Football Labor Market: A Communication and Talent Management Approach to Foresee Player Transfers
Graduate School of Communication 2018-2019 Master Thesis Supervisor: Dr. Pytrik Schafraad
Nicolò P. Ferrari 11825723
Master’s Programme: Communication Science Master’s Track: Corporate Communication
30/01/2019
Table of Contents
ABSTRACT ... I
INTRODUCTION ... 1
THEORETICAL FRAMEWORK ... 3
FOOTBALL LABOR MARKET: A TALENT MANAGEMENT APPROACH ... 3
FOOTBALL LABOR MARKET: A COMMUNICATION PERSPECTIVE ... 4
RESEARCH DESIGN AND DATA-COLLECTION ... 9
SAMPLING METHOD ... 9
CODING INSTRUMENTS AND VARIABLES ASSESSMENTS ... 10
INTERCODER RELIABILITY ... 11
ANALYSIS ... 11
RESULTS ... 13
HYPOTHESES TESTING: TALENT MANAGEMENT AND PLAYER’S MEDIA SALIENCE (H1 – H2) ... 13
HYPOTHESES TESTING: TOPIC SALIENCE (H3) ... 14
HYPOTHESES TESTING: TOPIC EVALUATION (H4) ... 15
DISCUSSION AND CONCLUSION ... 17
TALENT MANAGEMENT APPROACH ... 17
COMMUNICATION PERSPECTIVE ... 18
IMPLICATIONS ... 19
LIMITATIONS AND FUTURE RESEARCH ... 20
REFERENCE LIST ... 22
APPENDIX A ... 25
Abstract
The football labor market has been widely studied by adopting a strict talent management approach to help organizations to foresee football player’s career trajectories. However, this research underpins a combined approach by using, on one side, talent management variables (e.g., measurement of performance, age…) and a communication perspective developed by using a theoretical framework the first and second level of agenda-setting theory. A content analysis was conducted on a sample of 300 articles of La Gazzetta dello Sport identifying overall media coverage of players during transfer windows, topics related to players and overall evaluation. Data were collected about Serie A league players during two different periods: the summer window, starting on the 1st of July and finishing the 31st of August 2017 and the winter one from the 3rd
until the 31st of January 2018. Several logistic regressions featuring both talent and media variables
were conducted, showing how a dual approach is a critical strategic procedure for football firms to foresee potential player’s transfers. From a theoretical point of view, agenda-setting theory was successfully deployed in an entirely new domain of research, showing how football media coverage shares some similarities with general coverage. From a managerial point of view, this research shows how tracking player's transfers can help football organization to anticipate media coverage and foresee a potential loss of a player or a possible acquisition.
Keywords: Football, Media Coverage, Talent Management, Agenda Setting Theory, Football Labor Market
Football Labor Market: A Communication and Talent Management Approach to Foresee Player Transfers
Introduction
The growing shift from players considered as football clubs’ properties towards more independent economic, and media entities (Frick, 2007) presents new challenges to football organizations, showing how footballers gained more decision-making power on their career trajectories (Roderick, 2012). It has been assessed that the average contract length is about 2.5 years (Frick, 2007), and often players seek for increasing their economic and professional status by changing football organization (Thijs, 2017). Previous studies argued that news media coverage plays a fundamental role in improving football players salaries (Lehmann & Schulze, 2007), and reputation (Lucifora & Simmons, 2003). Consequently, due to the effects of fast and constant online mediatization of players' career trajectories, news media outlet become a stage to increase personal popularity (Frank & Nüesh, 2008). Previous research has explored football organizations and player’s transfers using a strict business-oriented and talent-management approach (Lucifora & Simmons, 2003; Frick, 2007; Frank & Nüesh, 2008; Herm, Callsen-Bracke, & Kreis, 2014; Thijs, 2017).
This study underpins both a media coverage perspective combined with talent management features. The aim is to explore the research gap concerning how media coverage relates to sports management. The agenda-setting theory, translated from a political to a corporate context by Caroll and McCombs (2003) was deployed as the theoretical column of this research. Specifically, the objective is to assess how football organizations can anticipate news media outlets by following two different points of investigation: a communication perspective and talent management overview. The former regards how football organizations can track potential player’s transfers by monitoring the amount of media coverage related to them. The latter concerns how specific talent attributes of players can be tracked since they represent indicators of a potential transfer (age, performance, salary…). This research aims to test this dual approach and its benefits. In fact, by only adopting a business-oriented perspective a firm could risk reputation damage due to a lack of proactivity when dealing with their players and external stakeholders (Manoli, 2016). Additionally, strategic communication and management in sports function as a tool to influence and persuade internal and external stakeholders: both have vital elements to increase the probability of success of a firm (Pedersen, 2012). Therefore, by having a combined point of view, this research finally
aims to test the relative strength this approach has and explore the potential differences existing between talent and communication management of football talents.
To conclude, from a societal point of view, a variety of stakeholders are involved during these economic interchanges, starting from the clubs (internal stakeholders), the fans and the media (stake-watchers) and legal football associations (stakekeepers) (Fassin, 2009). The identification of this relation will shed light on what level of transparency internal actors show towards media coverage, fans, and regulative organizations. Thus, the underlying research questions of this study assert: RQ (1) To what extent do transfer topics, portrayed in online news media coverage of “La Gazzetta Dello Sport”, relate to football exchanges during the Serie A transfer market seasons of 2017/2018? RQ (2) To what extent player’s features relate to football exchanges in the Serie A market seasons of 2017/2018?
Theoretical Framework
Football and business organizations share both similarities and differences. The primary distinction between these two types of firms regards their final objective: while business companies focus mostly on gaining profits, sports management focal point consists in improving team’s performance such as winning tournaments league, provide a full-entertaining stakeholder experiences and meet the community needs (Hoye, Smith, Nicholson, Steward, & Westerbeek, 2006). This distinction results in different management styles and consequently these two types of organizations show different approaches regarding their relations with stakeholders in order to achieve organizational success. This research focuses in particular over two key sectors that have relevance towards the football domain: the labor market management and its relationships with the media (Frick, 2007; García; 2011).
