Service source and channel choice in G2C service environments: a ...

doi:10.1111/j.1365-2575.2011.00388.x Info Systems J (2012) 22, 313–341

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Service source and channel choice in G2C service environments: a model comparison in the anti/counter-terrorism domain1 isj_388

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JinKyu Lee* & H. Raghav Rao† *Spears School of Business, Oklahoma State University, 317 North Hall, 700 N. Greenwood Ave., Tulsa, OK 74106, USA, email: [email protected], and † Management Science and Systems, University at Buffalo, WCU visiting Professor, SSME, Sogang University, Korea; 325C Jacobs Management Center, SUNY Buffalo, Amherst, NY 14260, USA, email: [email protected]

Abstract. This paper compares the relative advantages of two models (a) a two-factor (i.e. source and channel) choice model; and (b) a theory of planned behaviour (TPB)–based acceptance model, developed to explain electronic government (e-government) service adoption. The models were empirically validated in the government-to-citizen (G2C) anti/counter-terrorism (ACT) service domain by a telephone survey administered to a sample of 500 US residents systematically drawn from the mainland USA. The structured telephone survey questionnaire measured respondents’ intentions to use Web-based ACT services and their beliefs and attitudes towards various ACT service providers (e.g. the Federal Bureau of Investigation, Department of Homeland Security, local police, nongovernmental organisations (NGOs) and news companies) and media (e.g. Web, email, telephone, TV, newspapers, postal mail). The results of multiple-regression analysis of the 500 interview responses show that the source–channel choice model can explain the service usage intentions as well as, or better than, the TPB-based acceptance model. Both service source and channel exerted consistent and substantive effects on citizens’ intention to use ACT services, explaining, on average, more than 43% of variance in the dependent variable. In contrast, subjective norms in the TPB-based model seemed to have a marginal effect. Privacy protection and integrity of the service source appeared to be key antecedents of source preference, while the influence of channel attributes on channel preference varied widely depending on the type of services. This study provides an empirical basis for the validity and applicability of the source–channel choice

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This study has been supported by the US National Science Foundation under grant numbers IIS-0548917and IIS-

0809186. This research has also been funded in part by NSF under grants 0916612, 1134853 and by the World Class University program funded by the Ministry of Education, Science and Technology through the National Research Foundation of Korea (R31-20002). The usual disclaimer applies.

© 2011 Blackwell Publishing Ltd

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model and offers insightful and prescriptive knowledge for e-government initiatives and service providers. Keywords: E-government, Usage intention, Service source, Service channel, Preference model, Theory of planned behavior INTRODUCTION

This paper focuses on citizens’ intention to use electronic government (e-government) services in the anti/counter-terrorism (ACT) domain, using two different models: (a) a source–channel preference-based choice model; and (b) a theory of planned behaviour (TPB)-based attitudenorm-control model. Over the past decade, several researchers have attempted to explain citizens’ acceptance of e-government services (Carter & Bélanger, 2005; Fu et al., 2006; Lee & Rao, 2009) by adopting and extending technology acceptance and diffusion models that were originally developed in the private sector (Gefen et al., 2003; Venkatesh et al., 2003). While previous research has offered valuable insights into the factors that influence citizens’ acceptance and use of a certain e-government service, there remain some areas that have not been clarified as yet. One such area is related to the issue of multi-channel management (Chan & Pan, 2005; Pieterson & Ebbers, 2008), which has recently attracted increased research attention in both the public and private sectors. The technology acceptance/diffusion models that were introduced in the past do not explicitly take into account the multi-channel environments in which many government agencies are operating these days. For example, some citizens may prefer to file their tax returns using an e-government service channel (e.g. the Internal Revenue Service [IRS] e-filing program in the United States), whereas others may want to continue to use the traditional paper- and mail-based channel or telephone filing method (Lee & Rao, 2009). In a multi-channel environment, governments are expected to make balanced, yet citizen-centric decisions regarding service channel offerings, rather than coercing citizens to accept certain e-government services based solely on the cost savings produced by the electronic channel (Ebbers et al., 2008). Thus government agencies should ask, ‘Is this “e-channel” the right medium for our service?’ before they ask, ‘Why don’t people use our new e-government services?’ Another related issue is citizen-centric service integration and provisioning of a single point of contact (Layne & Lee, 2001; Weerakkody & Choudrie, 2005; Sarikas & Weerakkody, 2007). Unlike private-sector companies, which compete with one another, government agencies are expected to cooperate with other agencies and redesign their cross-agency operations so as to provide more efficient public services. Consequently, government agencies need to decide who should be the single point of contact (i.e. the service source) while horizontally integrating their services around citizens’ needs. Thus, the second question that should be asked before the ‘Why don’t people use our new e-government services?’ question is ‘Is this the best agency to be the contact point for citizens?’ These two questions correspond to the channel choice and source choice problems that citizens face when they need a government service. Unfortunately, previously suggested

© 2011 Blackwell Publishing Ltd, Information Systems Journal 22, 313–341

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models do not explicitly take into account these channel and source choice problems because they were originally developed for single-technology/channel (e.g. organisational IT, Web-based service) and non-cooperating stand-alone source (i.e. private organisation, independent/single agency) environments. This paper attempts to narrow this gap by introducing a novel perspective that views citizens’ acceptance of an e-government service as a function of the individuals’ preferences for the service source (i.e. provider) and channel (i.e. medium) of the service. This simple, two-factor preference model is also empirically examined and compared with a more conventional model based on the theory of planned behaviour (Ajzen, 1991; Taylor & Todd, 1995) in the anti/counter-terrorism domain. To date, only a limited amount of research has focused on e-government services for the general public (i.e. government-to-citizen [G2C], citizen-to-government [C2G]) in the ACT domain (Lee et al., 2003; Lee & Rao, 2007). Although disaster/crisis management research (Baum et al., 2007; Chen et al., 2009; Mills et al., 2009) usually includes terrorist-induced emergencies, most disaster/crisis management studies carried out in the past have emphasised the technologies for, or interactions among, responder agencies (i.e. government-togovernment [G2G] ) (Bharosa et al., 2009; 2010; Yang et al., 2009), leaving citizens outside the loop (Janssen et al., 2010). Government Web sites can be major information sources as to what happened and what citizens should do in a time of crisis (Caldwell et al., 2001). They can also serve as a C2G means of communication (Lee & Rao, 2007). Indeed, the internet-based e-government service channel would be the only bidirectional channel capable of handling the huge surge of information dissemination and processing needs to assist citizens in a panic. Unsurprisingly, many government organisations, including the US Department of Homeland Security (DHS) and the Federal Emergency Management Agency (FEMA – a sub-agency of DHS), the Federal Bureau of Investigation (FBI) and some local police departments have offered citizens various ACT services such as behavioural instructions for various types of terrorist threats (e.g. anthrax, chemical attacks), a ‘bounty list’ of most-wanted terrorists and dedicated Web links to collect public tips and leads for terrorism investigation (Lee & Rao, 2007). For example, in response to the 11 September 2001 terrorist attacks, FBI put up a public tips and leads form page on its Web site where citizens can submit information to help the agency track down terrorists in the United States. Although the needs for public safety have not been diminished, ACT e-government services have become less widely publicised as the war on terror has gradually lost its momentum in the years since the September 11 attacks. The lower visibility of ACT e-government services has two important implications. First, most citizens would be unaware of or would have a difficult time locating available e-government services (Weerakkody & Choudrie, 2005) when a disaster strikes again. This applies not only to the ACT domain but also to the wider disaster management area that deals with high-impact low-frequency threats to the public. Second, the fading away of these ACT programs might signify that the hype promoting e-government services, which was backed by the huge influx of funding in the ACT domain in the wake of the September 11 attacks, has diminished. In today’s more mature e-government environment, ACT agencies, as well as other government organisations, have to be deliberate and conscious when designing a new


