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Abstract

The COVID-19 pandemic influenced every industry dependent on human interaction, including an unprecedented influence on sport, as many events were canceled or played in front of empty stadiums. Since 2020, this natural experiment has been a unique focus of academic literature, though with attention to on-field effects. Based on exchange theory in relationship marketing, this study makes a novel contribution by questioning the impact of exchange uncertainty on sponsorship decision-making. Accordingly, we isolate the pandemic's effect on managerial commitment by comparing sponsor retention across multiple eras of Formula One (F1), finding evidence that uncertainty in (un)fair exchange affected sponsor retention during the pandemic-influenced 2020 season, yet improved trust in subsequent seasons. Results are encouraging for marketers in that the effects of the pandemic had a smaller impact than initial industry predictions, while also rebounding significantly following an unprecedented global event, underscoring the resilient exchange relationships within global sport.

Keywords

exchange theory relationship marketing managerial decision-making uncertainty COVID-19 B2B relationships survival analysis

The COVID-19 pandemic, the effects of which became widespread on a global basis in March of 2020, introduced high levels of uncertainty across many dimensions of everyday life, influencing the fields of medicine, sociology, economics, and politics. Similarly, the COVID-19 pandemic's effects on the sport industry were unprecedented, leaving a legacy of canceled events and empty stadiums (Grix et al., 2021). Notably, many global events, such as the 2020 Summer Olympic Games, were rescheduled and played in empty stadiums in 2021, as was the Euro2020 soccer tournament (Liu et al., 2024). The Formula One (F1) season was also severely affected in 2020, as it was shortened from 22 to 17 races and restricted to Europe and the Middle East, without fans present at Grands Prix sites for the first eight races. In North America, many major events such as the 2020 National Collegiate Athletic Association (NCAA) Division I men's and women's basketball tournaments were canceled, while other events and leagues such as National Basketball Association (NBA) games were rescheduled and played in front of empty stands in neutral site facilities closed to the public.

While these circumstances created a natural experiment for studies of crowd effects and other homefield advantages, there has been a paucity of studies investigating the pandemic's impact on the business side of sport and event organizations. Based on exchange theory in relationship marketing (RM) (Morgan & Hunt, 1994; McCarville & Copeland, 1994), this longitudinal study begins to rectify this void by isolating the effects of the pandemic during the 2020 season on sponsorship relationships that represent a substantial portion of revenue for sport organizations (Ozanian & Knight, 2023). The marketplace for sponsorship during the pandemic was severely impacted by uncertainty due to the substantial limitations in the delivery of the assets and rights contractually obligated to sponsors by their sponsored properties. Those assets and rights were based on the expected event schedule, including fans’ attention prior to the onset of the pandemic, thereby calling into question the fairness within the sponsorship exchange relationship. While the pandemic raised challenges to core principles of exchange (i.e., marginal utility, fairness), RM tenets of trust and commitment can mitigate uncertainty and reduce the propensity to leave (Morgan & Hunt, 1994).

“The uncertainty of the COVID-19 pandemic has accelerated shifts in the way rights holders sell sponsorship, brands make sponsorship investments, and how commercial partnerships are activated,” claimed research from Nielsen (2021, p. 2). However, a lack of empirical analysis means this assertion remains unproven. How did the uncertainty created by a global pandemic affect managerial decision-making related to sponsorship? Did the exchange relationships inherent to sponsorship dissolve at a higher rate in 2020, and if so, was the recovery slow or rapid? The purpose of this paper is to empirically address these questions by comparing the stability of sponsor relationships prior to, during, and after the pandemic-influenced season of 2020.

Given the global nature of the pandemic, we focus on the worldwide market for F1 racing team sponsorships and utilize a longitudinal approach, and accordingly isolate effects by analyzing data from both before and after the pandemic. Broad, trade-based estimates of the reduction in overall sponsorship spending as a result of COVID-19 ranged widely, between a low of 23% (ESA, 2021) to a high of 46% (Bradford & Sher, 2020). Importantly, this study is the first to specify a peer-reviewed methodology to empirically estimate changes in decision-making attributed to the uncertainty of exchange during the start of the pandemic in 2020, as well as to analyze multiple years of data following the pandemic's onset in order to assess any recovery. Sport and entertainment properties rely on RM via sponsorship exchange for their organizational survival (Cobbs et al., 2017; Jensen & Cornwell, 2017), and sponsoring brands make relational commitments to sponsored properties based on trust that such commercial opportunities will influence the brands’ various target markets (Farrelly & Quester, 2005; Meenaghan, 1983). Thus, this research represents an important contribution to multiple literatures, including pandemic effects on sponsorship, RM exchange, and managerial decision-making under high uncertainty.

The subsequent section provides a brief review of the literature related to the pandemic's effects on spectator sport, primarily from the sports economics literature. We then outline how scholars have described uncertainty associated with management decision-making, followed by a review of sponsorship as a business-to-business (B2B) exchange relationship subject to managerial decisions. Next, an overview of the survival modeling method employed in this study is presented along with a description of the sporting context of F1 racing. Results, discussion, and a conclusion that acknowledges the implications and limitations conclude the paper.

Literature Review

COVID-19's Effect on Sport

The vast majority of the literature on the effects of the COVID-19 pandemic related to the sport industry has focused on the natural experiments created by the sudden onset of a global pandemic. Once spectator sport competitions resumed, arenas or stadiums remained empty of fans. An unintended consequence of this action was for the first time in recent history, researchers in a wide variety of different academic fields could now isolate and study how teams, athletes, and other stakeholders, such as officials, might act or perform differently in the absence of crowds at events. Multiple studies, specifically in the sport of football (i.e., soccer), used the opportunity presented by empty arenas and stadiums to investigate the effects of homefield advantage on both players and officials (e.g., Leitner et al., 2023).

Several studies documented a player performance effect when crowds were absent (i.e., Benz & Lopez, 2023; Bilalić et al., 2021; Caselli et al., 2023; Cross & Uhrig, 2023; Ferraresi & Gucciardi, 2021; Fischer & Haucap, 2021; Hill & Van Yperen, 2021; McCarrick et al., 2021) and also found that match officials and bookmakers were not immune to effects from an absence of fans (Bilalić et al., 2021; Bryson et al., 2021; Hill & Van Yperen, 2021; Leitner et al., 2023; McCarrick et al., 2021; Reade et al., 2022; Winkelmann et al., 2021). Beyond football, a few studies examined similar effects of the (lack of) crowds on homefield advantage or officials in basketball (De Angelis & Reade, 2023; Gong, 2022), baseball (Losak & Sabel, 2021) hockey (Guérette et al., 2021; Thrane, 2024), or a combination of North American sports (Higgs & Stavness, 2021; Szabó, 2022). However, the realized effects of the pandemic on the spectator sport industry were not limited to sporting, on-field outcomes.

COVID-19 Business Effects

Prior to the onset of the pandemic, sponsorship had grown annually since the conclusion of the last economic recession in 2010 and had become an integral part of the marketing mix for brand marketers (IEG, 2018). According to Bradford and Sher (2020), sport sponsorship investment was projected to grow by 4.9% to $135.3 billion in 2020. The pandemic altered this projection by $61.6 billion, to $73.7 billion, or a reduction of approximately 46% (Bradford & Sher, 2020). According to the European Sponsorship Association (ESA), the total value of Europe's sponsorship market was reduced by 23% in 2020, falling from €30.69 billion in 2019 to €23.63 billion in 2020 (ESA, 2021). According to the sports marketing agency Two Circles, in 2020 global sport sponsorship spending was reduced by $17.2 billion due to the pandemic, which estimated the loss of revenue at 37%, from an estimated $46.1 billion to $28.9 billion (Cutler, 2020). IEG (2020) estimated that the total value of make-goods needing to be replaced due to the COVID-related shutdown to be equal to a 38% reduction in value for sponsors (approximately $10 billion), across both sports and entertainment. As stated, these estimates suggest that the pandemic's impact on various sponsorship markets varied from a low of 23% (ESA, 2021) to a high of 46% (Bradford & Sher, 2020). Others offered more moderate estimates, ranging from 37% (Cutler, 2020) to 38% (IEG, 2020).

