Abstract
In an effort to decrease the number of missed calls and to improve officiating during sport events, various sports leagues have implemented media technology (e.g., Video Assistant Referee, Hawk-Eye). More importantly, the use of officiating technology has significantly influenced spectatorship in various ways (e.g., perception of and attitude toward the technology). Although officiating technology is an impressive tool for communicating final decisions to spectators, few scholars have examined how spectators perceive the use of officiating technology, and no psychometric measurement scale exists that measures this perception. To fill this void, we developed and validated the Performance Expectancy of Officiating Technology (PEOT) scale to measure the perceptions of spectators, one of the most important stakeholder groups in the industry. We identified four sub-dimensions of PEOT: fair judgment, enjoyability, efficient game operation, and convenience of review; a multi-dimensional framework that provides a psychometrically sound approach to assessment. The results reveal that PEOT had a positive and direct impact on attitude toward and intention to watch sport events. In addition, attitude partially mediated the relationship between performance expectancy and intention to watch sport events. Theoretical and practical implications are discussed.
Sport officials (i.e., referees, umpires, judges) play a critical role in competition (Kittel et al., 2019; Lirgg et al., 2016). They are necessary participants in sport, charged with applying the rules and administering the competition in a fair and proper manner (Biedzynski, 1994). Although technological advancements have made sports faster, more powerful, and more enjoyable, fair decisions have become increasingly difficult to consistently make (Spitz et al., 2021; Tamir & Bar-Eli, 2020). Because high-tech digital cameras that allow slow-motion replay of important game situations have not always been available to officials, over the years, many controversial calls have unjustly influenced game outcomes (MacMahon et al., 2014; Spitz et al., 2018). Furthermore, poor judgment and incorrect calls can compromise the integrity of sport and dampen the fan experience. For example, England failed to advance in the 19th International Federation of Association Football (FIFA) World Cup tournament because the referee denied Frank Lampard (England Midfielder) an equalizing goal even though the ball was well over the goal line. Armando Galarraga, a pitcher for the Detroit Tigers, did not accomplish the 21st perfect game in Major League Baseball (MLB) history due to a missed call by the first-base umpire. These and other similar incidents added to the discussion about the use of technology in an effort to help referees make accurate decisions.
Over the last decade, various sport events have introduced innovative officiating technologies. The Hawk-Eye system, for example, now used at several major tennis tournaments (e.g., Wimbledon and U.S. Open), allows a referee to request an instant replay review when a player disputes a line call (Collins & Evans, 2008). This system displays computerized video of the line call on a large screen inside the stadium and on the televised broadcast. In addition, most major professional sport leagues (e.g., National Football League [NFL] and MLB) and non-profit sport organizations (e.g., FIFA and the National Collegiate Athletic Association [NCAA]) have introduced Video Assistant Referee (VAR) to minimize errors that might substantially influence game outcomes. Sports leagues and organizations have implemented various officiating technologies. The main goal of each type, however, is the same: to improve the quality of refereeing decisions, avoid controversial mistakes, and maximize fairness, which is are among the most critical values of sport competition. Although the intent is obvious to stakeholders, approaches of this technology may vary depending on needs and interests. For instance, sport organizations adopt officiating technology, referees/officials use it, and TV networks provide replay videos. Spectators generally consume review videos provided by media service companies (TV Networks: ESPN, CBS, FOX, etc.). Most decisions (i.e., useful information for spectators) aided by officiating technology are accessible to spectators as visual content. Game officials make decisions based on the information that innovative technologies offer, while spectators watch the mediated content during or after the review situations. Thus, for spectators, the use of officiating technology at sport events is a process of reviewing a replay video when it becomes available to them.
From the earliest stages of its adoption, fans and commentators have complained that officiating technologies slow down the game and violate the tradition and spirit of the sport. Even in the face of controversy, however, officiating technologies have enhanced quality of judgment and helped referees make accurate calls. In addition, sport media technology has had a significant impact on spectatorship (Cummins & Hahn, 2013; O’Reilly & Rahinel, 2006; Skey et al., 2018). Huge screens now offer live instant replays of on-field action for fans. Television viewers not only enjoy high-definition images but also hear expert analysis and view instant replays from various camera angles (Collins & Evans, 2012). These features have enabled sport spectators to determine whether a referee’s decision is fair in controversial situations. Previous findings indicate that the number of offsides and fouls, and the degree of home-team advantage, decreased significantly after the introduction of VAR in the Chinese Super League (480 games during the 2017–2018 season; Han et al., 2020), in the Italian Serie A (544 games during the 2016–2017 and 2017–2018 seasons; Carlos et al., 2019), and the German Bundesliga (480 games during the 2016–2017 and 2017–2018 seasons; Carlos et al., 2019). Spitz et al. (2021) reported that VAR increased the accuracy of officials’ decisions from 92.1% to 98.3% in different national soccer associations.
