Professional sport leagues are both commercial products and public products. As China’s sports leagues transition from state-governed properties to a more commercial model, professional sports are facing a crucial turning point on balancing stakeholders pertaining to its commercial/market value and social value. How to develop a sustainable business and management model of professional sports teams, fitting Chinese culture and the calls on free economy, has become a major challenge to scholars, practitioners and entrepreneurs in China’s sport industry. Every organisation has its relationships with many groups ("stakeholders") that affect and are affected by its decisions, which are dynamic in a constantly changing world. Stakeholder theory was developed through concerns regarding the nature of these relationships in terms of both processes and outcomes for both organisation and stakeholders, and the changing nature of the environment.

Marketing communication has undergone significant transformation in recent years. Influencers, who generate content on social networking sites (SNSs), have had a dramatic impact on consumers’ purchase decisions. Brands and marketers are therefore the most effective and powerful marketing tools. Social media influencers, so-called “YouTubers,” have become online opinion leaders by creating and posting content on social media, in contrast to celebrities who are well-known via traditional media in general. Previous studies regarding influencers have focused on the factors that contribute to the effectiveness of influencer marketing across various contexts or person-related factors. However, previous studies have not only elicited mixed findings concerning the effects of influencer marketing, but also reveal a lack of basic understanding of the mechanisms by which video content and influencers themselves impact consumer, particularly in the high-end product industry. Present-day social media influencers are not only information providers but also friendly communicators rather than inaccessible celebrities. Favorable attitudes towards influencers may play a crucial role in consumer behavior when purchasing high-end products in which have high prices and uncertainties. While some studies include the construct of the perception of influencers in their models, there is a lack of research on the effect of influencers and video content. Thus, we expect our empirical research to provide a comprehensive understanding of the nature of social contagion, which is highlighted by social media influencers.

Digital channels have been rising to the major shopping paths from the past few years, yet it is interesting to notice that more and more digital retailers advance into offline channel nowadays due to the benefits. For example, the digital retailing giant Amazon opened its first bricks-and-mortar bookstore in 2015. Dell, which distributes its products only in the catalog and digital channels, entered the leading retail stores such as Best Buy and Walmart. The digital retailers’ strategy trend that moves into the real world reveals the unique and powerful capabilities of the offline distribution channel and the importance of launching products in the offline channel. Previous studies in marketing have investigated various complement and substitute impacts of offline channel introduction (i.e., Avery et al. 2012, Wang and Goldfarb 2017). However, most of the literature focus on the pc-based online channel and catalog, there is little research about the impact of offline channel introduction on smartphone-based mobile channel. Although mobile channel is similar to online channel in many ways (i.e., internet access and convenient shopping), it can be distinguished from online channel in terms of search and access, leading to a different interplay with offline channel. This research, therefore, aims to investigate how product launching in offline channel affects purchases in mobile channel versus online channel, and deepen the understanding by exploring the moderating effect of offline store intensity. Besides, we also explore the interaction between two digital channels: online channel and mobile channel. As most of the multi-channel retailers offer products in both online and mobile channels, it is incremental to know the interplay between these two digital channels. Using data from a representative multichannel retailer selling beauty products, we find the following three empirical results. First, product introduction in offline channel has a positive effect on online and mobile sales, and the effect is greater for mobile. Second, as offline store increases, the positive impact of offline product launch weakens on online channel, and even turns to negative once the number of offline stores reaches a threshold. However, the influence on mobile channel stays the same. The results reflect that the complementary interplay between offline and mobile channel is relatively stronger than between online channel. Finally, within the digital channels, online purchases only increase with the growth of sales performance within online channel, whereas mobile purchases are positively affected by both within mobile channel sales and across online channel sales. Our findings contribute critical academic and managerial implications for multichannel retailing.