The existing academic literature on the effects of the COVID-19 pandemic has largely focused on the on-field, sporting effects of the natural experiment whereby competitions took place in empty or severely restricted spectator venues. In contrast, there has been a scarcity of empirical studies investigating the pandemic's off-field impact on the business side of sport organizations. Moreover, this preceding review of the academic literature focused on the effects of the pandemic reveals that no study to date has utilized longitudinal data to examine how the pandemic influenced managerial commitment, including decisions made both prior to and following the onset of the pandemic. In one study of off-field outcomes, Reade and Singleton (2021) found that attendance at matches in England, Italy, and Germany was negatively impacted by the prior day's announcements of cases and deaths (Reade & Singleton, 2021). Likewise, Alabi and Urquhart (2023) analyzed panel data from 2005 to 2021 that characterized the financial performance of English football clubs, establishing that the profitability of the clubs deteriorated during the pandemic. The effects of the pandemic introduced what Caggiano et al. (2020, p. 1) termed “COVID-19-induced uncertainty.”

In 2020, not only were the long-term effects of the pandemic unknown, but the duration of the pandemic and its short-term effects were also indefinite. This uncertainty impacted not only consumer confidence but also management relationships between B2B partners, such as sport organizations, events, and athletes (i.e., sporting properties), and the media channels and corporate brands that support them through broadcasts, advertising, and sponsorships. As expounded upon below, the commitment to each of these B2B relationships relies on the realization of a valued benefit (i.e., marginal utility) and an expectation of fair exchange (McCarville & Copeland, 1994), which were threatened by the uncertainty of the pandemic. A stream of pre-pandemic literature illustrates the salience of uncertainty in management decision-making (see Sniazhko, 2019 for a full review).

Uncertainty

Broadly defined, uncertainty is a circumstance in which scarce knowledge is available to determine which of several states of nature will occur or have occurred (Anderson et al., 1981). The pandemic-induced uncertainty of 2020 was strongly associated with situational ambiguity that results in task uncertainty for organizational decisions, characterized as an inability to act with determination due to the absence of cause-effect understanding, environmental dependencies, and internal interdependencies (Thompson, 1967). The COVID-19 pandemic introduced layers of uncertainty across the three typical dimensions described in the literature: environmental, industry, and firm uncertainty (Sniazhko, 2019). For example, in the sport of F1, environmental uncertainty impacted where and when the race events could resume (Cooper, 2021); industry uncertainty raised questions about if and how team and series personnel could travel to revised events (Barretto, 2020); and firm uncertainty was manifest in the cash flow challenges faced by teams and their sponsors (Mitchell-Malm, 2022).

When uncertainty reigns, trust is scarce (Morgan & Hunt, 1994), and business and marketing managers are forced to engage in coping tactics such as reduction, acknowledgment, or suppression of uncertainty (Lipshitz & Strauss, 1997). More specifically, in the case of sponsorship in 2020, brand managers had to decide to continue their commitment to the sponsored sport and suppress the uncertainty of return on their marketing investment, or to take steps to dissolve the relationship and thereby reduce the uncertainty associated with future commitments and contracted deliverables. The next section further explicates the sponsorship exchange relationship.

Exchange Theory

Sponsorship was defined by Meenaghan (1983, p. 9) as “the provision of assistance, either financial or in-kind, to an activity by a commercial organization for the purpose of achieving commercial objectives.” Conceptually, sponsorship represents a B2B marketing relationship whereby the “commercial organization” is a sponsoring firm and the “activity” is the sponsored property, such as a sports team, league, event, or athlete (Nickell et al., 2011, p. 578). Inherently, the sponsorship relationship is based on an exchange of resources, whereby the sponsoring firm commits financial or in-kind provisions to the sponsored property in exchange for promotional assets (McCarville & Copeland, 1994).

Exchange theory is the framework within which sponsorship manifests in sport, given that the objectives of each partner to the exchange are realized through their collective commitment of resources to improve each other's competitive position (Blalock & Wilken, 1979; Jensen et al., 2024). This resource exchange is based on three principles: (1) rationality, whereby actors have a conscious objective in mind; (2) marginal utility, whereby a valued benefit is not achieved in absence of the exchange; and (3) fair exchange, whereby the benefit realized is equivalent to a reference expectation based on previous exchange experiences or a perceived analogous exchange (Turner, 1986).

The theory of RM explains that when the three exchange principles are satisfied, trust and felt commitment are nurtured in the relationship and the likelihood of continued marketing exchange grows (Farrelly & Quester, 2005; Jensen & Cornwell, 2017; Morgan & Hunt, 1994). However, when a partner to the exchange suspects a violation of one or more of the three principles, trust in the relationship is jeopardized, commitment wanes, and the sponsorship risks dissolution (Cook & Gillmore, 1984; Hessling et al., 2018).

Research Hypotheses

Both B2B and B2C sponsors were severely impacted when the pandemic imposed substantial limitations on sporting properties’ ability to deliver the assets and rights contractually obligated to sponsors, thereby calling into question two principles of exchange relationships: (1) the marginal utility or realized benefit of exchange, and (2) the expectation of fairness (Turner, 1986). For example, canceled events negated brand exposure and both on-site hospitality and networking opportunities for all sponsors. Subsequently, when sporting events resumed with no spectators, sponsor exposure was limited to visuals within the broadcast received by viewers. As a result, managers’ trust and commitment were tested, and they made decisions on the continuation or dissolution of their relationships with sponsored properties amid environmental, industry, and potentially firm layers of uncertainty inflicted by the pandemic.

Amid the pandemic's uncertainty, sponsorship managers reportedly reacted in different ways that reflect the common coping tactics from the literature (Lipshitz & Strauss, 1997). Anecdotally, some sponsors acknowledged the uncertainty but maintained their trust and commitments to sport properties in spite of many events being canceled and the associated sponsorship assets not being delivered. Alternatively, other sponsors lost trust in the relationship and reduced the uncertainty of exchange by ceasing their contractually obligated payments (Patel, 2020). As a middle ground, some sponsors suppressed the uncertainty by requesting future commitments (i.e., make-goods) to replace the lost value of undelivered sponsorship rights and assets (Lefton, 2020). Based on these manifestations of RM in sponsorship exchange, we expect that the uncertainty of the pandemic resulted in less stable sponsorship relationships in 2020, compared to prior years, and grounding the following hypothesis:
H1: During the uncertainty of the pandemic-influenced 2020 season, sponsors would be significantly less likely to remain committed to sponsorship agreements when compared to prior and subsequent years.

In the latter half of 2020, as the pandemic's epidemiological effects were better understood, uncertainty began to slowly subside as environmental dynamism was reduced (Achrol & Stern, 1988). Protocols on how to safely attend sporting events were established that permitted limited fans to attend F1 Grands Prix, as well as Bundesliga, Major League Baseball (MLB), National Football League (NFL), and collegiate (i.e., amateur) events. While this progress re-established a proportion of ticket revenue for sport properties, the commitment of sponsors remained questionable due to shortfalls in the value realized (i.e., marginal utility) in sponsor exchange. However, RM theory suggests that with environmental and industry uncertainty falling, relationships that had endured the challenge by resisting opportunistic behavior are more likely to be reinforced with trust and strengthened commitment (Morgan & Hunt, 1994). Consequently, these enduring sponsorships are less likely to dissolve in the years following the pandemic-influenced season of 2020. Thus:
H2: In the years following 2020, sponsors exhibit greater relationship commitment through a reduced likelihood of dissolving sponsorship agreements, compared to the era prior to the pandemic.