Football Labor Market: A Talent Management Approach
Football talent management represents one of the most critical strategies to enhance performance and, therefore, a club’s reputation (Lucifora & Simmons, 2003). High investments characterize football labor market management to attract, motivate and retain players (Montanari, Silvestri, & Bof, 2008). These investments on salaries differ significantly by general employees’ remuneration. By making a comparison between different compensation systems and individual performance, football labor market displays an example of “labor-intensive context” meaning that human resources are highly correlated to performance (Montanari, Silvestri, & Bof, 2008). Therefore, because of the importance of increasing performance, players' salaries in Europe football market has never decreased from 2002 reaching 25.5 billion Euro investments in 2016/17 (Deloitte, Annual Review of Football, 2018).
By acknowledging the importance of this market, an additional element of professional football trading must be introduced. As argued by Roderick (2006), the new component consists of “career uncertainty”. When using this term, the author refers to the fact that career trajectories, compared to business organization’s employees, are very different. Players’ careers develop as unplanned, with relatively short contracts, paired with the possibility to experience contingencies (such as injuries). These traits can modify drastically the agreements football clubs have with players (Roderick, 2006).
Moreover, the drastic change of transfer regulation and liberalization of the football labor market in 2003 (Niemann, Garcia, & Grant, 2014) increased player’s and firms decisional power over
player’s career trajectory and makes these internal stakeholders more challenging to retain (Frick, 2007). Whereas talent management results to be very difficult to strategically plan, existing literature has examined how certain characteristics correlates directly to player’s market value (Herm, Callsen-Bracke, & Kreis, 2014) and consequently to career mobility (Frick, 2007; Montanari, Silvestri, & Bof, 2008; Thijs, 2017). This research will specifically focus on the research done by Thijs (2017) and Frick (2007), that found a correlation between player’s permanent transfers or temporary loan and four different features.
The first involves the negative association between performance and football transfers, divided into a number of goals and assists collected in the previous (or current) regular season. The second concerns the association between player’s age and transfer. The younger a player is, the more likely a transfer will happen. The third involves players current salary, specifically on how a higher salary relates to a higher chance of getting a transfer. The fourth refers to the total number of transfers players have done during their careers. In fact, a player who transfers more in his past is more likely to transfer again. These features represent indicators able to help foresee transfer exchanges and therefore constitutes valuable insight on how football firms can track in-house football players (Thijs, 2017). Furthermore, in line with the researches mentioned above, this study hypothesizes:
H1(a) Player’s performance negatively relate to his transfer. H1(b) Player’s age positively relates to his transfer.
H1(c) Player’s salary positively relates to his transfer.
H1(d) Player’s total number of transfers positively relates to his transfer. Football Labor Market: A Communication Perspective
After analyzing the football transfer by following a talent management perspective, the following step is to underpin a communication perspective. To begin with, football organizations differ in their relationship with the media compared to a general organization. Several differences need to be highlighted to understand the potential usefulness of interaction between media and football clubs and the different features they share together.
Firstly, from a business perspective, firms consider media as a valuable tool to communicate with stakeholders, enhance reputation, and promote their brand or products (Patriotta, Gond, & Schultz, 2011). Existing literature has focused on how PR should establish a two-way symmetrical interaction with their audience to enhance positive reputational outcomes (Grunig,
Grunig, Sriramesh, Huang, & Lyra, 1995). However, it has also been argued that a one-way communication style is predominant, were companies lack of "listening", and only try to steer the media to achieve their goals (Macnamara, 2016).
In a categorization provided by Fassin (2009), there are three significant actors a company needs to account for. The first actors are stakeholders, who hold a real stake inside a company, such as investors. The second actors are stakewatchers, who "watch over the stake", such as parties that monitor a company, protecting the stake of consumers. The third actors are stakekeepers, such as news media, that tent to act regulators, meaning that organizations do not hold responsibilities towards them (compared to the previous two actors) as they monitor companies and exert pressure. This distinction, however, is not as linear concerning news media and football organizations. In fact, football organizations and news media share a relation of interdependency (Cleland, 2009).
Furthermore, the media system (broadcast revenue) constitutes 45% of total revenue compared to the 38% of commercial revenue and 17% for the matchday revenue (Deloitte, 2018). This implies that the media have a direct “stake” inside football firms, changing from the role of stakekeepers to stakeholders. Consequently, since every football firms possesses responsibilities towards the media and vice versa (e.g., exclusive broadcast of live matches, interview with football players, direct connection of journalist with management departments of clubs), establishing and maintaining relations with news media is a crucial strategy that carries mutual benefits for the both actors involved (Birkner & Nölleke, 2016). Although this exclusivity is limited to certain media outlets, this relationship is particularly relevant since clubs provide information to these actors that, successively spread content to the public and other media outlets.
After highlighting the importance of this interdependent relationship, it is relevant to explain how media coverage relates to the labor market. Existing literature argues that news media are a vital variable for players to enhance both the popularity of players and teams (Frank & Nüesh, 2008). Players’ popularity is directly related to an increase in market value, income and status, which can benefit clubs by increasing players’ value (Birkner & Nölleke, 2016). Previous studies did not examine the relation between news media coverage and the transfer market. In order to do so, this research will draw upon the agenda-setting theory by Caroll and McCombs (2003) to explore the relationship between news media and football transfers. The researchers Caroll and McCombs (2003) translated the agenda-setting theory, originally designed by McCombs & Shaw (1972) to a corporate context from a political one, to provide firms with a theoretical framework to investigate the relationship between media and public. This theory consists of two different
levels: the first level affirms that the salience of an object in the media agenda is translated into the salience of those in the public agenda (Caroll & McCombs, 2003). In other words, the public select certain salient cues present in the media and prioritize certain information over others due to media exposure. The second level of the AST relates to the substantive and evaluative attributions these objects receive. In other words, the second level considers how the public is affected by different topics of the news (substantive level) and on how the public is influenced by how these topics are evaluated (evaluative level). In the sports coverage domain, this theory has been previously adopted to provide a conceptual framework for managers to obtain branding and marketing advantages (Wallace, Wilson, & Miloch, 2011). In addition, existing literature shows that, by steering the public opinion through media coverage on delicate issues (usage of anabolic steroids in the U.S Baseball Major League), sports policymakers have been influenced to take action (introducing of drug testing) (Denham, 2004).
