and 74 years, with a mean age of 32.0 years. The educational range included all levels from basic education to university studies. Participation in the experiment was voluntary and participants received 20 Euros as an incentive in return. The collection of data took place in a purpose-built laboratory equipped with a comfortable armchair, a tablemounted eye-tracking device and a wide-screen television (see Figure 2). This setup was standardized for all participants. The procedure was as follows: First, participants received a briefing during which the study’s actual objective was clouded. After calibration, the participants were asked to watch the first sport telecasts as they would do at home. Subsequent to the presentation the participants were asked to recall all brands which appeared during the film. The sequence of the films was randomized to control for order or fatigue effects. After the last film, brand familiarity and prior purchase behavior was assessed. The 26 stimulus films were assigned to four different types of sports: namely soccer, handball, Formula One and biathlon. Further, four different types of sport telecasts (live reporting, highlight show, interview sequence
Processing Sponsorship Information in Sport Telecasts 525
Figure 2 — Experiment set-up.
and victory ceremony) were identified. [Note: interview sequences in front of a sponsor backdrop are unusual in German handball coverage, and victory ceremonies are rarely held in soccer and handball telecasts. Thus these types were not included in the 26 stimulus presentations.] Table 1 gives an overview over the allocation of stimulus films. Since this study’s stimulus material consisted of real life telecasts, it was impossible to provide equal sponsor signage exposure time for each sponsoring brand. Rather, our approach controls the effect of variable exposure by measuring the seconds of on-screen time for each sponsor and entering these variables to the multivariate models. Whereas interview sequences and victory ceremonies were rather short in duration (27–33 s), stimulus films containing live reporting and highlight shows had a duration between 2 min 57 s and 5 min 55 s. For each sponsoring brand which appeared on screen for at least one second, exposure time, on-screen share, and on-screen clutter was measured. A computer imagerecognition system was employed which automatically detects sponsor signage in video material. Thus, seconds of exposure (exposure time) for each sponsor and signage placement was counted as well as the size of each sponsor signage (on-screen share) in terms of pixel coverage. Further, the amount of simultaneously exposed sponsor signage exposures (on-screen clutter) was assessed. Visual attention to sponsorship information was measured by eye-tracking. In preparation for the data analysis, AOI (areas of interest) for each sponsor signage on screen had been marked throughout the stimulus material. By matching the participant’s gaze coordinates with the AOI coordinates in each time frame, the computer software (SMI BeGaze) could determine the duration of every glance on sponsorship information. A tablemounted eye-tracking device (SMI RED) was used to record the participants’ eye movements with a frequency of 60 Hz. Individual calibration of each participant with
nine different calibration points and four validation points on the television screen was performed to ensure good data quality. The measurement of eye movements reveals to what extent sponsorship information is attended, but does not reveal if this sensory-received information was cognitively processed. Therefore, postexposure questioning was employed. To assess sponsor recall, a face-to-face recall test was used without giving any prompt. Participants were asked the following: “Could you tell me, which sponsors appeared in the last film?”. To conceal the study’s purpose, two sport-related buffer questions were included at this point, for example “Who scored for team X?”. The correct answer was always given in the film. At the end of the experiment a list of all brands was handed out to the participants. Brand familiarity was measured by asking the participants if they knew a brand before that day. Prior purchase behavior was measured by asking participants if they have ever bought a brand.
Data Analysis The database holds one set for each participant and sponsor. Since more than one sponsor appeared in all of the stimulus films, each visible sponsor per participant was treated as a new data set. By this procedure, we can analyze the variance in attention and memory as a function of sponsor-related variables (exposure, placement) and brand-related variables (brand familiarity and prior purchase behavior). Within the data analysis, the type of telecast did not show a significant effect on the viewer’s response. Therefore this variable was excluded from the analysis. Regarding different types of sport, we found differing results in terms of attention and recall. We took this variance into account by estimating one separate model for each type of sport. Table 2 provides an overview of variables in the database that were used in the analyses.