Contribution to Business-to-Business Decision-Making

A recently published analysis of two decades of research on sponsorship highlighted an overabundance of studies focused on individual consumers’ responses to sponsorship, and a relative dearth of studies that investigate the sponsor's perspective (Cornwell & Kwon, 2020). Moreover, there is increasing interest in explicating corporate decisions, particularly the reasons why a sponsor may choose to end its exchange with a property (e.g., van Rijn et al., 2019). While sponsor relationships have a firm foundation in the B2B strategic alliance literature (Farrelly & Quester, 2005; Farrelly et al., 2006; Nickell et al., 2011) and the sponsorship literature (Jensen & Cornwell, 2021), the dynamics of how unforeseen events (e.g., a global pandemic) influence these exchange relationships remain under-researched (Cobbs, 2011). Thus, this study not only features a managerially relevant issue in the effects of the COVID-19 pandemic on sponsorship decision-making, but also contributes to the B2B alliance, RM, and sport literature in a novel way. This study's focus is on the end of sponsorship relationships, specifically which factors may or may not be predictive of the end of such relationships, as called for by Halinen and Tähtinen (2002). Note that this study diverges from past research in its capacity, via the analysis of longitudinal data, to isolate how the effects and surrounding uncertainty of an unforeseen event impact decisions to continue or dissolve the sponsorship exchange relationship, representing a novel contribution to this stream of research.

In the remainder of this paper, we address the empirical challenge of better understanding how the uncertainty caused by the pandemic affected sponsorship in 2020 and the immediately following years. Rigorously, we employ a myriad of potentially confounding control variables to account for factors apart from the uncertainty related to the pandemic, thereby providing a better approximation of causal inference. We also analyze periods before and after the onset of the pandemic, comparing them to the primary year of COVID-19 uncertainty (2020) which is left out of the model to serve as a reference variable. These analytic procedures are designed to isolate effects during the period in question, as well as determine whether any effects were short or long term.

Broadly, anecdotal evidence suggests that sport business has demonstrated significant recovery and transformation since the COVID-19 pandemic, surpassing pre-pandemic revenue levels through strategic diversification and broadcast innovation (Howard & Best, 2022). A key driver of this resurgence has been lucrative long-term media rights deals, exemplified by the NBA's recent 11-year, $76 billion agreements with Disney, Amazon, and NBC, reflecting the growing value placed on live sports content across both traditional and streaming platforms (McCaskill, 2024). The widespread legalization and increasing popularity of sports betting in America has also emerged as a significant new revenue stream (WLA, 2025), and the post-pandemic period has seen a notable rise in investment and visibility for women's sports, with viewership and engagement surging across various leagues and events (Kaufman, 2025). With the inclusion of several years of sponsorship data following the height of the pandemic, this paper empirically assesses the recovery of sponsorship as uncertainty dissipated.

To execute the study, nearly 30 years of data on sponsor decision-making related to F1 racing teams were compiled. Importantly, data from 3 years following the affected 2020 season (i.e., 2021–2023), in addition to decades of data before the pandemic-influenced season, are analyzed to isolate the effects of uncertainty in 2020. As stated, we expect the probability of a sponsor exiting sponsorship agreements in the years preceding the onset of the pandemic, as well as the years that follow, will be lower than during the 2020 pandemic-influenced season. Given the paucity of research examining and isolating the effects of the pandemic on the decision-making of corporate firms, this research is expected to not only improve our understanding of the effects of unexpected and/or unplanned events but also help sport organizations and brands to contingency plan to better prepare for future events that may cause interruptions in sponsorship delivery or activation.

Method

Context

F1 is a multinational motorsport economy supported through sustained international popularity. In the season following the onset of the pandemic in 2021, a total television audience of 1.55 billion viewers representing 445 million unique individuals tuned into F1 Grands Prix (F1, 2022). This series’ audience spans six continents with more than 100 million domestic viewers in six different nations. F1 utilizes several tactics to maximize the development of an international audience while placating the series’ historical European foundation (Jensen et al., 2014). First contested as an international championship series in 1950, the 2024 F1 schedule visited 21 countries amid 24 Grand Prix races, compared to the 2020 season, which visited 12 countries amid 17 Grand Prix races. Ten F1 teams claiming seven different countries of origin contest each Grand Prix with two drivers per team. The 20 competing drivers in 2023 represented 15 different nationalities. In 2020, a similar schedule was suspended in March, just before the season's inaugural Grand Prix in Melbourne, Australia. Four months later, in July, the season commenced with 13 races exclusively in Europe, several of which were held at venues without fans, and the season concluded in December with four races in the Middle East.

Nonetheless, emerging markets for F1 include China, Indonesia, and the U.S., where three annual races now occur, and the Grands Prix audience has doubled since 2018 to more than 20 million viewers (F1, 2022; Smith, 2024). According to estimates (Ozanian & Knight, 2023), F1 team values have appreciated exponentially since 2019, rising 276% to an average of US$1.88 billion. Two primary activities have been cited for the massive growth in value. First, the teams collectively implemented a spending cap in 2021 to limit certain key costs at US$135 million. Second, the wide popularity of the Netflix series “Drive to Survive,” which provides viewers with an unfiltered, behind-the-scenes look at F1, has turned millions of new eyes to the sport since its debut in 2019 (Robinson, 2021). Growth in viewership and interest in F1 enhances the commercial value of team sponsorships, which can fund up to 70% of team budgets (Jensen & Cobbs 2014).

Dependent Variable

In order to investigate the question of whether the uncertainty caused by the effects of the global pandemic-influenced sponsorship decision-making, a dataset was compiled containing the sponsor of each team in each year, according to the procedure detailed by Cobbs et al. (2017, p. 100). The dataset originated with ChicaneF1, which catalogs historical team and sponsor pairings (Davies & Lawrence, 2023); subsequently, the data was triangulated with similar online and published sources to substantiate its reliability (e.g., SportsPro Media, 2007, 2013, 2014, 2015; Schlegelmilch, 2012). Sponsoring firms included in the dataset represent some of the most valuable multinational brands in the world, including airlines such as Emirates Air and Korean Air; alcoholic beverages such as Budweiser and Johnnie Walker; financial services firms such as Santander and RBS; fashion brands such as Hugo Boss and Michael Kors; tech firms such as Hewlett Packard and IBM; auto manufacturers such as Volkswagen and Fiat; telecommunications firms such as Siemens and Vodafone; and consumer electronics brands such as Philips and Samsung. The next step in the process was to note if each firm's sponsorship ceased or continued at the conclusion of each season. To accomplish this, we constructed a dichotomous variable (0 = Continuing, 1 = Ended) indicating whether each sponsorship continued or concluded through the end of each season, including at the end of the 2020 season impacted by the COVID-19 pandemic and three subsequent seasons (2021–2023). Importantly, once the sponsorship ends it is removed from the dataset, or it is considered censored and continues at present day. In addition, a time variable is inputted noting each year the sponsor remains with the team. Together, this event variable, time variable, and a specific ID variable for each sponsorship are the necessary components for computing the hazard function, or the probability of event occurrence, using the survival analysis approach (see Jensen & Turner, 2018 for a thorough methodological review). It is important to note that within the dataset there are multiple rows representing each year the sponsor remains, which is a necessary component in order to include time-varying covariates within the dataset, which as indicated in Table 1 includes the performance-related variables, economic variables, and clutter (i.e., number of team sponsors in each year).

Table 1.
Descriptive Statistics for Independent Variables.

