Internet Business Models for Broadcasters: How Television Stations

Perceive and Integrate the Internet

Submitted for Publication to the Journal of Broadcasting and Electronic Media

By

Sylvia M. Chan-Olmsted, Ph.D.

Department of Telecommunication

College of Journalism and Communications

University of Florida

Gainesville, FL 32608

chanolmsted@jou.ufl.edu

and

Louisa Ha, Ph.D.

Department of Telecommunications

Bowling Green State University

louisah@bgnet.bgsu.edu

Internet Business Models for Broadcasters: How Television Stations

Perceive and Integrate the Internet

Abstract

This study examines the Internet business strategy as it applies in the broadcast television industry by proposing a framework of Internet business models for the television broadcasters and, drawing on this framework, assessing the broadcasters’ current Internet operation patterns.

We found that the television stations have focused their online activities on building audience relationships, rather than generating online ad sales. The Internet is used as a “support” to complement the off-line core products.

Internet Business Models for Broadcasters: How Television Stations

Perceive and Integrate the Internet

The exponential growth of the Internet has changed the rules of competition in many industry sectors. The “reach” and “speed” of the development, coupled with the unique characteristics of interactivity and personalization, amplify the need for innovative business strategies from the competing media incumbents in their attempt to counter and/or leverage the rising popularity of this new market entrant.

The strategic importance of the Internet is especially evident for the television industry as television and the Internet develop a symbiotic relationship that has significant financial implications. Television provides the most desirable marketing communication channels for Internet marketers. With millions of Websites available on the Net, the Internet is the most cluttered medium in the world. To succeed in marketing an online brand, a marketer most likely will need the distribution of communication messages via a mass medium such as broadcast television to create broad awareness of the product or service and/or a niche medium such as cable television to connect with target markets.

On the other hand, the increasingly critical role of the Internet in American media consumers’ daily lives has led to a re-orientation of business strategy and operations by the leading “mass” medium, the television broadcasters. For example, both television stations and networks now frequently cross-promote their online and off-line content, especially for news and sports-related television programming (Grande, 1998; Greene 2000).[1] NBC recently launched a multi-platform advertising plan to focus on cross-platform advertising sales using its cable and broadcast networks, television stations, and Internet properties in an attempt to move away from an ad-reliance business model and reshape its business into a more interactive lifestyles management, information, and entertainment company,[2] (Mermigas, Aug 13, 2001). Disney and Fox formed a new joint venture, movies.com, to distribute movies digitally through broadband Internet connections or cable video-on-demand services[3] (Healey & Verrier, 2001). With the arrival of digital television, many television broadcasters are contemplating the feasibility of Web-enhanced television applications such as on-screen links to advertisers’ Web addresses, localized news services, late-breaking news, sports statistics, interactive polling, background to documentary material, online chat, and links to movie trailers and ticketing services (Pavlik, 2001; Nelson, 2001; Kerschbaumer, 2001). There has also been a shift in the thinking of leading Internet television companies towards using the Web to enhance the television experience, rather than using the television as merely an alternative Web access device (Thompson, 2000).

As revolutionary as the Internet is for the television industry, little literature has examined the changes in business models, operations, and perceptions in response to the Internet among the broadcast media incumbents. Media scholars have mostly investigated the impact of the Internet on the ideology of public broadcasting and the society (Hills & Michalis, 2000; Havick, 2000), the general interrelationship between the Internet and television and the Internet in the context of digital television, multimedia, and broadband communication (Owen, 1999; Chan-Olmsted & Kang, 2002; Picard, 2000), Internet radio Web-casting and online content of television broadcasters (Coyle, 2000; Chan-Olmsted & Park, 2000), and regulatory implications of Internet broadcasting (Fan, 2000). In his work on the economics of Web-based delivery of television content, Waterman (2000) suggested that Internet technology enhances the efficiency of television delivery through more efficient market segmentation and price discrimination. He also proposed that Internet television would follow the “cable television” model, offering a dichotomous mix of less expensive, niche Internet original content for targeted segments and relatively expensive, general appeal syndicated programming for a mass audience, more likely under a direct-payment method than the e-commerce or advertising model because of the diminishing initial need of avoiding a fee-based system and the increasing availability of greater bandwidth capacity for products of greater consumer value (Waterman, 2000).

Television stations have adopted a variety of Internet strategies, from outsourcing the Internet operations, utilizing the Web to create interactive advertising experience, employing the Internet as a marketing tool for on-air content or station brands, and positioning Websites as local portals, to producing content for enhanced television (Greene, 2000; Kerschbaumer, 2000). A 1999 NAB survey of television stations revealed that the majority of television stations (70%) maintained their own Internet operations, while the rest either outsourced their operations to a third party, used a combination of outsourcing and internal management, or had their parent network maintain the Internet function. The news and promotions departments were most frequently the unit that maintained the Internet operations if they were carried out in-house. The survey also found that the majority of the stations did not have full-time staff designated for Internet-related tasks and had focused on utilizing their Internet operations to generate advertising revenues rather than develop e-commerce or content on demand (Nitschke, 1999). Another survey of stations’ Web strategies found that while broadcast groups such as CBS stations are exploring ways to package existing sales forces, content, and promotion into profitable Internet businesses, Fox stations are focusing on online branding and developing local personalities and content with a national infrastructure and technical support. As NBC stations emphasize the building of local portals with their news products, station groups such as Tribune Broadcasting that also own newspaper properties are integrating the print and broadcasting Internet operations to provide a more competitive local content. On the other hand, ABC stations are working toward a Web version of network-station programming relationships, while the Chris-Craft Industries stations are developing niches that are database-driven (e.g., used cars and employment listings) and have immediate revenue potentials. According to the same survey, most station groups have invested in the broadband and wireless sector and some are supplying information content to their local broadband systems to ensure a presence in the growing broadband market (Greene, 2000).

The present study attempts to extend the relevant empirical literature in Internet business strategy as it applies in the broadcast television industry by proposing a framework of Internet business models for the television broadcasters and, drawing on this framework, assessing the broadcasters’ current Internet operation patterns. The following section presents relevant literature with respect to Internet strategy and two strategic competition perspectives with which this study is grounded. Subsequent sections present the framework development and then the empirical analysis and results. The paper concludes with a discussion of the findings and with directions for future research.

Literature Review and Theoretical Background

An important body of literature has sought to address the changes and values that the Internet brought to a conventional marketplace. Many media scholars have studied the factors that might impact a firm’s Internet strategy and proposed a range of Internet business models. A review of this literature will establish the rationale for the selections of the factors that shape the strategic directions of and the strategic options available to the television broadcasters.

