Antecedents and consequences of Internet channel performance

ARTICLE IN PRESS Journal of Retailing and Consumer Services 16 (2009) 163–173

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Antecedents and consequences of Internet channel performance Agnieszka Wolk , Bernd Skiera 1 School of Business and Economics, Department of Marketing, University of Frankfurt am Main, Mertonstr. 17, 60054 Frankfurt am Main, Germany

a r t i c l e in fo

Keywords: Internet channel Company performance Electronic commerce Distribution management

abstract Internet distribution channels may be either advantageous or detrimental for a company. Therefore, this study analyzes their performance, antecedents, and effect on company performance. Using survey data from multichannel retailers and structural equation model methodology, the authors show that Internet channel performance contributes to both financial and strategic company performance, with a greater effect on the latter. Similar and uncoordinated channels hinder Internet channel performance, but experience with direct channels and channel power are not required to pursue an Internet channel successfully. Customer migration and managerial commitment to the Internet channel have strong positive influences on financial performance. Overall, the results encourage the adoption and development of Internet channels. & 2008 Elsevier Ltd. All rights reserved.

1. Introduction The emergence and popularity of the Internet has encouraged an increasing number of companies to use it as an additional distribution channel (e.g., Ellis-Chadwick et al., 2002), which provides many opportunities, including access to new markets, increased sales, and reduced costs (Geyskens et al., 2002). In addition, consumers who have switched to the Internet channel or use multiple channels are more profitable than their offline counterparts (Gensler et al., 2008; Hitt and Frei, 2002). However, the Internet channel also poses some threats: it requires a high initial investment but takes off rather slowly (only 3.5% of retail sales were conducted online in 2007; US Census Bureau, 2007), and it may cannibalize other channels. Competition in the online environment also may decrease prices and margins (Degeratu et al., 2000) or prompt channel conflict. Furthermore, the Internet channel could lead to diminished customer profitability as a result of less loyalty or lower purchase frequency (Ansari et al., 2008; Sullivan and Thomas, 2004). As a result, it remains unclear whether the Internet channel itself provides strong performance and, even if it does, whether this performance contributes to the overall performance of the company. Yet, channel performance evaluation is crucial for designing an appropriate multichannel distribution strategy in terms of optimal channel mix, channel design, level of channel independence, and resource allocation across channels (Neslin  Corresponding author. Tel.: +49 69 798 28862; fax: +49 69 79 28973.

E-mail addresses: [email protected], [email protected] (A. Wolk), [email protected] (B. Skiera). URL: http://www.ecommerce.wiwi.uni-frankfurt.de (B. Skiera). 1 Tel.: +49 69 798 22378; fax: +49 69 79 28973. 0969-6989/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jretconser.2008.11.010

et al., 2006). Information about channel performance also helps determine appropriate pricing, assortment, and service level decisions (Neslin et al., 2006). Despite this importance, previous research into the performance of Internet channels and their contribution to company performance remains very scarce. Existing studies analyze the effects of introducing an Internet channel on expected future cash flows, measured by the company’s stock prices (Geyskens et al., 2002; Lee and Grewal, 2004). Yet these investigations fail to consider whether the Internet channel actually generates sales and profits for the company. In addition, the results of these studies might be confounded in mature stages, because some successful Internet channel introductions do not lead to strong Internet channel performance. Consequently, even though many companies have followed market pressures and adopted Internet channels, little is known about their performance. Furthermore, the market valuation used in previous studies has several weaknesses, in that it overvalues new economy stocks (Higson and Briginshaw, 2000) and possibly reflects investors’ overly optimistic attitudes toward Internet-related investments. Finally, studies based on stock prices may provide insights for large, listed companies, for which an Internet channel likely attracts many new consumers (Brynjolfsson and Smith, 2000), but it neglects smaller companies whose Internet channel performance is less clear. Neslin et al. (2006) thus highlight the gap between the importance of this topic and existing research in this area and call for additional research into channel performance evaluations and the effects on company performance. This study responds to that call by analyzing the performance of Internet channels and their influence on company performance, as well as their antecedents during the mature stage. We define Internet channel performance as a composite measure of the

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revenues generated by the Internet channel, as well as its growth and future potential. With regard to company performance, we distinguish between financial (i.e., global company sales, cost, and profitability) and strategic (i.e., global market share, competitiveness, and strategic position) performance (Cavusgil and Zou, 1994; Zou and Cavusgil, 2002). Furthermore, we build on work by Geyskens et al. (2002) to analyze drivers of Internet channel performance, including not only the factors they propose but also the effects of the support offered to the Internet channel and channel strategy characteristics, such as channel similarity and cooperation. Thus, we shed more light on the effect of channel cooperation, identified by Rangaswamy and van Bruggen (2005) and Neslin et al. (2006) as a major challenge for multichannel retailers. We also analyze channel cannibalization, its antecedents, and its effect on company performance, basing our results on a survey of 142 multichannel companies. In turn, we contribute to existing literature by analyzing the effect of Internet channel performance during the mature Internet stage on both strategic and financial performance in a multichannel context. We show that Internet channel performance has a stronger positive effect on strategic than on financial performance, and in its mature stage, Internet channel performance mostly depends on management support. In contrast with previous predictions (e.g., Alba et al., 1997; Brynjolfsson and Smith, 2000; Geyskens et al., 2002), we find that a company does not need to be large or have channel power or experience with other direct channels to operate an Internet channel successfully. In addition, we empirically show that similar and uncoordinated channels hinder Internet channel and company performance. Finally, we contribute to channel cannibalization literature (e.g., Biyalogorsky and Naik, 2003; Deleersnyder et al., 2002), in that we not only analyze the degree of channel cannibalization but also determine its consequences for company performance. The remainder of this article is organized as follows: First, we describe the theoretical framework and develop a conceptual model. Second, we describe the methodology and present the results of our empirical study. Third, we discuss the conclusions and provide some implications of our results.