Count (%)

Variables Measure (N = 7490) M SD Min, Max

Industrial factors

Economic growth (GDP) Cont. 1.82 2.74 −12.97, 34.86

Inflation (CPI) Cont. 2.18 2.83 −4.86, 121.74

Alcoholic beverage Binary 166 (2.22%)

Non-alcoholic beverage Binary 109 (1.46%)

Automotive Binary 1671 (22.31%)

Apparel Binary 356 (4.75%)

Bank Binary 25 (0.33%)

Retail Binary 721 (9.63%)

Telecommunications Binary 354 (4.73%)

Insurance Binary 61 (0.81%)

Food Binary 173 (2.31%)

QSR Binary 2 (0.03%)

Tech Binary 1459 (19.48%)

B2B Binary 3342 (44.62%)

Geographic factors

Africa Binary 8 (0.11%)

Asia Binary 932 (12.44%)

Australia Binary 54 (0.72%)

Europe Binary 4509 (60.20%)

North America Binary 1917 (25.59%)

South America Binary 69 (0.92%)

Domestic-sponsored team Binary 1807 (24.13%)

Domestic event Binary 5556 (74.18%)

Brand-related factors

Brand equity Binary 753 (10.05%)

Congruence Binary 4599 (61.40%)

Financial accountability Binary 3881 (51.82%)

Team-related factors

Sponsor clutter Cont. 29.26 9.88 3, 59

Drivers performance Cont. 3.66 4.12 0, 12

Historical team performance Cont. 4.66 3.09 0, 12

Recent team performance Cont. 134.63 181.40 −13.7, 860

Time-related factors

Era 1 (1996–2009) Binary 3708 (49.51%)

Era 2 (2010–2019) Binary 2656 (35.46%)

COVID (2020) Binary 265 (3.54%)

Post-COVID (2021–2023) Binary 860 (11.48%)

B2B, business-to-business; GDP, Gross Domestic Product; CPI, Consumer Price Index.

In terms of the sample analyzed, it includes all team sponsorships in F1 from the start of the modern era of F1 in 1996 through to the start of the 2024 season. In total, within the sample are 2,508 sponsorships across 7,490 observation-years, or an average duration of 2.99 years per sponsorship. It also is helpful to review the number of sponsorships and observations by era, across the overall sample. As indicated in Table 1, a total of 49.5% of observations occurred in Era 1 (1996–2009), 35.5% from Era 2 (2010–2019), and 11.5% in the 3 years following the pandemic-influenced season of 2020. There were a total of 265 observations in 2020, which is similar to subsequent years (251 in 2021, 285 in 2022, and 330 in 2023). Thus, the number of sponsorships active in 2020 is similar to the mean (287) over the subsequent three seasons.

Control Variables

Table 1 includes an overview of the various descriptive statistics for the study's control variables, the inclusion of which serves to isolate uncertainty during the timeframe of the pandemic by accounting for a myriad of other factors relevant to sponsorship decision-making. To begin, a group of variables that represent economic and industrial factors largely outside of the control of decision-makers was entered into the model first, ensuring they are controlled for throughout the analysis. Given the possibility that the economy may influence marketing-related decisions (e.g., Chang & Chan-Olmsted, 2005), growth in the economy in each sponsor's home country was reflected by including in the model the growth rate in Gross Domestic Product in each year (The World Bank Group, 2023). Data on growth in inflation in each sponsor's home country was also collected from The World Bank's inflation dataset (The World Bank Group, 2023). Growth in inflation is measured by the Consumer Price Index (CPI), which is a universally accepted measure of changes in prices (Boskin et al., 1998). Next entered into the model was a group of industrial factors that reflected each sponsor's industry sector, as well as customer orientation (i.e., B2B/B2C). A firm being categorized as B2B (rather than consumer-facing) has been found throughout the literature to influence a firm's objectives they hope to achieve from sponsorship (Henseler et al., 2011), returns (Mahar et al., 2005), which organizations they choose to sponsor (Cunningham et al., 2009), and whether they generate sales to other businesses aligned with the sponsorship (Cobbs & Hylton, 2012). In addition, Jensen (2024) found that B2B firms are 11.1% less likely to exit sponsorships, likely due to a long-term sales horizon and a more extensive, multi-stage sales process (Jensen & Cornwell, 2021). Thus, status as primarily a B2B firm was controlled for in the model, and present in 44.6% of cases, per Table 1.

A group of variables adapted from prior studies was included that indicate geographic factors, including the continent of each sponsor's corporate headquarters (e.g., Jensen, 2024), regional proximity (Woisetschläger et al., 2017), and the presence of a domestic event (e.g., Cobbs et al., 2017). Aligning with Cobbs et al. (2012), regional proximity between the sponsoring firm and sponsored property was reflected by a binary variable indicating whether the sponsor's home country (i.e., location of corporate headquarters) was the same as the flag being flown by each F1 team, which was present in 24.1% of cases. Research has found that regional proximity between a sponsor and a sponsored property may influence consumer perceptions of the sponsorship, as local sponsors are perceived as better-fitting (Woisetschläger et al., 2017). However, prior research noted that investors in sponsoring firms were skeptical of sponsorship with domestic teams (Cobbs et al., 2012) and Jensen et al. (2021) found sponsors with regional proximity pay a premium of more than $4.1 million per F1 sponsorship.

Given that the global calendar of Grands Prix changes annually, a binary variable was constructed and inserted for observations (i.e., sponsorship years) in which a race was held in the same country as a sponsor's headquarters, which may spur domestic activation of the sponsorship but also invite agency conflicts in corporate hospitality and other executive perks (e.g., Jensen et al., 2014). Finally, research has confirmed that the location of a firm's corporate headquarters may influence sponsorship-related decision-making, as Jensen (2024) found firms based in Asia were 11.8% less likely and firms based in South America 30.6% more likely to exit sponsorships, compared to those based in North America. Thus, firm location was controlled for in the model.

Next, brand-related characteristics were considered, including multiple factors that have a strong foundation in the literature to influence decision-making and success in sponsorship-linked marketing. Considering the findings of Jensen and Cornwell (2017) and Jensen et al. (2022) that congruence may influence the length of sponsorships, a dichotomous variable reflected whether the sponsoring firm's brand was congruent or incongruent based on categorization by two independent judges who are experts in the sponsorship-linked marketing literature and from different institutions than the authors, with any disagreements resolved after further discussion. These criteria for determining congruence were first established by Cornwell et al. (2005) and were subsequently employed across a number of sponsorship studies (Clark et al. 2009; Jensen, 2021, 2024; Jensen et al., 2024; Jensen & Cornwell, 2017, 2021; Jensen et al., 2022; Mazodier & Rezaee, 2013). As noted in Table 1, 61.4% of cases involved congruent sponsorship-brand pairings. This same inter-rater methodology was employed to categorize each sponsor as primarily B2B or B2C, as mentioned earlier.

Given its role as an influential sponsorship-related outcome (Cornwell et al., 2001), brand equity was also controlled for in the model. High levels of brand equity were reflected in the data by indicating if the brand has previously been included in Interbrand's annual ranking of the 100 best global brands, an approach utilized in multiple studies investigating the influence of brand equity on sponsorship (Jensen, 2021, 2024; Jensen et al., 2024; Jensen & Cornwell, 2017, 2021; Jensen et al., 2020; Jensen et al., 2022; Mazodier & Rezaee, 2013). Per Table 1, 10.1% of cases were classified as involving brands with high brand equity. We also indicated whether the sponsoring firm is publicly or privately owned, with 51.8% of cases involving publicly traded firms. Being publicly traded has been found to influence sponsorship decision-making in numerous prior studies (e.g., Jensen et al., 2024; Jensen & Smith, 2024), with Jensen (2024) finding that public corporations were 22.3% less likely to exit sponsorships. Research has consistently found that more sponsors (i.e., clutter) leads to a reduction in a consumer's ability to recall and/or recognize sponsorships (Breuer & Rumpf, 2012; Cornwell et al., 2000). With the potential for clutter to adversely affect brand partnerships (Jensen et al., 2024; Jensen & Cornwell, 2017; Jensen et al., 2022), the total number of sponsors for each team in each year was quantified and included as a covariate in the model. On average, the sponsorship portfolios of F1 teams included approximately 29 brand partners (SD = 9.88).

Finally, variables were constructed that operationalized both historical and recent organizational performance for every F1 team. In a prior study, a club's performance was a statistically significant predictor of a sponsor's decision to renew its sponsorship, with every point scored per match reducing the probability of exit by 54.4% (Jensen, 2021). Thus, it was important that organizational performance was controlled for in the model. Three different focal variables were utilized to operationalize sponsored team performance. To operationalize historical performance, variables reflecting the total championships won by each team's drivers (Cobbs et al., 2017; Cobbs et al., 2022) were paired with a variable reflecting the team's best historical position to date in the constructors’ championship (Jensen & Cobbs, 2014; Jensen et al., 2024). Finally, in order to operationalize recent performance, each team's total constructors’ points in each year were inserted into the dataset (Cobbs et al., 2017; Cobbs et al., 2022; Jensen & Cobbs, 2014).