526 Breuer and Rumpf
Table 1 Allocation of Stimulus Films Type of Sport
Type of Telecast
Total
Live Reporting
Highlight Show
Interview Sequence
Victory Ceremony
Soccer
4
4
2
-
10
Handball
2
2
-
-
4
Formula One
2
2
1
1
6
Biathlon
2
2
1
1
6
Total
10
10
4
2
26
Table 2 Overview of Variables in the Database Variable
Method
Unit
Description of Measurement
Exposure Time Exposure Analysis
s
Seconds of exposure (on-screen time) for each sponsor and signage placement were counted by an automatic image recognition system.
On-Screen Share
%
Size of each sponsor signage was measured in terms of pixel coverage and put into relation to full screen pixels.
On-Screen Clutter
-
Amount of simultaneously exposed sponsor signage exposures was captured by an automatic image recognition system.
Glance Duration
Eye-tracking s
Dynamic AOIs (areas of interest) were drawn for each sponsor and signage placement within the stimulus films. For each of the 85 participants, glances on sponsor signage were recorded and automatically assessed.
Sponsor Recall
Questioning
0|1
Unaided recall was assessed subsequent to every stimulus film by asking for brand names.
Brand Familiarity
0|1
Each participant was asked to tick-off familiar brands on a list.
Brand Customer
0|1
Each participant was asked to tick-off brands he or she had purchased before that day.
To test the seven developed hypotheses multiple regression analysis was employed. Multiple regression analysis is commonly used to analyze more complex relationships between a single dependent variable and several independent variables (Hair, Black, Babin, & Anderson, 2010). In our data analysis, the regression coefficients describe the importance of each independent variable in creating ‘glance duration’ and ‘sponsor recall’. Four linear regression models, one for each type of sport, explain the dependent variable ‘glance duration’ as an indicator of attention. Whereas linear regression is applicable to metric dependent measures, logistic regression allows predicting the outcome of binary dependent variables (Hair et al., 2010; Tabachnick & Fidell, 2008). Thus, in our data analysis logistic regression estimates the odds ratio of sponsor recall as a binary indicator of memory.
For both the linear regression and logistic regression stepwise estimation has been used. The stepwise approach follows a sequential process of adding or dropping independent variables from the equation according to their explanatory power they add to the regression model. Therefore, the final regression models only contain statistically significant independent variables (Tabachnick & Fidell, 2008).
Results A summary of descriptive statistics of metric measures is provided in Table 3. The ‘exposure time’ for each sponsor signage had a mean of 30.4 s and reached a maximum of 460.9 s. The average ‘on-screen share’ had a mean of 1.7% in relation to the full screen and ranged from 0.1 to 11.8 per cent. The ‘on-screen clutter’ measure, which
Processing Sponsorship Information in Sport Telecasts 527
Table 3 Descriptive Statistics of Metric Measures Exposure Time [s] On-Screen Share [%] On-Screen Clutter Glance Duration [s]
N 12‘600 12‘600 12‘600 12‘600
indicates the average count of other sponsor signages on screen had a mean of 3.7 and varied from 0.0 (no other sponsor signage on screen) to 12.0 competitors. ‘Glance duration’ for each single sponsor signage had a mean of 0.8 s throughout the sample. Table 4 shows descriptive statistics for the binary measures. Overall, 19.6% of sponsors were correctly recalled by the participant while 80.4% were not mentioned. Concerning brand familiarity, 63.9% of sponsoring brands were known by the participants while 36.1% were unknown. Further, 31.4% of sponsoring brands had been purchased frequently or at least occasionally by the participants, while 68.6% had not been purchased to date. The linear regression models show adjusted explained variances of 44% for soccer, 36% for handball, 64% for Formula One and 60% for biathlon. The Nagelkerke R2 for the logistic