2. Literature review Many empirical studies focus on distribution channel strategies and the various factors that may influence channel performance. Buchanan (1992) analyzes the influence of trade partner dependence and environmental uncertainty on channel members’ performance. Many other studies focus on the influence of interdependence and power on channel performance (e.g., Brown et al., 1995; Lusch and Brown, 1996). Gaski and Nevin (1985) consider the influence of coercive and noncoercive power sources, whereas Jap (1999) investigates the effect of coordination effort and investments on channel performance, measured as reported channel profits. Empirical studies also note the influence of various factors on channel performance, including channel member monitoring (Bello and Gilliland, 1997), channel communication (e.g., Anderson et al., 1987; Anderson and Weitz, 1989), control (Anderson et al., 1987), structure of the channel relationship, and applied contract design between channel members (Lusch and Brown, 1996). Finally, Desiraju and Moorthy (1997) reveal that performance requirements, understood as specific agreements between a manufacturer and retailer with regard to elements such as price and service, influence channel performance. Whereas these studies analyze relationships within a single offline channel, more complex distribution systems including Internet channel have recently grown in popularity. Therefore,

Gensler et al. (2007) analyze the performance of the Internet channel but only in terms of consumers’ loyalty and switching behavior, neglecting other aspects such as cost or competitive position. Geyskens et al. (2002) study the effect of adding an Internet channel in the newspaper sector, in which online content is available for free, and find a positive effect on the company stock prices for 70% of the companies they analyze, but a negative effect for 30% of them. Geyskens et al. (2002) also report that Internet channel additions appear more successful for powerful early followers with a few direct channels that support their Internet channel addition with publicity. Similarly, Lee and Grewal (2004) find a positive influence of adopting the Internet as a communication channel on firm market valuation, though they find no effect when the firm adopts the Internet as a sales channel. Thus, though these studies analyze the influence of introducing an online channel on the company’s market valuation in the early market stage, they do little to clarify the effects during a more mature stage.

3. Theoretical framework 3.1. Effect of Internet channel performance on company performance A company’s performance consists of two major dimensions: financial and strategic (Cavusgil and Zou, 1994; Samiee and Roth, 1992; Zou and Cavusgil, 2002). As the most important goal for a company, financial performance comprises sales growth, cost levels, and, eventually, realized profits. In contrast, strategic performance relates to the company’s market position (e.g., market share, competitiveness), which in the long run influences the company’s financial well-being. Changes in the distribution system, and especially the introduction of new distribution channels, should influence both of these dimensions. By including the Internet into an existing distribution system, companies can realize several advantages. First, they might reach and acquire new customers and thereby increase sales. Second, the Internet offers the possibility of increasing sales to existing customers, because of its constant availability and interactivity, the convenience of buying from home, the ease of communicating relevant information, and the ability to provide personalized offers. These advantages together may increase customer loyalty and strengthen long-term relationships with existing customers (Geyskens et al., 2002), which should prompt increased customer profitability (Gensler et al., 2008; Hitt and Frei, 2002). Third, as Kumar and Venkatesan (2005) show, multichannel customers generate higher revenues, so the Internet channel should contribute positively to company performance by increasing overall sales and profits, as well as the strategic position of the company in the market. Reality, however, indicates that the advantages offered by the Internet are not quite so easy to realize and that improved Internet channel performance might not lead to improved company performance. Online shopping poses many risks and inconveniences, including the lack of physical product examinations, postponed availability, minimal if any human interaction, and online payment concerns. As a result, many consumers still prefer offline channels (Geuens et al., 2003), and Internet channels have gained ground slowly, which suggests they may not justify the investment required. In 2006, for example, online shopping comprised only 3.5% of total retail sales (US Census Bureau, 2007). In addition, by introducing an Internet channel, companies enter a world of fiercer price competition due to the more transparent market, competitors that are only a click away, and reduced switching costs (Brynjolfsson and Smith, 2000). To compete in an online market, companies often must match their

