The Importance of Sports Network Externality

(2007) 16, 54 65 & 2007 Operational Research Society Ltd. All rights reserved 0960-085X/07 $30.00 www.palgrave-journals.com/ejis Perceived network externalities and communication technology acceptance Troy J. Strader 1, Sridhar N. Ramaswami 2 and Philip A. Houle 1 1 College of Business & Public Administration, Drake University, Des Moines, IA, U.S.A.; 2 Department of Marketing, College of Business, Iowa State University, Ames, IA, U.S.A. Correspondence: Troy J. Strader, College of Business & Public Administration, Drake University, 2507 University Avenue, Des Moines, IA 50311-4505, USA. Tel: þ 1 515 271-2753; Fax: þ 1 515 271-4518; E-mail: Troy.Strader@drake.edu Abstract Electronic mail (e-mail) and instant messaging (IM) systems represent two communication technologies that are potentially substitutable. A unique feature of e-mail and IM is that their value to an individual user increases as the number of other people adopting the system grows. This is referred to as a positive network externality. This externality makes it difficult for consumers to switch to other systems because of the potential loss of connectivity with network members. Further, as this externality grows, it has unintended negative consequences in the form of spim and spam. Including these three network externality effects positive, cross-impact, and negative the present study investigates the determinants of electronic communication system use based on an extended Technology Acceptance Model. The study findings suggest that user perceptions regarding network externalities have a positive impact on use of electronic communication systems while perceptions of problems associated with unsolicited messages and perceived usefulness of alternative systems do not significantly affect system use. This study contributes to our understanding of the factors that affect use of existing and newer alternative communication technologies. (2007) 16, 54 65. doi:10.1057/palgrave.ejis.3000657 Keywords: electronic mail; instant messaging; information technology acceptance; technology acceptance model; network externality; spam Received: 31 January 2006 Revised: 9 October 2006 Accepted: 21 December 2006 Introduction Because of the increasing relevance of emerging information technologies in the everyday life of individuals, understanding the factors that influence their adoption and use has become an important research topic (Venkatesh and Morris, 2000; Green and Hevner, 2000; Luo et al. (2000); Van Slyke et al., 2002). One important class of emerging information technologies is groupware technologies such as electronic mail (e-mail) and instant messaging (IM). An interesting feature of these groupware technologies is that they exhibit network externalities that is, their usefulness and value increase as the network of users expands (Chen and Lou, 2002; Kontzer, 2003). To capture the adoption of such technologies, therefore, it becomes necessary that the drivers of adoption include the features of the technology as well as network characteristics. Under the Technology Acceptance Model (TAM), the features of a technology are expressed from the customers perspective. Instead of focusing on physical features of a technology, the focus is shifted to the meaning of the features to the customer in terms of its usefulness and ease of use. The TAM model suggests that perceived usefulness and ease of use influence intent to use a technology; intent to use, in turn, drives actual adoption and use. A large number of technologies has been tested

Perceived network externalities Troy J. Strader et al 55 previously using the TAM model including e-mail, voice mail, word processors, spreadsheets, database programs, graphic systems, decision support systems, and Web browsers (Wang et al., 2004). In a majority of these studies, use of the TAM model was supported by the empirical results. To incorporate the network externality aspects as part of the TAM model, extensions have related externality to perceived usefulness and intent to use a technology (van den Hooff et al., 2005; Li, Chau and Lou, 2005). That is, as externality becomes more prevalent, the usefulness of a technology increases; this, in turn, is expected to impact intent to use in a positive manner. Using a social pressure argument, externality also has been posited to have a direct impact on intent to use, regardless of the perceived usefulness of a technology. The present study identifies and addresses three gaps in the existing literature for systems and technologies that exhibit network externality properties. First, while previous literature has investigated the adoption of a large number of information technologies (as listed above), there are few studies that have focused on the IM system. One reason for this is the relative newness of this technology. IM has become increasingly important in recent times because of the increase in its application potential. Its customers were primarily young users to begin with; however, its users have become more varied in recent years and additionally, it has found increasing application in work settings. By examining IM adoption using the TAM model with externalities, not only does the study apply TAM in a new setting, it also identifies some previously unknown (new) drivers of IM. Second, previous studies have examined technologies one-at-a-time and thus have not captured potential interdependencies that may be present among related technologies. Specifically, the issue of cross-impact of externality addresses the question: does the network size of one technology affect the adoption and use of another technology? This question is important as network size could act as a deterrent to adoption and diffusion of newer, succession products and technologies. For example, it is possible that because the people one communicates with continue to use e-mail, that individual may hesitate to use IM even if they believe that IM is a better technology than e-mail. The cross-impact could also act in a reverse direction, where the new technology once adopted prevents migration back to the older technology. This may happen in cases where the newer technology offers utility improvements that are substantively significant. The present study addresses the cross-impact issue for the first time. Third, most previous studies have examined the positive externalities and ignored potential negative externalities of a technology. The present study addresses the negative externality issue. As network size has increased, so have spam in the case of e-mail and spim in the case of IM. The nuisance and insecurity created by spim and spam could actually result in weakening the usefulness perceptions of customers with associated negative impact on adoption and extent of use of the systems. The study determines the extent to which spim and spam are seen as nuisance factors and captures their impact on usefulness of the two systems. The remaining sections are organized as follows: first we discuss the conceptual model describing the adoption phenomenon the TAM and how network externalities-related constructs can be incorporated into an extended model applied to the context of electronic communications system adoption. This is followed by presentation of the research methodology including a description of the survey methodology, construct items and measures, and the analytical method. Finally, the results and conclusions are presented. Conceptual framework The traditional model Over the years, a large number of studies have been conducted on identifying the factors affecting user acceptance of information technologies. These studies are based on a variety of frameworks including diffusion of innovation (Rogers, 1995), the theory of reasoned action (Fishbein and Ajzen, 1975), the theory of planned behavior (Ajzen, 1991), and the TAM (Davis et al., 1989). Many of these studies involved single-user information technology acceptance for spreadsheets, databases, and similar software. Less attention has been given to transactional information technologies, such as electronic communication systems (ECS), where system use requires more than one participant. In this study, we investigate the determinants of ECS adoption using the TAM with extensions made for network externalityrelated factors. The TAM model has been applied successfully in a wide variety of contexts, such as computer usage behavior (Davis, 1989), acceptance of information technology (e.g., Venkatesh and Davis, 1996; Dishaw and Strong, 1999), use of Internet technology (e.g., Teo et al., 1999; Lederer et al., 2000; Moon and Kim, 2001), and adoption of cellular phones (Kwon and Chidambaram, 2000). We refer to our extended model as the Communication TAM (CTAM). The TAM model was adapted from Fishbein and Ajzen s theory of reasoned action TRA (1975). TRA suggests that a person s acceptance of an information system (IS) is determined by his intention to accept it. The intention, in turn, is determined by the person s attitude toward the IS. Davis (1989) replaced the attitude concept with two distinct belief variables perceived usefulness and perceived ease of use. Thus, TAM posits that intent to adopt a new technology by consumers is determined by two important beliefs regarding the technology perceived usefulness and perceived ease of use (Davis et al., 1989; Adams et al., 1992; Venkatesh et al., 2002). Because of its excellent track record in explaining why people accept or reject an innovation, TAM is considered today as the dominant model for investigating acceptance of innovations by users (Templeton and Byrd, 2003).