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e-government service, so that they can maximise the benefits while minimising the chance of failure. As a result, e-government initiatives in the ACT domain have been forced to redesign their public services both to become more effective and to operate under a tighter budget. The primary objective of this paper is to provide e-government researchers and practitioners with alternative models of e-government service acceptance. As described earlier, many G2C e-government services operate in an environment different from e-commerce and organisational information technology (IT) environments. In particular, multi-channel service delivery and one-stop service via inter-agency business process consolidation are two important and distinct characteristics of many government services, which pose both opportunities and challenges to e-government initiatives. The new theoretical framework presented here, which views e-government service acceptance as a function of both source and channel preferences, can accommodate these two characteristics. To validate the usability and applicability of the channel–source preference model, the study described here empirically tested the new twofactor preference model and compared it with a more conventional TPB-based model. This comparison revealed the relative performance levels of the two models, thereby contributing an objective baseline for future research. The two models tested in the same e-government service contexts (i.e. ACT information gathering and reporting) also demonstrated how the different models can serve different research objectives and questions. Note that the two models were not compared so as to recommend replacing one or the other. Rather, the results of this study provide a chance to appreciate different e-government research contexts (e.g. Web site design/improvement vs. e-government projects prioritisation, integrated service design/channel mix) and the fitness of the compared models in different contexts. Accordingly, this research has several academic and practical implications. This study specifically addresses the multi-source and multi-channel environments by making a distinction between source choice and channel choice in the e-government service adoption decision process. In the ACT domain, where various government agencies can use multiple service channels to interact with citizens, user acceptance of an online ACT service can confound the source choice and the channel choice problems. Therefore, it is critical to articulate the impact of each choice on the final service acceptance, so as to develop an efficient, effective and readily acceptable source–channel mix. Equally important is the knowledge of the relative strength of the alternative theoretical perspectives. The empirical results of the model comparison provide a valuable baseline on which researchers and practitioners can make informed decisions as well as extend the base models to explain additional information systems (IS) adoption behaviours in a specific context.

THEORETICAL BACKGROUND

Theories of technology acceptance and use The technology acceptance model (TAM) (Davis, 1989) has been one of the most influential and widely cited works in the area of IT adoption and acceptance. TAM started as a prediction



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model that explained users’ acceptance of a new technology with two major determinants: perceived usefulness and perceived ease of use in an organisational setting, based on the theory of reasoned behaviour (TRA) (Fishbein & Ajzen, 1975). Adopting a socio-psychological orientation, TRA considers a human behaviour as a direct function of the subject’s intention to perform that behaviour, which is in turn mainly determined by the subject’s attitude towards the specific behaviour and perceived social pressure (i.e. subjective norm), all formed by related beliefs. TRA was extended to create TPB by incorporating the concept of practical controllability of the behaviour in question (i.e. perceived behavioural control), which is hypothesised to influence the actual behaviour directly as well as indirectly via behavioural intention (Ajzen, 1991). TAM has also been extended and extensively tested to include a number of antecedents of technology adoption behaviour, not only in organisational IT contexts but also in e-commerce and e-government contexts. For example, Venkatesh et al. (2003) conducted a comprehensive review of previous technology adoption studies, which integrated the major theoretical perspectives (i.e. TRA, TAM, TPB, motivational model, diffusion of innovation and social cognitive theory). They then used this knowledge to develop the unified theory of acceptance and use of technology (UTAUT), which features four core determinants of behavioural intention: performance expectancy (i.e. perceived usefulness, relative advantage, outcome expectations), effort expectancy (i.e. perceived ease of use), social influence (i.e. subjective norm, social factors) and facilitating conditions (i.e. perceived behavioural control, facilitating conditions). The empirical test of the UTAUT model revealed that only performance expectancy has consistent and strong positive effects on IT usage intention; the other factors are moderated by the specific context of the research (e.g. voluntary vs. mandatory use, demography of the user group) (Venkatesh et al., 2003). The findings from this line of research can, therefore, help managers estimate the chance of success for new IT adoption, or to develop solutions to encourage user acceptance within the given context. Like the TAM-based research stream, trust-centric models attracted enormous attention when they were introduced, and quickly assumed a major role in the various internet-based e-commerce acceptance studies conducted in recent years. The ready embrace of such models is partly attributable to the widespread uncertainty that prevails in the internet environment where users of a Web-based technology (e.g. an online shopping system) become much more vulnerable to unexpected behaviours of online entities than organisational IT users dealing with their employers (Lee & Rao, 2007). The early e-commerce trust model (McKnight et al., 2002) was also based on the TRA-TPB lineage and views an online exchange behaviour as a result of an individual’s trusting beliefs (e.g. integrity, benevolence and competence of the trustee) in his or her exchange partner and institutional safeguards (e.g. legal and technological assurance) built around the exchange relationship. The e-commerce trust model was subsequently integrated with TAM-based models in both e-commerce (Gefen et al., 2003) and e-government service (Lee et al., 2003; Carter & Bélanger, 2005; Lee & Rao, 2009) areas. This line of TAM-plus-trust research suggests that citizens’ intention to use an e-government service is influenced by perceived attributes of the service system (e.g. perceived usefulness/ease of use, satisfaction), the service provider (e.g.


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trusting beliefs, online competence) and the environmental conditions (e.g. subjective norm, compatibility, structural assurance) (Carter & Bélanger, 2005; Gefen et al., 2005; Fu et al., 2006; Lee & Rao, 2009), while trust in and perceived online competence of the service provider can increase the perceived usefulness of the e-government service (Lee & Rao, 2009). While the previously mentioned theories and models have made a great contribution to the IS research field, some concerns and criticisms have been raised against the seemingly universal application of the core technology acceptance models (e.g. TAM, UTAUT, TAMplus-trust) to different domains with different environments. Initially, the technology acceptance models were developed to understand why people do or do not accept a new technology made available to them, with an assumption being made that high acceptance and usage are equivalent to IS success for the adopting organisation (Karahanna et al., 1999; DeLone & McLean, 2003; Lee et al., 2011). This is quite a different concern from the questions that e-government initiatives and citizens are facing in today’s multi-source/multichannel environments – namely, what is the best mix of the service source (i.e. provider) and channel (i.e. medium) for effective provisioning of the public service? For example, if a biochemical terrorist attack is suspected, will threatened people turn to local police, DHS or someone else for information and instructions? Will they call, email or rush into the agency’s office in person? The presence of an alternative is not considered in the original TRA so some choice problems may not be adequately explained by TRA-based models (Sheppard et al., 1988).

Theories of decision-making under uncertainty An alternative way to understand e-government service acceptance is to interpret the acceptance (or rejection) behaviours as a result of decision-making. Prospect theory2 (PT) (Tversky & Kahneman, 1992) is a economic-psychology theory of decision-making that extends the subjective expected utility (SEU) theory (Schoemaker, 1982) by accommodating bounded rationality/irrationality of human decision-makers under uncertainty. PT and the underlying SEU theory posit that decision makers evaluate the expected utility of their alternative options and choose the one that gives the best subjective expected utility (Dhami & al-Nowaihi, 2007). According to PT, human decision-makers may simplify the alternative evaluation formula at the early stage of their decision-making process, known as the editing phase, by purging the common components from their alternatives. In addition, people are concerned more about losing their status quo (also known as the ‘framing effect’) and put more weight on extreme cases, even if the objective probability of such events is very low. This consideration of bounded rationality enables PT to explain some inconsistencies found in technology acceptance behaviours. For example, Falk et al. (2007) argued, based on status quo bias theory (which is derived from PT), that customers may not accept a newly added service channel (i.e. 2

Prospect theory and expected utility theory have gone through several revisions, and the revised theories are sometimes referred to as cumulative prospect theory and rank-dependent expected utility theory, respectively. We use the original names to refer to both the original and revised theories in this paper.