Although this theory has mostly been applied to help PR professionals in negotiating different frames with news media actors to reach mutual sense-making (Cornelissen, Carroll, & Elving, 2010), several studies also adopted AST as a tool to predict stock market fluctuations (Strycharz, Strauss, & Trilling, 2018; Strauss, Vliegenthart, & Verhoeven, 2016). The empirical evidence of these researches displays a direct correlation between media coverage and complex strategic and economic changes executed by organizations. These findings on corporate branding and stock market predictions display the double nature of this theory, and its potential application backward: analyzing news media to collect information about football transfers. Therefore, AST provides a conceptual framework to help clubs that aim to steer media coverage to influence the media stream of a potential transfer positively by using specific topics and subsequent attribution. Furthermore, the AST can provide football corporation with a managerial tool allowing them to collect transfers insights from media coverage. As a consequence of this theoretical procedure, based on the first level of the AST this research aims to investigate:
H2 An increase over time in the amount of a player's news media coverage will positively related to his transfer.
After explaining the importance of AST deployed as a theoretical tool and the relation between the sources and the media actors involved in selecting, framing and producing transfer news content, the next step focuses on depicting which topics are salient in the news (second level of AST). As this theory explains, news coverage is associated with specific characteristics (Caroll
& McCombs, 2003). In football media coverage, previous talent management researches have demonstrated how different players have a set of static (e.g., physical characteristics) and dynamic (e.g., social and economic characteristics) features (Frick, 2007), and how these attributes can affect player’s market value (Herm, Callsen-Bracke, & Kreis, 2014) job mobility (Thijs, 2017) and popularity (Frank & Nüesh, 2008; Lucifora & Simmons, 2003). Drawing upon Frick (2007) and Thijs (2017) researches on football labour market, six different characteristics have been selected: performance (e.g., goal, assist..), physical characteristics (e.g. weight), economic traits (e.g. contract length), job mobility (e.g. previous team ownership), players human capital (e.g. motivational role in the team) and team characteristics (e.g. team ownership). The focus of this research is to analyze the same features underpinned in the talent management section, but by adopting a media perspective. Due to the novelty of this approach and the lack of literature, the relationship’s direction cannot be assumed concerning substantive level. Therefore, the aim resides on testing how these features are portrayed in the media, and if they also represent indicators able to foresee potential transfers.
H3(a) The presence of player’s performance relates to his transfer.
H3(b) The presence of player’s physical characteristics relates to his transfer. H3(c) The presence of player's economic traits relates to his transfer.
H3(d) The presence of player’s job mobility relates to his transfer.
H3(e) The presence of player’s players human capital relates to his transfer. H3(f) The presence of player’s team characteristics relates to his transfer.
The evaluative dimension of the second level of AST concerns with how the media presents, for example, the overall reputation of an organization, by using a different tone in the news content (Deephouse, Carroll, & McCombs, 2001). Deephouse (2001) has developed an evaluative categorization which divides news in negative, neutral and positive. Previous research in the financial domain has investigated the effects of tone in news media coverage, showing how it can affect people attitude formations towards the stock market (Scheufele & Tewksbury, 2007; Strycharz, Strauss, Trilling, 2018). By adapting the underlying theoretical use of AST, since the interdependence of media coverage and transfers information (Cleland, 2009), we can hypothesize:
H4(a) The positive (negative) evaluation of player’s performance relates positively (negatively) to his transfer.
(negatively) to his transfer.
H4(c) The positive (negative) evaluation of economic traits relates positively (negatively) to his transfer.
H4(d) The positive (negative) evaluation of a player's job mobility relates positively (negatively) to his transfer.
H4(e) The positive (negative) evaluation of a player's human capital relates positively (negatively) to his transfer.
H4(f) The positive (negative) evaluation of a player's team characteristics relates positively (negatively) to his transfer.
Research Design and Data-collection Sampling Method
This thesis aims to investigate sport media coverage in Italy by conducting a systematic quantitative content analysis. The sampling procedure is divided into three steps: selection of news media outlet and football organizations assessment, selection of football players and selection of sample units. The first decision concerns the selection of news media outlet and football organization assessment. This research considers one online news media outlet: “La Gazzetta Dello Sport” (2018). This online media channel represents the most followed online sport news outlets in Italy (Audiweb, 2017). This choice is driven by the high media coverage this league receives due to being one of the major four European tournaments. The selection of articles is randomized at a systematic level; the entire articles will constitute the sampling unit since football trading media products are shorter and full of detailed content compared to daily sports news.
This study undertakes two transfer windows of the Italian Serie A League: the summer window started the 1st of July and finished the 31st of August 2017 and the winter one from the 3rd
until the 31st of January 2018. To select which player's coverage will be deployed for this research,
a stratified sample technique will be adopted: the aim is to analyze 10 players with similar media coverage. The first strata consist of randomly select one team from the first four clubs ranked in Serie A league at the end of the 2017/2018 football season since their players receive greater and similar media visibility due to better performance and participation to European championships. The football organizations selected are: Juventus, Naples, Rome, Inter, Lazio, Milan, Atalanta.
Secondly, there will be the selection of football players coverage. Several criteria need to be considered during this stage: to begin with, the players must have had played at least five games during the season. This procedure will help to exclude footballers with low media coverage. Moreover, to have an equal representative sample size, five transferred players and five players that did not change football organization will be selected. In details, for the summer transfer season, three players will be chosen randomly that got exchanged and three that did not transfer. For the winter transfer season, two footballers will be selected that changed team and two will be chosen because they did not transfer. More players will be studied during the summer transfer season since it is twice as long compared to the winter one.
The last step regards the selection of individual’s media coverage. The name of the player will be adopted as a keyword on the La Gazzetta Dello Sport online archive to find all news related
to him. However, since the media coverage of those players is quite extensive, 30 articles for each player will be analyzed. The procedure is based on a systematic random selection starting from the first day of the transfer window until the end of it for both players who transferred and those who did not. In conclusion, this sampling method will lead to a final amount of 300 articles, exactly divided in half between players transferred or not, which represents a rich yet feasible corpus of analysis for this research.