regression model, which represents all four types of sport, is 33%. This indicates that the observed variables in the five models explain an acceptable percentage of the dependent variable’s variance. Table 5 presents the linear regression results which can be used to predict the attention for sponsorship information. A significant change in the dependent variable ‘glance duration’ was observed with regard to the entered variables for the exposure time of various placements, as well as ‘on-screen share’, ‘on-screen clutter’ (only significant in soccer and handball) and ‘test group’ (soccer, biathlon). Not surprisingly, all ‘exposure time’ coefficients have a positive impact on ‘glance duration’. This leads to the conclusion that the longer a sponsor signage is exposed the higher the viewer’s attention for this sponsorship information. Thus, hypothesis 1 is supported. Further, all four models show positive effects for ‘on-screen share’, meaning that larger sponsor brand exposures produce higher attention rates, which leads to the acceptance of hypothesis 2. Results regarding ‘on-screen clutter’ show Table 4 Descriptive Statistics of Binary Measures Unit
Yes [=1]
No [=0]
Sponsor Recall [0|1]
%
19.6
80.4
Brand Familiarity [0|1]
%
63.9
36.1
Brand Customer [0|1]
%
31.4
68.6
Note: N = 12,600
Mean 30.43 1.66 3.73 .80
SD 48.22 1.45 2.64 1.54
Min .36 .09 .00 .00
Max 460.92 11.80 11.97 18.86
a significant negative effect for soccer and handball, while this effect is not significant for Formula One and biathlon. Seemingly, in sports performed on a field, that is, soccer and handball, the increase in other sponsorship information significantly reduces the viewer’s attention. Thus, hypothesis 3 is partly supported. The exposure time variables reveal a high degree of difference regarding their B-values within each kind of sport. This means that the exposure’s impact on attention is influenced not only by the total time, size and exclusiveness of exposure but also by the placement of the sponsor signage. Accordingly, some placements have a stronger impact on the viewer’s attention than others. For example, holding all other variables constant, in soccer a second of exposure on the board in the first row (B = 0.020) is more than twice as effective as a second of exposure on a board in the second row (B = 0.009). Thus, hypothesis 4 is accepted. Even though we did not formulate a hypothesis on the different test groups it appears that the second group of participants (older than 40 years without an academic degree) shows higher attention for sponsoring information compared with the young academics. However, this effect only holds true for soccer and biathlon. In a second step, a logistic regression model with a likelihood ratio estimation method was used to predict sponsor recall from attention scores (glance duration) and whether the participant was familiar with the brand and had purchased it. Demographics as control variables were also included into the model. Table 6 shows the coefficients (B), their standard errors (SE), the Wald Chi-Square statistic, associated p-values (Sig.), odds ratio (Exp B) and the changes in odds. All measures were significant predictors of sponsor recall. For every second of increase in ‘glance duration’, the odds of sponsor recall (versus no sponsor recall) increased by 308.0%. Thus, hypothesis 5 is supported. However, the sponsor recall function was nonlinear with respect to glance duration, meaning that recall highly increases with every second of glance duration on a low attention level; then the effect bottoms out, and grows again on a high attention level. Concerning the binary measures, the model shows that if the brand was known by the participant the odds of sponsor recall increased by 548.8%. If the respondent was a brand customer, the odds of sponsor recall increased by 47.4%. Thus, hypothesis 6 and 7 are supported. With regard to the control variables, the model shows that, compared with females, male consumers are more likely to recall sponsors correctly.