56 Perceived network externalities Troy J. Strader et al Perceived usefulness of a certain technology can be defined as the user s evaluative belief about the degree to which that technology enables the user to build and maintain interpersonal relationships in a social context (Li et al., 2005). This contrasts with the meaning provided to this concept in a job setting that it represents beliefs regarding the degree to which using a specific application system will increase an employee s job performance (Davis et al., 1989). Regardless, it refers to the gains that the user makes when using a technology and is expected to have a positive influence on the user s attitudes and behaviors. Perceived ease of use is defined as the degree to which a person believes that using a particular system would be free of effort (Davis, 1989). According to Venkatesh (2000), a vast body of research in behavioral decision making (e.g., Payne et al., 1993) and IS (e.g., Todd and Benbasat, 1991, 1994) has demonstrated that individuals attempt to minimize effort in their behaviors. This concept is opposite in meaning to the relative complexity notion proposed by Rogers (1995), which he defines as the degree to which an innovation is perceived as difficult to understand and use. Using this concept in the context of adoption of innovations, Rogers proposes that as the level of complexity of an innovation increases, the slower is its rate of adoption within the target market. Using the same logic, we expect the intent to adopt the communication systems to be higher among individuals that perceive them to be easy to use. Based on the above discussion, we offer the following two hypotheses: H1: The greater the perceived usefulness of the communication system for managing inter-personal communication, the higher the intent to use the system. H2: The greater the perceived ease of use of the communication system for managing inter-personal communication, the higher the intent to use the system. The positive relationship between behavioral intentions and action has been assessed in numerous past studies involving the TAM. As in earlier studies, we include the following hypotheses regarding intention to use a system and actual system use. H3a: H3b: The higher the intent to use e-mail, the greater its actual use. ThehighertheintenttouseIM,thegreateritsactualuse. The extended model The present study makes three extensions to the traditional model. All three extensions are linked to the concept of network externality and its implications on choice of communication technologies by users. These are: (1) the impact of positive network externalities (i.e., network size) on perceived usefulness of e-mail and IM; (2) the impact of negative network externalities (i.e., spam and spim) on perceived usefulness of e-mail and IM, respectively; and (3) the indirect impact of network externality on intent to adopt a competing system. Extension 1: role of positive network externality Network externalities exist in markets where the utility that a user derives from consumption of a good increases with the number of other agents consuming the good (Katz and Shapiro, 1985, p. 424). There are three sources of network externalities (Katz and Shapiro, 1985). One source of network externalities is a direct physical effect of the number of users on the quality of the product or service. The value of a fax machine to one user increases as more fax machines are purchased and used by others. Another source is an indirect effect where the utility of a product increases with the number of users because the quality of the product is higher or there are more complementary products available (Katz and Shapiro, 1986; Farrell and Saloner, 1987). As the user base for Macintosh computers grows, there should be a resulting growth in compatible software that further enhances the value of the hardware. A third source of network externalities (which captures another form of indirect effect) exists in situations where increasing sales of a durable good produces greater quality and availability of post-purchase services related to the product. The greater the number of people buying a certain brand of washing machine, the greater the likelihood that service for the machines will be provided. The focus of the present study is on the first source the direct effect. Direct network externalities have been extensively studied in economics, marketing, and organizational strategy for more than two decades. Early studies focused on theoretical development (Katz and Shapiro, 1985, 1986). Mathematical models were utilized to identify the relationship between network externalities, user utility, competition, and resulting market outcomes (Economides and Himmelberg, 1995; Forman and Chen, 2003; Park, 2004). The contexts studied were primarily physical goods such as spreadsheet software and VCRs, and various technologies such as banking networks and telecommunication equipment. Consumers were assumed to be homogeneous and network size was used as a simple measure of the magnitude of network externalities that existed. These early studies set the stage for extending research to a wider range of perspectives (firms and individual users) and contexts (e.g., newer technology products and services). As far as the present study is concerned, positive consumption externalities exist when the utility that a user derives from use of a particular communication technology increases with the number of other people using the same service. In other words, the more the

Perceived network externalities Troy J. Strader et al 57 number of people on e-mail or IM, the more the utility individual consumers derive from these technologies. That is, H4a: H4b: The greater a user s network externality perceptions for e-mail, the more useful he/she perceives e-mail to be for communications. The greater a user s network externality perceptions for IM, the more useful he/she perceives IM to be for communications. Extension 2: role of negative network externality (spam and spim) For well over a decade, individuals have benefited from the simplicity and low cost of sending e-mail messages. But this simplicity and low marginal cost also comes with a price. It is cheap and easy for organizations and individuals to send large numbers of unsolicited, mostly unwanted, e-mail messages (Strader et al., 2005). These messages are commonly referred to as spam. Spam can be viewed as a negative result of network externalities where network growth increases value to organizations sending unsolicited messages because it increases the potential number of recipients. In 2003, approximately two billion spams were sent every day (Swartz, 2004). More recently, IM systems have begun to suffer from the same problems associated with e-mail spam. Unsolicited instant messages are referred to as spim. More than one billion spims were sent in 2003, and it is estimated that this number will grow four-fold each year for the next several years (Swartz, 2004). The question we address in this study is whether spam and spim are merely an annoyance, or are they negatively affecting user perceptions regarding the usefulness of these communications systems, which culminates in a decrease in system use. A review of popular press articles and surveys of Internet user activities indicates that one factor that may potentially have a negative impact on use of these systems is