Officiating technology at sport events can reduce erroneous calls, provide another source of entertainment for spectators, and help spectators better understand the decisions of game officials. The Hawk-Eye system was first officially adopted at the 2005 NASDAQ-100 Tennis Open (also known as the Miami Masters). Since the successful implementation of officiating technology at sport events, most sport organizations have utilized officiating technology (e.g., instant replay review) in their games. Winand and Schneiders (2018) found spectators were satisfied with and enjoyed VAR at soccer events and had a favorable attitude toward the use of officiating technology. Innovative technologies have dramatically changed the environment of spectatorship over the last decade. Officiating technologies (e.g., Hawk-Eye and VAR) are taking the lead in creating a better sport consumption experience. In addition, Minor League Baseball has introduced Artificial Intelligence (AI) technology to call balls and strikes at MLB-sponsored games. Despite the increasing importance of officiating technology, only a few scholars have investigated its impact (Han et al., 2020; Winand & Fergusson, 2018). Moreover, systematic research describing the technological components that fans expect from officiating technology is scarce, and multidimensional scales for measuring these components do not exist. Furthermore, from a spectator perspective, one of the most critical stakeholder groups in the industry, exploring how these expectations influence the outcomes of sport consumption can improve our understanding of officiating technology implementation at sport events.
To understand the expected benefits one might have when watching official reviews via digital media technology, exploring the relevant factors contributing to this context-specific sport fan behavior is vital. A theoretical framework is necessary to establish a psychometric measurement scale. Because the focus of the current study was the expected benefits of using officiating technology, we borrowed the concept performance expectancy, the extent or degree to which an individual perceives that using a system or technology results in accomplishing the intended goal (Venkatesh et al., 2003). The technological components are expected benefits of implementing officiating technology at a sport event; thus, we applied the technology acceptance model (TAM; Davis, 1989; Davis et al., 1992), which scholars have widely used in information systems research as a theoretical foundation. In addition, scholarly studies about the relationships among expectations related to officiating technology and spectator-oriented outcome variables are few. Therefore, the purpose of the current study was twofold: (a) to develop a psychometric scale for Performance Expectancy of Officiating Technology (PEOT), and (b) to validate this scale by examining the relationship between PEOT and spectator-oriented outcome variables (e.g., attitude and intention). To provide a systematic approach to examining officiating technology in the context of sport consumer behavior, we developed the PEOT scale based on the TAM (Davis, 1989; Davis et al., 1992).
The findings of the current study contribute to the literature by validating the concept of PEOT and its psychometric measurement scale. The PEOT scale can assist scholars and practitioners. Innovative technologies will continue to emerge, further improving the quality of sport consumption. As sport organizations and marketers launch projects to understand and improve the spectator experience, they can use the PEOT scale to identify and evaluate the use of officiating technology at sport events. Developing appropriate communication strategies plays a major role in attracting and retaining sport spectators. Knowing the expectations and desires of spectators with respect to officiating technology can help practitioners build meaningful relationships and create communication and marketing activities intended to induce favorable attitudes and behaviors.
Theoretical Background
Conceptualization of Performance Expectancy of Officiating Technology
A primary purpose of the study was to develop a scale for PEOT by identifying four sub-dimensions spectator perceptions. Since officiating technologies are adopted and used by sport organizations in order to minimize human error, which can substantially influence game outcome, spectators generally consume and experience review videos in order to meet their needs and interests. Sport organizations and/or broadcasting channels provide replay videos to improve the spectating experience. Therefore, the sub-dimensions of PEOT should reflect perceived benefits and should be mutually exclusive. PEOT is the perception among sport spectators of expected performance-related service outcomes from the use of officiating technology at sport events.