Impact of the Internet

The emergence of the Internet has undoubtedly changed the business environment in which the television stations operate. Casagranda, Ashill, and Stevens (1998) suggested that the Internet has altered industry structure by reducing the costs of coordination in the value chain, become a source of competitive advantage by providing companies with new ways to outperform their competitors, and spawned new businesses by providing more information. The Internet is also evolving to encourage direct interaction between producers and consumers in markets where consumers have more complete information about goods and services enabling them to exert substantial control (Hagel & Rayport 1997). Such changes in the relationships between value chain members are taking place in the U.S. television industry as some programming products have been offered exclusively online, bypassing the traditional over-the-air, cable, or theatrical channel members. In summary, the Internet seems to have heightened the use and demand for information and customer service, elevated the need of new business development for staying competitive, and, to a certain degree, changed the relationship between and operation of value chain (distribution channel) members.

Strategic Value of the Internet

Many scholars have presented extensive lists of the strategic value of the Internet (Ainscough & Luckett, 1996; Griffith & Palmer, 1999; Quelch & Klein, 1996; Cronin, 1996; Ranchhod & Gurau, 1999; Sterne, 1995; Van Doren, Fechner, & Green-Adelsberger, 2000). Most concluded that the Internet allows a business to access global markets, provides mass customization, reduces marketing costs, builds strong business relationships with a greater degree of channel coordination, develops business intelligence, offers heightened communication with various publics to improve corporate image, and improves customer communication and service. Specifically, Standing (2000) suggested that the Internet provides a tremendous opportunity for the Web retailing of “digital goods” such as software and music. Venkatraman (2000) further concluded that the Internet is emerging as a critical backbone of commerce (Venkatraman 2000). In summary, the value of the Internet seems to rest in the areas of marketing, customer service/communication, commerce/new markets, and efficiency within the value chain (channel relationships).

Factors that Influence Internet Strategic Approaches

Angehrn (1997) suggested that the differences in Internet business strategies might be a result of the differences in the nature of the product/service, the degree of organizational competency in integrating the existing business with the Internet, and the degree of changes required from the organization to adopt the Internet. McBride (1997) added the factors of dependency on the Internet for revenue (peripheral or central to the main business activities) and the size of organization.[4] It was also suggested that the number of existing or potential customers with Web access and information intensity of the products would impact the aggressiveness of an Internet strategy (Watson & Zinkhan 1997). When it comes to using the Internet as a distribution system of products, Ranchhod and Gurau (1999) concluded that the profile of the target market, product characteristics, types of organization, degree of competition, and the environmental differences in regulation, economic conditions, technology, and demand nature will largely determine the choice of strategies. On the other hand, Nel et al. (1999) argued that the Internet strategy should differ depending on whether a company is established already, or whether it is created solely to do business on the Internet, because the two would have very different purposes for their Web ventures. They suggested that the established company model, such as in the case of television stations, would have an information-to-transaction content progression pattern. Specifically, an established company would likely start with an Internet strategy that focuses on delivering image/product information to existing customers; then evolving to collect market information, offering better customer/internal support; and finally developing transactional capacity. Venkatraman (2000) proposed that a company may approach the Internet by: 1) building on the current business model through restructuring of cost base, building on existing strength, or providing enhanced service; 2) creating a new business model with different operations and cost structures; and/or 3) using scenarios experimentation by entering several segments of the Internet market quickly, simultaneously, and possibly through alliances to find the best Internet strategies. He also argued that it would be in a company’s best interest to structure the Internet operation as an independent unit, separate of the original business, when the Internet venture is used to form alliances, raise capital, and attract new talent; when there is a meaningful way to separate digital and physical operations without creating confusion in the minds of customers; and when the entire organization cannot be mobilized to migrate to the Internet world (Venkatraman, 2000).

Finally, Thompson (2000) pointed out that as companies try to migrate their brands online, they have to adapt to online channels. Websites can add value in different ways, whether they are commerce-enabled or merely promotional vehicles. He further suggested that each channel of the multichannel environment must offer differentiated services to suit different market segments and that technology is an enabler, not something that enhances brand value per se. Brands that stretch across the multichannel environment have the best chance of success. Even in instances where there are no direct revenue or cost benefits, there can be tangible benefits from gaining competitive advantages through the implementation of a focused Internet strategy (Thompson, 2000).

Internet Business Models

Lewis (2001) suggested four distinct categories of online business models: 1) advertising, 2) retail/e-commerce, 3) intermediaries, and 4) services. Most recently, many have concluded that, in the consumer product/service sector, a total reliance on ads or retail/e-commerce revenue stream is insufficient (Davis, Jan. 8, 2001). In fact, Lewis (2001) argued that a mixture of multiple revenue streams that monetizes content/database, uses e-commerce, and cross-sells through multiple channels is the key to success, and the only area that can still rely on ads as the primary revenue stream would be in the niche/specialist area. It was also suggested that an intermediary needs to optimize the Internet and offer sustainable economic efficiency that cannot be done elsewhere; simply moving margins from one to another is not enough. To succeed in the service sector, online companies may need to leverage their core competencies and become an online marketplace that provides the infrastructure and renting facility for other companies (Lewis, 2001).

Some have argued that the Internet advertising model also needs to be contextual to be effective (i.e., place relevant ads within the most relevant context to the targeted segment) (Lee & Turban, 2001; Pack, 2001). Others have identified some problems with the Internet advertising model, pointing out that most of the Internet ads were placed by Internet related companies which are too dependent on Internet only revenue sources. Also, unlike the traditional media sectors, there are too many advertising online sources/sites. Media buyers have quite a challenge segmenting the ads options (Death of a Business Model, 2000).[5]

With the recent downfall of many Internet firms that relied solely on online advertising or e-commerce revenues, some have concluded that a hybrid clicks-and-mortar Internet business model would fare better than an online-only model (Almasy & Wise, 2000). More Internet companies are investing in physical assets to complement and extend their online assets. The rationale is that Internet businesses may be easy to start and expand rapidly but also easy to replicate. By contrast, traditional businesses’ established physical assets and infrastructure, brand equity, and channel relationships are harder to replicate in a short time.

In sum, a multi-channel, multi-revenue streams business model that uses the Internet to enhance the core product of the established business (i.e., develop a contextual advantage in which e-commerce or online ads might become more effective), to increase profit (i.e., increase internal efficiency and/or improve a business process), to expand customer base and relationships (i.e., manage customer relationships and customer segmentation by value/not just traffic but traffic of high value/growth segments), and to exploit new related Internet ventures seem to provide the best chance of success when integrating the Internet into a traditional business.

Competitive Strategy Perspectives and Core Competencies

The range of Internet strategies implementable in a television station, however, is determined by its executives’ view on the utility of the Internet to the overall competitiveness of the station and limited to its ability to implement the desirable strategies. In other words, a television station’s choice of a certain Internet strategy depends on whether the strategy, as assessed by the management, may become a source of creating and sustaining competitive advantages for the station.