Predictor Variable of Interest: The Effects of COVID

The long duration of the dataset spans progressions in the business practices for F1 and its teams, making the dataset an ideal foundation from which to investigate the effects of the pandemic on sponsor decision-making. Moreover, there have been multiple distinct eras within the evolution of F1 sponsorship that have a firm basis in the literature, the effects of which have been noted in prior studies (e.g., Cobbs et al., 2017; Cobbs et al., 2022; Jensen et al., 2024). To begin, the modern era of Formula One began in 1996, as F1 acquired commercial rights at that time in exchange for ongoing team payments (e.g., the Concorde agreement). This agreement enhanced how teams were supported financially by the series operator. This first modern era ended in 2009 with the implementation of a revised point scoring scheme the following year. In 2010, rule changes initiated an inflated point distribution approach, which established the second era that began in 2010 and spans the 10-year period leading up to the start of the pandemic, ending in 2019. The 2020 pandemic-influenced season serves as the reference variable; therefore, it is excluded from the model to allow effects across the other eras to be compared to it. Finally, the 3-year period following the pandemic season (2021–2023) provides a contemporary basis of comparison in order to isolate the effects of the pandemic on sponsor decision-making. It also determines whether any effects were semipermanent or waned following the pandemic-influenced season, representing an important contribution of this study. Following the approach used within previous scholarship, our models denote the F1 evolutions through binary variables capturing in which era the observation occurred—1996–2009 (Era 1), 2010–2019 (Era 2), as well as the 2021–2023 post-COVID era—with the 2020 season serving as the reference. Accounting for these different eras is important, not only due to the institutional changes described, but also because the digital broadcast revolution occurred across these decades, which could impact brand partnerships in an international context (Wakefield et al., 2020).

Data Analysis

The quantitative methodology that was utilized in this study is survival analysis, traditionally employed in the academic fields of biostatistics and public health to investigate the effects of covariates on a person's lifetime (Box-Steffensmeier & Jones, 2004). In other fields such as political science or demography, the methodology has been utilized to analyze the effects of variables that may influence the durations of wars, United Nations (U.N.) missions, marriages, and political careers (Box-Steffensmeier & Jones, 2004). Survival analysis is effective in analyzing longitudinal data for two main reasons. First, the method accounts for censored observations, or said another way, events for which the final duration is currently unknown (i.e., currently ongoing). In the case of this study, it is robust enough to account for sponsorships that have not yet ended, as well as those that have concluded. In addition, a survival model can analyze the effect of either variables that do not change over time (i.e., time-invariant), or those that do (i.e., time-varying covariates; Singer & Willett, 2003).

Rather than a simple binary variable indicating that the event occurrence of interest has occurred in a particular time period, such as in the example of a logit model, a survival model features a dual-natured dependent variable that indicates both whether the event has occurred and how much time has elapsed until such an occurrence (Singer & Willett, 2003). This dependent variable is a probabilistic hazard function, defined as the rate at which the event occurs (i.e., the event has been experienced), given that the target event has not occurred or the duration has not ended prior to that particular time interval (Box-Steffensmeier & Jones, 2004; Jensen & Turner, 2017).

For this reason, in some academic fields, these models are referred to as time-to-event regression models. As explained by Box-Steffensmeier and Jones (2004, p. 183), “Moving from a focus solely on whether an event occurred to additionally considering when an event occurred can result in much greater analytical leverage on the problem at end.” In the case of this study, the use of a survival model is preferable to a panel logit model because the duration of time that the sponsor remains is an important consideration for the property sponsored, as well as when the sponsorship ended (i.e., did it occur in the pandemic-influenced season of 2020). The benefits of survival models in cases such as these were described thusly by Hoang and Rascher (1999, p. 77) in their study of NBA players: “Logistic regression is unsatisfying because it cannot incorporate the effect of duration or time spent in the state prior to the occurrence of the event. The effect of duration, measured by length of tenure in the NBA, is particularly important because we would expect that a player's risk of exiting will change the longer he remains in the league.” The benefits of assessing how covariates influence the probability of the event occurrence of interest is in addition to whether the sponsor remains active (i.e., censored) or not. The use of survival analysis in this case allows for such an investigation, whereas the application of a panel logit model would simply indicate whether (in the case of this study) a sponsor exited in each time period. Importantly, the event occurrence of interest can only be experienced one time, and in the case of this study the sponsorship is removed from the dataset once it has ended.

In the sport context, the methodology has been previously utilized to investigate the durations of playing careers in professional sport, as well as coaches across multiple professional sports and intercollegiate athletics (Hoang & Rascher, 1999; Salaga & Juravich, 2020; Salaga et al., 2020; Staw & Hoang, 1995; Volz, 2017; Wangrow et al., 2018; Wangrow et al., 2021). Other prior sport-related applications include the durations of sport organizations (Cobbs et al., 2017), relationships with donors (Jensen et al., 2020; Wanless et al., 2019), revisiting mega-event sites (Sun et al., 2025), and sponsorships of global mega-sport events (Jensen & Cornwell, 2017), facilities (Jensen et al., 2020), U.S.-based events (Jensen & Cornwell, 2021), shirt sponsorships (Jensen, 2021), league sponsorships (Jensen et al., 2022), event title sponsorships (Jensen & Smith, 2024), and team sponsorships (Jensen et al., 2024).

Note that the approach utilized in this study, including a longitudinal data structure that allows for the inclusion of time-varying covariates and the aforementioned identification of a binary event occurrence of interest, is the same as several other studies from the sport management/marketing literature, with other various types of event occurrences of interest. For example, Cobbs et al. (2017) investigated the collapse of sport organizations, finding that the team's performance (operationalized as points scored) significantly reduced the probability of the team dissolving. Volz (2017), Salaga and Juravich (2020), and Salaga et al. (2020) examined within the context of the NFL durations of career tenures for quarterbacks, coaches, and running backs, respectively. Volz (2017) found that Black quarterbacks had a lower probability of survival each week, with a Black quarterback being 1.98 to 2.46 times more likely to be benched, while Salaga and Juravich (2020) found that Non-White coaches did not have significantly different tenures and are not more likely to be fired. Salaga et al. (2020) found that running backs with greater workloads had longer careers, with every 100 touches being associated with a 12.2% reduction in the probability of the player exiting the league. In the context of college athletics, Salaga and Juravich (2025) found Non-White head basketball coaches have significantly shorter employment spells, while Wangrow et al. (2021) found team performance and being at a private university lessen the probability of dismissal. Similarly, Foster et al. (2025) found success in football increased an administrator's tenure at a university, while success in basketball prolonged his or her career.

Modeling Approach

Based mainly on the fact that there is no requirement for an a priori parametrization of the model's baseline hazard, the Cox proportional hazards model (Cox, 1972) is the most versatile and widely used survival model. The Cox model is also recommended when the nature of the data is discrete (Box-Steffensmeier & Jones, 2004). In this case, the event occurrence of interest (i.e., the end of the sponsorship) can only occur once per year. Note that Cobbs et al. (2017), Volz (2017), Salaga et al. (2020), Wangrow et al. (2021), Foster et al. (2025), and Salaga and Juravich (2025) also used the Cox proportional hazards model in their studies, given that their unit of measurement was also a year. Salaga and Juravich (2020) also utilized the Cox model, though their time period was weeks during the season, rather than years.