The primary role of officiating technologies is to assist referees as they reach final decisions and to reduce incorrect calls. Because these technologies can improve officiating, various sport leagues have implemented them and they have become a critical component of sport events (Spitz et al., 2021). From the perspective of spectatorship, officiating technology provides a high quality fan experience because it enhances the overall quality of refereeing and protects the integrity of the game (Carlos et al., 2019; Stoney & Fletcher, 2020; Tamir & Bar-Eli, 2020). In this way, officiating technology is a service component of sport events. One of the main goals of the current study was to identify the expectations of spectators about the quality of officiating technology use in sport events. Spectators not only expect fairness in disputed situations but also enjoyability from viewing instant replay videos (Kim & Kim, 2018). Therefore, performance expectancy is a service outcome of using officiating technology.
According to marketing theory, providing excellent service to consumers requires a proper understanding of consumer expectations (Bebko, 2000; Parasuraman et al., 1985). Consumer expectations are predictions about what is likely to happen in service or product transactions (Oliver, 1981). Previous findings show that consumer expectations play a major role in the behavioral decision-making framework. According to Expectation-Disconfirmation Theory (EDT), developed by Oliver (1980), prior experience is an essential factor in service expectation (Zeithaml et al., 1993). Consumers evaluate service quality by comparing their perceptions of actual service during and after the service encounter (Parasuraman et al., 1985). If the outcome meets or exceeds expectations, consumers are satisfied and have a favorable perception of service quality; if the outcome falls short, consumers are dissatisfied and have an unfavorable perception of service quality (Breitenbach & Van Doren, 1998).
Parasuraman et al. (1985) defined service quality as the gap between consumer expectations about service and the perception of service quality during and after the encounter. They measured consumer service quality based on a unidimensional construct: what a consumer feels a service provider should offer (Parasuraman et al., 1985). However, a unidimensional model of expectation fails to explain how consumer expectations relate to satisfaction (Tse & Wilton, 1988). Numerous scholars have used a multidimensional model to describe different levels of expectation (Boulding et al., 1993; Laroche et al., 2004; Spreng & Olshavsky, 1993; Tse & Wilton, 1988). For example, Laroche et al. (2004) proposed a higher-order model of service expectations by identifying four categories of service expectation attributes: situational (Bitner, 1990), affect (Geers & Lassiter, 1999), and technical/functional (Gronroos, 1984). Situational expectations include physical and social surroundings. Affect expectations are beliefs that lead to emotions. Technical expectations refer to the service delivered to consumers, and functional expectations refer to how, why, where, and when consumers receive it. Since officiating technology provides various advantages, performance expectancy is a multidimensional concept in which each dimension includes belief and evaluative components.
Because officiating technologies influence the quality of sport events, a scale of performance expectancy needs to measure spectator perceptions of their use. This idea ties in well with leading theories about adopting new technology. Scholars have examined how information systems (IS) adoption might improve efficiency and increase workforce performance. They proposed a number of beliefs and associated expectations: usefulness, ease of use, and enjoyment (TAM; Davis, 1989; Davis et al., 1992), relative advantage, compatibility, complexity, trialability, and visibility (Rogers, 1995). In line with these findings, the concept of performance expectancy is a critical cognitive reaction based on constructs such as usefulness, outcome expectations, and relative advantage (Davis, 1989; Rogers, 1995; Venkatash et al., 2003). For example, Rogers (1995) theorized IS expectations as a relative advantage in the adoption of innovative technology. Based on these previous conceptions, PEOT is the perception among spectators of the expected performance-related benefits of using officiating technology at sport events (Davis, 1989; Davis et al., 1992; Rogers, 1995, Venkatesh et al., 2003).
Research Model: Dimensions of PEOT
To formulate a conceptual framework for PEOT, we used TAM (Davis, 1989; Davis et al., 1992) to examine spectator perception of the use of officiating technology in sport events. Davis (1989) proposed two key determinants of intention to use new technology: perceived usefulness and perceived ease of use. Perceived usefulness is “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989, p. 320), and perceived ease of use is “the degree to which a person believes that using a particular system would be free of effort” (Davis, 1989, p. 320). In addition, Davis et al. (1992) found that perceived enjoyment, as an intrinsic motivation, also influences new technology acceptance. Perceived enjoyment is “the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated” (Davis et al., 1992, p. 1113).