Coding Instruments and Variables Assessments
A codebook was assessed in order to analyze the online media coverage players received during transfer windows (see Appendix A). The first independent variable this coding instrument aimed to evaluate was the overall media coverage regarding football players (H2). The objective consists of coding the number of words referring to a player. If the player was mention in any fashion in a sentence (i.e., full name, nickname, pronoun), the entire sentence was coded in reference to the player. Finally, the media coverage was measured by summing the number of words occurring in each sentence referred to the player and by dividing this number by the total amount of words of the article. After multiplying this result by 100, the outcome obtained measures the exact percentage of how much an article relates to the player.
The second group of independent variables (H3) aimed to analyze how the six distinctive characteristics selected from the empirical studies conducted by Thijs (2017) and Frick (2007) were portrayed in the news media: performance, physical characteristics, economic traits, job mobility, human capital, and team characteristics. The strategy adopted is equivalent of the one explained for coding overall media coverage. In particular, to assess the amount of coverage regarding a specific topic, a set of guidelines and terms were provided to help to decide if a sentence referred to that topic (the codebook in Appendix A provide in full the guidelines and terminology used for the coding of these variables). In case a topic was not present, it was coded as 0. Once again, after adding together all the words in each sentence related to a topic, dividing them by the total number of words of the article and multiplying by 100 the results obtained showed an exact percentage of the presence of the topic in the article. In case of the presence of a topic, the next independent variables group concern topics’ evaluation (H4). Topic evaluation was coded as 1 = Positive; 2 = Negative. The general rule applied in the evaluation assessment was to consider an article positive if it would picture a scenario where the transfer was likely to happen (and Vice versa). In addition, for each topic, precise guidelines, as well as examples, were provided
to help coders in the evaluation (see codebook appendix A). Afterward, topics variables were recoded to fit the statistical analysis: Negative was coded as -1, the absence of evaluation (missing values) was coded as 0, and positive was assessed as 1.
After explaining the codebook procedure developed to study news media coverage, data regarding players’ performance, age, salary and number of previous transfers were retrieved as independent variable testing (H1). The source deployed is a website called transfermarkt.com, which contains all data related to football players careers (Transfermark, 2019). The data were added to the SPSS dataset manually. Following the same procedure, the dependent variable was also retrieved from transfermarkt.com and coded 0 = Not Transferred; 1 = Transferred.
Intercoder Reliability
Firstly, one coder was instructed and familiarized to code the 5% of the articles underpinned by this research (N = 30). The codebook was operative after adjusting a few guidelines due to one supervised session with the coder. Krippendorffs’ Alpha was calculated for all variables. Concerning the first and second groups of variables (correspondingly H2 and H3) Kalpha’s results to be .73 or higher. However, two variables, V7 and V13 measuring topics’ evaluation appeared to be below the accepted threshold of .70 (Field, 2009). One explanation behind these results deals with the unequal distribution of cases and the rather small sample size which can affect Kalpha’s test results due to its inherent rigidity (Schafraad, 2009). Furthermore, because of an insufficient sample size for both variables (less than 30 cases each), results must be interpreted with caution. Table 1 in appendix B shows all variables and their corresponding Krippendorffs’ Alpha values.
Analysis
To test the hypotheses, this research adopted three different binary logistic regressions since the dependent variable is dichotomous. The dependent variable in all the three analyses is the transfer of a player (0 = No; 1 = Yes). Respectively, the independent variables in the first analysis are: players’ performance, age, salary, number of transfers and overall news media coverage (H1 and H2). The independent variables for the second test are the topics related to players’ coverage (H3). The last analysis considered as independent variables the topics’ evaluations (H4). To test the significance of the analyses, chi-square tests the null hypothesis's coefficients, with the constant equal to 0. Logistic regression result’s interpretation is similar to linear regression analysis. When the b value is equal to 1 by increasing one unit of the independent variable the probability of a
possible transfer or not remain the same. If b results to be > than 1, this means that at each increase of the independent variable, then the odds of a transfer happing are higher. Vice versa, b < 1 shows, at each unit increase of the independent variable, a decreasing probability of getting transferred.
Results
Hypotheses Testing: Talent Management and Player’s media salience (H1 – H2)
This research tested, by following a talent management and a communication approach, weather football players attributes and media coverage were indicators related to players’ transfers. The talent management variables (goal, assist, age, salary, number of previous transfers) and the players' overall media coverage constitute the independent variables. The transfer is the dependent variable. On average, 27.07% of an entire article talk about specific features related to a player. To test the group of hypotheses H1 and H2 a binary logistic regression was deployed. The model explains 51.4% of the variance concerning the combination of management and communication variables relating to transfers (Nagelkerke R2 = .51). In addition, the model results to be statistically
significant, confirming communication and talent management variables as indicators of transfers (x2 = 145.91(8), p = .000). However, also Hosmer and Lemeshow goodness of fit test appears to
be significant (x2 = 97.54(8), p = .000). This implies that the model is not a good fit for the analysis
and consequently results must be interpreted carefully. Table 2 displays the descriptive statistics of all variables deployed in the analysis, the weight and the odds ratios for each variable.
The first finding worth to elaborate on is that both talent management and the total amount of coverage of players are significant, supporting hypotheses H1a, H1b, H1c, H1d, and H2. Specifically, as the number of goals (exp b = .29, p = .000), assists (exp b = .70, p = .012) and players’ age (exp b = .29, p = .000) increases, there is a decreasing probability that a transfer will happen. In contrast, the odds of a successful transfer increase when player’s salary is higher (exp b = 2.89, p = .000), when players’ number of previous transfers is higher (exp b = 3.04, p = .000) and the media coverage regarding players grows (exp b = 1.03, p = .000). In other words, for any addition goal, assist or year in age, the odds of players’ transfer decrease respectively by 28.6%, 70.4%, and 29.8%. In contrast, for each additional million (annual salary), the number of previous transfer and percentage value of media coverage, the likelihood of a transfer will increase by a factor of 2.89, 3.04 and 1.03.