There are fundamentally two views regarding the sources of sustainable competitive advantages: the resource-based view (RBV) and the industrial organization (IO) perspective. While the former assumes that firms are heterogeneous in relation to the “resources” and “capabilities” on which they base their strategies and these resources and capabilities may not be perfectly mobile across firms, resulting in heterogeneity among industry participants (Barney, 1991), the latter views firms as a bundle of strategic activities aiming at adapting to industry environment by seeking an attractive position in the market (Williamson, 1991; McGahan & Porter, 1997). As the IO perspective emphasizes the power of the environmental factors in dictating a firm’s strategic behavior, the RBV view focuses on the internal competency of a firm in determining its strategic behavior.

Another notion that is often cited as the base of strategic competitiveness of a firm is “core competencies.”[6] These are unique combinations of “resources” and “capabilities” that can serve as a source of competitive advantages for a firm over its rivals (Prahalad & Hamel, 1990; Hitt et al., 2001; Habann, 2000). While “resources” here represent inputs into a firm’s production process such as capital equipment, employee skills, brand names, proprietary rights, and managerial know-how, “capabilities” represent a firm’s capacity or ability to integrate individual resources to achieve a desired objective (Amit & Schoemaker, 1993). Accordingly, core competency would influence the range of Internet strategies appropriate for a firm. Researchers have suggested that core competencies in a multimedia content company are those that contribute to bridging the gap between changing technologies and emerging customer needs. Specifically, they are likely to include the critical competencies of: 1) the creative use of content, 2) exclusive access to content (i.e., content ownership or exclusive licensing), 3) experience with marketing and publicity, and 4) access to distribution channels (Cardoso, 1996).

The Framework of Internet Business Models for Broadcasters

As business models that evaluate ways in which firms may leverage the Internet to develop competitive advantage are still evolving, it is quite a challenge for firms to decide on the extent and approaches of involvement with this new medium. The development of an appropriate business model is especially critical as well as intricate for the television industry as the Internet offers an alternative distribution channel for its products and strengthens its position with the audiences while at the same time it competes with television for audience attention.

Based on the literature reviewed, we propose a series of Internet business models for television broadcasters in Figure 1. This framework incorporates the following notions: 1) a television broadcaster’s core competencies are a result of the interaction between both the internal and external forces (i.e., the IO and RBV perspectives co-exist and shape a firm’s actual competencies); 2) a television broadcaster’s core competencies, including resources for and capabilities of the Internet operations, determine its strategic behavior; 3) a television broadcaster’s strategic options in regard to the Internet are either revenue, cost, or support-focused.

Within the resource-based perspective, consistent with previous findings, we propose that internal forces such as the types of core products,[7] changes required to integrate the Internet operations, dependency of the online revenue, relationships with channel members regarding the Internet, size of the organization, market position, alliances relating to the Internet, and brand management capability would affect a television broadcaster’s Internet competency. Within the IO perspective, we believe that external forces such as economic condition, regulatory environment, technological development, and the audience’s Internet adoption rate and patterns would impact a television broadcaster’s Internet competency. Adopting the strategic management tradition, we define Internet competency as a firm’s capabilities and resources in implementing Internet related activities.

Thus, based on its composition of resources for and capabilities of Internet operations, a television broadcaster may choose to utilize the Internet to generate revenues from the sales of online advertising space/sponsorships, e-commerce (i.e., selling either merchandise or per unit content online), content subscription (i.e., charging online users a monthly subscription fee for the right to access exclusive content online), content syndication (i.e., selling exclusive online content to other Websites), and/or affiliate programs (i.e., receiving a % of all sales generated by customers traveling through a station’s Website to the online storefront of the partner). It may use the Internet to reduce costs by managing its relationships with channel members like advertisers and programming syndicators more efficiently or by improving the overall operational efficiency within its station. The broadcaster may also utilize the Internet to support or complement its off-line operations by developing stronger customer relationships and collecting audience information through the Internet. We believe that most stations would have a combination of Internet operations that aim to accomplish multiple objectives. Following the proposed framework of analysis, four research questions are addressed in this study:

Research Question 1: What Internet strategies have the broadcast television stations adopted? Are they mostly revenue, cost, or support oriented?

Research Question 2: What business models are perceived by the television executives as appropriate for broadcast television stations?

Research Question 3: How do the internal forces, based on the RBV perspective, affect a television station’s Internet competency?[8]

Research Question 4: How do Internet competencies affect Internet strategies in this industry?

Method

Procedure

The present research relies on the use of perceived measures to operationalize Internet activities, resources, capabilities, and other related variables. The use of such self-reported measures is a well accepted practice in strategy research (Dess & Robinson, 1984; Robinson & Pearce, 1988; Venkatraman & Ramanujam, 1987; Spano & Lioukas, 2001). Our reliance on subjective responses from the executives can be justified by both practical and theoretical reasons. From a practical viewpoint, the use of perceptual data with respect to strategy, resources, and capabilities was necessitated mostly due to the non-availability of appropriate financial reports to capture such organizational information. Theoretically, research has suggested that managers’ perceptions often shape behavior and are more critical to strategy making and firm performance than some “mentally distant” objective indicators (Hambrick & Snow, 1977; Snow, 1976; Chattopadhyay et al., 1999). Spanos and Lioukas (2001) also argued that the social constructionist perspective supports the notion of “reality” as the parts of the information flows that the firm enacts through attention and belief, and thus managerial perceptions shape to a very important extent the strategic behavior of a firm.

A mail survey was administered in the fall of 2001 to all 1115 commercial broadcast television stations in the United States. Local television stations rather than national television networks were chosen so that the effects of strategy and capabilities could be studied independent of the confounding effects from a media conglomerate’s corporate strategic considerations. In the case of a station that is a part of a broadcast station group, the surveyed executive was instructed to base his/her answers on the particular station property, and the group ownership variable was controlled for in the statistical analysis (e.g., partial correlations were performed). Note that public television stations were excluded because of their different funding structures and accordingly the different premises in their approach to Internet business models.[9] A pilot survey of five participants was first conducted to test the questionnaire instrument. The pilot results were not included in the final analysis. The first wave of questionnaires was sent during the month of October 2001. A second wave of reminder questionnaires was delivered to non-respondents in the following month. All questionnaires were addressed by names to the current general managers of the 1115 stations. The rationale is that a “general manager” is typically the highest ranked executive in such local organizations. A total of 219 completed questionnaires were usable with an overall response rate of 20%, excluding returns due to mail delivery failures. The relatively lower response rate may be partially due to the Anthrax mail scare in media organizations, which occurred during approximately the same time period our mail survey was conducted. To test whether our respondents were different from the non-respondents, we examined the results for any differences in the means of all variables used in this study between early and late respondents. The rationale behind such an analysis is that late respondents (stations that responded in the second wave) are more similar to the general population than the early respondents (Armstrong and Overton, 1977). The only statistically significant difference found was for the “Nielsen market ranking” measure (F = 19.736, p < .05). It seems that stations located in major broadcast markets are relatively less likely to participate in the survey. Nevertheless, the comparison shows that non-response bias is not a serious issue in this study. In all, we believe that the response rate is adequate as it falls within the range of comparable surveys aiming at executives[10] (Falconer & Hodgett, 1999).