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online), unless they find an overwhelming reason to abandon the traditional (i.e. off-line) service channel that has satisfied the customers thus far. In the e-government area, Lee & Rao (2007) applied PT to categorise different types of risk and their counter-beliefs involved in the ACT service context and empirically examined their effects on citizens’ intentions to use Web-based ACT services. The results of this study revealed a positive effect of citizens’ belief in the service provider’s domain competence (i.e. competence in ACT operations) and a negative effect of perceived privacy risk (posed by the service provider) on intention to use the e-government services; both of these effects were stronger than the effect of relative usefulness of the e-government service. However, the positive effect of citizens’ belief in the service provider’s online competence (i.e. competence in providing Web-based services) on their perception of relative usefulness became non-significant when the analysis controlled for their experience with the e-government service. These findings, along with those from PT and earlier trust studies, suggest an explanation for some of the inconsistent findings about the role of trust/trusting beliefs in the literature. That is, trust3 matters only when (1) people can sense a risk in at least one of their options; (2) the risk cannot be fully addressed by other measures (e.g. contract, technology), so they have to rely on the mercy of other people; (3) the risk is not common to all alternative options; and (4) the difference that the trustee can make on the expected utility of the risky options is large enough to overturn the existing preference order (i.e. status quo) among the alternative options. People may not realise that there is a risk involved in their e-government service use, and the level of understanding of that point may differ across culture (Gefen et al., 2005). In addition, some risks may not be fully addressed by involved people. For example, the insecure technological infrastructure of the internet may be a serious concern for all kinds of e-services, but neither the service provider nor the user can do much about the fundamental security risk underlying all internet-based service systems (Lee & Rao, 2007). In a single-source multichannel environment where a monopolistic service provider offers multiple modes of service delivery, the risk of opportunistic behaviour and the trustworthiness of the service provider is a common factor to every available service channel, which will be cancelled out in the editing stage (Kahneman & Tversky, 1979). Finally, even in a multi-source environment, trust itself may not exert decisive impact on the final choice if the trustworthiness of all alternative service sources exceeds an acceptable threshold. Therefore, general trust (i.e. a combination of integrity, benevolence, competence) in alternative e-government service providers may fail to discriminate among the alternatives in a high-trust society such as the United States, where citizens tend to place trust in their government, government agencies and legal systems (Gefen et al., 2005). Not only trust, but any other factor that has been considered as a major determinant, including the core variables in TAM, may fall short in explaining some other technology acceptance phenomena across time, culture and context (Fu et al., 2006; Carter & Weerakkody, 2008; Lee 3 We limit the scope of trust here to trust in a (group of) human interaction partner, although some researchers have extended the trust concept to include beliefs about artefacts (e.g. legal system, IT tool/system, the internet).


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& Rao, 2009) because the types of risk and their counter-beliefs perceived by potential users will vary with the given alternatives, environments and time (Elke & Christopher, 1998). This shortcoming is even more likely to occur in the e-government field because the variety of roles that governments play creates heterogeneous contexts for a wide range of e-government services. Some important factors in one e-government service domain might have been dropped in a parsimonious IT adoption model evolved in the private sector environment or a different public service domain (e.g. mandatory taxation vs. voluntary public safety/national security). Given this possibility, a general model of e-government service acceptance should include a small number of direct determinants that exert stable effects across time and context, and can mediate the effects of a wide range of context-specific antecedents. Attitudes, perceived behavioural control, and subjective norms Attitude is a core concept in the TPB (and in the earlier TRA) model. Attitudes are known to be a direct determinant of behavioural intention along with the other two determinants: subjective norms and behavioural control. Because this heavily cited reference model is a sociopsychological theory of human behaviour, the theory should hold in explaining a wide range of human behaviour in various social contexts (Sheppard et al., 1988). Although the original TAM model and some later IT acceptance models followed the original structure of TRA/TPB and included attitude in their model (Agarwal & Prasad, 1999; Karahanna et al., 1999; Thrane et al., 2004), a large number of IT acceptance studies have omitted or justified the omission of the attitude concept in favour of parsimony or due to multi-collinearity with other contextspecific variables (Davis et al., 1989; Venkatesh et al., 2003; Carter & Bélanger, 2005; Meso et al., 2005; Fu et al., 2006; Lee & Rao, 2009). One issue with attitude is the multidimensionality of the concept, which often results in confusion in terms of definitions, operationalisation and measurements. Attitude has been contextualised as an ex-ante judgement about IT adoption and use, as well as an ex-post evaluation of previous experiences IT (Devaraj et al., 2006; Tsai & Huang, 2007). In addition, Brock & Sulsky (1994) identified two dimensions of attitude: general attitude towards an IT object and the belief about benefits from the IT. Wixom & Todd’s (2005) conceptualisation of attitude, for example, is closer to an ex-post evaluation (i.e. user satisfaction) of a general IT object (i.e. a Web site). Such object-based attitudes were found to be limited in their ability to predict IT acceptance and use due to the lack of the specification on the target behaviour (Wixom & Todd, 2005). On the other hand, attitude in TRA/TPB (i.e. attitude towards a behaviour) refers to an individual’s positive or negative feelings about performing a target behaviour (e.g. using an e-government service) (Ajzen & Fishbein, 1980). This attitude should be specific to the time, target and context of the behaviour of interest, to be a good predictor of the behaviour or behavioural intention (Ajzen, 2002). In other words, the causal effect and predictive power of a clearly specified attitude towards e-government service adoption should not be underestimated. Facilitating conditions and self-efficacy, which were suggested as determinants of perceived behavioural control (Taylor & Todd, 1995), have also been discounted in some TAM-