Table 5 Linear Regression Results (Stepwise) for Attention Variable
Soccer B
Formula One B
Handball B
Biathlon B
Constant
.275
.397
-.200
-.074
Exposure Time Board (row 1) [s]
.020**
-
-
-
Exposure Time Board (row 2) [s]
.009**
-
-
-
Exposure Time Cam Carpet [s]
.022**
-
-
-
Exposure Time Backdrop [s]
.026**
-
-
-
Exposure Time Sportswear [s]
.033**
-
-
-
Exposure Time Bench [s]
.028**
-
-
-
Exposure Time Jersey [s]
.032**
-
-
-
Exposure Time Board [s]
-
.021**
-
-
Exposure Time Floor [s]
-
.005**
-
-
Exposure Time Cubes [s]
-
.008**
-
-
Exposure Time Jersey [s]
-
.011*
-
-
Exposure Time Car (large logo) [s]
-
-
.069**
-
Exposure Time Car (small logo) [s]
-
-
.035**
-
Exposure Time Board [s]
-
-
.037**
-
Exposure Time Insert [s]
-
-
.034**
-
Exposure Time Press Conference [s]
-
-
.009*
-
Exposure Time Race Suit (large) [s]
-
-
.034**
-
Exposure Time Race Suit (small) [s]
-
-
.012**
-
Exposure Time Backdrop [s]
-
-
.049**
-
Exposure Time Victory Podium [s]
-
-
.020**
-
Exposure Time Sportswear [s]
-
-
.047**
-
Exposure Time Board [s]
-
-
-
.059**
Exposure Time Cam Carpet [s]
-
-
-
.014**
Exposure Time Rifle [s]
-
-
-
.043**
Exposure Time Inflatable [s]
-
-
-
.016**
Exposure Time Cap [s]
-
-
-
.013**
Exposure Time Backdrop [s]
-
-
-
.033**
Exposure Time Sportswear [s]
-
-
-
.048**
Exposure Time Jersey [s]
-
-
-
.040**
On-Screen Share [%]
.087**
.056**
.242**
.057**
On-Screen Clutter
-.071**
-.053**
n.s.
n.s.
Test Group [0|1]
.080*
n.s.
n.s.
.176**
Adj. R2
.442
.364
.644
.606
Sig. (p)
.000
.000
.000
.000
Note: Dependent variable: Glance Duration. B: nonstandardized regression coefficient **predictor is significant at p≤.01; * predictor is significant at p≤.05; n.s.: predictor is not significant
528
Processing Sponsorship Information in Sport Telecasts 529
Table 6 Logistic Regression Results (Stepwise) for Memory Variable Glance Duration [s] Glance
Duration2
B
SE
Wald
Sig.
Exp(B)
Change in Odds*
1.406
.054
686.260
.000
4.080
308.0%
[s]
-.184
.014
162.491
.000
.832
-16.8%
Glance Duration3 [s]
.007
.001
61.607
.000
1.007
.7%
Brand Familiarity [0|1]
1.870
.081
528.311
.000
6.488
548.8%
Brand Customer [0|1]
.388
.057
46.953
.000
1.474
47.4%
Gender
-.247
.055
20.107
.000
.781
-21.9%
Age
-.009
.002
15.738
.000
.991
-.9%
Education
.230
.037
37.756
.000
1.259
25.9%
Constant
-4.013
.178
506.857
.000
.018
-98.2%
Note: Nagelkerkes-R2 = .333; N = 12,600; dependent variable: sponsor recall * denominates the probability change of a sponsor being identified due to a one unit change in a given variable.
While age has a negative impact on recall, higher levels of education affect sponsor recall in a positive direction.
Discussion and Implications This paper brings more clarity to the understanding of sponsorship information processing in sport television situations. It contributes in three ways as it relates to (1) the role of viewer attention, (2) the importance of placement variables, and (3) the influence of exposure size and visual clutter. We will elaborate on each area as part of this discussion to highlight our contribution. Since exposure measures of sponsor signage can only represent the input to the ‘black-box’ of information processing and memory measures reflect only the output, we added attention as an intermediary construct. This put us in the position to analyze (1) which sponsorship information has been sensory-received by the television viewer, and (2) which of the sensory-received information has been cognitively processed. As a general conclusion from this study, we claim that the analysis of attention as a mediator is crucial in identifying the complex process of sponsorship information processing. With regard to sensory reception of sponsorship information, there is enough evidence to suggest that the generation of viewer attention is predictable from the placement of the sponsor signage and exposure variables. In line with spotlight theory, only a small amount of sponsorship information can reach the viewer’s attention, since the sport action captures the biggest part of that attention. Thus, our research reinforces older studies by d’Ydewalle et al. (1988) and d’Ydewalle and Tasmin (1993) who reported a very low ratio between exposure and attention for perimeter boards. With regard to the cognitive processing of sponsorship information, we found that accurate recall increases significantly with the degree of attention paid to the sponsor signage. However, saturation effects occur which truncate the recall probability. Besides attention,