Table 2
Talent management and Overall Media Coverage as Indicators of Football transfers (H1 – H2).
Variable Mean(SD) b s.e. p Wald
statistic Exp. b Talent Management Goal 1.60(1.43) -1.251 .244 .000 26.342 .286 Assist 2.07(1.88) -.351 .139 .012 6.378 .704 Age 26.02(3.25) -1.211 .182 .000 44.205 .298 Salary (Million) 2.28(1.99) 1.061 .177 .000 .177 2.889 No. clubs 4.47(1.90) 1.113 .191 .000 .191 3.045 Communication Total Coverage 27.07(29.14) .026 .006 .000 16.519 1.027 Nagelkerke R2 .514 -2 Log likelihood 269.979 x2 145.91 .000
Hypotheses Testing: Topic salience (H3)
This result section aims to test which topics related to football players’ media coverage are related to transfers. Specifically, a logistic regression analysis was conducted with news media covering performance, physical characteristics, economic traits, job mobility, human capital and team characteristics as independent variables and players' transfers as the dependent variable. The model explains the 23.9% of the variance (Nagelkerke R2 = .23) and chi-square result 59.40(6), p = .000
shows a significant relationship between topics in news media coverage and football transfers. Furthermore, to measure how well the model fit this analysis, Hosmer and Lemeshow goodness of fit test was carried out. Results found to be significant (x2 = 17.12(8), p = .029),
indicating a poor fit of this model for this analysis. Therefore, as previously mentioned, the outcome of this analysis must be interpreted with caution. Table 3 provides descriptive statistics for each variable, regression weights, and odds ratios. Out of six different topics, economic traits (exp b = 1.06, p = .001) and job mobility (exp b = 1.15, p = .000) yield a significant effect as indicators of football transfers. Therefore, H3c and H3d are statistically supported. In contrast,
media coverage related to performance (exp b = .985, p = .225), physical characteristics (exp b = .991, p = .666), human capital (exp b = 1.02, p = .085) and team characteristics (exp b = .985, p = .187) results are not significant. As a consequence, H3a, H3b, H3e, and H3f are rejected. Particularly, the logistic regression analysis showed how performance did not matter as indicators, however economic and job mobility news lead to a transfer.
Table 3
Topics related to players’ coverage as Indicators of Football transfers (H3). Variable
(Media Presence)
Mean(SD) b s.e. p Wald
statistic Exp. b Performance 6.79(10.59) -.015 .013 .225 1.296 .985 Physical C. 1.77(6.44) -.009 .020 .666 .186 .991 Economic Traits 6.27(12.31) .058 .017 .001 11.445 1.059 Job Mobility 3.02(6.93) .140 .035 .000 15.545 1.150 Human Capital 5.37(13.21) .023 .013 .085 2.966 1.023 Team C. 13.08(16.09) -.015 .011 .187 1.741 .985 Nagelkerke R2 .239 -2 Log likelihood 356.491 x2 59.397 .000
Hypotheses Testing: topic evaluation (H4)
The last analysis conducted focuses on the relation between the evaluative level of news media coverage (independent variable), specifically the tone related to performance, physical characteristics, economic traits, job mobility, human capital, and team characteristics and players’ transfer (dependent variable). A logistic regression shows that the model holds significance, as the chi-square test results to be x2 = 105.042 (8), p = .000. As Nagelkerke R2 = .39, the model accounts
for the 39.4% of the variance. Hosmer and Lemeshow test of goodness of fit in this analysis results to be not significant, indicating that logistic regression is a good fit for the data (x2 = 12.14(8), p
= .145). Table 4 reports all the descriptive statistics, regression weights, and odds ratios. The probability of a transfer happening will decrease by 30.7% when the media focuses on reporting
performance negatively (exp b = .445, p = .000). Therefore, H4a is supported, showing a relationship between this evaluation and players’ transfers. In contrast, when news media focuses on evaluating economic traits positively, transfers’ possibility will increase by 63.3% (exp b = 1.732, p = .040). This finding supports H4c.
Similarly, the likelihood of a transfer happing increases by 80.4% when news media evaluate human capital as positive (exp b = 3.977, p = .000) and by 73.3% when also team characteristics are portrayed as positive in the news media (exp b = 2.750, p = .000). These findings allow us to confirm, H4e and H4f. In contrast, the evaluation of physical characteristics did not yield significant effect on relation with football transfers (exp b = .645, p = .120), rejecting hypothesis H4b, as well as job mobility evaluation (exp b = 1.053, p = .880) rejecting H4d.
Table 4
Topics’ evaluations as Indicators of Football transfers (H4). Variable
(Tone)
Mean(SD) b s.e. p Wald
statistic Exp. b Performance .19(.68) -.809 .212 .000 14.521 .445 Physical C. .01(.37) .362 .395 .360 .837 1.436 Economic Traits .16(.56) .549 .268 .040 4.205 1.732 Job Mobility .11(.46) .051 .338 .880 .023 1.053 Human Capital .05(.48) 1.380 .361 .000 14.614 3.977 Team C. .07(.83) 1.012 .184 .000 30.196 2.750 Nagelkerke R2 .394 -2 Log likelihood 310.846 x2 105.042 .000
Discussion and Conclusion
This research aimed to study the role of talent management, combined with an in-depth analysis of news media coverage, as tools to help football firms and specifically PR professionals to manage football transfers. A content analysis of 300 articles of "La Gazzetta dello Sport”, as well as a talent management variables analysis were conducted in order to answer the research questions: RQ (1) To what extent do transfer topics, portrayed in online news media coverage of “La Gazzetta Dello Sport”, relate to football exchanges during the Serie A transfer market seasons of 2017/2018? RQ (2) To what extent player’s features relate to football exchanges in the Serie A market seasons of 2017/2018? Correspondingly, this study focused on the extent managers can identify players who are at risk of being transferred and benefit from analyzing news media coverage to anticipate other firms that have the intent to acquire in-house players. This operation is possible by following a dual approach: on one side, by adopting a talent management perspective, managers can observe if a player is at risk of being transferred. On the other, this study contributes to the sports communication literature by underling how a communication perspective is critical to help firms to monitor and react consequently by trying to steer the media coverage negotiation into a favorable strategic outcome.