perceptions regarding the growing number of unsolicited messages (Fallows, 2003; Biever, 2004; Bird, 2004; Schultz, 2004; Swartz, 2004). An overall finding of an October 2003 survey of e-mail users noted that spam is beginning to undermine the integrity of e-mail and to degrade the online experience (Fallows, 2003, p. i). Extension 3: impact of network externality of one system on adoption of a competing system The network externality concept implies that a particular technology (with associated network) will retain its edge as long as the network size is maintained or even increased. However, there are situations where one technology succeeds another and attempts to draw consumers from the earlier technology. In other words, there is potential for the network size to decrease and the externality benefits to fall for the first technology. This is likely the case for e-mail against IM; and it may be so for improvements in IM (such as between traditional IM and videosupported IM). The degree to which a network sees erosion in its size is likely to vary depending on relative cost and benefit perceptions of the individual user. With improvements in technology, a succeeding technology (IM) is likely to offer better features and thus have greater utility than its predecessor (e-mail). Studies have pointed out that IM provides unique benefits: information on availability of a person on the network at any given time, real-time communication with that person, expansion of buddy network, and better quality of communication. These improvements increase the utility of IM technology and make it more valuable to the user. However, this has to be matched against the potential loss in utility arising from switching from e-mail because not all people on a person s e-mail network may switch to the IM network. Some researchers have called this as loss of connectivity (Windrum and Birchenhall, 2005). Two points need to be made regarding user evaluations of costs and benefits. First, while TAM studies do not measure benefits and costs and their tradeoffs specifically, they measure perceived usefulness of a technology. Through this measure, it is possible to infer that when users consider a technology as useful, they perceive that technology to provide them with benefits they value more than the costs they incur. Second, the higher such perceptions of usefulness, the lower will be the likelihood of switching to a new technology. There may be some variability in this behavior as younger people and college students will likely experience greater switching to IM than older people. Overall, H5a: H5b: The more a user perceives that there are problems associated with unsolicited e-mail messages (spam), the less useful they perceive e-mail to be for communications. The more a user perceives that there are problems associated with unsolicited instant messages (spim), the less useful they perceive IM to be for communications. H6a: H6b: The more useful a user perceives e-mail to be, the lower their intention to use IM. The more useful a user perceives IM to be, the lower their intention to use e-mail. Figure 1 provides an overview of the proposed model. The basic constructs in the model include spam/spim perceptions, perceived network externalities, perceived usefulness, perceived ease of use, behavioral intention to use, and actual system use.

58 Perceived network externalities Troy J. Strader et al Spam (SPAM) Perceived Network Externalities E-Mail (NET_EM) _ Perceived Ease of Use E-Mail (PEOU_EM) Perceived Usefulness E-Mail (PU_EM) _ Behavioral Intention to use E-Mail (BI_EM) E-Mail Use (EM_USE) Table 1 Survey sample characteristics Characteristic Category Number Percentage (%) Gender Female 89 54.6 Male 73 44.8 Missing 1 0.6 Spim (SPIM) Perceived Network Externalities IM (NET_IM) Figure 1 use. Research method _ Perceived Usefulness IM (PU_IM) Perceived Ease of Use IM (PEOU_IM) Behavioral Intention to use IM (BI_IM) Data An online survey was used to collect data for this study during May/June 2005. The respondents were undergraduate and graduate students in a business college in a Midwestern private university in the United States. The student group was chosen for this study because they represent a group of individuals who is likely to be aware of both e-mail and IM and possibly may be users of at least one, if not both, systems. Extra credit points were given to students completing the survey. Approximately 300 students were invited to participate in the study and 163 surveys were completed for a response rate of 54.3%. The demographic makeup of the sample is described in Table 1. The descriptive statistics and correlations among the study constructs are shown in Table 2. Measures All model constructs, with the exception of actual system use, were measured using multi-item scales. The items were adapted from previous studies except for the measure of perceived network externalities. Use, behavioral intension to use, perceived ease of use, and perceived usefulness were adapted from Venkatesh and Davis (1996). The measures for perceptions of spam and spim were based on scales recommended by Strader et al. (2005). Finally, the measure of perceived network externalities was developed based on categories of users a person is most likely to wish to communicate with: friends, family members, and other individuals. The items were framed in a Likert format; each item was expressed in the form of a statement and respondents were asked to indicate the level to which they agreed or disagreed with the statement. The next section describes the components of each construct including reliability statistics. The scale items for the constructs are listed in Appendix A. _ IM Use (IM_USE) Proposed conceptual model to explain e-mail and IM Age 18 19 22 13.5 20 24 87 53.4 25 29 25 15.3 30 34 10 6.1 35 39 7 4.3 40 44 2 1.2 45 49 7 4.3 50 54 1 0.6 55 or older 1 0.6 Missing 1 0.6 Education High school 1 0.6 Some college 91 55.8 Undergraduate degree 10 6.1 Some graduate school 54 33.1 Graduate degree 6 3.7 Missing 1 0.6 Income I do not know 21 12.9 I would rather not say 17 10.4 Less than $50,000 25 15.3 $50,000 $74,999 31 19.0 $75,000 $99,999 32 19.6 $100,000 $124,999 19 11.7 $125,000 or more 17 10.4 Missing 1 0.6 Perceptions of spam/spim Two items were used to measure each of these two constructs. These items focus on the amount of time and effort wasted on dealing with unsolicited messages. The reliability coefficient for these two-item scales was 0.849 for spam and 0.912 for spim. Perceived network externalities Individuals may wish to communicate online with people who fit into one of the three categories: friends, family, and other individuals. Using these categories, three items were constructed to measure the perceived network externalities. Theoretically, an individual should receive greater utility from an electronic communication system when a larger number of friends, family, and other individuals use the same system. The reliability coefficient for these scales was 0.683 for e-mail and 0.747 for IM. Perceived ease of use The scales for perceived ease of use for e-mail and IM systems were adapted from Venkatesh and Davis (1996). The focus is on four dimensions of user perceptions regarding how easy it is to use these systems. This scale has been validated in numerous previous studies. The reliability coefficient for this scale was 0.864 for e-mail and 0.881 for IM.