Numerous findings indicate that TAM is one of the most powerful and effective models for explaining user acceptance of new technology (Igbaria et al., 1997; O’Cass & Fenech, 2003). TAM has predicted user acceptance of various technologies, including banking technology (Lai & Li, 2005), online shopping (Bruner & Kumar, 2005; Gefen et al., 2003), and mobile payments (Cocosila & Trabelsi, 2016; Slade et al., 2015). Consistent with previous new technology adoption literature, Ko et al. (2011) examined consumer perceptions of an electronic scoring system adopted in Taekwondo. Using TAM as a theoretical framework, they identified factors that influenced consumer attitude and purchase decision. They found that consumer perceptions of and attitude toward the new electronic scoring system were favorable and that key TAM factors (e.g., usefulness, ease of use, and enjoyment) positively influenced attitude and purchase intention.
Due to their unique nature, sport events are experiential products. The use of officiating technology at sport events might enhance the sport consumption experience. As defined earlier, PEOT is the perception among spectators of the expected performance-related service outcomes of the use of officiating technology at sport events. The sub-dimensions of performance expectancy should not focus on motivations to adopt officiating technology but on the expected benefits perceived by sport spectators. Based on previous findings, we conceptualized expected performance of officiating technology use in sport events. We identified four sub-dimensions of PEOT: (a) fair judgment, (b) enjoyability, (c) efficient game operation, and (d) convenience of review. To validate the PEOT scale further, we also tested the relationships between PEOT and (a) attitude toward sport events and (b) intention to watch.
Fair Judgment
The first sub-dimension, fair judgment, refers to the degree to which a spectator believes that using an officiating technology will improve the fairness of referee’s decisions (Davis, 1989, Ko et al., 2011; Rogers, 1995; Venkatesh et al., 2003). Fair judgment is akin to the concept of usefulness (Davis, 1989; Davis et al., 1992) and relative advantage (Rogers, 1995), which are cognitive motivations that drive new technology adoption. Various scholars have investigated usefulness as a predictor of new technology adoption (e.g., Davis, 1989; Venkatesh et al., 2003). In sport events, fair judgment is a key determinant of perceived event quality (Ko et al., 2011). Furthermore, missed calls by referees can seriously influence the financial status of a club or the career of a player (Craven, 1998). Therefore, fair judgment is an important dimension that influences spectator perception of officiating technology use.
Enjoyability
Enjoyability refers to the perception of the expected emotional benefit, including pleasure and fun, of using officiating technology in sport events (Davis et al., 1992; Lankton & Wilson, 2007). Enjoyability plays a key role in consumption behavior (Holbrook et al., 1984; Laroche et al., 2004; Venkatesh et al., 2003). Consumer experiences that are pleasurable, when using technology, prompt more users to adopt it (Davis et al., 1992). Hedonic values (e.g., enjoyability and fun) play a key role in information processing (Babin et al., 1994) and technology adoption (Davis et al., 1992). Thus, we included enjoyability as a significant dimension of spectator perception of officiating technology use.
Efficient Game Operation
Efficient game operation refers to the perception that the use of officiating technology allows a sport event to proceed smoothly (Collins, 2010). Prior to the adoption of officiating technology, spectators experienced game delays when coaches and/or players complained about the referee’s decisions, particularly after controversial outcomes. For years, officiating technology has helped minimize game delays caused by disputed decisions. Collins (2010) found that officiating technology allowed the game to proceed smoothly. The World Taekwondo Federation adopted an electronic scoring system to improve event operation. Ko et al. (2011) found that officiating technology increased game efficiency by removing the complaints of coaches and athletes about controversial calls. Therefore, we expected that efficient game operation would be a dimension of PEOT.
Convenience of Review
In TAM, ease of use is a critical determinant of technology adoption (Davis et al., 1992). As media technology has become more user friendly, spectators have had easy access to media that can influence consumption of spectator sports (Abeza et al., 2019; Billings et al., 2017; Ha et al., 2017; Rynarzewska, 2018). Scholars considered ease of use as a usability dimension of service quality expectation (Mckinney et al., 2002; Parasuraman et al., 2005). In the current study, convenience of review refers to spectator perception that officiating technology will make watching reviews more convenient (Davis, 1989; Lankton & Wilson, 2007; Venkatesh et al., 2003). Technological advances in media enable spectators to stream a game using a smart phone or tablet. In addition, spectators can review an important play several times using media technology, even if they have lost the live broadcast. Therefore, convenience of review is likely to influence performance expectancy.