Talent Management Approach
Specific key characteristics were found to play a role as indicators of a potential transfer. In line with the research carried by Thijs (2017), performance, age, salary and number of previous transfers "constitutes signals in the context of a player's career trajectory". Specifically, this study found a relationship between negative performance and the likelihood of transfer. One explanation is that firms that hold a skillful player might not be willing to sell him as much as one that is performing weaker. This implies that firms have more power than players on their career trajectories and can influence them to a certain extent on their future. An additional finding concerns the age of players. Results from the underlying research aligned with the CIES Football Observatory Monthly report (Poli, Ravenel, & Besson, 2015), suggesting that younger players have an increased probability of getting transferred. Similarly, data indicate that a player's salary is a determinant that can enhance the likelihood of football transfers. Player's age and salary findings are in line with existing literature arguing how these features reflect the values players add to the team, and therefore a younger player and a high salary are indicators of respectively potential talent and affirmed quality (Ruijg & van Ophem, 2015). Lastly, the aforementioned
research conducted by Thijs (2017) matched with the results of this study regarding the previous number of transfers. A higher number of prior transfers increase the probability of a player getting transferred.
Communication Perspective
News media coverage was found as a critical determinant in explaining football transfers. Previous studies did not focus on how news media portrayed transfers and therefore no satisfying answers have been investigated concerning this topic. As a consequence, this section will explore several possible explanations when interpreting results. To begin with, a correlation was found between an increase in media coverage and an increase in the probability of a player changing football team. This first main finding relates to the nature of the relationship between teams, players and media coverage and the accuracy of the information reported. The likelihood of a transfer is possible to be partially foreseen by monitoring news media coverage. This allows us to infer that media coverage possesses a level of accuracy in reporting about transfers. Therefore, this data shed light on the relationship between firms and news media outlets. To a certain degree, there is a confirmation of the interdependency and transparency between these two actors, suggesting how also firms provide information to the media.
A second result of this research concerns the type of news audience receives. Football transfers move millions of euros and can change the performance of an entire team. Therefore, during the transfer windows, more room for discussion is designated for a player’s transfer in news media compared, for example, to football results. Moreover, Italian football media coverage is characterized by a very particular “sensationalized” writing style, build upon the necessity of entertaining the reader “at all costs” (Mapelli, 2009). Related to the fact that football transfers can be perceived from the public as extremely important for the future of the team, journalists aim to grasp the public’s attention by providing information about transfers. This results with wider media coverage of players under the lens of a possible transfer. By focusing on media coverage, this research found how specific attributes related to footballers play a role as indicators of transfer.
Specifically, certain topics can play a role in helping managers to foresee a potential transfer. The findings suggest that when news provides economic traits information (e.g., possible transfer offer given by an external team with the intent of acquiring a player), there is a higher chance that a transfer would happen. While, for example, performance or physical information (did not yield an effect on transfers) often do not associate with a player necessarily leaving his
team, economic news tends to provide specific and detailed information on transfers. Similarly, news that concerns the job mobility of a player is often mentioned referring to a potential transfer as a component of his behavior when deciding for future firms’ destinations. A particular acknowledgment highlighted by this research focuses on team characteristics news. Although this news type was the most commonly found during the analysis, it did not show an effect on transfers. One reason behind this concerns the vague news flow used by journalists when a player is declared transferable by the football firm. In fact, without more specific news regarding, for example, an offer from a different football organization, journalists only report potential interests which could result as not accurate enough to define a potential acquisition clearly.
The tone of news topics was also assessed as a key indicator of a possible transfer. Generally, news media positive tone of a potential footballer exchange is a crucial detector for the transaction to happen. Vice versa, this research found that there is a decreased chance of a player's trade when tone regarding performance is negative. By focusing on the specific evaluation of each topic, a player's performance evaluation is in line with the talent management variables. When players exhibit a decrease in their skills, they become less appealing on the labor market and, therefore, news media coverage portrays them negatively.
Furthermore, economic traits tone as well as, human capital and team characteristics, increase the probabilities of a successful transfer when they portray the possibility of a player changing his team positively. This finding shows that in football domain tone is a useful insight compared to different domains, such as the financial where the tone of news has a minor influence on stock market fluctuations (Strycharz J. S., 2018). In specific, the evaluative level of agenda-setting theory resulted to have a stronger impact compared to the substantive one, confirming, to a certain degree, how the “sensationalized” writing style of news, constituted by a high amount of journalists opinions (Mapelli, 2009) is as effective as to the content of itself.
Implications
To begin with, this research enriched sports and specifically football literature, assessing the AST as the theoretical framework of investigation. Findings confirmed how the first level of agenda setting theory has a relationship with transfers, foreseeing football exchanges. Concerning the second level, what strikes the most is the stronger effect for tone (evaluative attributes) compared to topics (substantial attributes). This particular finding directly connects to the managerial implications this research aimed to highlights. To anticipate news media coverage, and try to steer
a positive outcome concerning players, football firms not only need to account for talent management key attributes but have to negotiate with media. Media coverage portraying a positive transfer outcome was found to be related to a higher chance of transfer. Therefore, if the aim for a team is to keep a player, the firm should focus on providing information, for example, that clearly states the willingness of keeping him. By trying to steer the media in this way, a firm would have higher chances of not spreading rumors about the availability of a player to leave and obtain an advantage in keeping an in-house player.
To conclude, strategic communication management, as well as analyzing football market by following a talent management approach, will enhance the odds of keeping a player by: first, utilizing talents variables to spot a potential player at risk of being transferred. Second, by steering the media coverage, increasing the possibility of keeping the player. In contrast, lack of proactivity could cause damage in reputation when firms are too slow or decide to not communicate to media, as they risk losing important assets of their teams.