Among the survey respondents, 23 % were CBS affiliates, 20 % NBC affiliates, 16 % ABC affiliates, 12 % Fox affiliates, 7 % UPN affiliates, 7 % WB affiliates, 5 % PaxNet affiliates, 5 % independents, and 3 % with multiple affiliations (2 % missing). As for market sizes, 18 % were located in the top 25 Nielsen markets, 18 % in market 26-50, 36 % in 51-100, and 28 % in 100+ markets. The average surveyed station was located in a market with 6 local signals. In regard to multiple ownership, 12 % were single-owned, 22 % were owned by a 2-5 station group, 15 % 6-10 station group, 22 % 11-20 station group, 17 % 21-30 station group, and 12 % 31+ station group.

Operational Measures

A mostly close-ended questionnaire was designed to measure the current Internet strategies and the factors that might have affected the Internet operations among the U.S. commercial television stations. Specifically, we assessed the internal forces that might influence a station’s resources and capabilities for its Internet operations as depicted in the proposed framework, a station’s resources and capabilities for its Internet-related operations as perceived by its top executive, and the station’s Internet-related activities as classified by revenue, cost, or support oriented utilities. Table 1 details the operational definitions used to measure these variables.

Among the internal factors, “core products” (i.e., core programming products) for television stations were operationalized as in the area of local news (including sports and weather), network programming, syndicated programming, or community-related (non-news) local programming. The four general programming types were chosen because they are universal to most commercial stations (Eastmand & Ferguson, 2001) and because of the difficulty to go beyond programming sources in such classifications.[11] All perception-related variables such as organizational changes required, online revenue dependency, and brand management capability were measured on a 1-7 scale with 1 being the lowest and 7 the highest. As for channel relationships, we chose to measure the relationship between a television station and its affiliated network by assessing the degree of Internet-related assistance it received from the network, ranging from a lot of assistance, some assistance, not much assistance, to no assistance at all. As bigger station groups may allow for more resource sharing opportunies, to assess the role of a station’s group size, we categorized each station into single-owned, 2-5 station group, 6-10 group, 11-20 group, 21-30 group, or 30+ group. While a station’s organizational size was measured by its total number of full-time employees, its market position was operationalized by dividing the self-reported overall ranking by the total number of local television stations in the market (e.g., number 1 station in a 5-station market would have a percentile ranking of 20%). Finally, using the industry trade reports as a guide, we identified a list of possible alliance partners in the survey. These dummy coded variables are alliances with ISPs, radio stations, newspapers, cable television systems, DSL providers, community websites, and online merchants.

To study Internet resources and capabilities for television stations, we first measured the overall resources devoted to the Internet operations as a percentage of the station budget and the number of full time personnel hired to perform Internet-related functions. “Part-time personnel,” “Outsourcing Internet operations,” and “Internet duties carried out by existing employees” are three other values that we also measured if a station does not have any full-time Internet staff. We further operationalized the specific resources devoted to each of the ten types of proposed Internet operations by measuring the percentage of Internet budget allocated for each area. General managers were also asked to report their assessed capabilities to implement overall Internet strategies and activities in each specified area using the same 7-point scale.

To measure each station’s Internet strategy, we first defined a “strategy” as an integrated and coordinated set of actions designed to gain a competitive advantage for the organization (Hitt, Ireland, & Hoskisson, 2001). Accordingly, we wanted to measure the role of the current Internet-related activities in a station relative to its overall operations in attaining market competitiveness. To this end, we ask the station executives to assess their Internet strategies by evaluating the importance of their current Internet practices (divided into the ten categories) in contributing to their stations’ competitiveness. To guage a station’s view on the necessity of each of these Internet strategies for a television station in the future, we also asked the managers to appraise the importance of each strategy for commercial television stations in five years. We further measured the executives’ assessment of the relative utility of the Internet by comparing the anticipated ideal percentage of the revenue contributions from various online operations and the traditional station revenues sources such as television ad sales and network compensations.

Results

Current Internet Business Models Adopted by Television Broadcasters

Most of the television stations (91%) devoted 1-5% of their overall budgets for Internet related functions. Over 59% of them hired full-time staff for online operations, while 20% used part-time employees, 19% outsourced their Internet functions, and 2% delegated the online duties to existing staff at the stations. For the stations that hired full-time Internet personnel, most of them (77%) have no more than two employees for the Internet operations. Despite the limited resources allocated, the station executives seemed to perceive their stations as average in carrying out their Internet strategies (M = 3.76) (see Table 2). They are most confident with their stations’ capabilities in using the Internet to manage audience relations (M = 4.28). They also see their capabilities in using the Internet to improve operational efficiency (M = 3.50) and gather audience intelligence (M = 3.47) as average. Managing client relations (M = 3.03), selling ads (M = 3.02), participating in affiliate programs (M = 2.58), implementing e-commerce (M = 2.18./2.02), and setting up content subscription/syndication (M = 1.89/1.77) were assessed as marginal. The executives seemed to feel that they are most unequipped to generate revenues from the sales of content online.

About a quarter of the stations’ resources are allocated for managing audience relations (e.g., email communication with audience), followed by online ad sales (19%), efficiency improvement (11%), audience data collection (9%), and client relations management (6%). Very limited resources have been devoted to other Internet operations. It’s interesting that while online ad sales commanded almost one fifth of a station’s Internet resources, the corresponding capability is rated to be below average.

The most important Internet strategy adopted by the television stations seems to be audience relations management (M = 4.74). Content syndication, subscription, e-commerce, and affiliate program participations were practiced minimally (M = 1.48-1.92). While using the Internet to improve operational efficiency (M = 3.69) and gather audience intelligence (M = 3.53) is somewhat important, online ad sales, contrary to previous studies, appears to be a less desirable strategy (M = 2.58). Survey results suggest that the commercial television stations have mostly adopted a support-focused Internet business model aiming to develop audience intelligence and improve the relationship between a station and its audience. Cost reduction strategies were the next important Internet strategies, while revenues-oriented options were, surprisingly, the least practiced. In sum, the commercial television stations seem to be treating the Internet as a complementary tool for improving the value of their existing off-line products, rather than a new medium for additional business opportunities.