Spectator-Focused Outcome Variables
The important outcome variables of spectator-based sport events in sport management research are attitude and behavioral intention. Attitude refers to an overall evaluation of how much individuals like or dislike an object, issue, person, or action (Petty & Cacioppo, 1986). Perceived value of a product is a fundamental factor in attitude toward the product (Fishbein & Ajzen, 1975). In addition, behavioral intention is an individual’s intention to perform various behaviors (Fishbein & Ajzen, 1975). The prediction of spectator behavior is a primary interest of sport marketers. Trail et al. (2003) found that behavioral intention is an appropriate forecast of actual spectator sport consumption. Therefore, in the current study, intention to watch a sport event was the dependent variable. However, the effect of performance expectancy on attitude and intention to watch remains unexplored. Thus, we posed the following research question:
Method
Participants
In exchange for extra credit, 275 college students enrolled at a major university in the southeastern United States participated in the study. An online survey link (Qualtrics) was distributed by instructors in a sport management program via email with a brief description of the study. All participants were voluntary and all responses were anonymous. After completing a consent form, participants received a brief prompt informing them the NCAA had implemented instant replay video technology to help officiate their games. American football (professional and college) had the second largest fan base in the United States, smaller only than Olympic sports (Jones, 2017); telecasts of the 392 regular-season events in 2019–2020 on ABC, ESPN, FOX, and other networks reached more than 145 million people and 47.5 million attended games (National Football Foundation, 2020). Survey participants were college students; thus, NCAA football was a familiar context in which to measure general perceptions of the use of officiating technology. Participants answered a series of questionnaire items about fair judgment, enjoyability, efficient game operation, convenience in review, attitude toward sport events, and intention to watch. Survey items were randomized to prevent any effects related to item order. Demographic items at the end of the survey included gender, age, and ethnicity. Through a screening procedure, we excluded 23 participants who did not fully complete the survey or completed the survey within a minute. The sample size (N = 252) exceeded the minimum criteria (i.e., greater than 200) to test the proposed measurement model (Hair et al., 2010; Weston & Gore, 2006). Of the 252 participants included in the analysis, 132 (52.4%) were male and 120 (47.6%) were female. The majority of the participants were 18–22 years old (M = 22.98 years), and 154 (61.1%) respondents were White/Caucasian, 60 (23.8%) respondents were Asian, 17 (6.7%) respondents were Hispanic, and 15 (6.0%) respondents were African American/Black.
The PEOT Scale
We identified four sub-dimensions of PEOT based on relevant previous findings (e.g., Davis, 1989, Davis et al., 1992; Ko et al., 2011). PEOT consists of four sub-scales (fair judgment, enjoyability, efficient game operation, and convenience of review) containing a total 12 items adapted from existing scales (Childers et al., 2001; Davis, 1989; Ko et al., 2011; Moon & Kim, 2001). For validity purposes, we asked a panel of experts to rate each item in terms of relevance, representativeness, and clarity. Panel members consisted of 10 doctoral students and three professors in sport management (Arnold et al., 2013; Kim et al., 2015; Yi & Gong, 2013). Based on their evaluation, we revised the items accordingly.
Fair judgment
Fair judgment refers to the degree to which a spectator believes that using officiating technology will improve the fairness of refereeing decisions (Davis, 1989, Ko et al., 2011; Rogers, 1995; Venkatesh et al., 2003). We measured fair judgment using a three-item, 7-point Likert-type scale (Strongly disagree = 1, Strongly agree = 7). These items were adapted from Davis (1989) and Ko et al. (2011). Items included “Using instant replays improves the referee’s officiating performance,” “I would find instant replays useful for fair judgment in officiating,” and “Using instant replays would improve fairness in officiating.” To check for internal consistency, we calculated Cronbach’s alpha (α = .84).
Enjoyability
Enjoyability is the spectator perception of the expected emotional benefit, including pleasure and fun, of using officiating technology in sport events (Davis et al., 1992; Lankton & Wilson, 2007). Three items were adapted from Davis et al. (1992) and Ko et al. (2011) assessed perceived enjoyability on a 7-point Likert-type scale (Strongly disagree = 1, Strongly agree = 7). Items included “In the events, it would be enjoyable to watch instant replays,” “I have experienced pleasure when the events have used instant replays,” and “Watching instant replays in the events would give me enjoyment.” Internal consistency of these three items was high (Cronbach’s α = .80).