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competitors’ prices. Moreover, the desire to reduce channel conflict and preserve channel integrity might decrease prices in other channels (Pan et al., 2002). As Ancarani and Shankar (2004) reveal, multichannel retailers offer lower price levels than do traditional retailers; thus, even if the Internet channel operates well, it may do so at the expense of lower prices in other channels, which in turn may be detrimental to company financial performance. Strong Internet channel performance also can threaten other distribution channels, which creates channel conflict that can deteriorate the relationship between a company and its distributors, decrease support and involvement levels on the distributors’ side, or even lead to termination of a contract (Frazier, 1999). Various studies report that channel conflict has a detrimental effect on company performance (e.g., Bucklin et al., 1997). Consequently, the company’s financial (i.e., lost sales from other distributors) and strategic (i.e., lower market coverage and market share) performance both may deteriorate as a result of an Internet channel. Even greater Internet channel sales might not contribute to the company’s financial performance if they come only from current customers who have switched channels rather than from new customers. Although Deleersnyder et al. (2002) and Biyalogorsky and Naik (2003) find only a small level of channel cannibalization and conclude that its threats are largely overstated, their studies refer to a rather early stage of the Internet, when the level of online purchases remained negligible (i.e., only .7% of total retail sales realized online in 1999, US Census Bureau (1999, 2007)). In contrast, consumer surveys show that only 6% of total sales in 1999 were incremental, indicating that 94% of them had been cannibalized from traditional retail channels (Christman, 1999). Furthermore, a more recent study indicates that though shortterm channel cannibalization may be unlikely, over the long term, the Internet channel may come to substitute for offline channels (Weltevreden, 2007). Although channel cannibalization alone does not necessarily imply negative consequences for a company, switches to the Internet channel together with other changes in consumer behavior, driven by that switch, can be very detrimental. For example, the Internet channel may decrease impulse and encourage rational shopping, while also increasing price search and the probability of switching to a competitor (Ansari et al., 2008; Degeratu et al., 2000; Sullivan and Thomas, 2004). Decreased impulse shopping and increased switching to competitors may result in a lower market share and diminished financial and strategic company performance. This discussion thus implies that introducing a new Internet channel, with its unique ability to generate sales, may have both positive and negative effects on company financial and strategic performance.

3.2. Factors influencing Internet channel performance Various factors might influence the performance of the Internet channel. Building on the work of Geyskens et al. (2002), we propose that performance depends on channel power, company size, company experience in managing direct channels, the timing of the Internet channel’s introduction, and economic environment. We also extend Geyskens et al.’s (2002) work by analyzing the support that the Internet channel receives and channel strategy characteristics, such as channel similarity and cooperation. These factors are of particular interest from a managerial perspective, because the company can actually control them. As such, we provide insights about instruments that a company might use to improve its Internet channel performance.

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3.2.1. Company characteristics Early research on Internet channels predicted that small companies, as opposed to larger companies that already possess high levels of market penetration because of their stores, would benefit most from the Internet channel (Alba et al., 1997). The Internet distribution channel then was expected to help small companies overcome geographical barriers and reach new markets. However, small firms lack sufficient reputation and credibility to attract many customers (Brynjolfsson and Smith, 2000). When conducting an online purchase, consumers prefer well-known and reliable partners. Additionally, results showing that large companies experience greater innovation performance (Gatignon and Xuereb, 1997) imply that they also should attain more success in a new channel introduction. Therefore, we hypothesize that the size of a company has a positive influence on Internet channel performance. Companies that operate direct channels likely possess knowledge that helps them successfully introduce and manage an Internet channel. However, the direct effect of company experience with other direct channels on Internet channel performance is not clear. On the one hand, companies with greater experience and knowledge in direct distribution channels should have tested various strategies and solutions that they could use to develop a successful Internet channel. On the other hand, this knowledge may not be appropriate for the Internet channel, which differs from other direct channels. Companies may fail to acknowledge the specific characteristics of the Internet, get stuck in their old routines, and fail to adjust their strategies appropriately (Singh and Lumsden, 1990). If the applied strategy turns out to be inappropriate, they may suffer poor Internet channel performance. Consequently, the net effect of a company’s experience remains ambiguous. Finally, channel power, defined as the company’s need to maintain a good relationship with its distribution partners because of its dependence on these partners (Kumar et al., 1995; Lusch and Brown, 1996), should enhance Internet channel performance. Low channel power may complicate the Internet channel introduction and operations with objections from strong existing distributor partners. Greater channel power, in contrast, encourages the company to act opportunistically to take advantage of and exploit other parties (Frazier and Rody, 1991). As a result, a company in such a situation may follow a self-oriented channel management strategy (Lee et al., 2003) and pursue the Internet channel without fear of opportunistic behaviors by its current distribution partners (Geyskens et al., 2002). Consequently, we expect a positive effect of channel power on Internet channel performance.

3.2.2. Introduction characteristics In the offline environment, ambiguity surrounds the effect of the time of entry. On the one hand, early entrants enjoy firstmover advantages that followers find very difficult to meet. On the other hand, followers may learn from the mistakes of the first mover, develop and pursue an improved strategy, and thus attract the first mover’s customers (Kerin et al., 1992). In the case of an Internet channel addition, however, the advantages of an early introduction (e.g., more credibility) presumably are greater than the threats of applying a suboptimal strategy, because changes are relatively easy and fast to make in an Internet channel (Gulati and Garino, 2000). Whereas adjustments to products may take significant time during the development and production stages, the Internet offers the advantage of greater flexibility. Therefore, first movers can easily adjust their mistakes. In addition, with time, a company can build its market position through greater consumer awareness and credibility. Therefore, we hypothesize