Anticipated Internet Business Models

We asked the managers to anticipate the importance of each Internet strategy for the overall television stations in five years to assess the potential value of each Internet strategy as peceived by the executives who will continue to appropriate resources to execute these strategies. All ten strategies increased in importance with audience intelligence (+1.67 in M) and merchandise e-commerce (+1.56 in M) leading the way in the magniture of changes (see Table 2). Audience intelligence (M = 5.20) and audience relations management (M = 5.37) were perceived to be the most significant Internet strategies of the future. While the stations still believe that it’s more important to use the Internet for operational efficiency improvement (M = 4.50) and client relations management (M = 4.29), they begin to see online ad sales (M = 4.06), client relations management, content e-commerce, and affliliate programs as more viable strategies in the future. The directions and degrees of changes seem to indicate a progression from treating the Internet as mainly a support tool to a potential vehicle for reduing cost and generating revenues.

We also asked the managers to identify the ideal revenue mix of the future for their firms, taking into account the revenue potentials of the Internet. The respondents indicated an ideal revenue mix of 69.7% for regular television ad sales, 7.1% for online ad sales, 3.1% for e-commerce, 2.5% for online content syndication/subscription, 1.2% for affiliate programs, and 1.8 for network compensation.[12] The approximate 14% of total online revenues seem to replace the dwindling compensation revenues that traditionally came from affiliated networks. Bivariate correlations were performed to measure the associations between the revenue mix variables and a station’s Internet competency. Pearson correlation reveals that the more Internet capable a station is, the higher the ideal percentage it envisions for online ad revenues in the future (r = .18; p < .01). Also, there seems to be some association between a station’s interest in Internet affiliate programs and its positive expectation of its existing broadcast network-affiliate relationship, as the more current resources a station devotes to generate Internet affiliate program revenues, the higher ideal percentage of network compensation (r = .20; p < .01) it expects.

Internal Factors and a Station’s Internet Competency

Multiple regression analysis was performed to assess the relative contributions of the internal factors on a station’s Internet capability (see Table 3). Standard tests for multicollinearity revealed no significant problems for the regression model.[13] The hierarchical regression model reveals that online revenue dependency (β = .34) and branding capability (β = .33) are the two most significant factors that predict a station’s Internet capability, followed by market position (β = -.18),[14] alliances with radio stations (β = -.15),[15] organizational size (β = .13), and changes required to integrate the Internet (β = -.12). News core product,[16] size of the parent station group, and channel relationship with the network were statistically insignificant in predicting the Internet capability. Overall, the equation accounted for 47% of the observed variance. We also performed regressional analyses for both the Internet budget and personnel resource variable on the internal factors. While the budget regression model was insignificant, the personnel resource model revealed three significant predictors, news core product (β = .27), the size of organization (β = .26), and Internet revenue dependency (β = .20). This equation accounted for 24% of the observed variance.

To assess the association between internal factors and a station’s specific Internet resources and capabilities, bivariate correlations were performed (see Table 4). Pearson correlations reveal that the more “organizational changes” needed to implement online strategies in a station, the lower its online ad sales resources/capabilities (r = -.15, p < .05; r = -.19, p < .01) and its client/audience relationship management capabilities(r = -.16, p < .05; r = -.14, p < .05). “Online revenue dependency” is perhaps the most significant variable that is related to a station’s specific Internet competency. It is significantly related to all specific capability variables. As for specific resource variables, the more a station depends on online revenues, the more resources it devotes for online ad sales (r = .29, p < .01) and e-commerce (r = .42-43, p < .01). While “channel relationship” is not statistically related to any specific competency variables, “organizational size” is positively related to a station’s capabilities to manage audience relationships (r = .18, p < .01) and collect audience data (r = .14, p < .05). As for “size of parent station group,” it seems the larger the station group a station belongs to, the more resources it would appropriate for online ad sales (r = .14, p < .05), but the less resources for e-commerce activities (r = -.13, p < .05; r = -.16, p < .05). Also, the more competitive a station is (i.e., lower market ranking %), the more likely for it to devote more full-time personnel, have better overall online capabilities, and use more resources for online ad sales, affiliate programs, and audience data collections. The leading stations are also more capable of selling ads online, offering content subscription/syndication, instituting affiliate programs, improving operational efficiency using online means, managing audience relationships online, and collecting audience data online. The managers’ perceived branding capability is positively related to resources and capabilities for online ad sales and the Internet capabilities of affiliate program participation, client/audience relationship management, operational efficiency, and online audience data collection.

We also examined the correlations between Internet competency and the internal categorical variable, perceived core product using Cramer’s V. Cramer’s V, measuring the degree of association based on Chi-squares, reveals significant correlation between the core product and capability variables such as the capability of implementing an online strategy (Cramer’s V = .21; p <.05), selling content online (Cramer’s V = .21; p <.05), and managing audience relations (Cramer’s V = .15; p <.01).

Internal Factors and Internet Strategies

To assess the relationships between internal factors and the Internet strategies adopted by the the stations, bivariate correlations were performed. Online revenue dependency is related significantly to all Internet strategies except audience relations management (r = .26-.90; p <.01). Also, the better channel relationship a station has with its network, the more likely for it to adopt the client relationship management (r = .15; p <.05) and audience intelligence (r = .19; p <.01) strategies. The more competitive a station is, the more likely for it to adopt online ad sales (r = .33; p <.01), content e-commerce (r = .18; p =.01), content subscription (r = .18; p <.05), audience relations management (r = .17; p <.05), and audience intelligence (r = .16; p <.05) strategy. The more capable a station is in branding, the more likely for it to utilize online ad sales (r = .24; p <.01), syndication (r = -.14; p <.05), operational efficiency (r = .17; p <.05), audience relations management (r = .43; p <.01), and audience intelligence (r = .24; p <.01) strategies. Alliances with ISPs are related to the content e-commerce (r = .14; p <.05), subscription (r = .14; p <.05), syndication (r = .16; p <.05), and affiliate program (r = .18; p <.01) strategies. Alliances with DSL providers are related only to a subscription strategy (r = .15; p <.05). Alliances with online merchants are related to the operational efficiency (r = .16; p <.05), audience relations management (r = .22; p <.01), and audience intelligence (r = .18; p <.01) strategies. Finally, alliances with local community Websites are related to the audience relations management (r = .17; p <.05) and audience intelligence (r = .18; p <.01) strategies.