Efficient game operation
Efficient game operation is the spectator perception that the use of officiating technology allows sport events to proceed smoothly (Collins, 2010). We measured efficient game operation using a three-item, 7-point Likert-type scale (Strongly disagree = 1, Strongly agree = 7). These items were adapted from Davis (1989) and Childers et al. (2001). Items included “The use of instant replays would improve efficiency in game operation,” “The use of instant replays would make game operation smooth,” and “The game operation would be efficient because of instant replays.” Internal consistency of these three items was high (Cronbach’s α = .92).
Convenience of review
Convenience of review refers to the spectator perception that using officiating technology will make watching reviews more convenient (Davis, 1989; Lankton & Wilson, 2007; Venkatesh et al., 2003). We measured convenience of review using a three-item, 7-point Likert-type scale (Strongly disagree = 1, Strongly agree = 7). These items were adapted from Childers et al. (2001). Items included “I would find it easy to follow instant replays through the media,” “Instant replays would allow me to review a judgment several times in a convenient manner,” and “Instant replays would allow me to review a referee’s judgment in a convenient manner.” Internal consistency of these three items was high (Cronbach’s α = .77).
Spectator-Focused Outcome Variables
Attitude toward sport events
We assessed attitude toward sport events using three items from Bagozzi et al. (1992). Participants indicated on a 7-point semantic differential scale how they felt about sport events: bad/good, negative/positive, and unfavorable/favorable. Internal consistency of these three items was high (Cronbach’s α = .91).
Intention to watch
We assessed intention to watch using three items from Till and Busler (1998). Participants indicated on a 7-point semantic differential scale their future intention to watch: very unlikely/very likely, improbable/probable, and impossible/possible. Internal consistency of these three items was high (Cronbach’s α = .98).
Results
Scale Validation: CFA for the First-Order-Factor Model
We conducted CFA for the first-order-factor model using the maximum likelihood method and AMOS 27.0. We assessed goodness-of-fit using the x2/df ratio, the comparative fit index (CFI), the Tucker Lewis index (TLI), the incremental fit index (IFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). The results revealed good model fit (χ2/df = 3.015, RMSEA = .090, CFI = .952, TLI = .934, IFI = .953. SRMR = .043; see Figure 1). All of the items demonstrated factor loadings greater than .60 (Hu & Bentler, 1999).
The first-order-factor model of PEOT.
CFA for the Second-Order-Factor Model
We examined the second-order-factor model to validate the structure of the construct based on the results of the CFA for the first-order-factor model. The overall fit of the second-order-factor model was good: χ2/df = 3.092, RMSEA = .091, CFI = .948, TLI = .932, IFI = .949. SRMR = .047; see Figure 2). All of the items demonstrated factor loadings greater than .60 (Hu & Bentler, 1999). The results revealed that all loadings of each item on their respective factor were significant (p < .001), ranging from .63 to .91. The first order factors also loaded significantly on the second-order factors (p < .001), ranging from .82 to 1.00.
The second-order-factor model of PEOT. Note. PEOT = Performance expectancy of officiating technology.
Construct Reliability and Discriminant Validity
We assessed the reliability of the four-factor model using Cronbach’s α, average variance extracted (AVE), and composite reliability (CR). All Cronbach’s α values were greater than the cut-off point of .70, ranging from .77 (convenience of review) to .98 (intention to watch; Hair et al., 2010). All AVE values were between .54 (convenience of review) and .79 (efficient game operation), a range considered acceptable (Hair et al., 2010). The CR coefficients were greater than .7, indicating good reliability (Hair et al., 2010). Tables 1 and 2 presents these results.
Results for Confirmatory Factor Analysis for First-Order-Factor Model.
Note. All variables were measured on a 7-point scale.
Item Correlation Matrix.
Note. FAIR = fair judgment, ENJO = enjoyability, EFFI = efficient game operation, CONV = convenience of review.