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that the duration of Internet channel operations has a positive influence on performance. To succeed, company projects require the company’s commitment, reflected in the amount of resources devoted to their realization. The resource-based view and empirical studies both imply that the resources available for a specific company project have a direct effect on its probability of success (e.g., Day and Wensley, 1988). Hence, we predict a positive influence of the company’s support on Internet channel performance. 3.2.3. Marketplace characteristics In addition to internal company factors, the success of an Internet distribution channel likely depends on the market in which the company operates. If we consider the Internet channel a form of distribution system innovation, we may follow Gatignon and Xuereb (1997), who show that innovations perform better in favorable market conditions, and propose that the economic environment influences Internet channel performance. Geyskens et al. (2002) also recognize the effect of the marketplace on the Internet distribution channel. Because a new channel provides consumers with another way to buy the company’s products, the success of an Internet channel may be sensitive to the economic environment in particular. In the case of a favorable economic situation and high demand for company products, the additional distributional channel should perform better than it would when surrounded by economic stagnation and low overall demand (Geyskens et al., 2002). Therefore, we propose that the economic environment positively influences Internet channel performance. 3.2.4. Channel characteristics In an extension of Geyskens et al.’s (2002) research, we propose several additional factors that may play important roles with regard to Internet channel performance. First, an innovation that is similar to an existing offer and does not propose a major advantage, that is, an incremental innovation, tends to be less successful in a market than a radical innovation (Gatignon and Xuereb, 1997). Therefore, similar to other innovations, we posit that an Internet channel succeeds only if it differentiates itself from other channels and offers consumers new possibilities and options not available elsewhere. An Internet channel that mirrors other distribution channels, in terms of such features as product assortment, communications, promotion strategy, and additional services, likely fails to win new consumers, who will not bear switching costs to obtain services they already have in other channels. Consequently, we propose that the more similar the Internet channel is to the existing distribution system, the poorer Internet channel performance will be. Second, previous research suggests that through mutual commitment and cooperation, channel parties can decrease the potential for channel conflict, help serve consumers better, and thereby increase mutual profitability (Jap and Ganesan, 2000; Kumar et al., 1995; Neslin et al., 2006). Channel cooperation also offers benefits such as reduced redundancy and costs, better cross-promotion, shared information across channels, and better brand recognition (Gulati and Garino, 2000). As a result, channels can improve their performance through close cooperation. Consequently, we propose that greater channel cooperation will lead to better Internet channel performance. 3.3. Factors influencing channel cannibalization A new distribution channel that is very similar to existing channels in terms of the offer and marketing strategy is more likely to attract the company’s existing customers rather than win new consumers (Deleersnyder et al., 2002). To acquire new

consumers, the company must provide a new offer that does not replicate those of the other channels, which have proved unattractive to those consumers in the past. Existing consumers familiar with current strategy in other channels, however, will generally find it easy to switch to an Internet channel that provides similar and already attractive offerings, together with the inherent Internet advantages. Therefore, we propose that greater similarity between existing and new distribution channels should increase channel cannibalization. The resources and support that a company invests in a new Internet channel also should attract the attention of existing consumers and prompt them to switch to the Internet channel. Consequently, we posit that company support for the Internet channel increases channel cannibalization. Finally, channel cooperation that allows for easy channel switching also may increase channel cannibalization. If the company runs a cross-channel promotion, such as allowing consumers to purchase products online and return them offline or supporting the Internet channel in offline stores, consumers confront information that likely prompts them to switch channels. Fig. 1 summarizes the conceptual model.

4. Methodology We use a structural equation model and the well-recognized partial least squares (PLS) methodology to analyze the links in our conceptual model. 4.1. Structural model Eqs. (1)–(4) show the structural model specification that links several of our proposed factors to Internet channel performance (Eq. (1)), strategic company performance (Eq. (2)), financial company performance (Eq. (3)), and channel cannibalization (Eq. (4)): ICP ¼ a0 þ a1 E þ a2 CP þ a3 SI þ a4 CS þ a5 EE þ a6 SU þ a7 CC þ a8 T þ 1 ,

(1)

SP ¼ b0 þ b1 ICP þ b2 CA þ 2 ,

(2)

FP ¼ d0 þ d1 ICP þ d2 CA þ 3 ,

(3)

and CA ¼ g0 þ g1 ICP þ g2 CS þ g3 SU þ g4 CC þ 4 ,

(4)

where ICP represents the Internet channel performance, E is the experience, CP refers to the channel power, SI is the size of the company, CS is the channel similarity, EE indicates the economic environment, SU equals the support, CC indicates the channel cooperation, T is the time of Internet channel introduction, SP represents the strategic performance, FP refers to the financial performance, and CA equals the channel cannibalization. 4.2. Measurement model With regard to company performance, we follow Zou and Cavusgil (2002) and distinguish financial from strategic performance using multiple-item measures on a five-point Likert scale. To measure Internet channel performance, we adapt Kumar et al.’s (1992) operationalization of reseller performance, but to verify the robustness of our analysis, we also use an alternative Internet channel performance measure that builds on Cavusgil and Zou’s (1994) ideas for measuring export ventures. Similar to Geyskens et al. (2002), we operationalize company size as the number of employees and total revenues, and our measure of experience takes into account the perceived level of a company’s experience