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based models because of their non-significant effects in the presence of core TAM/UTAU constructs (i.e. performance expectancy and effort expectancy constructs) (Venkatesh et al., 2003). The UTAUT model suggests that the facilitating condition does not influence IT usage intentions, but rather that only its interactions with age and experience can influence actual usage behaviours (Venkatesh et al., 2003). Nevertheless, the lack of effects from facilitating conditions and self-efficacy might derive from the invariance of data from the typical settings of organisational IT and e-commerce studies. In a typical organisational IT setting, potential users are provided with convenient access to the technology implemented in their workplaces (Davis et al., 1989); similarly, in e-commerce studies, access to the internet and the Web sites in question is often assumed as given among already-online customers. In contrast, in the G2C e-government context, the target of government services is the public, who may or may not have access to internet. The ‘digital divide’ is still a major hurdle for e-government services, even in leading and visionary e-government countries such as the United States and the UK (Weerakkody & Choudrie, 2005; Smith et al., 2009), as well as developing countries (Weerakkody et al., 2007). In the ACT service context, in which timely information flow is critical for public safety and national security, the importance of perceived availability/accessibility to ACT e-government services cannot be emphasised enough. To adequately explain the phenomena in the e-government environments, where typical assumptions for organisational IT or e-commerce environments do not hold, the concept of perceived behavioural control should be brought back and re-examined. Another factor that deserves more attention is social influence (e.g. subjective norms, images). Although this factor was shown to be significant in early technology acceptance studies (Venkatesh & Davis, 2000), the UTAUT model (Venkatesh et al., 2003) suggested that social influence affects technology acceptance only when certain conditions exist. For example, it exerts significant influence when the acceptance is mandatory, when the users are older women or when the users are in the early stages of individual experience with the IT. This finding, however, should not be interpreted as a reason to drop the social influence factor from the model. As discussed earlier, any factor can be non-significant when the study context steps out of the previously assumed boundary conditions (e.g. when use of IT is voluntary; when the IT adoption model is applied to explain continued use, rather than initial adoption). In many e-government research contexts dealing with heterogeneous groups of people, a wide scope of socio-cultural perspectives is essential (Sarikas & Weerakkody, 2007). Evidence of social influence is well documented outside the typical organisational IT adoption area. According to Abrahamson & Rosenkopf (1993), the sheer number of adopters of a new innovation can exert a ‘bandwagon effect’ that encourages non-adopters to accept and use the innovation. This bandwagon effect can be substantive when the return from an innovation is ambiguous (i.e. risky). Institutional bandwagon pressures come from the fear of being the only one who missed an important business opportunity, which leads to a belief that one would be better off staying in the majority even if they all make a wrong decision. In the ACT service context, citizens’ fear of being left behind in a vulnerable place under a terrorist attack and suffering from a relatively lower level of protection than other citizens is likely to increase as the number of adopters of a particular online ACT service increases. This type of social pressure has been often


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overlooked in the previous e-government service acceptance models, and thus should be re-evaluated. Source and channel preference Another approach to understanding e-government service acceptance is to adopt a different theoretical framework that can address the ‘choice’ problem at hand. A theoretical framework that assumes a multi-source/multi-channel environment allows for answering such questions as ‘Which agency should be designated to provide a particular public service?’, ‘Should we give a priority to an e-government development project for this particular service?’ and ‘Which agency or service medium is slowing down the adoption of the e-government service?’ The two most distinctive choice variables that citizens can think of in the ACT service context are the choice of service provider (i.e. source) and the choice of service medium (i.e. channel), each of which will become a major factor in the subjective expected utility calculation. Therefore, separation of source preference and channel preference may mirror what happens in citizens’ e-government service adoption decision-making process, while the concept of preference explicitly recognises the multiple alternatives and resulting valuation of the alternative in question. The preference of each decision variable (i.e. source, channel) can also represent all three TPB factors (i.e. behavioural attitude, perceived behavioural control and social influence) for the decision variable in the sense that a preference refers to individual’s choice of, or liking for, using a particular source or channel after taking into account other alternatives and their associated risks. To date, few studies have focused on source preferences. A recent paper on channel preference in the private sector suggested that consumers’ satisfaction with an online channel is strongly related to their preference for the channel (Devaraj et al., 2006). This study also identified that time responsiveness, personalisation, security and reliability are the conduct variables significantly related to the consumer satisfaction outcome with the channel. Therefore, different conducts and attributes of different ACT agencies and service channels might be expected to result in different levels of satisfaction and preference of the sources and channels. The cognitive-affective model of organisational communication suggests that the type of communication strategies affect and are affected by the attributes of the communication medium and the message, and that an individual is induced to choose the most appropriate communication medium and message form for the communication strategy that he or she employs (Te’eni, 2001). The attributes of the communication medium considered in this dynamic selection include channel capacity, interactivity and the adaptability of an available communication medium, while major attributes considered in the selection of the message form include size, distribution, organisation and formality of the message. The early mediarichness literature projected various media onto a single dimensional continuum of channel richness or the amount of information that can be transmitted during a communication. Te’eni’s (2001) model offers an important extension to the media-richness theory in that it recognises the multidimensionality of a communication medium and suggests a contingency structure between the sub-dimensions and the strategies.



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Channel attributes While Te’eni’s (2001) study is concerned with communication in organisational settings, citizens are also expected to distinguish different communication channels and strategically choose the best available channel when they use ACT services. The idea of a strategic choice of communication medium and the consideration of the message attributes offer insight into the ACT service channel choice. Dimensions of communication channels that are particularly relevant to ACT services and, therefore, are seriously considered by the communicating partners (i.e. ACT agencies and citizens) include, but are not limited to, network reliability, availability, timeliness, interactivity and privacy risk (Devaraj et al., 2006; Singh et al., 2009; Rajan et al., 2010). Network reliability refers to the robustness of a service channel. On 11 September 2001, the internet and mobile phone networks proved unreliable as some parts of the networks were overloaded and could not be used. Given that the major news Web sites were overloaded right after the 2001 attacks, and that ACT Web sites are inevitably strategic targets of denial of service (DoS) attacks, perception of network reliability of an ACT service channel will be an important factor in citizens’ choices of ACT service channels. Availability in this study reflects the ubiquity of the service channel access points. While the internet has been a major communication and marketing channel for younger generations, convenient internet access is not yet available for a large portion of population in many countries (Thrane et al., 2004; Dwivedi & Lal, 2007). People who do not have a broadband internet connection at home (37% of US adults, as of April 2009) (Horrigan, 2009a) or who stay outside without broadband wireless internet access (68% of US adults, as of April 2009) (Horrigan, 2009b) will not consider internet-based ACT services as a viable option. The timeliness concept captures information dissemination speed. While the internet has greatly improved the diffusion of information across a wide range of geographic locations, last-minute ACT information may appear more slowly on the Web than on TV or radio (e.g. live coverage) due to the additional time required for Web publication (e.g. media format conversion, data transfer to the Web site). Also, time-critical information reported through an ACT Web site or via email to an ACT service provider may not be utilised immediately because of the asynchronous data processing architecture (Rao et al., 2009) that prevails among traditional Web-based database applications. The recent development and diffusion of mobileinternet devices (e.g. internet-enabled 3G/4G mobile phones and tablets) and Web 2.0 technologies (e.g. instant sharing and redistribution of live or repackaged contents) represent a fascinating opportunity for e-government initiatives, but it also poses a challenge because citizens’ perceptions of the channel attributes, or conduct (Devaraj et al., 2006), of internetbased channels will change as they experience the convergence of the internet and cell phone networks. Timeliness of the channel is one dimension that will be heavily affected by this transformation in communication, but such transformation may also influence the full spectrum of citizen perceptions on internet channels. Accordingly, e-government initiatives may need to recognise the mobile internet as a separate service channel and explore the fit between its perceived channel attributes and the proposed e-government services. The concept of channel


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preference can be utilised to measure citizens’ acceptance of a mobile channel in the planning stage and help the e-government initiative determine its appropriateness. Interactivity of a service channel refers to a communication channel that provides an immediate response to an input, as suggested in Te’eni’s (2001) propositions. Immediate clarification of citizens’ questions and additional information requests will be a valuable attribute of ACT service channels for information learning. Likewise, immediate receipt confirmation of public leads, information about potential terrorists and terrorist acts that a citizen submits to an ACT agency, will provide extra assurance to citizens. Privacy of a service channel is the level of privacy assured by the channel. Depending on the information technology, messages between citizens and ACT service sources can be completely public (e.g. CB/GMRS/FM/AM radios), considered to be public (e.g. email, Web), considered to be private (e.g. telephone, postal mail), forced private (e.g. messages through an internet anonymiser such as Tor 4) or anywhere in between. When an individual needs to transmit private information to an ACT service provider or requires anonymity, the privacy of the service channel will be an important determinant of the public’s acceptance of the service channel (Lee & Rao, 2007). Our literature review indicates that today’s mainstream technology adoption models have evolved from TRA, but in the process of making a parsimonious model for private sector IT adoption contexts, they have dropped some social factors (e.g. behavioural control, subjective norms) in the original model. Also, such private-sector oriented models cannot answer some important questions that government organisations must answer (e.g. the best combination of government service source and channel, e-government project prioritisation). Service source and channels are likely to be the most salient factors that citizens would consider for public service adoption decisions. Therefore, a new perspective that understands citizens’ e-government service adoption as a function of their source and channel preferences can offer a valuable insight to e-government researchers and practitioners. Accordingly, we develop and compare two (i.e. a TPB-based vs. a source-channel preference-based) models in the following sections.

