همکاری زنجیره تامین فرانشیزی-فرانشیزر : به اشتراک گذاشتن اطلاعات مربوط به پیش بینی تقاضا در صنایع با تکنولوژی بالا

In order to maintain their competitive edges in the market, high-tech firms cannot simply rely on superior technology alone. In addition, due to rapid technological changes, market demands in the high-tech industries have become volatile and difficult to forecast. This study proposes an option (i.e. franchising) that high-tech firms can use in order to expand their markets and improve firm performance. We also utilize a Bayesian forecasting methodology in order to address information sharing between the franchisor and franchisee. Our study demonstrates how high-tech franchising firms can benefit from information sharing of demand forecasts when franchising in order to enhance franchise performance. We also show that a profit sharing mechanism that results in optimal profits for the franchisor and franchisee. The study fills the research gaps that currently exist in the franchising literature and provides important managerial implications for practitioners in B2B markets.

A substantial amount of venture capital pours into high-tech industries (e.g., pharmaceuticals, communication and electronic equipment and office computing machines) each year due to their high profits and growth potential. For instance, recent reports have indicated that the global electronic manufacturing services market, which consists of the computer applications segment; the consumer, industrial, medical and Aerospace segments; and the communications segment, had total revenues of $194.6 billion in 2010 (Datamonitor, 2011a) and that the global semiconductor market reached a value of $346.8 billion in 2010 (Datamonitor, 2011b). The global sales of consumer electronics reached $722 billion in 2010 and these sales are estimated to continue to grow (Euromonitor International, 2011). However, instead of relying on technological superiority, high-tech firms have increasingly focused on developing a variety of marketing programs to maintain their competitive edges (Traynor & Traynor, 2004). In North America, manufacturers sell consumer electronics through major retailers, such as Best Buy and Wal-Mart. In addition to these traditional distribution channels, some firms (e.g., Dell and SONY) have attempted distributing products via kiosks in shopping malls and other high traffic locations. Given the high competition in high-tech industries, firms should attempt to find more ways to expand their markets. Research has indicated that franchising could be an optimal solution to the channel design problem in some circumstances when high-tech firms decide to expand their markets (Ayal & Izraeli, 2002). The most recent example of franchising in the high-tech industry can be found in Apple's decision to expand its market in China. Apple has given its franchise to Hon Hai Precision Industry Co., Ltd., which will sell Apple's consumer electronics products in China via its established Cybermart Digital Square (Liu, 2009). Franchising plays an important role in the US economy and is becoming the world's fastest growing form of retailing (Kaufmann, Gordon, & Owers, 2000). In 1985, sales of franchising in the US were estimated at $529 billion (Lal, 1990). More recently, sales of franchising are more than $1.3 trillion and account for more than 10% of retail sales by all U.S. businesses (U.S. Department of Census, 2010). Franchising has become one of the most popular ways of doing business in today's marketplace. Entering and exiting the franchising industry is a common feature of franchisors (Lafontaine and Shaw, 1998 and Shane and Foo, 1999). Prior research on franchising has focused on investigating factors that have caused franchising failures. However, franchising performance remains under-researched (Alon et al., 2006 and Combs et al., 2004). Since franchising may serve as a good alternative to expanding markets for high-tech industries (Ayal & Izraeli, 2002), additional studies are needed to investigate how to enhance franchising performance. Prior research has suggested that sharing demand forecast information between franchisors and franchisees could be one possible method by which to improve franchising performance (Dant & Nasr, 1998). Within this study, we propose a specific model by which to improve franchising performance through information sharing between the franchisors and franchisees. Accurate demand forecasting is essential to businesses (Lancioni, 2000) as it influences profits substantially (Taylor & Xiao, 2010). For example, in order to meet various consumer needs, high-tech firms like Hewlett-Packard offer many innovative products. By providing multiple similar products, Hewlett-Packard's forecast accuracy reduced and, thus, profits were affected adversely (Ward, Zhang, Jain, Fry, & Olavson, 2010). Because of rapid technological changes and numerous product configurations, the demands in the high-tech industries are very volatile and difficult to forecast (Yelland, 2009). In addition, due to the current uncertain and turbulent economy (IDC, 2009), demand forecasting is more difficult than in previous years in the high-tech