Perceived network externalities Troy J. Strader et al 59 Table 2 Correlations and descriptive statistics SPAM NET_EM SPIM NET_IM PEOU_EM PU_EM PEOU_IM PU_IM BI_EM BI_IM EM_USE IM_USE Spam (SPAM) Perc. Network Ext. EM (NET_EM) 0.096 Spim (SPIM) 0.310 a 0.035 Perc. Network Ext. IM (NET_IM) 0.052 0.366 a 0.156 b Perc. Ease of Use EM (PEOU_EM) 0.086 0.343 a 0.029 0.161 b Perc. Usefulness EM (PU_EM) 0.038 0.385 a 0.174 b 0.236 a 0.415 a Perc. Ease of Use IM (PEOU_IM) 0.011 0.293 a 0.005 0.595 a 0.468 a 0.330 a Perc. Usefulness IM (PU_IM) 0.014 0.300 a 0.088 0.729 a 0.179 b 0.299 a 0.747 a Beh. Intention to Use EM (BI_EM) 0.018 0.333 a 0.006 0.007 0.310 a 0.395 a 0.129 0.118 Beh. Intention to Use IM (BI_IM) 0.070 0.255 a 0.065 0.645 a 0.178 b 0.217 a 0.578 a 0.792 a 0.124 EM Use (EM_USE) 0.109 0.404 a 0.147 c 0.014 0.357 a 0.269 a 0.079 0.058 0.332 a 0.055 IM Use (IM_Use) 0.034 0.181 b 0.021 0.658 a 0.118 0.095 0.663 a 0.786 a 0.055 0.825 a 0.006 Mean 5.14 6.27 3.02 5.03 6.13 5.94 5.59 5.23 6.66 5.17 6.71 4.72 Std. Deviation 1.71 0.88 1.79 1.57 0.86 1.03 1.42 1.79 0.65 2.00 0.96 2.47 a Po0.01; b Po0.05, c Po0.10. Perceived usefulness The two scales for perceived usefulness for e-mail and IM were also adapted from Venkatesh and Davis (1996). The focus is on four dimensions of user perceptions of the usefulness of the systems for communications purposes. Similar to ease of use, these scales have been validated in numerous previous studies and the present study provides further support for their reliability in studies of technology adoption. The reliability coefficient for this scale was 0.913 for e-mail and 0.955 for IM. Behavioral intention to use An important dependent variable in the study is user s intention to use an e-mail and/or IM system. This construct is important because of its high correlation with actual system use. Two-item scales for behavioral intention to use each of the two systems were developed from Venkatesh and Davis (1996). The reliability coefficient for this scale was 0.948 for e-mail and 0.983 for IM. E-mail and IM system use A single-item was used to measure system use. For example, individuals were asked to what extent they disagreed or agreed with the statement I often use electronic mail. Analytical method The relative importance of the various predictors for each construct in Figure 1 was investigated using a structural equation model. The model indicated the following relationships: PU EM ¼ f ðspam; NE EMÞ PU IM ¼ f ðspim; NE IMÞ BI EM ¼ f ðpeou EM; PU EM; PU IMÞ BI IM ¼ f ðpeou IM; PU IM; PU EMÞ USE EM ¼ f ðbi EMÞ USE IM ¼ f ðbi IMÞ In the preceding equations, SPAM and SPIM are the perceived level of time and effort wasted dealing with unsolicited e-mail and instant messages. NET_EM and NET_IM are the perceived network externalities associated with each system. PU_EM, PU_IM, PEOU_EM, and PEOU_IM are the primary TAM model constructs for perceived usefulness and perceived ease of use for each system. BI_EM and BI_IM are the behavioral intentions to use each system. USE_EM and USE_IM are the level of actual use of the system by the user. The structural model analytics indicated that two new paths not specified in the hypothesized model could be added to improve the fit statistics. The first new path is between perceived ease of use and perceived usefulness for both systems. This is not a relationship included in the original TAM model, but may result from the ECS systems being easy to use relative to software packages tested in earlier TAM studies. Second, a direct path from perceived network externalities to intention to use IM indicates the strength of the network externalities

60 Perceived network externalities Troy J. Strader et al construct in determining IM use. Network externalities for newer communication systems may be a stronger predictor of intention to use and actual use than just the perception that the system is useful. Neither new path involves a dramatic departure from the hypothesized model, but may indicate that factors affecting electronic communication system use vary depending on whether the technologies are in the growth or maturity phases of their life cycle. After adding these new paths to the hypothesized model, the fit statistics indicate the following: chi-square with 142 degrees of freedom ¼ 276.48 (P ¼ 0.000), GFI (goodness-of-fit index) ¼ 0.87, RMSR (root-mean-squared residual) ¼ 0.051, NNFI (non-normed fit index) ¼ 0.94, and CFI (comparative fit index) ¼ 0.95. The chi-square statistic, as well as the other indicators of relative and absolute fit (e.g., NNFI, CFI, and RMSEA), unequivocally suggest that the hypothesized structural model is a reasonably good representation of the variance covariance matrix of study measures. The parameter estimates for the hypothesized model are reported in Table 3 for e-mail and Table 4 for IM. Results The overall results suggest that the extended TAM model captures the adoption of IM better than that of e-mail. The R-square value for each of the three equations (see Tables 3 and 4) indicates a better fit in the case of IM. This result may hint that the TAM model is much more applicable to newer technologies. It is possible that the phenomenon of interest may shift from adoption or nonadoption for a new technology to extent of use for older technologies. Test of hypotheses The traditional model Hypotheses H1 and H2 suggest that perceived usefulness and ease of use should be positively associated with intent to use a system. These hypotheses come directly from the TAM model but are tested in the context of communication (transactional) technologies. Results show that while perceived usefulness of e-mail has a significant association with the intent to use e-mail, the same is not true for IM. The structural coefficient for e-mail is 0.37, while that for IM is 0.02. There is greater support for the hypothesis relating to the effects of perceived ease of use. This is another relationship that has been tested in previous TAM studies of acceptance of individual technologies. For e-mail, the findings show that the perceived ease of use has a positive effect on intention to use e-mail. The structural coefficient (0.21) is significant at the Po0.10 level. The coefficient for IM is 0.37 and significant at the Po0.01 level. Finally, hypothesis H3 tests the relationship between intention to use a system and actual use. The findings of this study support previous findings. For e-mail, the beta coefficient (0.31) is significant at the Po0.000 level. Similarly, the coefficient in the case of IM