MODEL DEVELOPMENT

TPB-based model vs. two-factor preference model We adopt citizens’ intention to use an e-government service as the dependent variable of our two models, assuming that wide adoption of e-government services is an important objective and, therefore, a good proxy measure of success for G2C e-government service development projects. In the study context, an e-government service refers to two specific ACT services: the provision of information about a potential terrorism threat to the public and the provision of a C2G communication channel for public leads and tips regarding terrorism activities. The first model explains the intention to use an e-government service based on the three factors suggested by the theory of planned behaviour: attitude towards, subjective norm of and 4

See Dingledine et al. (2004) Tor: The Second-Generation Onion Router, In Usenix Security.



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perceived control over the behaviour of using a Web site to receive the e-government service. As discussed earlier, the original structure of TPB can provide a general framework that is applicable to a wide range of e-government service contexts and avoid overlooking a relevant factor – which can happen when an applied model (e.g. TAM, UTAUT, online-trust) that was developed in one context is applied to a different context. Because one of the objectives of this study is to compare different theoretical perspectives, this basic TPB-based model should serve as a baseline model that can show the relative performance of the second model, yet can be extended in future e-government research by adding context-specific antecedents of the three direct determinants (i.e. attitude, perceived control, subjective norm). The second model considers citizens’ intention to use an e-government service as a function of personal preference based on two factors: the source of the service (i.e. service provider) and the channel of the service (i.e. service medium). While the first model, just like many other IT adoption models, dichotomises the decision options (i.e. use vs. don’t use), this two-factor (i.e. source and channel) preference model frames the situation as a multiple-choice problem wherein citizens need to pick one solution out of many alternative solutions that can lead to the same terminal objective. Nevertheless, given the limited knowledge and bounded rationality of lay people, this simplified decision-making model includes only the two most salient aspects of the given alternatives – the service provider and the service medium. As discussed earlier, the simplification mechanism reduces the number of factors and possible outcomes to be considered by reformulating (the editing phase in the prospect theory) the selection problem in such a way that common factors and outcomes across alternatives cancel one another out, leaving only the salient factors to be taken into account in the final comparison (Kahneman & Tversky, 1979). In this approach, the preference of a source (or channel) represents the result of multiple comparisons between the estimated subjective utilities expected from using available service providers (or service media). Figure 1 depicts the two models explaining citizens’ intention to use e-government services in the ACT domain. These models should not be considered as two exclusive and incompatible views, however. The first model and its associated approach are based on a general theory developed to explain various human behaviours, while the second model simulates a decision-making process with the minimum set of decision factors, because not all possible TPB factors can be considered in a complex selection problem. The chain of influence from past experience to belief, to attitude and finally to a decision, as suggested in TPB, is a common underlying premise in the both perspectives. The two-factor preference model focuses on only the later part of the chain. Subsequently, the second model can be extended by adding beliefs or attitudes towards the behaviour of using a particular ACT service provider, regardless of the service medium, and vice versa, as the determinants of each of the two preferences. The three specific research questions to be addressed by the model comparison are as follows: 1 Do both source and channel preferences exert significant and substantial influences on e-government service acceptance? 2 Can the two-factor preference model explain citizens’ e-government service acceptance as well as or better than the TPB-based baseline model?


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TPB-based Model

Attitude towards e-gov service use

Subjective norm of e-gov service use

Perceived control over e-gov service use

Intention to use e-gov service

For ACT information gathering

For ACT information reporting

Service source preference

Service channel preference

Two-factor Preference Model Figure 1. Two analytical frameworks for e-government service adoption: TPB-based model vs. two-factor preference model.

3 Do both models exhibit consistent relationships and stable performance across different service sources and channels in the ACT domain?

Mean comparison of source and channel attributes This research examines interviewees’ trusting beliefs (i.e. domain competence, online competence, privacy protection, integrity) and perceived channel attributes (i.e. reliability, privacy risk, timeliness, interactivity, availability) for some alternative ACT service sources and channels so as to provide domain-specific prescriptive knowledge to ACT e-government initiatives and researchers. Providing a foothold for model extension, we posit that the source-specific counter-risk beliefs (i.e. trusting beliefs in ACT service providers) can differentiate the preference levels of alternative service sources, whereas channel attributes can differentiate the preference levels of alternative service channels. To test these relationships, the study first compares the preference levels of five alternative ACT service providers (i.e. DHS, FBI, local police, news companies and non-governmental



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ACT research organisations)5 and six service media (i.e. Web, TV, telephone, email, postal mail and newspapers) for the two types of ACT services (i.e. information gathering and reporting).6 The five alternative ACT service providers were selected to cover both public and private entities that possess varying degrees of authority, familiarity and expertise in the ACT domain. All five alternatives offered some sort of ACT information or ACT information collection facility on their Web sites during the research period. The Web and the other five service channels include common telecommunication channels and mass media, both internet-based (i.e. Web and email) and conventional (i.e. TV, phone, postal mail, printed newspapers). Given the wide range of ACT service sources and channels, our data are likely to include considerable variance in the preference scores of the sources and channels. Upon identification of the most preferred and least preferred ACT service sources and channels for each service type, the mean values of the source and channel attributes were compared pair-wise (i.e. most vs. least preferred). The specific hypotheses for the mean comparison were as follows: Source attributes H1-1. Citizens’ belief in the domain competence of the most preferred ACT service source is significantly stronger than that of the least preferred source. H1-2. Citizens’ belief in the online competence of the most preferred ACT service source is significantly stronger than that of the least preferred source. H1-3. Citizens’ belief in the willingness to protect privacy of the most preferred ACT service source is significantly stronger than that of the least preferred source. H1-4. Citizens’ belief in the integrity of the most preferred ACT service source is stronger than that of the least preferred source. Channel attributes H2-1. Citizens’ perception of the reliability of the most preferred ACT service channel is significantly higher than that of the least preferred channel. H2-2. Citizens’ perception of the privacy risk of the most preferred ACT service channel is significantly lower than that of the least preferred channel. H2-3. Citizens’ perception of the timeliness of the most preferred ACT service channel is significantly higher than that of the least preferred channel. H2-4. Citizens’ perception of the interactivity of the most preferred ACT service channel is significantly higher than that of the least preferred channel. 5

For categorical items (i.e. news media companies, non-governmental ACT research organisations), the interviewer

provided examples (e.g. CNN, The Washington Post/RAND Corporation, Terrorism Research Center). 6 For each service type, only applicable media were examined (i.e. Web, TV, phone and printed newspapers for information gathering vs. Web, phone, email and postal mail for information reporting)


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H2-5. Citizens’ perception of the availability of the most preferred ACT service channel is significantly higher than that of the least preferred channel.