brand-related variables also influence cognitive processing. Firstly, television viewers who are familiar with a brand are more likely to recall the brand after exposure; secondly, viewers who have purchased the brand before exposure show better recall scores. These findings parallel those of Hoek, Gendall, Jeffcoat, and Orsman (1997), as well as Pham and Johar (2001) and Wakefield et al. (2007), and fit comfortably into the theory of the associative network model which suggests that memory will be greater for those brands that are prior-associated in the viewer’s memory. Little research to date has empirically studied the influence of placement variables on sponsoring effectiveness. This is surprising because the optimal placing of the sponsor signage at the venue (e.g., on a perimeter board or athlete’s jersey) is regarded as a key decision in sponsorship management. Based on our results, we argue that some placements are superior in capturing attention, independent of their exposure time. Especially, sponsor signages that are placed on the athlete’s gear, or at least close to sport action, provide a greater chance of attracting viewer attention. In an effort to maximize the efficiency of sponsorship rights, managers should choose those placements at the venue that generate the best ratio between exposure time and attention. However, the exposure-to-attention ratio of a particular placement (e.g., backdrop) can vary with different venues or camera settings. An interview backdrop, for example, can be either dedicated exclusively to a single sponsor signage or it can be visually-overloaded by more than twenty smaller sponsor signages. Thus, the efficiency of sponsorship communication in terms of capturing attention is also dependent on the size and exclusiveness of sponsor signage exposure. There is evidence from advertising research that larger brand logos increase the consumer’s attention (Pieters & Wedel, 2004). However, this interrelation has not been examined in a sponsorship context. On the basis of the current findings, increasing the on-screen size of
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the sponsor signage helps significantly to draw attention to the sponsorship information in all four types of sport. Thus, managers should be advised to select placements which allow presenting large size sponsor signage. Besides size, the exclusiveness of signage exposure seems to matter. To put it the other way around: visual clutter will reduce the viewer’s attention. Even though this effect could only be demonstrated for football and handball telecasts, the finding is consistent with overload theory and indicates that too much sponsorship information exposed simultaneously can hinder the absorption of that information. Therefore, sponsoring managers should consider the amount of simultaneously exposed sponsorship information as a threat. An athlete’s jersey, for example, which holds ten or more different sponsor signages should be avoided, since the impact of exposure on attention will be marginal. As a general implication, we recommend that viewer attention be regarded as the crucial resource in the sponsorship communication process. The wide-spread approach of benchmarking different sponsorship rights on the basis of sponsor signage exposure alone is insufficient, because exposure per se does not increase attention and recall. Rather, the right selection of placements, the optimization of on-screen size, and the reduction of visual clutter will maximize the return on investment. Therefore, sport entities / agencies should realize that a reduced number of sponsor signages can produce better results for the sponsoring partners. In return, sponsoring managers should be willing to spend more for superior placements and a less visually cluttered sport environment since the sport viewers’ attention, and thus their recall, can be increased significantly by use of this strategy.
Limitations Even though this study contributed to our understanding of sponsorship information processing, there are several important limitations of this research. Firstly, we have analyzed the process by which attention to sponsorship information leads to recall, but the affective response as a function of attention remains unexplored. Tripodi, Hirons, Bednall, and Sutherland (2003) point out that sponsor recall measures a viewer’s ability to retrieve the brand but it does not reveal the favorability of such an association. Therefore follow-up research is needed to examine whether attention is also a valid predictor of affective information processing. A second limitation concerns the way we measured attention. Even though eye-tracking can be regarded as the most advanced technique to measure attention, there is still a shortcoming. Attention research has identified a high correlation between eye movements and attention (Pieters & Wedel, 2004), however, “the orienting of attention is not always dependent on the movement of the eyes, that is, it is possible to attend to an object while maintaining gaze elsewhere” (Duchwoski, 2007, p. 9).
Lastly, the findings from this research are based on a laboratory experiment. Experimental studies are often faced with the criticism that they do not reflect the ‘real world’ due to the artificiality of the laboratory environment (Schram, 2005). Since the artificial setting of the experiment affects the external validity of the results, we should generalize the findings with caution.
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