In addition to producing a coefficient for each predictor variable in the model, which determines whether that variable is increasing or decreasing the probability of event occurrence, survival models also feature a hazard ratio (HR) for each variable. The HR is the anti-log of each variable's coefficient and is interpreted in a similar way as the odds ratio in a logit model (i.e., when HR > 1, then HR – 1 = % increase in probability of sponsorship dissolution; when HR < 1, then 1 – HR = % decrease in the probability of sponsorship dissolution). The decision was made to cluster standard errors by sponsorship, in order to provide robustness to heteroskedasticity. As indicated below, the Cox model does not contain a constant term (β0), which is instead absorbed into the baseline hazard function of the model. The scalar form of the model is reflected in the equation below:
$h_{i} (t) = \exp (β_{1} x_{1 i} + β_{2} x_{2 i} + β_{3} x_{3 i} + \dots + β_{k} x_{k i}) h_{0} (t)$

Results

Descriptive Statistics

Prior to inserting covariates in the model in order to account for other factors, we begin by reviewing base rates of event occurrence in the years before and after the onset of COVID, or the hazard function, on an annual basis. In Era 1, sponsors exited at a rate of 34.3% and in the decade preceding COVID (Era 2), the probability of sponsor exit was 25.5%, as indicated in Figure 1. In the 3 prior years leading up to the onset of the pandemic (2017–2019), it had averaged 21.8%. During 2020, the probability of sponsor exit increased to 27.2%. In the years 2021–2023, sponsor exit averaged 21.0%, including 16.7% in 2021, 22.5% in 2022, and 23.9% in 2023. Next, we examine the probability of sponsor exit once covariates are inserted into the model.

Figure 1.
Hazard functions for Formula One (F1) sponsorships (1996–2023).

Covariates: Economic, Industry, and Geographic Factors

As indicated in Table 2, all five groups of factors inserted into the model, including industrial (χ(14) = 358.66, p < .001), geographic (χ(7) = 39.30, p < .001), team-related (χ(3) = 104.09, p < .001), brand-related (χ(4) = 235.94, p < .001), and time-related (χ(3) = 25.91, p < .001), explained a statistically significant amount of variance in the probability of sponsor exit when introduced into the model. The best-fitting models according to AIC and BIC are Models 5 (AIC = 30125.3) and Model 4 (BIC = 30330.2). The mean variance inflation factor was 2.05, providing some evidence that collinearity is not an issue within the model.

Table 2.
Hierarchical Survival Analysis Modeling Results.

Model 1 Model 2 Model 3 Model 4 Model 5

Industrial factors

Economic growth (GDP) .99 (.01) .99 (.01) .99 (.01) .99 (.01) .99 (.01)

Inflation (CPI) 1.0*** (.00) 1.0* (.00) 1.0 (.00) 1.0* (.00) 1.0* (.00)

Alcoholic beverage .89 (.09) .90 (.09) .96 (.09) .99 (.11) .99 (.00)

Non-alcoholic beverage .88 (.16) .89 (.16) 1.0 (.17) 1.0 (.16) .99 (.16)

Automotive .85 (.04) .87 (.04) .98 (.06) 1.0 (.06) .99 (.06)

Apparel .81* (.08) .83 (.08) .91 (.09) .91 (.09) .92 (.09)

Bank 1.4 (.32) 1.4 (.29) 1.2 (.27) 1.3 (.32) 1.4 (.31)

Retail .85* (.06) .86* (.06) .81** (.05) .87* (.06) .88 (.06)

Telecommunications .86 (.08) .88 (.08) 1.0 (.09) 1.1 (.11) 1.05 (.10)

Insurance 1.0 (.17) 1.1 (.18) 1.0 (.16) 1.0 (.17) 1.05 (.16)

Food 1.0 (.12) 1.0 (.12) 1.0 (.13) .91 (.11) .89 (.11)

QSR 2.6* (.19) 2.6* (.16) 3.4* (.46) 2.5* (.49) 2.5 (.66)

Tech .86 (.04) .87** (.05) .94 (.05) .93 (.05) .92 (.05)

B2B 1.0 (.05) 1.0 (.05) .97 (.04) .95 (.04) .94 (.04)

Geographic factors

Africa 2.2* (.41) 1.9* (.34) 2.1* (.14) 2.1* (.17)

Asia 1.1 (.06) 1.1 (.07) 1.1* (.07) 1.1 (.07)

Australia 1.2 (.24) 1.2 (.24) 1.3 (.25) 1.3 (.25)

North America 1.0 (.05) 1.05 (.05) 1.1 (.05) 1.1* (.05)

South America 1.5* (.23) 1.6 (.24) 1.6 (.23) 1.6** (.24)

Domestic-sponsored team .96 (.04) .94 (.04) .92 (.04) .90* (.04)

Domestic event .89 (.04) .89 (.04) .91* (.04) .92* (.04)

Brand-related factors

Brand equity .77 (.06) .80 (.06) .78 (.06)

Congruence .86 (.04) .83* (.04) .83 (.04)

Financial accountability .74* (.03) .80* (.03) .78*** (.03)

Team-related factors

Sponsor clutter 1.0* (.00) 1.0* (.00)

Driver performance .96* (.01) .96* (.01)

Team historical performance 1.0 (.01) 1.02* (.01)

Team recent Performance .99* (.00) .99 (.00)

Time-related factors

Era 1 (1996–2009) .97 (.12)

Era 2 (2010–2019) .79 (.09)

Post-COVID (2021–2023) .71* (.11)

AIC 30372.2 30372.1 30300.1 30137.9 30125.3

BIC 30468.4 30516.4 30465.0 30330.2 30338.3

Log-likelihood −15172.12 −15165.05 −15126.04 −15040.93 −15031.66

Wald χ² 358.66* 886.84* 289.93* 1044.92* 721.85***

Results from Cox proportional hazards model. Hazard ratios (HR) are listed, with robust standard errors in parentheses. HR > 1 indicates increase in probability of sponsorship dissolution; HR < 1 indicates decrease in probability of sponsorship dissolution. p < .05; p < .01; p < .001.

B2B, business-to-business; GDP, Gross Domestic Product; CPI, Consumer Price Index.

Examining the results of Model 5 once all factors were entered, the model indicates there were several covariates that influenced sponsor decision-making; therefore, it was prudent to include them in the model to help isolate the effects of the pandemic. First, inflation in the form of the CPI was influential, indicating that every one percent increase in CPI in the home country of the sponsor increased the probability a sponsor will exit by 1.1% (HR = 1.01), and the effect was significant (z = 2.54, p = .011). Among the geographic variables, sponsors in Africa (HR = 2.10), North America (HR = 1.11), and South America (HR = 1.59) were significantly more likely to exit than those in Europe. In addition, sponsors in the same home country as the team were 9.6% less likely to exit (HR = .90) while those who had events in their home country were 8.1% less likely to exit (HR = .92).

Covariates: Brand and Team-Related Factors

Consistent with the sponsorship-linked marketing literature, all three brand-related factors were significant predictors of sponsor renewal/exit, including brand equity, congruence, and financial accountability (i.e., public vs. private). High brand equity firms were 22.4% less likely to exit (HR = .7760), congruent brands were 17.1% less likely to exit (HR = .8291), and publicly traded firms were 21.6% less likely to exit (HR = .7841). Several of the team-related variables were significant predictors of sponsor decision-making, including sponsor clutter, driver performance, and both historical and recent team performance. Every sponsor added by each team increases the probability that every sponsor will exit by .75% (HR = 1.01). This means every 10 sponsors added increases the chances of sponsor exit by 7.54%, while in a purely theoretical scenario, a team adding 100 sponsors would increase the probability of exit for every sponsor by 75.4%. Every championship earned by a team's drivers decreases the probability of sponsor exit by 3.5% (HR = .9646). In terms of team performance, for every one position decrease in the team's best historical finishing position, the probability its sponsors will exit increases by 2.9% (HR = 1.03). For every point earned in each season, the probability of sponsors exiting decreases by .06% (HR = .9994). Or, every 10 points earned decreases the probability of sponsor exit by .64% and every 100 points earned decreases the rate of exit by 6.4%.