This paper aims to expand our understanding on the success factors of small businesses, which comprise of more than 90 percent of all businesses in U.S. in 2016. One of the most critical issues behind small business success is the competition, which becomes increasingly intense. Not only small businesses fiercely compete with larger competitors (e.g. Emergence of mega-retailers such as Wal-Mart has intensified the competition in the grocery industry, and, as a result, many mom and pop stores have gone out of business.), but also the competition against each other (i.e. competition between small businesses) becomes increasingly aggressive. Yet, the current literature in marketing have less investigated the issue of competition between small businesses, while issues on competition between small and large businesses have been somewhat explored. Another phenomenon in small business that has not received much attention is the competition between generalist and specialist firms. This phenomenon of specialist versus generalist competition is in fact frequently observed in many industries. Therefore, we study competition between small businesses, focusing on the competition between generalist and specialist small businesses. We examine how competitive intensity, as well as market environmental factors, affect the performance of small businesses. Specifically, we decompose the competitive intensity into two types, one between generalists and the other between specialists, in order to identify the differential effects of competition between generalist and specialist, and examine their impacts on the generalist and specialist performance. Given the research questions above, we develop the following hypotheses based on the past research in marketing. First, we expect competition has a positive effect on generalist performance, while we expect the opposite effect on specialist performance. We also expect that the effect of competition becomes weaker, as the competition becomes more intense. That is, the positive (negative) impact of competition on generalist (specialist) performance becomes less significant as there are more competitors in the market. We further expect that competition between the same type of businesses (e.g. between generalists) has a positive effect on their performance, while competition between the difference types (e.g. between generalist and specialist) has a negative effect on their performance. Moreover, we expect that market environmental factors have differential effects on the performance of generalist and specialist. To test the aforementioned hypotheses on the small business competition between generalist and specialist, we collected data from the health care industry on private physician practices (offices) in Korea. Out data contain, for each practice, monthly sales, number of doctors, number of nurses, type of practice, number of beds and zip code it is located in. We also have data on average consumer spending, average medical spending, percentage of patients over sixty years old for each zip code. Moreover, we have data on competition between the same type of offices (e.g. between generalists and between specialists) and competition between different types (e.g. between generalist and specialist). Note that our data collected from the Korean health care industry fit our research questions well. First, the majority of medical service providers in Korea are small private practices with an average number of two doctors, and the share of generalist and specialist practices are about half-and-half. Second, unlike the U.S. health care industry, generalist physicians in Korea usually practice a number of different fields, while specialist physicians focus on their own specialties. Third, patients in Korea do not usually distinguish between generalist and specialist offices, and they do not usually have a primary care physician. As a result, patients can easily switch between physicians, and in fact the switching is highly likely, as all medical information is centralized by government. Our main findings are as follows. First, we find that competition has a positive effect on generalist performance, while it has a negative effect on specialist performance. Specifically, we find that generalist benefits from competition with both generalist and specialist, while specialist suffers from the competition with both specialist and generalist. As competition becomes intense, meaning the number of physician offices increases, it would attract more patients to visit the area where physician offices are clustered (clustering effect), while it becomes easier for patients to switch from one to the other nearby offices. In particular, as generalist usually treats multiple fields (specialties), generalist tends to benefit from the patients who switch from specialist. In other words, generalists benefit from competition, as they free ride on clustering of physicians including specialists, while specialists would suffer from competition. Second, our findings show that as the competition becomes more intense, its effect on business performance becomes weaker. That is, a high level of competition weakens the benefits and damages imposed on the performance of generalist and specialist, respectively. When there are more physician offices to switch, the effect of free riding becomes weaker, as patients have more options to choose from. Thus, the benefit of generalist from free riding becomes weaker, as well as the negative effect on specialist performance. Moreover, our findings suggest that market environmental factors do influence the business performance. Specifically, the performance of both generalist and specialist improves as the number of doctors increases. However, an increase in the number of nurses has a different effect on generalist and specialist. Employing a larger group of nurses has a negative effect on generalist because it might cause the operation of the office to be less efficient. However, since specialist’s practice usually involves a more technical and sophisticated processes, a larger group of nurses could make the office more efficient having a positive impact on the sales performance. Similarly, we find the effects of other environmental factors have differential impacts on the performance of specialist versus generalist.

We investigate the effect of offline social interactions on online shopping demand and the moderating role of online channel preference in this offline-online relationship. To be specific, we intend to obtain empirical evidence by answering the following questions. First, do offline social interactions affect online demand? Second, to what degree do the active versus passive kinds of offline social interactions have the differential influence on online shopping demand? Third, how does online channel preference affect the effect of offline social interaction on online shopping demand? Drawing on the related literature in the fields of social interactions and Internet retailing, we hypothesize that the active kind of offline social interactions exerts positive influence on online shopping demand whereas the passive kind of offline social interactions has negative effects. We further hypothesize that online channel preference weakens the influence that offline social interactions has for online shopping demand. Both the positive impact of active interactions and the negative impact of passive interactions diminish in determining online shopping demand as online channel preference gets greater. We obtained sales data between January 2008 and April 2010 from a leading Internet retailer that sells baby products in the U.S. The data includes the information of zip codelevel sales and shipping days. We merged this proprietary data with the following three commercial datasets purchased from ESRI (Environmental Systems Research Institute): (1) 2011 Civic Activities Market Potential, (2) 2011 Internet Market Potential, and (3) 2011 Baby Products Market Potential). Each of these datasets includes the information of offline social interactions, online shopping preferences and offline baby product sales, respectively. Finally, as we focus on the zip code-level interplay between offline social interactions and online demand, we control for regional demographics and market condition. As such, we obtained the 2010 Census data and 2009 ACS (American Community Survey) data to account for overall local environments (e.g. population density of children aged less than five years, percentage with college education). Our empirical analyses and hypotheses testing provide the following important findings. First, active offline social interactions have positive effects on online shopping demand. This indicates that active social interactions reflect information exchange among long ties, and this informational influence in turn reduces any risk and uncertainty associated with online shopping. Second, passive offline social interactions have negative effects on online shopping demand. This suggests that passive social interactions take place among local ties and generate normative influence to conform to the expectations of others about shopping behavior, making online shopping as a new channel less attractive there. Third, online channel preference is significantly positive on online shopping demand, confirming prior studies on the relationship between channel preference and demand (Changchit et al. 2014; Valentini et al. 2011). Fourth, the positive effect that active offline social interactions have for online shopping demand decreases as online channel preference increases. Regions with strong online channel preference are likely to have well-established channel propensity and the informational influence of social interactions in reducing uncertainty becomes weaker. As such, social interactions do not play a role in spreading information about the online marketplace in regions where online channel benefits are well understood (Burt 1992, 2005; Harrigan et al. 2012). Lastly, the negative influence of passive offline social interactions gets smaller as online channel preference gets greater. Online channel preference reflects the locally-determined attractiveness of the online marketplace, and this in turn weakens normative influence to conform to the expectations and shopping behaviors of local ties.