RESEARCH DESIGN AND METHODOLOGY

This study adopted a telephone survey method to collect data. Participants of the survey were systematically sampled to represent the adult (18 years or older) residents of the lower 48 (contiguous) states in the United States, using a telephone list. To set the situational contexts of the questions, respondents were asked to imagine a hypothetical situation in which they might want to collect information about biochemical terrorism, and another situation in which they might want to report potential terrorism activities. The telephone survey was outsourced to a market research company that used an automated system to generate random telephone numbers; it continued to conduct the actual telephone interviews until the target number7 of usable responses (n = 500) was collected according to stratified sampling criteria. The criteria were specified by the researchers to assure that the sizes of the state-level subsamples reflected the geographical and gender distributions of the US population. The data collection took place when terrorism was an increasing concern worldwide8 (in 2006). It took approximately 1 month to achieve the target sample size (i.e. 500 usable responses).

Survey instrument Telephone interview scripts and questions were developed based on the literature in the IS adoption area and preliminary studies of the authors. While many questionnaires used in previous studies have employed multi-item measures, the current survey instrument consisted of single-item measures mainly because of the time constraints in the telephone survey. The questionnaire included measures for intention to use, in terms of subjective probability (a percentage), the two types of ACT services (i.e. ACT information gathering and information reporting) for two ACT agencies (i.e. FBI and DHS). Also included in the instrument were two sets of antecedents, as shown in Figure 1. The first set included the measures for TPB factors – attitude towards behaviour, social norm and behavioural control – which were measured in a 5-point agreement scale item. The TPB factors should be specified in a way that corresponds to the time, target and context of the behaviour of interest so as to be good predictors of the behaviour or behavioural intention (Ajzen, 2002). Accordingly, the interview scripts and questions in the questionnaire sought to measure the positive and negative feelings associated with the behaviour (Fishbein & Ajzen, 1975) of using a Web site of a particular ACT service provider in a particular circumstance (i.e. learning about a possible ongoing bioterrorism attack and 7

This target sample size was set to facilitate use of the most data-demanding analysis technique in a larger research

project context. The 500 usable responses are well above the sample size requirement for the multiple regression analyses presented in this paper. 8 http://www.msnbc.msn.com/id/18399660/ns/world_news-terrorism/



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reporting a terrorism suspect). The subjective norm measures sought to capture the descriptive norm of performing the behaviour in question so as to avoid the low variability problem (Ajzen, 2002). These measures used the American public as the reference group for the decision-maker in the study context based on the bandwagon effects suggested in the diffusion of innovation theory (Abrahamson & Rosenkopf, 1993). The behavioural control measures represent the perception of responders’ controllability over their own behaviour of using a particular Web site under the given situation. The preference values in the second set were measured in a 10-point preference scale. The use of different measurement scales was intended, in part, to alleviate the potential problems of common method variance (Podasakoff et al., 2003). Each respondent was asked about two service sources (i.e. FBI and DHS) for the model comparison measures. A sample set (for the FBI-Web option only) of the measurement items and scripts for the telephone interview appears in Table A1 in the Appendix. For the mean comparison of source and channel attributes, each interviewee was asked to provide his or her preference levels and perceptions on all alternative sources and channels for each service type. The duration of telephone interview varied, but the average was approximately 15 minutes. Data analysis Comparative analyses of the TPB-based model and the two-factor preference model were done by using a multiple regression analysis technique. Although the single-item measures did not allow more vigorous tests, especially the reliability and validity of the measures (Dow et al., 2008), the basic statistics of the data, including correlations and Exploratory Factor Analysis results, were inspected to ensure there were no indications of problems in the data and models (e.g. multi-collinearity). The telephone interview method also compensated for the limitation of the measurement instruments, providing for a higher level of data quality. The person-toperson interactive data collection method prevented respondents from providing insincere or indifferent answers without reading the questions – a problem that prevails in many unattended paper/Web-based questionnaire surveys. Preliminary analysis on the average source–channel preference scores shows interesting results about the ACT service providers and service media examined in the study (Table 1). The second and third columns in Table 1 show the preference scores of the service sources and channels for the two types of ACT services. The preference scores were measured independently, and interaction effects between sources and channels were not considered. As shown in the table, local police turned out to be the most preferred service provider, among the examined sources, for both types of ACT services (i.e. information gathering and reporting), while news companies were the least preferred source for ACT information. In terms of channel preference, TV was the most preferred medium, among the surveyed channels, for information gathering. Interestingly, the telephone was the most preferred medium for information reporting, yet the least preferred medium for information gathering. Based on these results, we conducted a series of paired-sample t-tests that compared various source and channel attributes perceived by individuals. The results of the paired


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Table 1. Preferences for ACT service sources and channels Information gathering Source preference Police DHS NGO FBI News companies Channel preference TV Web Newspapers Phone

Information reporting

5.88 5.42 4.67 4.59 4.04

8.53 7.52 6.71 3.41 3.03

Police FBI DHS NGO News companies

7.86 6.49 6.41 5.36

8.21 3.55 3.40 2.70

Phone email Web Mail

All preference scores were measured in 10-point interval scale. The most and least preferred sources and channels are shown in bold.

sample t-tests are presented in the following section. We used a general statistical package (SPSS) to conduct the various basic statistical tests as well as the main multiple-regression analysis for model comparison.

RESULTS

Tables 2 and 3 present the results of regression analyses that compared the TPB-based model and the two-factor source–channel preference model. The results in Table 2 are calculated with the first type of dependent variable – intention to use an e-government service for ACT information gathering; the results in Table 3 are for the other type of service – ACT information reporting. Each table includes results of two comparisons, one for each ACT agency (i.e. FBI and DHS). At a glance, both models appeared to work well, consistently accounting for more than 30% of the variance in the dependent variables for all four cases. The second and third columns in Table 2 (information gathering) indicate that all of the tested independent variables had significant effects (p < 0.001) on the dependent variable. When the two models’ explanatory powers are compared, the two-factor source–channel preference model seems to explain considerably more variance (9.7% in the FBI case; 7.5% in the DHS case) in the dependent variable with the smaller number of independent variables. For the information reporting service (Table 3), the results of the comparison of explanatory power are somewhat less obvious. Compared to the TPB-based model, the preference model could explain 5.4% more variance in the dependent variable when it was tested against the DHS-Web site data set, but it explained 2% less of the variance in the FBI-Web site data set. Nevertheless, the overall result suggests that the explanatory power of the two-factor preference model is as strong as, if not stronger than, the conventional TBP model, explaining, on average, more than 43% of the variance in e-government service usage intentions of the public.