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Company performance: (1) Financial performance (2) Strategic performance

Internet channel performance

Channel cannibalization

Firm characteristics

Marketplace characteristics

Introduction characteristics

Channel characteristics

Size Experience Channel power

Demand

Timing Support

Channel similarity Channel cooperation

Fig. 1. Summarizes the proposed effects in our conceptual model.

in managing direct channels and the period during which it has operated them. The channel power measure is based on scales from Kumar et al. (1995) and Lusch and Brown (1996), and economic environment equals the demand level for the company’s products, combined with the overall economic situation. Furthermore, we operationalize timing as the length of time the company has operated the Internet channel and order of entry; for our support measure, we consider the overall investment in the Internet channel, management commitment, and level of publicity used to support the Internet channel (Geyskens et al., 2002). To measure channel similarity, we include aspects such as communication and promotion strategy, company product offers, and additional services, whereas for channel cooperation, we consider the extent of channel integration, interchannel support, and cooperative coexistence. Finally, we measure channel cannibalization as the extent to which the company realizes online sales because of a loss in offline sales. We provide the details of these measures in the Appendix.

5. Empirical study 5.1. Data The sample consists of 142 companies from Germany, Austria, and Switzerland that responded during October 2005–March 2006 to a mailed questionnaire. The criterion for inclusion is that all companies must operate multiple channels, including an Internet channel. The response rate equals 21%, and the sample size meets the requirements for the PLS analysis. In single-informant studies, the possible threat of biased responses arises because of the selective perceptions of the informant (Lindell and Whitney, 2001; Podsakoff et al., 2003), known as common method variance (CMV), which may result in inflated correlations between the variables and thus misleading conclusions (Lindell and Whitney, 2001). Although some studies suggest that the CMV problem is not overly severe (Crampton and Wagner, 1994; Harrison et al., 1996), we follow Lindell and Whitney’s (2001) approach and test for its presence in our data set by investigating whether the correlations between the predictor and criterion variables remain significant after we control for CMV. Using the lowest correlation between the variables as a CMV estimate (Lindell and Whitney, 2001), we find that CMV does not significantly influence the correlations. Consequently, we con-

clude that our data set does not suffer from CMV and proceed with the empirical analysis of our sample. 5.2. Descriptive results Our results indicate that most managers evaluate Internet channels rather positively. As Table 1 indicates, overall company performance improves after the introduction of the Internet channel in 70% of all companies surveyed. Moreover, 55% of companies report an improvement in their strategic performance, and 36% note improvements in their financial performance. The results also suggest a positive evaluation of the Internet channel with respect to other factors. For example, 66% of companies indicate that their Internet channel attained a high level of market penetration compared with other distribution channels, and as many as 76% agree that the Internet channel has grown steadily. However, the results are not so positive for all companies. As many as 18% (8%) of the analyzed companies disagree that their financial performance (overall performance) increased after the Internet channel introduction. On average, 13% of the analyzed companies state that the Internet channel has performed below their expectations. These results show that we have gathered data from both successful and unsuccessful companies, which enables us to determine the antecedents and consequences of Internet channel performance. However, we cannot completely rule out self-selection effects, such that managers may have taken part in the survey only if their Internet channel has performed well. We note though that our results are consistent with those of Geyskens et al. (2002), who report that 70% of companies experience an increase in stock prices after an Internet channel introduction. In addition, our results support the notion that most purchases still occur in offline channels. Only 11% of companies generate more revenue through their Internet channel compared with other channels. Furthermore, only 24% agree that the Internet channel would either continue to be a major source of revenue or soon become one. Finally, 28% of the companies have experienced channel conflict after their Internet channel adoption, and 39% report channel cannibalization. 5.3. Results for measurement model We evaluate our measurement model by analyzing (1) individual item reliability (i.e., item loadings), (2) the convergent

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Table 1 Influence of Internet channel adoption on company performance. The company performance improved due to Internet channel adoption

Strategic performance

Financial performance

Overall performance

Agree or strongly agree (%) Neither agree nor disagree (%) Disagree or strongly disagree (%) Average

55 41 4 3.64

36 46 18 3.19

70 22 8 3.95

Table 2 Measurement model. Factors

Items

Loadings

t-Statistics

Internal consistency

Average variance extracted (AVE)

Discriminant validitya

Financial performance

FP1 FP2 FP3

0.99 0.99 0.93

54.73 64.10 3.43

0.98

0.94

0.21

Strategic performance

SP1 SP2 SP3

0.98 0.98 0.83

20.94 26.61 2.13

0.95

0.87

0.26

Online channel performance

OCP1 OCP2 OCP3 OCP4 OCP5

0.99 0.99 0.99 0.99 0.78

7.73 13.20 5.23 5.05 2.22

0.98

0.91

0.23

Support

SU1 SU2 SU3

0.84 0.61 0.86

2.85 1.70 2.88

0.82

0.61

0.24

Channel similarity

CS1 CS2 CS3 CS4 CS5 CS6

0.94 0.91 0.91 0.94 0.94 0.88

5.90 5.08 4.38 5.74 5.85 9.64

0.97

0.84

0.04

Channel cooperation

CC1 CC2 CC3 CC4

0.68 0.89 0.77 0.88

2.07 3.62 2.09 2.72

0.88

0.65

0.16

Channel power

CP1 CP2 CP3

0.89 0.89 0.61

2.74 2.70 2.93

0.85

0.65

0.01

Economic environment

EE1 EE2

0.82 0.50

2.02 1.67

0.62

0.46

0.02

Channel cannibalization

CA1 CA2 CA3

0.92 0.88 0.95

2.62 2.73 2.10

0.94

0.84

0.24

Number of observations: 142. a Average of the squared correlations of the particular construct with all other constructs.