industry (e.g., PC and consumer electronics). As such, efforts have been made to discover better ways of producing accurate forecasts (e.g., McDade et al., 2010 and Meredith, 2006). For instance, Sun Microsystems Inc., a vendor of enterprise computing products with about $14 billion in annual sales, has devoted its effort to developing models to forecast its demand (Yelland, 2009). In B-to-B markets, the accuracy of demand forecasts could be improved via information sharing between different members, which would allow firms to respond to changes in customer and competitive demands in a real time manner (Lancioni, 2000). As such, mastering more accurate demand forecast information contributes to better managerial decisions, such as an optimal pricing policy and inventory levels (Christopher, 1999). For instance, firms can adjust the inventory level according to the market demand and reduce the costs of inventory shortage with increased demand information accuracy. There has been a growing interest in improving supply chain performance for all members of a supply chain through demand forecast information sharing (e.g., Mishra et al., 2007 and Mukhopadhyay et al., 2008). In many cases, supply chain players make decisions with limited demand forecast information. A significant benefit of this information sharing is that the shared information (i.e., consumer demand information and sales trend, etc.) improves information accuracy and distribution planning. For example, retailer Best Buy Co. and PC manufacturer Lenovo Group Ltd. have benefited from exchanging information during product distribution. A recent report shows that Lenovo significantly outperformed the market in its PC shipments among the top five vendors (Gartner, 2011). Researchers also indicate that information sharing would be especially useful for improving firm performance in the high-tech industry (Lee, So, & Tang, 2000). Therefore, the case of demand forecast information sharing between the franchisor and franchisee has gained interest as it is expected to enhance franchising performance. Researchers have identified factors, such as incidences of repeat purchases and the age of the relationship between the focal franchisor and franchisee, that influence a franchisee's willingness to share information (Dant & Nasr, 1998). However, further research is needed in order to encourage information sharing between both parties in such a way that is beneficial to both parties. One stream of research on information sharing in supply chain management identifies the benefits of information sharing in regard to inventory and replenishment (e.g. Parlar & Weng, 1997). Another stream of research has examined the conditions that facilitate information sharing (Li, 2002) and how information sharing impacts price decisions. However, few comprehensive models exist to identify the ways by which to encourage information sharing and bring benefits to channel members. Thus, one of our research objectives is to develop a model that facilitates information sharing between the franchisor and franchisee and, subsequently, brings about optimal profits for both parties. We demonstrate how to enhance franchising performance through information sharing between the franchisor and franchisee. Developing such a model in regard to franchising will contribute to the existing franchising literature. This research will also contribute to an emergent stream of research on profit sharing in B2B channel relationships. Previous studies have examined the determinants, practices and consequences of profit sharing (e.g., Fang et al., 2008, Jap, 2001, Wagner et al., 2010 and Wagner and Lindemann, 2008). However, little research has been conducted on the mechanism of profit sharing and bargaining in the franchising system. Extending prior research, this research will demonstrate that a profit sharing mechanism that motivates information sharing between the franchisors and franchisees result in optimal profits for the franchisor and franchisee, which further encourages future collaboration. In addition, although substantial research has focused on the coordination between the franchisors and franchisees, prior research has been conducted under the assumption that market demand is certain. Little is known in regard to whether the observed effects could be generalized to when market demand is uncertain. In order to fill the aforementioned research gaps, based on the Bayesian forecasting theory (DeGroot, 1970), this study proposes a win–win information sharing model that encourages franchisors and franchisees to share demand forecast information through pricing strategies and inventory- and replenishment-related savings. The rest of the paper is organized as follows. Section 2 provides a summary of the relevant literature. Section 3 presents our modeling framework. Section 4 analyzes the cases of non-information sharing and information sharing under the Stackelberg game. Section 5 compares the impact of different information strategies on the performances of the franchisor and the franchisee. We present numerical examples in Section 6. Conclusions and managerial implications are presented in Section 7.