is significant at the Po0.000 level. The extended model The first extension pertains to the impact of positive network externalities. The expectation is that perceptions of network size will drive perceptions of usefulness of a system. Results show this to be the case. For e-mail, the beta coefficient value for this relationship is 0.50, which is significant at the Po0.000 level. For IM, the relationship is also positive (b ¼ 0.36) and significant at the Po0.000 level. The second extension examines the negative aspects of network externality, whereby larger network size offers a positive inducement to use of spam and spim by advertisers and unscrupulous agents. The expectation is that spam and spim will become a source of nuisance with the capacity to negatively impact the usefulness of the communication systems. The findings of the study indicate that consumers are not impacted negatively by either spam or spim. The third extension examines the cross-impact arising from positive network externality. When externality is high, the perceived usefulness of a system is likely to be high. The latter, in turn, implies that consumers may find an alternative system as less valuable and hence show a lower intent to use that system. These hypotheses would be unique to the CTAM for considering choices between alternative communication technologies that Table 3 Parameter estimates for model in Figure 1 (e-mail) Predictor Perceived usefulness of e-mail Intent to use e-mail Actual use of e-mail b t b t b t Spam 0.06 0.69 Network externality for e-mail 0.50 3.65 a Perceived ease of use of e-mail 0.36 3.50 a 0.21 1.81 b Perceived usefulness of e-mail 0.37 3.67 a Perceived usefulness of IM 0.02 0.22 Intent to use e-mail 0.31 4.44 a R 2 0.37 a 0.21 a 0.11 a a Po0.01; b Po0.10.

Perceived network externalities Troy J. Strader et al 61 could conceivably both provide the same function. Results do not support this assertion. No significant associations are found in either case. Empirically derived relationships As mentioned earlier, three relationships were suggested by the solution to the structural model. The first two are direct paths from perceived ease of use to perceived usefulness for both technologies. Those who consider IM or e-mail as easy to use also find it more useful. The third path reflects a direct relationship of network externality of IM to intent to use IM (b ¼ 0.57; Po0.01). This is an interesting finding as it implies that higher network size of IM is sufficient to induce intent to try that ECS, regardless of usefulness perceptions. The results above are summarized and related to the several hypotheses in Table 5. Discussion of results The purpose of this study was to conceptualize and test the relevance of factors that lead to consumer use of e-mail and IM systems. The basis for the study was the TAM with three network externality-related extensions that capture unique characteristics of transactional, communication-oriented, information technologies. These extensions pertain to positive network externalities (network size), negative externalities (spam and spim), and cross-network externality (impact of network size of e-mail on IM use and vice versa). Of these three extensions, the empirical test of the model provided support for only the positive network externalities factor. Before we examine the implications of the study s findings, the study limitations are discussed next. Limitations Several limitations of our study are noteworthy. First, because the study was based on survey measures that were cross-sectional in nature, caution must be exercised in drawing cause effect inferences. The results, therefore, might not be interpreted as proof of causal relationships, but rather as lending support for a prior causal scheme. The development of a time-series database and testing of the externality usefulness intent linkage in a longitudinal framework would provide more insights into probable causation. Second, the study is based on a sample of college students. Unlike other consumer studies, the choice of students is appropriate as they are the primary targets for a service like IM. All the same, replication of this study using other sample groups will provide greater validation for the generalizability of the study s findings. Third, the study used two products that experience network effects e-mail and IM. The study design could be used to test other sets of products that exhibit similar network effects and technology succession property. Finally, because the study data were collected from a single source, there is a likelihood of problems arising from common method variance. The measurement model tested using the study measures indicated that this was not a major issue in the present study. Despite these limitations, the results offer useful insights into the relevance of network externality for adoption and use of network products. In the next section, key findings of the study are discussed and managerial implications are drawn from them. Study framework implications The traditional model Overall, there was good support for the traditional TAM model for both e-mail and IM. Except for the non-relationship between perceived usefulness and intent to use IM, all other relationships were significant at the Po0.01 level. These results provide validation for the use of TAM to capture consumer adoption of communication technologies such as e-mail and IM. Interestingly, maybe because of its newness, not too many previous studies have examined IM adoption. IM adoption is still in the early stages of adoption with less than 35% of the population having adopted it (as suggested by the study s sample). More studies are needed to not only track this phenomenon, but also to identify conditions that facilitate migration of consumers from e-mail to IM. Positive network externalities (perceived level of use by other individuals) One of the purposes of the study was to examine whether perceived level of use of a communication system by friends, family, and the overall population would affect perceptions of its usefulness. There is strong evidence to support the idea that perceived network Table 4 Parameter estimates for model in Figure 1 (IM) Predictor Perceived usefulness of IM Intent to use IM Actual use of IM b t b t b t Spim 0.01 0.28 Network externality for IM 0.36 5.17 a 0.57 6.12 a Perceived ease of use of IM 0.66 9.81 a 0.37 3.53 a Perceived usefulness of IM 0.02 0.15 Perceived usefulness of e-mail 0.03 0.50 Intent to use IM 1.02 18.44 a R 2 0.69 a 0.55 a 0.68 a a Po0.01.