Time-Related Eras and COVID

Finally, the binary variables indicating the various F1 eras described above were entered. These included the years spanning the first modern era of F1 from 1996 to 2009, the 10 years preceding the onset of the pandemic from 2010 to 2019, and the 3 years following the pandemic in 2021–2023. As stated, the pandemic-influenced season of 2020 was left out of the model for use as a reference to the other eras. During the initial modern era of F1 (1996–2009), sponsors were 3.4% less likely to exit than in the 2020 season (HR = .9660). Sponsor renewals during these years were not significantly different than 2020 (z = –0.28, p = .777). In the decade preceding the pandemic, sponsors were 21.3% less likely to exit (HR = .7871), and the results were marginally significant (z = –1.92, p = .055). Given this result, we find limited support for H1. In the 3 years following the onset of the pandemic, sponsors have been 28.5% less likely to exit (HR = .7148), and the results were significant at the α = .05 level (z = –2.20, p = .028). This result provides substantial support for H2.

Discussion

To begin, it is helpful to review and discuss how the effects found in this study compare to the industry's initial estimates of the effects of the pandemic. As stated, in the 10-year period prior to the pandemic and the three years following, our model suggests sponsors were 21.3% and 28.5% less likely to exit (i.e., more likely to renew), respectively, compared to the reference year of 2020. This result suggests that while decision-making during 2020 was affected, the effects were considerably less than most industry estimates. In fact, effects of the pandemic during the 2020 season on the probability of sponsor exit, when compared to the preceding decade, were only marginally significant. However, in the three years following the pandemic sponsors were significantly less likely to exit. Thus, results suggest the pandemic's effects were more fleeting (i.e., less long-term), as this study's model suggests sponsor renewals have exceeded pre-pandemic levels in the three years following the pandemic. To put our results in monetary terms, with each F1 sponsor paying an average of $5.28 million (Jensen et al., 2021) and an average of 29 sponsors per team (Table 1), the pandemic season was responsible for a marginal loss of 3.5 sponsors per team, when compared to sponsor renewal data from 27 other F1 seasons and controlling for a wide variety of other factors that may influence that decision. Therefore, our estimates approximate that the pandemic resulted in an average sponsorship loss of $19 million per team in 2020, or $228 million across the entire sport.

While significant, the results are encouraging as one might attempt to assess the potential impacts of future events that may disrupt global sports such as F1, the Olympic Games, or the FIFA World Cup. Overall, results indicate that the impact of the pandemic on sponsorship was not as significant as many had first anticipated. Rather than an impact on revenue spanning 37% (Cutler, 2020), 38% (IEG, 2020), or 46% (Bradford & Sher, 2020), the impact was likely closer to between only 3% to 12% of total revenue, given that Ozanian and Knight's (2023) estimates of team revenue range from $160 million (Williams) to $700 million (Mercedes). Perhaps more importantly, results indicate that while the introduction of pandemic-induced uncertainty was significant in that sponsors were 21% less likely to exit prior to the pandemic, the effect was fleeting, and within a year, sponsors’ continuation rates matched or mildly exceeded pre-pandemic levels. Not only should this result increase confidence of sport business leaders that sport can be largely insulated long-term from the uncertainty caused by an unexpected global event, but also that the business is robust and can bounce back relatively quickly afterwards. Such resilience underscores the enduring market power of sport that provides confidence for sponsors’ long-term strategic decisions to invest in the medium as a marketing tool¹.

Theoretical Implications

This study found that while controlling for a host of potentially confounding variables such as industrial, geographic, team, and brand-related factors, sponsors were 3.9% less likely to exit in the years 1996–2009 and 21.3% less likely to exit in the years 2010–2019, compared to the reference year of the pandemic-influenced season in 2020. While considerably lower than the broader trade estimates, this empirical quantification of exchange relationship instability at the height of the pandemic's influence on sporting events (2020) is consistent with the expectation of exchange theory in conditions of uncertainty. Specifically, the three layers of uncertainty introduced by COVID (absence of cause-and-effect understanding; environmental, and internal) called into question marginal utility and fair exchange, thereby violating core principles of exchange and endangering the sponsor relationship. Since the sponsorship exchange is an exercise in RM (Cobbs, 2011; Farrelly & Quester, 2005), we also note how the pandemic's uncertainty weakened trust in the relationship's capacity to meet exchange expectations, despite the intended commitments.

Once pandemic-related uncertainty had waned and a more normal course of commerce had returned starting in 2021, sponsors were 28.5% less likely to exit in the years following the onset of the pandemic (i.e., from 2021–2023), compared to 2020. This striking result matches the theoretical expectation that once the layers of uncertainty were resolved, the exchange principle(s) were restored, and perhaps more importantly, sponsored properties had demonstrated a previously unrealized commitment to executing fair exchange amid the uncertainty, thereby re-establishing trust and leading to sponsorship market resiliency and robustness. Accordingly, our finding is consistent with a prediction from Washington Commanders Chief Partnership Officer Ryan Moreland in 2022, who predicted a 20% year-over-year increase in sponsorship sales at the time (Lefton, 2022). These results also suggest that while sponsors were less likely to renew during pandemic-induced uncertainty in 2020, effects were more fleeting than prior estimates, and that sponsor trust in exchange increased significantly after the initial effects of the pandemic.

In summary, by employing exchange theory as applied to the pandemic uncertainty, we can understand why more sponsors exited in 2020, compared to prior and later periods in F1. Specifically, COVID produced heighted uncertainty as to the principles of marginal utility (realization of the benefit due to exchange) and fair exchange (equality of resources given and received). Accordingly, decision-makers for sponsoring brands engaged in different coping tactics (i.e., reduction, acknowledgement, or suppression) to manage this uncertainty (Lipshitz & Strauss, 1997), albeit with sponsorship dissolution levels below trade expectations. Yet, once the uncertainty subsided and the principles of exchange were re-established, sponsorship relationships stabilized and even achieved a level of stability higher than preceding years.

Limitations and Future Research

While this study represents the first and most encompassing analysis of the pandemic's impact on sport sponsorship decision-making, there are multiple limitations of the study that must be acknowledged. First and perhaps most important is the fact that in the absence of definitive revenue data from sponsorship, this study utilized the rate of sponsor renewal as its dependent variable. Whether a sponsor decides to renew is but one important metric. Ideally, associated data would be gathered and accompany the renewal rate in order to improve the generalizability of the study's findings. However, actual sponsorship revenue on a per-event, team, or league basis is typically proprietary.

Second, it is unknown if the impact of the pandemic on the sport of F1 is generalizable to other global sports. An argument can be made that the introduction of pandemic-induced uncertainty was less impactful on F1 than other events, as F1 was able to stage a (albeit) shortened season and host many events with fans in the latter part of the 2020 season. In contrast, the 2020 Summer Olympic Games were rescheduled, with the event staged in 2021 in the absence of fans, and the 2020 NCAA Men's and Women's Basketball Tournaments were canceled. In another example, the 2020 NBA playoffs were able to be staged, but were hosted at Disneyworld in a bubble with no fans. Future research is necessary in order to compare and contrast this study's findings to those of other sports, which may help determine whether this study's findings are generalizable to other global sport leagues, teams, and events.

Finally, this study is based on longitudinal, quantitative data that characterizes decades of sponsorship exchange relationships. While the findings quantify an aberration in relationship continuity in 2020 that correlates to the pandemic and its drastic effects on sporting events and associated sponsor exchange, our theoretical approach infers uncertainty as the causal mechanism and restored trust and commitment to the sponsor relationship as the source of resilience. Alternatively, the rebound in sponsor relationships following the worst of the pandemic may result from the culling of the weakest relationships within a short timeframe. Nonetheless, future research that takes a qualitative, survey-based, or mixed methods approach could strengthen our inference through interviews with decision-makers and reflective measures of comparative uncertainty, trust, and commitment. Nonetheless, this study provides a robust, empirical analysis of the sponsorship trajectory in a global sport before, during, and after the unprecedented events of 2020.