Nomological Validity: Structural Equation Model
To assess the extent to which the four dimensions of PEOT fit into a theoretical network, we examined attitude toward sport events and intention to watch as consequences of performance expectancy of officiating technology. Scholars have identified both attitude and intention as critical predictors of actual spectator behavior (Mahony & Moorman, 1999). Thus, we measured the nomological validity of PEOT by examining the relationships among performance expectancy, attitude toward sport events, and intention to watch. To establish nomological validity and answer RQ1, we reused the dataset from scale validation and used structural equation modeling. The overall fit of the model was good: χ2/df = 2.675, p < .001, RMSEA = .082, CFI = .949, TLI = .939, IFI = .949. SRMR = .056; see Figure 3). The results indicate that direct paths from PE to attitude toward sport events (standardized path coefficient = .25, p < .001) and intention to watch (standardized path coefficient = .15, p < .05) were significantly positive. Correlation coefficients between PEOT and attitude toward sport events (r = .26, p < .001) and PEOT and intention to watch (r = .23, p < .001) were moderately weak. In addition, attitude partially mediated the relationship between performance expectancy and intention to watch sport events.
The results of structural equation modeling. Note. PEOT = Performance expectancy of officiating technology. *p = .05. **p = .01. ***p = .001.
Discussion
The use of officiating technology at sport events is a relatively new area of study in mediated sport consumption research. Scholars have largely focused on analyzing its effectiveness in communicating decision messages to game officials (Carlos et al., 2019; Han et al., 2020; Spitz et al., 2021) and, more recently, on how a manager or coach perceives its impact on sport events (Chen & Davidson, 2021). According to a recent Nielsen report (Bauder, 2021), the NCAA Men’s Basketball Championship was the most-watched program (16.92 million viewers) April 5–11, 2021. As indicated earlier, 145 million fans watched the 2019–2020 regular season games of NCAA football, while 47.5 million fans attended live games (National Football Foundation, 2020). Although officiating technologies are widely used at various sport events and sport spectators are obviously an important stakeholder group, few scholars have addressed the concept and dimensionality of performance expectancy of officiating technology. In addition, systematic research requires psychometrically sound measurement scales.
To improve spectatorship, sport organizations need to position themselves to thrive in the new normal, after the pandemic, by addressing key opportunities, from fan engagement to cutting-edge technology. Officiating technology has received considerable attention as it offers a reliable and fun media consumption experience (Winand & Schneiders, 2018). Even as sport communication research on the use of officiating technology at sport events revolves around sport organizations, managers/coaches, and referees, the spectator domain remains essential, given that spectator perceptions are increasingly influential in mediated sport consumption. The importance of spectator engagement and interest make examining their expectations of officiating technology a vital task. Therefore, the primary purpose of the current study was to develop a construct of PEOT and investigate the relationships among PEOT, attitude toward sport events, and intention to watch. We proposed the PEOT scale, 12 items capturing fair judgment, enjoyability, efficient game operation, and convenience of review, all dimensions of PEOT. Finally, we examined the relationship between PEOT and spectator-oriented outcome variables to validate the scale. Our examination of how the perceived value of officiating technology influenced the attitude and behavioral intention of spectators yielded findings with theoretical and managerial implications in the area of sport communication and media technology.
Theoretical Implications
The most meaningful theoretical contribution of the current study is the conceptualization and development of the PEOT scale, a multi-dimensional tool based on the perspective of spectators, one of the most important stakeholder groups in the sport industry. To the best of our knowledge, the PEOT scale is the first to measure perceptions of the use of officiating technology in sport events. By applying previous findings about technology adoption, we identified four dimensions of PEOT and confirmed them using scale validation methods: fair judgment, enjoyability, efficient game operation, and convenience of review. Construct validity indicated that the four dimensions were sufficient to measure a single construct (i.e., PEOT). In addition, the results revealed that convenience of review and fair judgment were the strongest predictors of PEOT. This finding is consistent with previous findings about information technology adoption, findings that demonstrate that perceived usefulness and perceived ease of use are the primary antecedents of technology adoption (Davis, 1989; Davis et al., 1992; O’Cass & Fenech, 2003; Park & Kim, 2014). In the current study, we identified sub-dimensions of this unique service quality of new technology in sport events.
Second, the results introduce a reliable and valid measurement scale for evaluating the use of officiating technology in sport events. The PEOT scale enables sport communication researchers to examine potential antecedents and consequences of using officiating technology and allows researchers to assess various and systematic perceptions of officiating technologies in a parsimonious way.