validity of the construct (i.e., significance of item loadings, Kumar et al., 1992), (3) internal consistency (Fornell and Larcker, 1981), (4) construct reliability (i.e., average variance extracted [AVE]), and (5) discriminant validity between constructs. We do not include total revenue in the analysis because of the many missing values. In Table 2, we depict the high degree of individual item reliability and convergent validity; the items are significant (at

least at po.10) with loadings greater than .5, which fulfills the recommendation proposed by Hulland (1999). We drop two insignificant items at this stage. With one exception, the internal consistency measures, suggested by Fornell and Larcker (1981), exceed the .7 threshold for each factor, as recommended by Nunnally (1978). Furthermore, the AVE of the constructs range from .46 to .94, which indicates good construct reliability in most

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cases (Fornell and Larcker, 1981). We also note the satisfactory discriminant validity among the constructs, because the average of the squared correlations of a particular construct with all other constructs is always lower than its AVE (Fornell and Larcker, 1981). On the basis of these results, we conclude that our constructs offer sufficient measurement precision. 5.4. Results for structural model In Table 3, we provide the results from the structural model, which explains 53% of the variance for strategic performance and 43% for financial performance. In addition, it explains 56% of Internet channel performance and 66% of channel cannibalization. As Table 3 shows, Internet channel performance has a positive effect on strategic and financial performance (b1 ¼ .48, d1 ¼ .27, po.01). Because both variables use the same measurement scale, we can compare the coefficients, which reveal that Internet channel performance has a greater influence on strategic performance than on financial performance. Surprisingly, we also find a positive effect of channel cannibalization on strategic and financial performance (b2 ¼ .31, d2 ¼ .44, po.01), which implies

Table 3 Results of the structural model. Coefficient

R2

Dependent variable

Independent variable

Strategic performance

Internet channel performance Channel cannibalization

0.48 0.31

0.53

Financial performance

Internet channel performance Channel cannibalization

0.27 0.44

0.43

Internet channel performance

Experience Channel power Size of company Channel similarity Economic environment Support Channel cooperation Time

0.29 0.16 0.04 0.20 0.04 0.65 0.20 0.16

0.55

Channel cannibalization

Internet channel performance Channel similarity Support Channel cooperation

0.35 0.05 0.66 0.22

0.66

Maximum of variance inflation factor (VIF) ¼ 2.63.  Significant at 0.01.  Significant at 0.05.

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that companies appreciate channel cannibalization and observe positive effects when consumers migrate from offline channels to the Internet. We also note that cannibalization has a greater effect on financial performance than on strategic performance, possibly because of the cost reduction effects of channel cannibalization (e.g., Gulati and Garino, 2000). Next, we analyze the factors that influence Internet channel performance. A company’s experience in direct channels has a significant, negative effect on Internet channel performance (a1 ¼ .29, po.01), whereas Geyskens et al. (2002), who analyze the intensity and scope of experience, find no significant effect of intensity and a negative effect of the scope. Thus, these results in combination imply that experience in direct channels may not benefit a company, because the Internet channel is very different from other direct channels. Channel power also has a significantly negative effect on Internet channel performance (a2 ¼ .16, po.05), in contrast with Geyskens et al.’s (2002) finding of a positive effect of channel power on the market valuation of companies with Internet channel additions. We posit that companies with less channel power might not be able to negotiate advantageous conditions with their distributors and thus can only apply them in the Internet channel, which translates into stronger Internet channel performance. Channel similarity also has a significantly negative effect on Internet channel performance (a4 ¼ .20, po.01), which indicates that without additional incentives, consumers are less likely to buy online. As we expected, support for the Internet channel and channel cooperation have significant and positive influences on Internet channel performance (a6 ¼ .65, po.01 and a7 ¼ .20, po.05, respectively). Finally, we find that the companies that have operated their Internet channel for longer experience stronger Internet channel performance (a8 ¼ .16, po.05). Similar to Geyskens et al. (2002), we find no significant effect of company size or the economic environment on Internet channel performance. In a subsequent step, we analyze factors that influence channel cannibalization and find a positive effect of Internet channel performance (g1 ¼ .35, po.01). Furthermore, we observe that support for Internet channels has positive effects and channel cooperation has negative effects (g1 ¼ .66, g4 ¼ .22, respectively; po.01). Channel similarity has no significant effects on channel cannibalization. For a robustness check of our results, we use an alternative measure of Internet channel performance that captures the extent to which companies achieve various aims using their Internet channels (for measurement details, see the Appendix). The results indicate the high validity of the alternative measurement of Internet channel performance: (1) All loadings are significant and greater than .6, (2) the internal consistency measure is .94, (3) the