Internet Competencies and Internet Strategies

Pearson correlations reveal that the more Internet capable a station is, the more likely for it to adopt the online ad sales, audience data collection, audience relations management, e-commerce (both merchandise and content), operational efficiency, subscription, syndication, and affiliate program strategies (in that order, see Table 5). Also, the more Internet capable a station is, the more likely for it to anticipate the potential importance of the online ad sales, merchandise e-commerce, audience data collection, affiliate programs, audience relations management, content e-commerce, syndication, and client relations management, in that order. As for online resources in the format of budget, the more Internet budget a station has, the more likely for it to adopt content e-commerce, client relations management, subscription, audience data collection, syndication, online ad sales, and merchandize e-commerce, in that order. In terms of the anticipated potential of each strategy in relation to the Internet budget, content e-commerce is the most statistically significant strategy. The bivariate correlations also reveal that the more online personnel a station has, the more likely for it to adopt online ad sales, audience data collection, audience relations management, content e-commerce, and merchandise e-commerce strategies, in that order. Only the anticipated role of online ad sales strategy is statistically related to the personnel resources variable. All specific Internet resources/capabilities variables are statistically related to their corresponding strategies except for online resources for content subscription and, to some degree, online resources for content syndication.

Discussion and Conclusions

Our findings generally confirm existing literature in Internet business strategy, but provide a somewhat different picture of Internet practices for the broadcasters than what was reported previously. Compared to prior studies of Internet operations at television stations, more personnel has been allocated for Internet related functions. Nevertheless, television broadcasters still devote a relatively limited budget to their Internet operations. Contrary to previous findings, the stations have focused their online activities on building audience relationships, rather than generating online ad sales. In other words, the Internet is used as a “support” to complement their off-line core products. Stations also felt inadequate in selling or syndicating content via the Internet. The dichotomous “cable television” content delivery business model suggested by Waterman (2000) has yet to materialize. It’s interesting that while the stations anticipated a share as high as 7% of the future total revenues coming from online ad sales, they also rated their online ad sales capabilities as only average and allocated most of their online resources for audience relations management. The stations’ emphasis on the “support” utility of the Internet confirms Nel et al.’s (1999) notion that an established business would tend to approach the Internet with an information (support) to transaction progression pattern. The strategic focus on “audience relations and intelligence” also corroborates Thompson’s (2000) view on the importance of a multichannel presence and the tangible benefits a business may derive from online activities that are not revenues or cost based.

It is evident that the broadcasters see an increasing revenue potential for their Internet operations as they anticipated a future revenue share as high as 14% from activities via this medium. The sources of online revenues, however, seem to concentrate on ad sales, affiliate programs, and e-commerce. The revenue potential of “content” is minimal as perceived by the managers. While continuing to view the strategy of audience relations management as critical, the stations begin to value the importance of audience intelligence and the cost-cutting utility of the Internet in managing client relations. The findings of this study point to a mix of business models that first utilize the Internet as a supplemental medium for developing a relationship with the audience of an off-line core product, and continue to increase the value of this relationship by building better audience intelligence, which then, in turn, improves the stations’ ability to sell online ads and implement e-commerce, thus harvesting the value of its Internet operations (see Figure 2).

Consistent with previous studies, we found that online revenue dependency and branding capability are the most significant predictors of Internet capability for the broadcasters. As for Internet resources, a television station is more likely to devote more personnel for online operations when it has a news core product, is bigger, and sees online revenues as relatively more important (as a part of the overall revenues). Though the alliance factor was mostly not a significant predictor for Internet competency, the choice of alliance partners through the review of bivariate correlations reveals some interesting facts in the strategic alliance patterns of the broadcasters. For example, a television station is more likely to ally with community Websites when it wants to practice the audience relations management and audience intelligence strategies. On the other hand, it is more likely to ally with a DSL provider when it is interested in a content subscription strategy. In all, our study confirms the notion that a broadcaster’s Internet competency is critically dependent on its manager’s view of the relative revenue contribution of the Internet, even though the broadcasters’ online focus, at least presently, is not on revenue-generating strategies. Nevertheless, we also found that Internet competency is most influential in the adoption of an online ad sales strategy. This again reinforces our view of the necessary progression of online business models (for the broadcast television industry) that begins with a support emphasis to establish the value of the Internet operations and later capitalizes on the more refined online competency to generate additional revenues.

The main limitation of this study is in its ability to account for the responses from leading stations in the nation’s biggest broadcast markets as the responded stations are proportionally concentrated in mid-sized markets. Future research may re-evaluate the concept of off-line core products by ways of station performance (i.e., utilize ratings to identify a station’s core product) and examine the role of a broadcaster’s Internet operations in relation to its core product. A longitudinal study that tracks the broadcasters’ resources and capabilities would also provide better insight on the impact of various organizational factors. With the trend toward contextual advertising, it would be interesting to investigate the broadcasters’ capability in providing such advertising or e-commerce environment.