Third, to establish nomological validity of the PEOT Scale, we explored the relationship between PEOT and spectator-oriented outcome variables (i.e., attitude toward sport events and intention to watch). The test of the structural model suggested that the PEOT scale is psychometrically sound and that the proposed associations were strong. We found that high PEOT made attitude toward sport events more favorable and increased intention to watch; likewise, more favorable attitude toward sport events strengthened intention to watch. Consistent with previous findings (Davis, 1989; Venkatesh et al., 2003), positive relationships between PEOT and attitude toward sport events and intention to watch emerged. These results suggest that PEOT induced more favorable attitude toward sport events and intention to watch (Ko et al., 2011). Thus, the findings provide a better way to understand the unique nature of officiating technology from the perspective of sport spectators.
Managerial Implications
The results of this study have several implications for practitioners and managers. For sport marketers, developing appropriate communication strategies is critical to attracting and retaining sport spectators. Because officiating technologies in sport events have grown rapidly, sport marketers need to understand how spectators perceive the use of those technologies and what kinds of expectations spectators have. For example, FIFA used goal-line technology for the first time in the 2014 World Cup, the most-watched mega event in the world. Since then, FIFA has used VAR in various competitions. Therefore, sport managers and marketers need to understand whether these tools meet the needs of sport spectators. The findings suggest that spectators expect officiating technology to provide fairness, enjoyable content, efficient game operation, and convenience of review. The PEOT scale can help sport practitioners evaluate different expectations of officiating technology. In addition, because the results revealed that convenience of review and fair judgment were the strongest predictors of PEOT, sports practitioners should consider how they can enhance event service quality using officiating technology. Finally, the relationships among the constructs indicate a sequential link. If spectators perceive high PEOT, they are likely to have a favorable attitude toward sport events and a higher intention to watch. Therefore, sport businesses and organizations can use our findings when considering whether to introduce or expand officiating technology in sport events.
(De)Limitations and Suggestions for Future Research
Given the research gap in consumer behavior associated with officiating technology, the aim of the current study was to provide a starting point for examining spectator perceptions of the use of officiating technology in sport events. However, the study has some limitations that open pathways for future research. First, we only considered the instant replay technology adopted by NCAA football. Sport spectators might have different expectations for different types of sports. Whether the proposed model is equally applicable to various sport events, including individual competitions (e.g., tennis) and team competitions (e.g., baseball), is worth exploring. In fact, different sport events use different officiating technologies (e.g., Hawk-Eye in tennis or VAR in soccer). Scholars should consider various officiating technologies in the context of various sport events. Moreover, some of the new emerging technology has significantly changed the way spectators consume sport events. For example, in 2019, robotic umpires (i.e., TrackMan system; ball and strike judgements) were experimented with in baseball by the Atlantic League and Arizona Fall League. Examining how the use of robotic umpires in sport events alters spectators’ evaluation of ball and strike calls and trust in umpire may open a pathway for future study (Bogage, 2019). Second, our sample consisted exclusively of college students. These participants likely came from the digital-technology generation, which has experienced lifelong use of communication and media technology. Therefore, our findings might have limited generalizability. Scholars should further validate PEOT by using a broader samples with different age segments. In addition, we did not explore the role of gender; thus, scholars should consider gender differences. Third, we used only two spectator-oriented outcome variables to validate the PEOT scale. In conjunction with various constructs of consumer behavior (e.g., satisfaction, trust, and perceived value), scholars should consider investigating relationships between PEOT and consumer behavioral outcome variables. They should also consider the potential moderating effects of other variables (e.g., degree of sport involvement). Fourth, although we tested four sub-dimensions to measure PEOT, unidentified confounders might have influenced the findings. Scholars might consider identifying unique dimensions qualitatively using in-depth interviews or quantitatively employing Exploratory Factor Analysis (EFA). Finally, we developed the PEOT Scale and suggested a research model for understanding how spectators respond to officiating technology use in sport events based only on the perspective of sport spectators. However, referees and athletes might be more concerned with the quality of officiating technology than its entertainment factors. Scholars should consider the perceptions of officiating technology held by athletes, referees, and sport organizations.
Despite these limitations, our findings validate a measurement tool for assessing PEOT from the perspective of sport spectators that marketers and researchers can use to enhance the quality of spectatorship at sport events.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