		Count (%)
Industrial factors
Economic growth (GDP)	Cont.		1.82	2.74	−12.97, 34.86
Inflation (CPI)	Cont.		2.18	2.83	−4.86, 121.74
Alcoholic beverage	Binary	166 (2.22%)
Non-alcoholic beverage	Binary	109 (1.46%)
Automotive	Binary	1671 (22.31%)
Apparel	Binary	356 (4.75%)
Bank	Binary	25 (0.33%)
Retail	Binary	721 (9.63%)
Telecommunications	Binary	354 (4.73%)
Insurance	Binary	61 (0.81%)
Food	Binary	173 (2.31%)
QSR	Binary	2 (0.03%)
Tech	Binary	1459 (19.48%)
B2B	Binary	3342 (44.62%)
Geographic factors
Africa	Binary	8 (0.11%)
Asia	Binary	932 (12.44%)
Australia	Binary	54 (0.72%)
Europe	Binary	4509 (60.20%)
North America	Binary	1917 (25.59%)
South America	Binary	69 (0.92%)
Domestic-sponsored team	Binary	1807 (24.13%)
Domestic event	Binary	5556 (74.18%)
Brand-related factors
Brand equity	Binary	753 (10.05%)
Congruence	Binary	4599 (61.40%)
Financial accountability	Binary	3881 (51.82%)
Team-related factors
Sponsor clutter	Cont.		29.26	9.88	3, 59
Drivers performance	Cont.		3.66	4.12	0, 12
Historical team performance	Cont.		4.66	3.09	0, 12
Recent team performance	Cont.		134.63	181.40	−13.7, 860
Time-related factors
Era 1 (1996–2009)	Binary	3708 (49.51%)
Era 2 (2010–2019)	Binary	2656 (35.46%)
COVID (2020)	Binary	265 (3.54%)
Post-COVID (2021–2023)	Binary	860 (11.48%)

	Model 1	Model 2	Model 3	Model 4	Model 5
Industrial factors
Economic growth (GDP)	.99 (.01)	.99 (.01)	.99 (.01)	.99 (.01)	.99 (.01)
Inflation (CPI)	1.0*** (.00)	1.0* (.00)	1.0 (.00)	1.0* (.00)	1.0* (.00)
Alcoholic beverage	.89 (.09)	.90 (.09)	.96 (.09)	.99 (.11)	.99 (.00)
Non-alcoholic beverage	.88 (.16)	.89 (.16)	1.0 (.17)	1.0 (.16)	.99 (.16)
Automotive	.85** (.04)	.87** (.04)	.98 (.06)	1.0 (.06)	.99 (.06)
Apparel	.81* (.08)	.83 (.08)	.91 (.09)	.91 (.09)	.92 (.09)
Bank	1.4 (.32)	1.4 (.29)	1.2 (.27)	1.3 (.32)	1.4 (.31)
Retail	.85* (.06)	.86* (.06)	.81** (.05)	.87* (.06)	.88 (.06)
Telecommunications	.86 (.08)	.88 (.08)	1.0 (.09)	1.1 (.11)	1.05 (.10)
Insurance	1.0 (.17)	1.1 (.18)	1.0 (.16)	1.0 (.17)	1.05 (.16)
Food	1.0 (.12)	1.0 (.12)	1.0 (.13)	.91 (.11)	.89 (.11)
QSR	2.6*** (.19)	2.6*** (.16)	3.4*** (.46)	2.5*** (.49)	2.5** (.66)
Tech	.86** (.04)	.87** (.05)	.94 (.05)	.93 (.05)	.92 (.05)
B2B	1.0 (.05)	1.0 (.05)	.97 (.04)	.95 (.04)	.94 (.04)
Geographic factors
Africa		2.2*** (.41)	1.9*** (.34)	2.1*** (.14)	2.1*** (.17)
Asia		1.1 (.06)	1.1 (.07)	1.1* (.07)	1.1 (.07)
Australia		1.2 (.24)	1.2 (.24)	1.3 (.25)	1.3 (.25)
North America		1.0 (.05)	1.05 (.05)	1.1 (.05)	1.1* (.05)
South America		1.5* (.23)	1.6** (.24)	1.6** (.23)	1.6** (.24)
Domestic-sponsored team		.96 (.04)	.94 (.04)	.92 (.04)	.90* (.04)
Domestic event		.89** (.04)	.89** (.04)	.91* (.04)	.92* (.04)
Brand-related factors
Brand equity			.77** (.06)	.80** (.06)	.78** (.06)
Congruence			.86** (.04)	.83*** (.04)	.83** (.04)
Financial accountability			.74*** (.03)	.80*** (.03)	.78*** (.03)
Team-related factors
Sponsor clutter				1.0*** (.00)	1.0*** (.00)
Driver performance				.96*** (.01)	.96*** (.01)
Team historical performance				1.0** (.01)	1.02*** (.01)
Team recent Performance				.99*** (.00)	.99** (.00)
Time-related factors
Era 1 (1996–2009)					.97 (.12)
Era 2 (2010–2019)					.79 (.09)
Post-COVID (2021–2023)					.71* (.11)
AIC	30372.2	30372.1	30300.1	30137.9	30125.3
BIC	30468.4	30516.4	30465.0	30330.2	30338.3
Log-likelihood	−15172.12	−15165.05	−15126.04	−15040.93	−15031.66
Wald χ²	358.66***	886.84***	289.93***	1044.92***	721.85***

Footnotes

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Notes

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		Count (%)
Variables	Measure	(N = 7490)	M	SD	Min, Max
Industrial factors
Economic growth (GDP)	Cont.		1.82	2.74	−12.97, 34.86
Inflation (CPI)	Cont.		2.18	2.83	−4.86, 121.74
Alcoholic beverage	Binary	166 (2.22%)
Non-alcoholic beverage	Binary	109 (1.46%)
Automotive	Binary	1671 (22.31%)
Apparel	Binary	356 (4.75%)
Bank	Binary	25 (0.33%)
Retail	Binary	721 (9.63%)
Telecommunications	Binary	354 (4.73%)
Insurance	Binary	61 (0.81%)
Food	Binary	173 (2.31%)
QSR	Binary	2 (0.03%)
Tech	Binary	1459 (19.48%)
B2B	Binary	3342 (44.62%)
Geographic factors
Africa	Binary	8 (0.11%)
Asia	Binary	932 (12.44%)
Australia	Binary	54 (0.72%)
Europe	Binary	4509 (60.20%)
North America	Binary	1917 (25.59%)
South America	Binary	69 (0.92%)
Domestic-sponsored team	Binary	1807 (24.13%)
Domestic event	Binary	5556 (74.18%)
Brand-related factors
Brand equity	Binary	753 (10.05%)
Congruence	Binary	4599 (61.40%)
Financial accountability	Binary	3881 (51.82%)
Team-related factors
Sponsor clutter	Cont.		29.26	9.88	3, 59
Drivers performance	Cont.		3.66	4.12	0, 12
Historical team performance	Cont.		4.66	3.09	0, 12
Recent team performance	Cont.		134.63	181.40	−13.7, 860
Time-related factors
Era 1 (1996–2009)	Binary	3708 (49.51%)
Era 2 (2010–2019)	Binary	2656 (35.46%)
COVID (2020)	Binary	265 (3.54%)
Post-COVID (2021–2023)	Binary	860 (11.48%)

Examining Managerial Decision-Making in a Time of Uncertainty: A Longitudinal Investigation of Pandemic Effects

Abstract

Keywords

Literature Review

COVID-19's Effect on Sport

COVID-19 Business Effects

Uncertainty

Exchange Theory

Research Hypotheses

Contribution to Business-to-Business Decision-Making

Method

Context

Dependent Variable

Control Variables

Predictor Variable of Interest: The Effects of COVID

Data Analysis

Modeling Approach

Results

Descriptive Statistics

Covariates: Economic, Industry, and Geographic Factors

Covariates: Brand and Team-Related Factors

Time-Related Eras and COVID

Discussion

Theoretical Implications

Limitations and Future Research

Footnotes

Funding

Declaration of Conflicting Interests

Notes

References