Limitations and Future Research
A first limitation is related to the sample of this research. Only a limited number of players were studied, and only coming from the Italian Serie A league. Furthermore, different labor markets have different rules and ways to negotiate during football exchanges. In addition, Italian sports news is characterized by a sensational writing style, which could differ from other sports news culture and journalism. Although this research depicted a first column able to explains to a certain degree the relations between media and football clubs in Italy, future research should improve this the range of this dual approach by comparing Italian news media coverage with different football coverage in other countries by studying several football players.
A second limitation concerns the source of information. La Gazzetta dello Sport was chosen since the focus of this research was to study the relationship between media coverage and football teams. However, several actors can play a role concerning football transfers. Future research should also focus on comparing this outlet with players statements on social media and football organizations reports. For example, player’s social media could potentially be a source of information since the growing popularity of them and increased decision power over their career’s trajectories (Roderick, 2012). Although this research explores the surface of how football transfers can be anticipated by monitoring the media, by adding other sources of information, future
researchers would be able to provide a more complete overview on the football labor market domain.
The last limitation concerns the relationship direction between football news and transfers. This research found a relationship between media and football firms. Although these insights present useful indicators of a potential transfer, they must still be interpreted as helpers for football organizations since the direction of this relation is ambivalent. In other words, this research found a bi-directional relationship between media coverage and football clubs. It is not possible to assess a causal relationship between these two factors (e.g., increase of player's media coverage cause player's transfer, or vice versa). Future research should focus on investigating this bi-directional relationship, the possibility to predict transfer news media coverage from football transfers, which would give the opportunity to test whether transfers could also be seen as indicators for the news selection process. By building of this future research ideas, scholars will enhance the probability of understanding this relatively new field of research, unveiling new managerial and academic insights related to the domain of football communication.
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Appendix A
Codebook – Football Online Coverage Content Analysis Introduction:
The purpose of this study is to investigate the level of transparency online football news media outlets retain by focusing on the analysis of overall news media coverage of football players, division of media coverage on topics related to the player and final evaluation level.
In this project, articles are coded from an online archive. To be able to acquire similar selections, only one search term is underpinned. The basic search term is “player X name”. In a later stage, a variety of corporate topics are assessed following the guidelines provided by Frick (2007) and Thijs (2017). Concerning the administrative data, the information was retrieved from Transfermarkt.com, a website that collects information about football players in Europe.
Evaluation point of view:
To avoid confusion on how to evaluate (negative or positive) the sub-topics related to the potential transfer of a football player, there is one general rule to follow: consider the article as negative (positive) when it pictures a scenario where the transfer is unlikely (likely) to happen. All the examples help to answer the code would refer to the player Higuain.
ADMINISTRATIVE DATA (NOT TO BE CODED): 0.1 Name of the coder
Nicolò Ferrari 0.2 Sample
“Name of the player” is mentioned in the article. 300 articles total. Number of articles per player (if possible) = 30.
First section = 10% mutual coding N = 30 News outlet: La Gazzetta Dello Sport 0.3 Player’s nationality
0.4 Player’s age
0.5 No. of previous transfers 0.6 Salary
0.7 Player X got transferred 0 = no; 1 = yes
0.9 No. of assists
GENERAL DATA AND CONTROL VARIABLES V1 Coder ID
Nicolò Ferrari (actual coder)
Reliability test (first 10% of articles)
V2 Sample
“Name of the player” is mentioned in the article. 300 articles total. Number of articles per player (if possible) = 30.
First section = 10% mutual coding N = 30 News outlet: La Gazzetta Dello Sport V3 Date:
Given in full (e.g. dd/mm/yyyy) V4 Number of words
Copy paste from the first word of the article to the last word before the stripe. Do not include title, start with the first word of the article. Count also captions of pictures and all text until the last word.
OVERALL COVERAGE OF FOOTBALL PLAYER (H2) V5 “How much of the article is about (Player X)?”
Total number of words referring to Player X in the article
To assess this question, it is necessary to take into account the number of words of each sentence referring to Player X.
The first step consists of assessing media coverage related to a player: if a sentence mentions the name of Player X, the whole sentence counts as media coverage referring to Player X (e.g., "while Cristiano Ronaldo could arrive to Juventus during this summer window, Higuain might decide to leave Juventus”)
The second step consists in making a sum of all the words found in the sentences referring to Player X. For example, if two sentences in the article relate to one player, the assessment counts the sum of the words in both sentences: (sentence 1 = 20 words) + (sentence 2 = 21 words) = 41 words in total.
To understand which sentences refer to Player X the general rule is to code each sentence (Name, nickname, pronoun) that directly or indirectly relate to him.
FOOTBALL TOPICS CODING (H3) AND EVALUATION (H4): Next, you assess the type of article through several variables:
The articles will be divided based on the football topics they present. The analysis will be carried out by dividing articles based on the major categorization of the topic (divided into sub-groups), referring to the work of Frick (2007) and Thijs (2017), adapted to fit this research. Six different characteristics have been selected:
Performance (e.g., goal, assist, etc.), physical characteristics (e.g., weight), economic traits (e.g., contract length), job mobility (e.g., previous team ownership), players human capital (e.g., motivational role in the team) and team characteristics (e.g., team ownership).
Remember: You can choose more than one topic/sub-group. Performance (Substantive Level AST):
V6 “How much of the article is devoted to the topic performance of Player X?” Total number of words referring to Player X performance
General definition:
“the action or process of performing a physical football related task or function on the field.”
Assessment degree of topic’s presence:
To assess this question, it is necessary to take into account the number of words in each sentence referring to Player X performance.
The first step consists in assessing media coverage related to Player X performance: if a sentence mentions the name of Player X, the whole sentence counts as media coverage referring to this topic.
The second step consists in making a sum of all the words found in the sentences referring to Player X performance. For example, if two sentences in the article relate to one player, the assessment counts the sum of words in both sentences: (sentence 1 = 20 words) + (sentence 2 = 21 words) = 41 words in total.
To understand which sentences refer to Player X performance, the general rule is to code each sentence that directly or indirectly relates to him based on the subtopics explained below.
Subtopics to help decision process: Goals: goals scored by Player X. Assists: assists given by Player X.
Yellow card: yellow cards picked up by Player X. Red card: red cards picked up by Player X.