Table 4 Measurement model for alternative online channel performance. Factors

Items

Loadings

t-Statistics

Internal consistency

AVE

Discriminant validity*

Internet channel performance (alternative measure)

OCPA1

0.73

2.56

0.94

0.62

0.21

OCPA2 OCPA3 OCPA4 OCPA5 OCPA6 OCPA7 OCPA8 OCPA9

0.90 0.73 0.69 0.72 0.64 0.90 0.83 0.90

3.18 2.65 5.13 3.16 2.53 3.23 4.89 3.38

*Average of the squared correlations of the particular construct with all other constructs.

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Table 5 Results of the structural model: robustness check. R2

Dependent variable

Independent variable

Coefficient

Strategic performance

Internet channel performance (A) Channel cannibalization

0.48 0.31

0.53

Financial performance

Internet channel performance (A) Channel cannibalization

0.48 0.31

0.52

Internet channel performance (A)

Experience Channel power Size of company Channel similarity Economic environment Support Channel cooperation Time

0.27 0.14 0.00 0.02 0.01 0.61 0.23 0.13

0.55

Channel cannibalization

Internet channel performance (A) Channel similarity Support Channel cooperation

0.37 0.02 0.68 0.25

0.67

Maximum of variance inflation factor (VIF) ¼ 2.63. (A) Indicates the use of the alternative measurement.  Significant at 0.01.  Significant at 0.05.  Significant at 0.1.

AVE equals .62, and (4) the average squared correlation between Internet channel performance and other constructs is .31 (Table 4). In Table 5, we clarify that the results of both structural models are generally consistent. Only the factors that influence Internet channel performance differ slightly, in that channel similarity does not significantly influence Internet channel performance. This rather small difference suggests that our overall model is robust. 5.5. Total effects We use path analysis to calculate the total influence of one variable on another. The total effect is the sum of the direct effect (see Table 3) and the indirect effects (i.e., effects of one variable on another, mediated by at least one other variable in the system). We calculate the indirect effects by multiplying the path coefficients of all variables on the path and, in Table 6, show that company performance is positively influenced by Internet channel performance, channel cannibalization, the support given to the Internet channel, and channel cooperation. Its performance also increases with time. However, channel power, experience in direct channels, and channel similarity harm company performance.

6. Conclusions The results of our empirical study show that Internet channel performance increases both the strategic and financial performance of the company. Therefore, despite the various threats associated with the Internet channel, the net effect of its performance on the company is positive. Internet channel performance has a stronger effect on strategic than on financial company performance, which implies that the Internet channel helps improve the company’s competitive position in the market, though it is less successful in generating additional revenues and profits. These results further indicate that many companies may have introduced their Internet channels in response to market

Table 6 Size of the total effects. Independent variable

Strategic performance

Financial performance

Online channel performance Channel cannibalization Experience Channel power Size of company Channel similarity Economic environment Support Channel cooperation Time

0.59 0.31 0.17 0.09 0.02 0.10 0.02 0.59 0.05 0.09

0.42 0.44 0.12 0.07 0.02 0.06 0.02 0.57 0.00 0.07

 All direct and indirect effects significant at 0.01.  All direct and indirect effects significant at least at 0.05.

pressures and, as such, improved their competitive situation. Even though their financial performance has not improved significantly, the threat remains that such performance actually might have decreased had the company not responded to this market pressure. We also identify and analyze factors that influence Internet channel performance and find that support from the company has the highest positive effect on the Internet channel performance, which underlines the critical importance of managerial commitment. Furthermore, channel cooperation has a positive effect on Internet channel performance, which implies that joint channel strategies and efforts, integration, and mutual support help the company successfully operate its new distribution channel. As expected, the time during which the company has operated the channel has a positive influence on its performance. In contrast, our results imply that the Internet channel will fail if it is similar to an existing offline channel, because it does not offer consumers any additional incentives. With regard to experience, our results match Geyskens et al.’s (2002) and show that companies do not need to possess experience with other direct channels to operate an Internet channel successfully.

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Lessons learned in other direct channels actually do not provide any assistance for the Internet channel and can even have negative effects on its performance. In contrast to Geyskens et al. (2002), we show that companies with low channel power benefit from stronger Internet channel performance than companies with high channel power. These results imply a difference in the factors that drive a successful Internet channel introduction and those that drive strong Internet channel performance. That is, in early stages, channel power may have been necessary to pursue an Internet channel, because of the strong opposition from existing distributors. However, the threats related to the Internet channel were highly overestimated, and it turns out that even companies with low channel power can successfully operate such a channel. Furthermore, companies with low channel power that cannot negotiate advantageous conditions with their distributors might best make use of them through the Internet channel, which may translate into stronger Internet channel performance. Finally, the size of the company has no effect on the Internet channel performance, in contrast with predictions that larger companies with better reputations and more credibility can attract consumers in online environment (Brynjolfsson and Smith, 2000). As an extension to existing studies in the area of channel cannibalization, we surprisingly find that channel cannibalization improves company performance, especially its financial performance, which implies that channel migration represents a desirable factor. Our results, though unexpected, match those of

