Innovations are the foundation of an enterprise’s sustainable development, which is particularly important for sports firms in an evolving Chinese sport industrial environment. Analyzing publicly-listed sports firms on The New Third Board (NTB) in China, this study examined the influence of corporate financial capability and corporate governance structure on firms’ R&D intensity through a series of multiple regression models. Findings revealed that corporate financial capability is an important determinant of R&D intensity, and corporate governance structure has a small but meaningful effect on R&D intensity. Specifically, for Chinese sports firms, several financial capability indicators, such as return on equity, accounts receivable turnover, assets turnover, and profit growth rate, have positive relationships with R&D intensity; however, other financial capability indicators, such as leverage and cash flow, have negative relationships with R&D intensity. Limited evidence was found to support the notion that corporate governance significantly influences R&D intensity, although sports firms with good governance mechanisms are more likely to increase the positive effects of financial capabilities on R&D intensity while decreasing the negative effects. Discussions were centered on planning and executing R&D activities in sports companies.

Purpose: Since 2014, “sport for all” has been promoted as a new national strategy in China, which injects powerful dynamism and vitality for its development in numerous aspects. However, there has been very little feedback on sport service provision in community, and the satisfaction level of community participants is largely unmeasured. To promote physical and mental health of residents and form a stronger foundation of sport culture, more attention should be directed to community sports. The purpose of this paper is to examine the impact of community-sport service provisions on participants’ satisfaction and, in turn, on their sport participation behavior. Design/methodology/approach: Community-sport program participants in China (n=576) responded to a survey measuring the proposed concepts. Findings: Structural equation modeling analyses revealed that community-sport services in the areas of sport facility, grassroots sport organizations and sport activity programs had strong influences on participant satisfaction and, in turn, their desire for participation, which highlighted the demand for high-quality sport service provision by community. Originality/value: The study contributed to the literature by proposing two clear dimensions (core sport service and peripheral sport service) for the measurement of public sport service provision in community sports. A second theoretical contribution of the study relates to the clarification of the relationship between the two dimensions of community-sport service provision (both core and peripheral services) and community participants’ satisfaction levels.

Players selected in the annual Major League Baseball First-Year Player Draft have a choice of whether to enter the system or pursue a career outside of professional baseball. As with any choice, there are opportunity costs associated with their decision. The purpose of this study was to analyze those opportunity costs by comparing draftees’ expected, non-signing-bonus income to the income of standard laborers. We estimated expected income over the first six seasons of players’ careers by combining minor and major league salary data with the probabilities obtained from C5.0 classification models that were applied to a large set of player-related data. Unlike traditional classification methods that produce the probabilities for landing in one of two classes, C5.0 classification models are able to produce the simultaneous probabilities of a player landing in several different classes, or out of professional baseball, in each season. The results show that, all else equal, college fielders drafted in the first 89–90 picks and college pitchers drafted in the first 32 picks (college graduates) or 171 picks (some college) should sign contracts. Pitchers drafted out of high school should sign contracts if they are picked in the first 78 picks, as should fielders selected in the top 118 picks. Beyond these ranges, signing bonuses or higher levels of performance are needed to limit the opportunity costs of pursuing professional baseball. Such findings are relevant to draftees making contract decisions and baseball stakeholders dealing with recent calls for improved wages and labor conditions in the minors.

Every March a sample of the top Division I men’s basketball programs in the National Collegiate Athletic Association (NCAA) gather to compete in March Madness, a grueling single elimination tournament that captures the attention of millions of viewers and shines a prominent spotlight on the 68 teams that are competing for college basketball’s national championship. Interspersed amongst the numerous financial incentives that exist for each university, and the millions of dollars that are wagered on brackets and bets, are the suggestions of media members, coaches, and players as to which factors are important to teams in their quest for success. One common suggestion argues that player experience provides a benefit to teams as they attempt to handle the pressure and maintain their composure amidst one of the most hectic postseasons in all of sport. However, there have been few studies conducted to analyze the effects that the two primary categories of player experience (i.e., prior postseason experience and class rank) have on the performances of March Madness teams. Therefore, this study sought to test the validity of the assumption by using a series of empirical models to analyze player performance and experience data from the 693 games that took place during the 2007 to 2017 March Madness tournaments. The findings suggest that simply having a higher class rank than an opponent offers no discernible advantage at any stage of the competition, but that possessing more prior March Madness experience may significantly improve a team’s margin of victory in the later rounds.

The rapid emergence of artificial intelligence and its derivative machine learning tools has been embraced with enthusiasm in sport consumer behavior research lately. In light of the era of big data, digitalization, and automation, the main goal of this book chapter is to provide readers with an integrative overview of the nuts and bolts of machine learning, how advanced machine learning techniques, as a novel and legitimate methodological approach, can enhance sport consumer behavior research and theory development. We seek to shed light on the significance, opportunities, benefits, and challenges of machine learning applications for sport marketing scholars to understand, decipher, and predict consumer behavior and psychology through algorithm transparency, causal inference, and visualized . An empirical illustration is presented to thoroughly discuss how machine learning can complement the conventional approaches concerning reliability, validity, utility, explainability, and prediction precision. <!----> <!----> <!----> <!---->