National league matches: matches played by Player X in his team/national league. International league matches: Player X active in international matches for the country. Game system: use of specific modules om the field related to Player X. e.g., With Higuain, A.C. Milan would play 4-3-3.
In case this topic is not present, code as 0. In this way, you will be directed to the assessment of the next topic.
Performance (Evaluative Level AST):
V7 “How would you evaluate the overall tone of the article regarding Player X Performance?”
1 = Negative; 2 = Positive.
General rules: One of the two answers must be provided. In the case of indecision or further analysis, write on V18 additional details when conducting the analysis. Consider the article as negative (positive) when it pictures a scenario where the transfer is unlikely (likely) to happen.
Assessment Negative Evaluation:
“Negative” = Evaluate where Player X is mentioned negatively related to his performance: Assess this option if the article is more negative than positive. Several examples are taken into account:
If a player is associated with negative performance: "The last season Higuain scored only 10 goals, where the 2016/2017 one he reached 23 goals".
If a player is associated with a continuative trend of negative performance: “it appears that Higuain in the last four matches lost his efficacy when shooting." If a player is mentioned in a prediction of negative performance trend “By scoring less, Higuain market value changed drastically.”
Subgroups negative assessment:
Goal and assists: when a player is described as not in line with expected goal and assists performance. (E.g. "Higuain is not keeping up with the past season, where at this point, he already had scored 7 goals."
Yellow and red car: when a player foul on the field is mentioned in such a way that it creates damage in the visibility of the player. “Higuain often was sanctioned with yellow cards during the past season due to protests against the referee” Not to code a positive explanation about the temper of Player X (“Higuain is a fighter on the field, and therefore he gets several yellow cards”)
National league matches: if a player supposed to take part in national team matches is instead not called to play.
International league matches: if a player supposed to take part to international team matches instead is not called to play.
Assessment Positive Evaluation:
"Positive" = Evaluate where Player X is mentioned positively related to his performance: Assess this option if the article is more positive than negative. Several examples are taken into account (e.g., “The last season Higuain scored 23 goals, reaching his personal record of goals scored in one season”).
If a player is associated with positive performance: "The last season Higuain 20 goals, double the amount of 2016/2017 season."
If a player is associated with a continuative trend of positive performance: “Higuain in the last four matches scored with high efficacy: 10 shots on target, five goals." If a player is mentioned in a prediction of positive performance trend: “By scoring at this rhythm, Higuain market value will increase drastically.”
Goal and assists: when a player is described as in line or over the expected goal and assists performance. E.g. "Higuain is keeping up with the past season goals count: 12 goals in 20 matches."
Yellow and red car: when a player’s foul on the field is mentioned as positive in increasing the visibility of the player. “Higuain showed a clean sanction record the past season: 2 yellow cards and no red card”. To code as positive the characteristic of the temper of Player X (“Higuain is a fighter on the field, and therefore he gets several yellow cards”)
National league matches: if a player takes part in national team matches.
International league matches: if a player takes part in international team matches.
Physical Characteristics (Substantive Level AST):
V8 “How much of the article is devoted to the topic Physical Characteristics of Player X?”
Total number of words referring to Player X in the article related to his physical characteristics
General Definition:
“Physical characteristics are defined as traits or features about football players, related specifically to their body constitution and skills.”
Assessment degree of topic’s presence:
To assess this question, it is necessary to take into account the number of words in each sentence referring to Player X’s physical characteristics.
The first step consists in assessing media coverage related to Player X’s physical characteristics: if a sentence mentions Player X’s physical characteristics, the whole sentence counts as media coverage referring to this topic.
The second step consists in making a sum of all the words found in the sentences referring to Player X’s physical characteristics. For example, if two sentences in the article relate to one player, the assessment counts the sum of the words in both sentences: (sentence 1 = 20 words) + (sentence 2 = 21 words) = 41 words in total. To understand which sentences refer to Player X’s physical characteristics, the
general rule is to code each sentence that directly or indirectly relates to him based on the subtopics explained below.
Subtopics to help decision process: Age
Height
Left/right foot Weight
Injury: both actual and potential injuries (e.g. “In the past, Higuain has been injured and therefore did not constitute a reliable resource for Juventus during the
championship”). Power
Strength Stamina
In case this topic is not present, code as 0. In this way, you will be directed to the assessment of the next topic.
Physical Characteristics (Evaluative Level AST):
V9 “How would you evaluate the overall tone of the article regarding Player X’s physical characteristics?”
1 = Negative; 2 = Positive.
General rules: One of the two answers must be provided. In the case of indecision or further analysis, write on V18 additional details when conducting the analysis. Consider the article as negative (positive) when it pictures a scenario where the transfer is unlikely (likely) to happen.
Assessment negative evaluation:
“Negative” = Evaluate where Player X is mentioned negatively related to his physical characteristics. Assess this option if the article is more negative than positive. Several examples are taken into account:
If a player is associated with, in general, negative physical characteristics: "Higuain, during the summer, gained too much weight and now he is less reactive when playing."
If a player is associated with a continuative trend of negative physical characteristics: “it appears that Higuain would need to rest for two more months due to a knee injury."
If a player is mentioned in a prediction of negative physical characteristics trend "Higuain often is injured, therefore A.C. Milan might decide not to risk investing money on buying him."
Subgroups negative assessment:
Age, height: when a player’s age or height is considered a negative aspect during a transfer. (E.g. “Higuain, 31 years old, might be too expensive for A.C. Milan, which is considering investing money in younger players”.)
Power, agility, stamina, strength: when a player’s ability is portrayed as missing or not sufficient for the level required (e.g. "Higuain demonstrated not to be sufficiently strong or fast, compared to other valuable international players").
Injury: when an injury is portrayed as an obstacle in a player’s career and therefore affect his chances of being exchanged (e.g. “Higuain have been often injured in the past, therefore it is difficult to predict if it would happen or not again”).
Assessment Positive Evaluation:
“Positive” = Evaluate where Player X is mentioned positively related to his performance: Assess this option if the article is more positive than negative. Several examples are taken into account (e.g. “the fact that Higuain is 30 years old and