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Table 2. Model comparison: information gathering Variable

Standardised beta

Dependent variable: Intention to use FBI Web site 1. TPB-based model Attitude towards the behaviour 0.275 Subjective norm 0.144 Perceived behavioural control 0.398 2. Two-factor preference model Source preference (FBI) 0.296 Channel preference (Web) 0.614 Dependent variable: Intention to use DHS eebsite 1. TPB-based model Attitude towards the behaviour 0.294 Subjective norm 0.153 Perceived behavioural control 0.379 2. Two-factor preference model Source preference (DHS) 0.358 Channel preference (Web) 0.600

Significance level

Adjusted R2

0.000 0.000 0.000

0.366

0.000 0.000

0.463

0.000 0.000 0.000

0.397

0.000 0.000

0.472

Significance level

Adjusted R2

0.000 0.749 0.000

0.435

0.000 0.000

0.415

0.000 0.022 0.000

0.331

0.000 0.000

0.385

Table 3. Model comparison: information reporting Variable

Standardised beta

Dependent variable: Intention to use FBI Web site 1. TPB-Based Model Attitude towards the behaviour 0.450 Subjective norm 0.013 Perceived behavioural control 0.324 2. Two-factor preference model Source preference (FBI) 0.196 Channel preference (Web) 0.573 Dependent variable: Intention to use DHS Web site 1. TPB-based model Attitude towards the behaviour 0.302 Subjective norm 0.101 Perceived behavioural control 0.336 2. Two-factor preference model Source preference (DHS) 0.270 Channel preference (Web) 0.524

A closer look at the results of each TPB-based model testing suggests that perceived behavioural control plays the major role (b = 0.324–0.398, p < 0.001) in adopting both types of ACT e-government services (i.e. information gathering and reporting), while attitude towards the behaviour (b = 0.275–0.450, p < 0.001) exerts a stronger influence for information reporting services (Table 3). In contrast, social pressure (i.e. subjective norm) failed to show a statisti-


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cally or substantively significant impact (b = 0.013, p > 0.05; b = 0.101, p = 0.022) in the information reporting cases (Table 3). Its effects are statistically significant in information gathering cases (Table 2) but are relatively smaller than the other two factors. Regarding the two-factor preference model, both source preference and channel preference are statistically and substantively significant determinants of citizens’ e-government adoption intentions. However, channel preference showed much stronger effects (b = 0.524–0.614, p < 0.001) relative to source preference (b = 0.196–0.358, p < 0.001) across all four cases. While source preference maintained a respective influence in all cases, its effect became less deterministic for reporting information. Nevertheless, the presented results cannot confirm the loss in the impact size across the service types; thus this issue should be further explored in a future study. Overall, the model comparison results suggest three conclusions:

• • •

Both source and channel preference exert significant and substantial influences to e-government service acceptance. The explanatory power of the two-factor preference model is comparable to or stronger than the TPB-based model in our samples. Both the TPB-based model and the two-factor preference models exhibit consistent relationships and stable performance across different service sources and channels, except that subjective norm that became non-significant in one out of four cases.

Context-specific findings Tables 4–8 present interview respondents’ perceptions of various attributes of alternative ACT service providers and service media. Table 4 compares the respondents’ perceptions of DHS – the highest government authority in the ACT domain – and the local police – a lower-level authority only remotely related to ACT. Table 5 compares the most preferred (local police) and least preferred (news companies) ACT service sources for both service types. Keep in mind that the local police were preferred over all other ACT service sources surveyed, including DHS and FBI. According to the results of this study, citizens believe that DHS is as competent as or slightly more competent than local police in ACT operations and online services. At the same time, DHS is perceived as having a lower level of integrity and being less likely to protect citizens’ privacy than local police. Local police are perceived to offer significantly stronger domain (ACT)

Table 4. Perceived source attributes: dhs vs. local police Mean

ACT competence Online competence Privacy protection Integrity

DHS

Police

Mean difference

Significance (2-tailed)

3.32 3.32 2.87 3.26

3.29 3.00 3.36 3.61

0.026 0.320 -0.487 -0.356

0.727 0.000 0.000 0.000

All perceptions were measured using a 5-point interval scale, where 1 = lowest level and 5 = highest level.



Table 5. Perceived source attributes: local police (most preferred) vs. news companies (least preferred) Mean

Police

News companies

Mean difference


3.28 3.01 3.36 3.60

2.30 2.63 1.93 2.18

0.978 0.374 1.422 1.419

0.000 0.000 0.000 0.000

ACT competence Online competence Privacy protection Integrity

Table 6. Perceived channel attributes: Web vs. TV Mean

Reliability Privacy risk Timeliness Interactivity Availability

Web

TV

Mean difference


3.31 3.50 3.96 3.49 3.92

3.25 2.11 3.94 1.99 4.71

0.057 1.387 0.018 1.509 -0.790

0.308 0.000 0.776 0.000 0.000

Table 7. Perceived channel attributes: Web vs. phone Mean


Web

Phone

Mean difference


3.32 3.50 3.95 3.49 3.92

3.32 3.58 3.74 4.17 4.61

-0.002 -0.079 0.210 -0.672 -0.697

0.971 0.312 0.003 0.000 0.000

Table 8. Perceived channel attributes: phone (most preferred) vs. postal mail (least preferred) Mean


Phone

Postal mail

Mean difference


3.33 3.54 3.74 4.21 4.60

3.31 2.77 3.11 2.64 4.66

0.020 0.766 0.624 1.576 -0.060

0.666 0.000 0.000 0.000 0.076


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competence (H1-1), online competence (H1-2), privacy protection (H1-3) and integrity (H1-4) than news companies (i.e. the least preferred source). Collectively, these results suggest that perceived privacy protection and integrity are critical attributes for ACT service providers, while domain competence and online competence may not play a deterministic role. The next three tables show the media attributes perceived by the surveyed citizens. Tables 6 and 7 compare the Web – a major e-government service channel – with the most preferred service channels for ACT information gathering (TV) and information reporting (telephone), respectively. As mentioned earlier (see Table 1), the telephone was also the least preferred channel for information gathering. Table 8 compares the telephone channel with the postal mail, which was the least preferred information reporting channel. The data suggest that no single media attribute has a dominant and deterministic impact on the channel preferences for ACT information gathering. TV was the most preferred channel for information gathering; although its perceived interactivity was significantly lower (p < 0.001) than Web, the second most preferred information collection channel. While it may have a positive influence, the high availability of TV cannot be the sole predictor of the strong channel preference because the least preferred channel (i.e. telephone) also has a significantly higher (p < 0.001) availability than Web. In terms of ACT information reporting, the picture is clearer. The perceived interactivity (H2-4) of the telephone channel was significantly higher (p < 0.001) than that of the Web, the second least preferred medium for information reporting. The same pattern was observed in the comparison with the postal mail, the least preferred medium. Timeliness (H2-3) of the telephone was significantly higher (p < 0.001) than that of postal mail but was lower than that of the Web, suggesting that timeliness alone cannot determine channel preference. Reliability (H2-1) and availability (H2-5) may also have positive influences, but neither has a dominant effect on the channel preference for reporting ACT information. Privacy risk (H2-2) does not seem to be a major concern, as the telephone was perceived as a much riskier medium than the postal mail. While these comparisons can provide valuable insights into public perceptions on various ACT service sources and channels, the existence or non-existence of a significant difference between the service sources and channels should not be interpreted as a determinant of source or channel preferences. We present these results of our exploratory analyses to provide some leads for future extension of the source–channel preference model, as suggested in the model development section. Nevertheless, further research needs to be done to theorise causal relationships between these attributes and the preferences, and perhaps other factors in the TPB-based model.

LIMITATIONS AND FUTURE RESEARCH

This study explored a new perspective with which to frame and analyze citizens’ acceptance of e-government services. As a study conducted in an area still in the developmental stage, the current research has several limitations that should be addressed in future research. The models



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compared in this paper provide alternative frameworks for e-government service adoption analyses but do not offer many context-specific managerial prescriptions for non-ACT domains. They should be refined and extended in each domain of e-government services that share the same boundary conditions. As a first step, the factors previously suggested in the TAM, UTAUT or online-trust theories can be re-conceptualised according to the specific contexts of an e-government domain and rearranged as determinants of the three TPB factors or of the two preference factors. For example, relational trust in a government agency may influence source preference in the two-factor model. Such trust may be also postulated to serve as a determinant of attitude towards use of the agency’s e-government services in the TPB-based model. Likewise, some ‘digital divide’ factors (e.g. gender, education, income level) may be related to channel preference or linked to the behavioural control or social norm constructs. The measurement instrument employed in this study used single-item measures for the modelled concepts. While the interactive, personal interview methodology used might have minimised potential reliability problems in the data, the single-item measures did not allow for statistical testing of the reliability and validity of the measures. In conjunction with model extension efforts, a future study should develop a multi-item measures and test the suggested models using a structural equation modelling technique. The current research also did not take into account the special characteristics of the latest internet-based technologies (e.g. Web 2.0, 3G/4G mobile network, cloud computing). Because these advances have the potential to bring some revolutionary changes in the wider socio-technical systems, a follow-up study might examine their distinct channel characteristics and effects on the multi-channel service environments, as well as on citizens’ perceptions on internet-based service channels.