62 Perceived network externalities Troy J. Strader et al Table 5 Summary of hypotheses and findings H# Hypothesis Supported? H1 The greater the perceived usefulness of the communication system for managing inter-personal communication, the higher the intent to use the system E-mail, yes IM, no H2 The greater the perceived ease of use of the communication system for managing inter-personal Yes communication, the higher the intent to use the system H3a The higher the intent to use e-mail, the greater its actual use Yes H3b The higher the intent to use IM, the greater its actual use Yes H4a The greater a user s network externality perceptions for e-mail, the more useful he/she perceives Yes e-mail to be for communications H4b The greater a user s network externality perceptions for IM, the more useful he/she perceives IM Yes to be for communications H5a The more a user perceives that there are problems associated with unsolicited e-mail messages No (spam), the less useful they perceive e-mail to be for communications H5b The more a user perceives that there are problems associated with unsolicited instant messages No (spim), the less useful they perceive instant messaging to be for communications H6a The more useful a user perceives e-mail to be, the lower their intention to use instant messaging No H6b The more useful a user perceives instant messaging to be, the lower their intention to use e-mail No externalities affect perceived usefulness. Usefulness, in turn, affects intention to use a system as well as actual system use. Although not hypothesized, results also showed that network externalities influence use of IM directly. In other words, if the network that an individual belongs adopts IM or is planning to adopt IM, that individual will also adopt IM regardless of its level of usefulness. More than the utility of the system, the social influence or the need to belong become important factors in one s attitude toward the system. Overall, the evidence for positive network externality is strong. This implies, then, that conventional wisdom that focuses on increasing the intrinsic value of a product will be insufficient for success in the context of network effects. The network issue has become a particularly important one for developing and marketing newer technologies as they attempt to gain a critical mass of users. Negative externality perceptions of spam/spim Spam and spim are perceived as negative and of nuisance value by users. They are typically unsolicited and unwanted. They may also go beyond a simple annoyance and bring viruses that can harm someone s personal computer. The results of our study of this factor are clear. The time and effort wasted dealing with spam and spim do not appear to significantly affect a user s perception of the usefulness of a given communications system. Maybe, they have become such a common occurrence that individuals have learned to deal with them. It is apparent that the utility derived from e-mail and IM systems far outweigh their negative externality. Cross-externality: perceived usefulness and its effect on alternative system use One facet of the conceptual model proposed that the perceived usefulness of one electronic communication system may affect people s intention to use an alternative system. For example, if someone finds e-mail to be highly useful, the question is will that result in lower intention to use a newer technology like IM? As with perceptions regarding spam/spim, no significant association was found between perceived usefulness of e- mail and intention to use IM, and vice versa. The systems do not appear to be substitutes. This may also explain why individuals now have phones at home, carry cell phones, have e-mail accounts, and use IM. The older technologies are not replaced. They are added to a portfolio of available communication methods that give users more and more options. The value seems to come from having choices rather than selecting one optimal communication system. Managerial implications The results of this study have implications for e-mail and IM service providers. First, individuals have learned to cope with spam as well as its newer variation, spim. Software can be used to block many unsolicited messages, and those messages that get through can often be recognized as spam and deleted. Since perceptions regarding spam and spim have not been shown to effect perceptions of communication system usefulness, it is unlikely that users will want to pay much for spam blocking solutions. The result is that service providers should provide spam blocking services but may only be able to charge a nominal fee or nothing. A second implication is based on the finding that usefulness of one system does not negatively affect intention to use an alternative system. The implication for service providers is that they should not market newer communication systems (IM) by pointing to flaws in existing systems (e-mail). New systems appear to provide an alternative means of communication for users, but not necessarily a substitute. Introduction of new ECS may lead to an overall growth in communications, through multiple means, between individuals rather than a continual process of switching to new technologies.