In this paper, we assumed a franchisor–franchisee supply chain where both the franchisor and the franchisee have private information about the market demand. In this setting, we analyzed the profits for the supply chain under a non-information sharing scenario and under an information sharing scenario. We first derived the equilibrium prices and the profits under the two scenarios then investigated the impact of information sharing on the performances of the franchisor and the franchisee. We found that the franchisor always prefers to share the franchisee's information, particularly when the franchisor royalty fee is equal to zero. However, the franchisee would like to share its information with the franchisor only when the franchisor can provide a lower wholesale price or craft a profit sharing mechanism to the franchisee. Thus, when the franchisor engages in profit sharing with the franchisee, a multi-attribute utility bargaining model is used to implement profit sharing for the franchisor and the franchisee to motivate the franchisee to share its information, so that an information sharing equilibrium can be reached. Our numerical examples further illustrated and verified our analytical findings and provided more managerial insights. Our research addresses important issues in the franchise supply chain research and profit sharing and bargaining in channel relationships. Our findings provide valuable managerial implications for firms in the high-tech industry that employ franchising in order to expand their market with rapidly changing market demands in a volatile economy. Theoretically, our paper contributes to the growing research about the value of demand forecast information sharing in a franchisor–franchisee supply chain. Specifically, we investigate how to improve franchising performance, an area that has been under-researched. We demonstrate that information sharing between franchisors and franchisees will benefit both parties and enhance franchising performance. In addition, this research contributes to the emergent research stream on profit sharing in B2B channel relationships. Extending prior research that has focused on identifying antecedents or consequences of profit sharing, this research demonstrates a profit sharing mechanism between the franchisor and franchisee and show how an optimal profit bargaining scheme can be achieved. In the proposed profit sharing mechanism, the more information that is shared between the franchisor and franchisee, the higher the increased profit gain that each partner will share. The mechanism will encourage information sharing between the franchisor and franchisee and result in optimal profits for the franchisor and franchisee. Such a profit sharing mechanism is likely to enhance the quality of the relationship (e.g., collaboration satisfaction and future collaboration) between the franchisor and franchisee because of the shared profit gain (Jap, 2001 and Wagner et al., 2010). This research also develops a more comprehensive Bayesian forecasting model in order to capture the impact of information sharing on price decision and franchise supply chain performance under uncertain market demand conditions. Franchising is becoming the world's fastest growing form of retail (Dant et al., 2007 and U.S. Department of Census, 2010). Thus, it is managerially important to develop mechanisms of coordination between franchisors and their franchisees in order to improve channel coordination and enhance supply chain performance. In our research, we use a game theory model in order to show how the franchisor can offer a wholesale discount or craft a profit-sharing mechanism in order to transfer part of the increased profit to the franchisee as an incentive for sharing information. This way both the franchisor and franchisee benefit from the demand forecast information sharing. Information sharing is particularly important when the franchisor royalty fee is equal to zero. In the business world, the supply chain consisting of a franchisee (e.g. auto parts franchises, computer and consumer electronic products franchises, etc.) and its franchisor is a typical example for our research. Marketers in high-tech industries may want to look for potential partners in order to authorize their franchises in order to expand their markets and improve their performances in the market. This research shows that franchising is a good alternative that high-tech firms could adopt. Furthermore, creating accurate demand forecasts for high-tech products is becoming a challenging task for firms and, in order to enhance the franchising performance, marketers need to encourage information sharing between franchising partners in order to bring higher profitability to the whole franchising system. Our research suggests that when market demand information is shared, the franchisor can profit from savings in inventory and shortage costs, while the franchisee can profit from a lower wholesale price. That is, due to the increased information accuracy, the franchisor has better control of the costs of inventory disposal and shortage. On the other hand, when receiving a lower wholesale price from the franchisor, the franchisee can set a lower retail price, which leads to increased demand and, thus, higher profits. In regard to the ways by which to facilitate information sharing between the franchisor and franchisee, our research also offers some suggestions. It has been suggested that a franchisor's lower royalty fees and wholesale prices as well as a profit-sharing mechanism would motivate a franchisee's willingness to share market demand information, resulting in optimal profits to all franchising partners. In order to ensure the success of the information sharing, an optimal profit sharing scheme should be established that is acceptable to both the franchisor and franchisee. The optimal profit sharing scheme would be determined by the relative bargaining strength of the franchising partners. High-tech firms are able to use the managerial insights from our research to improve their marketing decisions and profits. Our analysis has some limitations. We assumed a theoretical demand function in this paper. While this theoretical demand model has been used extensively in economics and marketing, it is desirable to examine whether the qualitative implications are able to be generalized to the empirical study through real data. This research can be extended in several directions. First, in this paper our analysis is based on a single period model; therefore, it is a good idea to investigate how information sharing might work in a multi-period and what the long-term impact of profit sharing will be (e.g., Wagner et al., 2010). Second, our proposed model was not tested empirically. It is highly suggested that future research empirically test the proposed model with data collected from high-tech franchising industries. Third, our research did not consider the possible franchisees' psychological factors of information sharing. For instance, the franchisee might share information because of a moral obligation to pay back for being part of the franchise system, instead of a royalty fee. Future research should investigate the effects of these psychological factors on information sharing. Fourth, if the research is to consider whether the franchisee needs know-how or expertise from the franchisor or if the franchisee wants to be the franchisor's first choice for the next product launch, then it would be valuable to examine whether the franchisee may be motivated to share information without lower wholesale prices to the franchisee or without a profit sharing mechanism being in place with the franchisee. Also, it is valuable to examine whether the size of high-tech firms plays a major role in the information sharing franchising supply chain. In addition, our research did not prove that any change in the profit was due to information sharing and not other factors. Future research might want to address this issue by collecting data from high-tech industries in order to examine if our derived theoretical result can be generalized to an empirical study. Finally, aside from considering other factors, future research should extend our study to include other franchisees in a competitive market.