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Christman (1999) and Gulati and Garino (2000), who highlight the positive aspects of channel cannibalization in terms of decreased costs and the retention of customers who prefer the Internet channel. Our results thus encourage companies to adopt and develop Internet channels, because doing so will increase performance, even in the presence of channel cannibalization. Moreover, moving consumers to the Internet channel has a positive effect on company performance and therefore should not be considered a threat. Our results further imply that the success of the Internet channel depends mostly on factors that a company can control and influence rather than on historically or environmentally determined characteristics. The most important aspect of Internet channel success is the management commitment to and support of the Internet channel, though to be successful, the channel also should differentiate itself from existing channels. In contrast with previous predictions (e.g., Alba et al., 1997; Brynjolfsson and Smith, 2000; Geyskens et al., 2002), we note that a company does not have to be large or possess channel power and experience with a direct channel to operate an Internet channel successfully. This result should encourage and underline the possibilities for many small companies.

Appendix See Table A1.

Table A1 Factor Strategic performance

Item

References

 The strategic position of your company in the market became much stronger due to the introduction of Zou and Cavusgil (2002) Internet as a distribution channel. (1—strongly disagree, 5—strongly agree for all items of a factor)

 Compared to your major competitors, your company became much more competitive in the global market due to the introduction of the Internet distribution channel

 Your company market share increased due to the introduction of the Internet distribution channel Financial performance

 Global sales of your company increased due to the introduction of Internet as a distribution channel. Zou and Cavusgil (2002) (1—strongly disagree, 5—strongly agree for all items of a factor)

 Global profits of your company increased due to the introduction of the Internet as a distribution channel

 The company was able to decrease costs due to the introduction of the internet distribution channel Internet channel performance

 Compared to other distribution channels, the online channel has achieved a high level of market    

Kumar et al. (1992), Cavusgil and Zou (1994) penetration for the company (1—strongly disagree, 5—strongly agree for all items of a factor) The performance of the online distribution channel has been very good The online channel will either continue to be a major source of revenue for the company or will soon become one In the coming years, the revenues generated by the online channel are expected to grow faster than that by other channels The online channel has grown steadily

Internet channel performance (alternative measure)

 Please, state to what extent the aims related to the introduction of the Internet channel were fulfilled Cavusgil and Zou (1994)

Size of company

 What is the number if employees in your company?  What is the company total revenue? (due to many missing values did not enter the analysis)

Experience

 How would you judge your company experience in managing direct channels? (1—very inexperienced,

(1—high below expectations, 5—high above expectations for all items of a factor) J Gain foothold in the online environment J Increase the awareness of the company J Respond to competitive pressure J Improve the company’s image J Expand strategically into new markets J Improve the distribution system of your company J Generate sales J Generate profits J Contribute to company performance

5—very experienced)

 How long the company is engaged in direct distribution channels? (due to insignificant loading did not enter the analysis)

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Table A1 (continued ) Factor

Channel power

Item

 How many firms are there in your field of business that could provide you with a distribution  

Timing

References

Kumar et al. (1995), Lusch and Brown (1996) comparable to the one you currently have? (1—very few, 5—very many) How expensive would it be for your company to replace the existent distributor(s)? (1—very expensive, 5—very cheap) How many channels does your company operate?

 When did your company introduce Internet distribution channel?  Compared to your competitors, how would you judge the timing of your company Internet channel

Geyskens et al. (2002)

introduction? (1—very late, 5—very early) (due to insignificant loading did not enter the analysis) Support

 To what extent did your company use publicity to promote the Internet distribution channel? (1—very – inextensive, 5—very extensive)

 How would you judge the overall (money/resource/time/effort) investment in the Internet channel introduction? (1—very low, 5—very high)

 How would you judge the extent of management commitment for the online channel? (1—very low, 5—very high) Economic environment

 How would you judge the level of demand for your company’s products? (1—very low, 5—very high) Geyskens et al. (2002),  How do you describe the economic environment in your business area in the last few years? (1—very Atuahene-Gima (1995) unfavorable, 5—very favorable)

Channel similarity

 To what extent are the following aspects similar across channels? (1—very different, 5—very similar –      

Channel cooperation

 How do you judge the co-existence of different distribution channels of your company? (1—very   

Cannibalization

for all items of a factor) Communication strategy Promotion strategy Product or service offered Additional services (e.g., guarantee, delivery, return policy) Quality of the channel service Accessibility

 Introduction of the Internet distribution channel decreased sales from other channels. (1—strongly  

–

uncooperative, 5—very cooperative) How would you judge the support of other distribution channels offered to the online channel? (1—very unsupportive, 5—very supportive) How would you judge the level of integration between the offline and online businesses? (1—very disintegrated, 5—very integrated) How would you judge the role of the online channel compared to offline channels? (1—very substitute, 5—very complementary) –

disagree, 5—strongly agree for all items of a factor) Sales over the Internet distribution channel came from buyers who would have bought the product anyway over existing distribution channels The online channel enhanced the profitability/sales of offline channels (reversed)

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