Perceived network externalities Troy J. Strader et al 63 Finally, perceptions regarding the number of other people who use a particular communication system have a powerful effect on perceptions of usefulness and intention to use a system. Marketing of new communication systems should take this into account. And it seems they have. T-Mobile refers to communicating with friends and family in their advertising. Cingular has a Family Talk service plan. And MSN Messenger advertising mentions that you can use their service to chat online with friends, family, or colleagues. This finding also has strategic implications for firms providing communication services, online auctions, and other online services. When a new communication system is developed, it will be difficult to add users early on because they know that not many other people will be using the system. But after the communication system reaches a critical mass of users its use will take off with less marketing effort. For a further discussion of the difficulties associated with competing in highly networked markets, see Chakravorti (2004). Future research directions As discussed earlier, the model proposed in the present study should be tested in different contexts using different sets of products and different samples to evaluate its generalizability. One problem is in finding products that are somewhat substitutable and that show network externality effects. For technology successions that have network externalities, one research direction would be to evaluate the role of relative network externalities. As people migrate from one technology to another, the externality benefits shrink for one and expand for another. This phenomenon can be captured and expressed in the form of relative network externalities that is, the externality of one expressed as a ratio of externality of another. Two questions that arise are: how important is relative network externality when compared to the externality for a technology? What characteristics define people that exhibit higher relative network externality. If a technology successor is able to identify people who have a higher potential score for relative externality, the targeting efforts of firms could become more efficient. One research possibility is therefore to identify characteristics of individuals in terms of demographics, psychographics, and network profile that exhibit faster migration between technologies. Technology succession literature also has argued that for achieving market success, the increased usefulness of a succession technology (assuming new technologies have better features than existing technologies) and its associated welfare improvements need to make up for the loss of connectivity with the communication group arising from migration to that technology. Evaluating this equation may provide information on minimum welfare improvements that are needed and implications on product improvements. There is no empirical study that examines this trade-off to date, and future researchers can empirically investigate this issue for products that exhibit network effects. Finally, one implication of externality is to maximize the installed base of users for a product. Two strategies are available for achieving this goal: be a pioneer and be first in the market; and/or use a launch strategy that will ramp up the base quickly. The effectiveness of the two strategies has not been evaluated to date and could form a fertile area for future research. The message to managers is that it may not be a good idea to heavily invest in improving product features at the expense of efforts to increase the installed base of customers (Lee and O Connor, 2003). About the authors Troy J. Strader is Associate Professor of Information Systems at the Drake University College of Business & Public Administration. He received his Ph.D. in Information Systems from the University of Illinois at Urbana-Champaign. His research interests include communication technology adoption, online financial services, online consumer behavior, and mobile commerce. Sridhar N. Ramaswami is Professor of Marketing in the College of Business at Iowa State University. Dr. Ramaswami received his Ph.D. in Marketing from the University of Texas, Austin. His research focuses on consumer behavior in online markets, marketing of financial services, sales management, and brand equity management. Philip A. Houle is Associate Professor of Information Systems in the College of Business & Public Administration at Drake University. He received his Ph.D. in Computer and Information Control Sciences from the University of Minnesota. He served as the top Information Technology Administrator at Drake University from 1999 to 2001. References ADAMS D, NELSON R and TODD P (1992) Perceived usefulness, ease of use, and usage of information technology: a replication. MIS Quarterly 16(2), 227 250. AJZEN I (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes 50(2), 179 211. BIEVER C (2004) Spam being rapidly outpaced by spim. Retrieved November 1, 2005, from http://www.newscientist.com/article. ns?id ¼ dn4822. BIRD D (2004) Think spam is tough? Try fighting spim. Retrieved November 1, 2005, from http://www.instantmessagingplanet.com/ enterprise/article.php/3365281. CHAKRAVORTI B (2004) The new rules for bringing innovations to market. Harvard Business Review 82(3), 59 67. CHEN Y and LOU H (2002) Toward an understanding of the behavioral intention to use a groupware application. Journal of End User Computing 14(4), 1 16.

64 Perceived network externalities Troy J. Strader et al DAVIS F (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3), 319 340. DAVIS F, BAGOZZI R and WARSHAW P (1989) User acceptance of computer technology: a comparison of two theoretical models. Management Science 35(8), 982 1002. DISHAW M and STRONG D (1999) Extending the technology acceptance model with task-technology fit constructs. Information and Management 36(1), 9 21. ECONOMIDES N and HIMMELBERG C (1995) Critical mass and network size with application to the U.S. fax market. In Toward a Competitive Telecommunications Industry: Selected Papers from the 1994 Telecommunication Policy Research Conference (BROCK G, Ed) Lawrence Erlbaum Associated, New York. FALLOWS D (2003) Spam: how it is hurting email and degrading life on the Internet. Pew Internet & American Life Project. Retrieved July 19, 2005 from http://www.pewinternet.org. FARRELL J and SALONER G (1987) Competition, compatibility and standards: the economics of horses, penguins and lemmings. In Product Standardization as a Tool of Competitive Strategy (GABEL HL, Ed), pp 1 22, North Holland, New York. FISHBEIN M and AJZEN I (1975) Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Boston. FORMAN C and CHEN P-Y (2003) Network effects and switching costs in the market for routers and switches. Working Paper, Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, PA. GREEN G and HEVNER A (2000) The successful diffusion of innovations: guidance for software development organizations. IEEE Software 17(6), 96 103. KATZ M and SHAPIRO C (1985) Network externalities, competition, and compatibility. American Economic Review 75(3), 424 440. KATZ MandSHAPIRO C (1986) Technology adoption in the presence of network externalities. Journal of Political Economy 94(4), 822 841. KONTZER T (2003) Corporate IM ready to take off. Information Week. Retrieved June 15, 2003, from www.informationweek.com/story/ IWK20030404S0032. KWON H and CHIDAMBARAM L (2000) A test of the technology acceptance model: the case of cellular telephone adoption. Proceedings of the 33rd Annual Hawaii International Conference on Systems Sciences. 