Figure 1: A Framework for Analyzing Broadcasters’ Internet Business Models

Table 1: Variables and Their Operational Definitions

Variables		Operational Definitions
Internal Factors
Core product	· Local news, network programming, syndicated programming, community-related, (non-news) local programming (categorical variable)
Organizational changes	· Perceived personnel/organizational changes required to implement the ideal online strategies (1-5 scale with 1-no changes, 2-not many changes, 3-some changes, 4-major changes, 5-tremendous changes)
Online revenue dependency	· Perceived organizational dependency on the revenues from online ventures (1-7 perception scale)
Channel relationships	· Degree of assistance to the station’s Internet operations from the affiliated network (1-4 scale with 1-no assitance at all, 2-not much assistance, 3-some assistance, 4-a lot of assistance)
Size of parent station group	· Single-owned, 2-5 station group, 6-10 station group, 11-20 station group, 21-30 station group, 31+ station group (coded from 1-6)
Size of organization	· Number of full-time station employees
Market position	· Station’s percentile ranking in the market (self-reported overall ranking over total number of local television stations in the market in ranking percentile, ; e.g., #1 station in a 10 station market = 10%, #2 station in a 10 station market = 20%., etc.)
Alliances	· Current or planned alliances with ISP, online newspaper, online radio, DSL provider, cable system operator, local community website, online merchant, and others (dummy coded)
Branding capability	· Perceived capability to brand the organization (1-7 perception scale)
Internet Resources and Capabilities
Overall online capabilities	· Perceived overall organizational capability to implement Internet strategies and activities (1-7 perception scale)
Overall online resources	· Overall resources devoted to the Internet operations (% of budget in categories: 1=1-5%, 2=6-10%, 3=11-15%, 4=16-20%, 5=21-25%, 6=25+%; number of full- time personnel)
Specific online resources	· Specific resources devoted to the operations of online ad sales, merchandise e-commerce, per-unit content e-commerce, content subscription, content syndication, affiliate program, client relations management, audience relations management, operational efficiency, and audience data collection (% of budget allocated for each area)
Specific online capabilities	· Perceived organizational capability to implement each of the identified online activities (1-7 perception scale)
Internet Strategies (current and anticipated)
Online advertising strategy	· Importance of online ad sales activities to the organization currently (1-7 perception scale) · Importance of online ad sales activities to the organization in five years (1-7 perception scale) · Desired % of revenue contribution from online ad sales in five years
E-commerce strategy (Merchandise)	· Importance of selling merchandise online to the organization currently (1-7 perception scale) · Importance of selling merchandise online to the organization in five years (1-7 perception scale) · Desired % of revenue contribution from selling merchandise online in five years
E-commerce strategy (Per-unit online content)	· Importance of selling per-unit content online to the organization currently (1-7 perception scale) · Importance of selling per-unit content online to the organization in five years (1-7 perception scale) · Desired % of revenue contribution from selling per-unit content online in five years
Subscription strategy	· Importance of content subscription online operations to the organization currently (1-7 perception scale) · Importance of content subscription online operations to the organization in five years (1-7 perception scale) · Desired % of revenue contribution from content subscription online operations in five years
Syndication strategy	· Importance of content syndication online operations to the organization currently (1-7 perception scale) · Importance of content syndication online operations to the organization in five years (1-7 perception scale) · Desired % of revenue contribution from content syndication online operations in five years
Revenue-sharing affiliate program strategy	· Importance of revenue-sharing online affiliate program to the organization currently (1-7 perception scale) · Importance of revenue-sharing online affiliate program to the organization in five years (1-7 perception scale) · Desired % of revenue contribution from revenue-sharing online affiliate program in five years
Channel relations management strategy	· Importance of online operations that manage channel relations to the organization currently (1-7 perception scale) · Importance of online operations that manage channel relations to the organization in five years (1-7 perception scale)
Customer relations management strategy	· Importance of online operations that manage customer relations to the organization currently (1-7 perception scale) · Importance of online operations that manage customer relations to the organization in five years (1-7 perception scale)
Operational efficiency strategy	· Importance of online operations that improve efficiency to the organization currently (1-7 perception scale) · Importance of online operations that improve efficiency to the organization in five years (1-7 perception scale)
Audience intelligence strategy	· Importance of online operations that collect audience information to the organization currently (1-7 perception scale) · Importance of online operations that collect audience information to the organization in five years (1-7 perception scale)

Table 2: Means and Standard Deviations of the Internal Factors, Internet Resources/Capabilities, and Internet Strategies.

Variable Mean S.D.

Internal Factors

Organizational Changes 3.19 .92

Online Revenue Dependency 1.78 1.18

Channel Relationship 1.89 .89

Size of Organization 30.10 7.89

Size of Station Group 3.48 1.58

Market Position 45.97 27.52

Branding Capability 5.30 1.56

Internet Resources and Capabilities

Overall Online Resources – % of Budget 1.11 .38

Overall Online Resources – No. of Full-time Employees* 1.56 1.09

Overall Online Capabilities 3.76 1.77

Specific Resources/Capabilities – Online Ad Sales** 18.78 %/3.02 27.49/1.82

Specific Resources/Capabilities – M. E-commerce 1.51 %/2.18 6.64/1.61

Specific Resources/Capabilities – C. E-commerce 1.48 %/2.02 6.49/1.45

Specific Resources/Capabilities – Content Subscription .32 %/1.89 1.67/1.49

Specific Resources/Capabilities – Content Syndication .43 %/1.77 2.21/1.36

Specific Resources/Capabilities – Affiliate Program .78 %/2.58 3.28/1.63

Specific Resources/Capabilities – Client Relations Mgmt. 5.74 %/3.03 11.70/1.76

Specific Resources/Capabilities – Audience Relations Mgmt. 25.00 %/4.28 29.02/1.83

Specific Resources/Capabilities – Operational Efficiency 11.34 %/3.50 20.94/1.74

Specific Resources/Capabilities – Audience Intelligence 9.15 %/3.47 15.95/1.90

Current Internet Strategy/Anticipated Role of the Strategy

Online Ad Sales Strategy 2.58/4.06 (+1.48) 2.02/1.79

M. E-commerce Strategy 1.60/3.16 (+1.56) 1.31/1.57

C. E-commerce Strategy 1.63/3.07 (+1.44) 1.33/1.59

Subscription Strategy 1.50/2.70 (+1.20) 1.33/1.69

Syndication Strategy 1.48/2.63 (+1.15) 1.22/1.51

Affiliate Program Strategy 1.92/3.33 (+1.41) 1.51/1.60

Client Relations Management Strategy 2.83/4.29 (+1.46) 1.75/1.67

Audience Relations Management Strategy 4.74/5.37 (+0.63) 1.84/1.50

Operational Efficiency Strategy 3.69/4.50 (+0.81) 1.81/1.65

Audience Intelligence Strategy 3.53/5.20 (+1.67) 2.01/1.58

*Based on 129 stations that reported to have full-time employees dedicated for online operations.

**Respondents were instructed that their budgets for all Internet operations should add up to 100%. However, the average %s do not add up to 100% exactly.

Table 3: Regression of Internet Competency# on Internal Factors

Independent Variable B Beta t F R²

Nature of core products + .20/.43 .06/.27 .70/2.84*** 8.96***/3.22*** .47/.24

Changes required to integrate -.23/-.01 -.12/-.01 -1.96**/-.16

the Internet

Relationship with channel .13/.03 .07/.04 1.09/.58

members concerning the Internet

Dependency on the Internet .48/.12 .34/.20 5.70***/2.74***

revenue

Size of the organization .20/.18 .13/.26 2.01**/3.40***

Size of parent station group -.01/-.01 -.01/-.01 -.18/-.17

Market position -.01/.00 -.18/.03 -.2.31**/.28

Brand management capability .38/.05 .33/.11 5.20***/1.42

Alliances concerning -.99/-- -.15/-- -2.20**/--

the Internet^

#The first number is for Internet capability while the second number is for Internet personnel resources.

+The core product variable was recoded to a dummy variable with 1 being a local news core product and 0 being all other core products.

*p<.10; **p<.05; ***p<.01.

^Only the alliances with radio stations are significant among all alliance variables.