33, pp 1023 1029. LEDERER A, MAUPIN D, SENA MandZHUANG Y (2000) The technology acceptance model and the World Wide Web. Decision Support Systems 29(3), 269 282. LEE Y and O CONNOR G (2003) Network product launch strategy for network effects products. Journal of the Academy of Marketing Science 31(3), 241 255. LI D, CHAU PandLOU H (2005) Understanding individual adoption of instant messaging: an empirical investigation. Journal of the Association for Information Systems 6(4), 102 129. LUO H, LUO W and STRONG D (2000) Perceived critical mass effect on groupware acceptance. 9, 91 103. MOON JandKIM Y (2001) Extending the TAM for a world-wide web context. Information and Management 38(4), 217 230. PARK S (2004) Quantitative analysis of network externalities in competing technologies: the VCR case. The Review of Economics and Statistics 86(4), 937 945. PAYNE W, BETTMAN J and JOHNSON E (1993) The Adaptive Decision-Maker. Cambridge University Press, New York. ROGERS E (1995) Diffusion of Innovations, 4th edn. The Free Press; New York. SCHULTZ M (2004) How to stop IM and spim abuse. Retrieved November 1, 2005, from http://news.zdnet.com/2100-9595_22-5201055.html. STRADER T, HOULE P and RAMASWAMI S (2005) Spam, spim, and user perceptions of e-mail and instant messaging usefulness. International Journal of E-Business Research 1(4), 51 57. SWARTZ J (2004) Spam s irritating cousin, spim, on the loose. Retrieved November 1, 2005, from http://www.usatoday.com/tech/news/ 2004-03-01-spim_x.htm. TEMPLETON G and BYRD T (2003) Determinants of the relative advantage of structured SDM during the adoption stage of implementation. Information Technology and Management 4(4), 409 428. TEO T, LIM VandLAI R (1999) Intrinsic and extrinsic motivation in Internet usage. Omega, International Journal of Management Science 27, 25 37. TODD P and BENBASAT I (1991) An experimental investigation of the impact of computer-based decision aids on the decision making process. Information Systems Research 2(2), 87 115. TODD P and BENBASAT I (1994) The influence of DSS on choice strategies: an experimental analysis of the role of cognitive effort. Organizational Behavior and Human Decision Processes 60, 36 74. VAN DEN HOOFF B,GROOT J and DE JONGE S (2005) Situational influences on the use of communications technologies. Journal of Business Communication 41(2), 4 27. VAN SLYKE C, COMUNALE C and BELANGER F (2002) Gender differences in perceptions of Web-based shopping. Communications of the ACM 45(7), 82 86. VENKATESH V (2000) Determinants of perceived ease of use: integrating control, intrinsic motivation and emotion into the technology. Information Systems Research 11(4), 342 365. VENKATESH V and DAVIS F (1996) A model of the antecedents of perceived ease of use: development and test. Decision Sciences 27(3), 451 481. VENKATESH V and MORRIS M (2000) A theoretical extension of the technology acceptance model. Management Science 46(2), 186 204. VENKATESH V, SPEIER C and MORRIS M (2002) User acceptance enablers in individual decision making about technology: toward an integrated model. Decision Sciences 33(3), 297 316. WANG C, HSU Y and FANG W (2004) Acceptance of technology with network externalities: an empirical study of internet instant messaging services. Journal of Information Technology Theory and Application 6(4), 15 28. WINDRUM P and BIRCHENHALL C (2005) Structural change in the presence of network externalities: a co-evolutionary model of technological successions. Journal of Evolutionary Economics 15, 123 148. Appendix A Scale items and constructs (Likert scale where 1 ¼ strongly disagree and 7 ¼ strongly agree) Spam perceptions (SPAM) I waste a lot of time processing unwanted e-mail. Unwanted e-mail makes it difficult to sort through useful. Perceived network externalities e-mail (NETEM) Many people use electronic mail. Many of my friends use electronic mail. Many of my family members use electronic mail. Spim perceptions (SPIM) I waste a lot of time processing unwanted instant messages. Unwanted instant messages make it difficult to sort through useful instant messages. Perceived network externalities Instant Messaging (NETIM) Many people use instant messaging. Many of my friends use instant messaging. Many of my family members use instant messaging. Perceived ease of use e-mail (PEOUEM) My interaction with an electronic mail system is clear and understandable. Interacting with an electronic mail system would not require a lot of my mental effort. I find that an electronic mail system is easy to use. I would find it easy to get an electronic mail system to do what I want it to do.

Perceived network externalities Troy J. Strader et al 65 Perceived usefulness e-mail (PUEM) Using an electronic mail system improves my communication with friends, family, and other individuals. I am able to keep abreast of things happening with my friends, family, and other individuals because of the electronic mail system. I am able to keep up my relationships with friends, family, and other individuals because of the electronic mail system. I find an electronic mail system would be useful in my communication with friends, family, and other individuals. Perceived ease of use Instant Messaging (PEOUIM) My interaction with an instant messaging system is clear and understandable. Interacting with an instant messaging system would not require a lot of my mental effort. I find that an instant messaging system is easy to use. I would find it easy to get an instant messaging system to do what I want it to do. Perceived usefulness Instant Messaging (PUIM) Using an instant messaging system improves my communication with friends, family, and other individuals. I am able to keep abreast of things happening with my friends, family, and other individuals because of the instant messaging system. I am able to keep up my relationships with friends, family, and other individuals because of the instant messaging system. I find an instant messaging system would be useful in my communication with friends, family, and other individuals. Behavioral intention to use e-mail (BIEM) Assuming I had access to an electronic mail system, I intend to use it. Given that I had access to an electronic mail system, I predict that I would use it. Behavioral intention to use Instant Messaging (BIIM) Assuming I had access to an instant messaging system, I intend to use it. Given that I had access to an instant messaging system, I predict that I would use it. Use e-mail (EMUSE) I often use electronic mail. Use Instant Messaging (IMUSE) I often use instant messaging. Demographics (categorized) Gender Age Education Income