Table 4: Correlation Analysis of Internal Factors and Specific Internet Resources and Capabilities

Organizational Online Revenue Channel Organization Group Market % Branding

Changes Dependency Relationship Size Size Ranking Capability

Spec. Res./Cap. -.153*/-.190** .286**/.470** -.036/-.014 .006/.059 .139* /-.037 -.272** /-.284** .264**/.287**

-Online Ad sales

Spec. Res./Cap. -.074/-.081 .416**/.392** .004/.053 -.079/.056 -.133*/-.014 -.045/-.129 -.133/.067

-Product E-commerce

Spec. Res./Cap. -.060/-.037 .425**/.429** .003/.029 -.070/-.028 -.157*/-.056 -.047 /-.131 -.123/.060

-Content E-commerce

Spec. Res./Cap. -.024/-.056 .015/.401** .077/-.025 .091/.019 -.005/-.056 -.026 /-.196** .006/.049

-Content Subscription

Spec. Res./Cap. -.061/-.059 .061/.419** .074/-.063 -.119/-.017 -.042/-.058 -.049/-.216** .074/-.009

-Content Syndication

Spec. Res./Cap. -.056/-.106 .100/.316** .128/.083 -.102/-.033 -.080/-.042 -.167*/-.180* .092/.197**

-Affiliate Program

Spec. Res./Cap. -.022/-.164* -.109/.260** .032/.116 .126/.041 -.075/-.103 .119/-.108 .059/.200**

-Client Relations Mgmt.

Spec. Res./Cap. .189/-.140* -.073/.278** .069/.063 -.024/.181** .034/.049 .12*/-.228** -.011/.401**

-Audience Relations Mgmt.

Spec. Res./Cap. .066/-.067 -.066/.234** .065/.076 .049/.054 -.060/-.048 .042/-.201** .031/.234**

-Operational Efficiency

Spec. Res./Cap. .102/-.091 -.090/.269** .020/.138 .031/.138* .034/-.003 -.179**/-.220** .062/.266**

-Audience Intelligence

Table 5: Correlation Analysis of a Station’s Internet Resources/Capabilities and Its Current Internet Strategy and the Anticipated Role of Each Strategy

Online Ad M. E-commerce C. E-commerce Subscription Syndication Affiliate Client Relp Aud. Relp Efficiency Aud. Data

Overall Online .514**/.343** .272**/.276** .268**/.235** .230**/.131 .229**/.199** .205**/.262** .135/.152* .388**/.251** .241**/.129 .390**/.264**

Capabilities

Online Resources .161*/.129 .155*/.147* .209**/.225** .192**/.155* .182**/.171* .071/.143* .196**/-.013 .057/.033 .028/.022 .186**/.106

-Overall Budget

Online Resources .312**/.188** .142*/.018 .173**/.047 .064/-.024 .135/-.001 .082/.006 .042/-.014 .233**/.085 .132/.050 .246**/.116

-Overall Personnel

Spec. Capability .545**/.367**

-Online Ad sales

Spec. Resources .489**/.375**

-Online Ad sales

Spec. Capability .453**/.482**

-Product E-commerce

Spec. Resources .426**/.218**

-Product E-commerce

Spec. Capability .450**/.427**

-Content E-commerce

Spec. Resources .354**/.189**

-Content E-commerce

Spec. Capability .452**/.399**

-Content Subscription

Spec. Resources .-.035/.126

-Content Subscription

Spec. Capability .418**/.321**

-Content Syndication

Spec. Resources .083/.165*

-Content Syndication

Spec. Capability .218**/.340**

-Affiliate Program

Spec. Resources .172*/.194**

-Affiliate Program

Spec. Capability .365**/.515**

-Client Relations Mgmt.

Spec. Resources .189**/.321**

-Client Relations Mgmt.

Spec. Capability .596**/.527**

-Audience Relations Mgmt.

Spec. Resources .166*/.234*

-Audience Relations Mgmt.

Spec. Capability .517**/.561**

-Operational Efficiency

Spec. Resources .153*/.288**

-Operational Efficiency

Spec. Capability .723**/.548**

-Audience Intelligence

Spec. Resources .258**/.225**

-Audience Intelligence

+First number is for the relationship between the Internet resources/capabilities and current strategy; second number is the anticipated future importance of the strategy.

SUPPORT

COST

REVENUES

Figure 2: The Internet Business Models of Television Broadcasters

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Endnotes

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[1] The cross-promotion seems to be working as more than 75 % of the respondents to a survey said they have visited a Website after seeing an ad for it on TV or a mention of it during or after a show. See Channel Surfing, 2000.

[2] Such as providing personalized financial and general news information to viewers with an interactive television device and connecting advertisers with these viewers more efficiently.

[3] This alliance will include exclusive access (distribution rights) on new releases from Disney, and Fox. Cable systems would have to negotiate with movies.com for video-on-demand rights. The movies may be offered with value-added interview, behind-the-scene segments (Joint ventures/channel access/core value-adding).

[4] Larger organizations have been slower to recognize the importance of the Net and suffer from greater organizational inertia. Large organizations also tend to take the risks and problems associated with the Internet more seriously.

[5] There is substantial differentiation occurring on the Web. Website differentiation leads to audience differentiation and is the basis for setting advertising rates and schedules. Advertising opportunities are often segmented based on the categories of search engines; online publishers and traditional content providers; narrowcast, special interest and niche content websites; and webzines and web broadcast/intelligent agent services, making the media planning process time-consuming and challenging.

[6] Core competencies are a resource-based view of strategic competitiveness.

[7] For example, a strong core product of news content may be more Internet transferable than a core entertainment product due to the current spectrum constraint.

[8] Note that even though we subscribe to both RBV and IO competitive strategy perspectives, we will empirically test only the proposed internal forces because the latter’s unit of analysis is “industry” rather than “firm” and we are investigating only the broadcast television industry (i.e., there is no variation in the environmental factors proposed).

[9] Low-power stations were also excluded from this survey because of their limited programming products and resources and the often different funding structures.

[10] Falconer and Hodgett (1999) suggested that lower response rates (between 10-35%) are typical for large mail surveys of businesses. Loch et al. (1992) reported a 20% response rate to a survey of 657 senior U.S. managers on threats to information systems. Dekleva (1992) achieved response rates of 12.2% and 22.4% in two surveys of 1000 and 500 U.S. information systems managers. Raymond et al. (1995), in their study of the relationship between organization structure and information technology, garnered a 16% response rate from a sample of 1000 Canadian CEOs.

[11] Network programming would include all television programs delivered to the station because of its affiliation status. Syndicated programming would include all programs that the station has purchased or bartered for the right to air them on its schedule. The goal of the classification is to differentiate a station’s ability to integrate its on-air and online product. For example, it would be easier and more beneficial for a station to integrate a core product of local news or local community programming with its online operation.

[12] Typically, ad revenues would comprise of at least 80-85% of a station’s total revenues. There is also a category of “others.” The respondents were instructed that all items should add up to 100%. However, the answers sometimes do not reflect the 100% total.

[13]Collinearity statistics such as tolerance are between .69 and .92, and all VIFs are smaller than 2.

[14] The smaller the number for market position, the more competitive (leading) a station is.

[15] All alliance variables were insignificant except the radio alliance variable, which is negatively predicting the Internet capability of a station.

[16] To test the importance of a “local news core product,” we recoded the core product variable to a dummy variable measuring the presence of a local news core product.