CONCLUSION

The study empirically examined two alternative models – a TPB-based model and two-factor preference model – of online e-government service acceptance, using telephone interview data from 500 US citizens. The two models were compared in four service contexts: two service types (i.e. information gathering and reporting) and two ACT service sources (i.e. FBI and DHS). In addition, citizens’ perceptions of various ACT service sources and channels were surveyed and analysed to identify the source and channel attributes that can influence citizens’ preference for a certain ACT service source and channel. The results of regression analyses showed that the novel two-factor (i.e. source and channel) preference model outperformed or was on a par with the TPB-based model in explaining the variance in citizens’ intention to use a Web-based ACT service. This finding supports our assumption that the service source (i.e. provider) and channel (i.e. medium) are two important aspects that citizens consider when they make an e-government service adoption decision. Serious mismatches were observed between citizens’ preferences and the current assignments of service responsibilities in our study context. Citizens preferred to deal with local police the most. This preference was noted with both types of ACT services and held up over the other ACT service sources examined in the study, including higher authorities (e.g. DHS and FBI) within this domain. Another surprising result was that news media


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companies, which have traditionally played a major role in disseminating information about disasters and terrorist attacks, were the least preferred source for both service types. In terms of service channels, TV was the most preferred medium for information gathering, and telephone was the most preferred medium for information reporting. Interestingly, the most preferred channel (i.e. telephone) was also the least preferred medium for information gathering. These findings make important contributions to the research communities in the IS and e-government field. First, this study offers a new, empirically validated theoretical model to explain e-government service acceptance behaviours. On the one hand, the more traditional TPB-based model posits that citizens can either accept or resist an e-government service, explaining their acceptance decision based on their attitudes, norms and control. On the other hand, the preference model portrays the decision as a ‘choice among multiple options’ problem. This perspective is more realistic for many public services that are offered by multiple, collaborative agencies through various communication channels. The two-factor preference model provides several valuable insights into e-government service adoption issues, which have not been explored in previous TPB-based models. With this two-dimensional view, the locations of potential adoption problems can be clearly identified. For instance, our test results suggest that selecting a less preferred service medium will have a more detrimental impact – compared to selecting a less preferred service provider – on the acceptance of a new service in the ACT domain. Nevertheless, service source preference does exert a statistically and substantively significant influence on the acceptance decision and should be selected carefully when designing a new e-government service. It is also important to note that the choice of a model specification should be directed, in part, by the purpose of the e-government adoption research. For example, a simple preference model will offer many useful insights when a national/federal-level authority wants to develop a multi-channel service policy for a service domain. In contrast, an extended TPB-based model (i.e. a model with many potential determinants of the attitude, norm and behavioural control factors) will serve better when an agency tries to promote its existing e-government services. This study also found that privacy protection and integrity considerations are consistently related to source preference, whereas domain competence and online competence considerations may have marginal effects. Several channel attributes seem to have varying effects on the channel preference level, contingent upon the type and context of the service. For the information-gathering type of service, its high availability seemed to make TV more preferable to other alternative information dissemination channels, while their high ratings in terms of interactivity and timeliness could promote preferences for the telephone and the Web as information-reporting types of services. Although further testing and research are necessary to elucidate the details of these relationships, this study recognised the fact that one source or channel attribute can have a positive correlation with the source or channel preference for one service type, yet show a negative correlation for another service type. Therefore, future research should draw a clear boundary when predicting a preference while developing a better analysis method to identify major determinants of source and channel preferences. The current study has some managerial implications for e-government initiatives as well. The source–channel preference model suggests that national/federal governments seek the



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best combination of the service source and channel when they design a new e-government service. In the current study context, citizens preferred to deal with their local police rather than with FBI or DHS – even though the latter agencies are actually in charge in many ACT incidents. Citizens also prefer to watch TV for information gathering and to make telephone calls to report tips, yet do not want these services provided by the news media companies that often own or operate TV networks. A possible solution for this dilemma may be a new type of ACT service arrangement wherein local police departments become a liaison between citizens and the various other ACT agencies. Public announcements about an ongoing ACT incident might be made by a local police chief appearing on TV, while the incident-related tips could be reported to the local police department; this department would act as the primary point of contact for the public, while filtering and forwarding the public’s reports to the appropriate ACT agencies based on the contents. Given that the Web was citizens’ second most preferred channel for information gathering, local police departments might allocate a small cross-site subscription area (e.g. iFrame) on their Web pages to the agency in charge (e.g. FBI). With this set-up, any information update on the leading agency’s Web site would appear instantly on the local police’s Web site. As new or converged media spread among the public, e-government service initiatives need to constantly monitor public perceptions of the new channel attributes and attempt to take advantage of them. For example, given that TV and the Web were the first and second preferred sources for ACT information gathering, a mobile TV service, such as the DMB (digital mobile broadcasting) phone popular in South Korea, seems to be a perfect solution for disaster information dissemination service. Citizens’ perception of availability of the Web will also change rapidly as 3G/4G mobile networks replace the existing 2G networks. Therefore, e-government initiatives should give a priority to Web-based information dissemination services and carefully appoint the right (i.e. most preferred) government agency as the Webbased service provider. Similarly, email and texting/SMS will be excellent choices for timesensitive information reporting: email was the second preferred information reporting channel and is now accessible on many mobile phones – the first preference for the type of service. E-government initiatives, therefore, need to build mobile message-handling capacity to process such a public-to-government information flow in an effective manner while preserving the high interactivity and timeliness of those service channels.

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Biographies JinKyu Lee is Assistant Professor of Management Science and Information Systems in the Spears School of Business and a faculty associate at the Center for Telecommunications and Network Security (CTANS), Oklahoma State University. He holds a Ph.D. (2007) in

MIS from the School of Management, University at Buffalo, Master of Information Systems (1999) from Griffith University, Australia, and B.B.A. (1996) from Yonsei University, Korea. His current research interests include inter-organisational information sharing and organisational transformation, effective use of information and communication technologies in public sector, and effective information security and privacy. Prof. Lee has published research articles in various academic journals and conferences including Decision Support Systems, Communications of the ACM, Information Systems Frontiers, IEEE Transactions on Systems, Man and Cybernetics, ICIS and HICSS. He has also served as a guest editor and associate editor for special issues of leading journals and conferences such as MIS Quarterly, Information Systems Frontiers, and ICIS. Some of his research and educational projects have been supported by National Science Foundation (NSF), National Security Agency (NSA), and Department of Defense (DoD). Prof. Lee’s full C.V. is available @ http://spears.okstate.edu/directory/47msis/559-jinle H. Raghav Rao is SUNY Distinguished Service Professor at the University at Buffalo. He holds Ph.D. (1987) degree from Krannert Graduate School, Purdue University, M.B.A. (1981) from University of Delhi, India, and B. Tech. (1979) from Indian Institute of Technology, India. His research interest includes Information and Decision Theory, e-Government and e-Commerce, Information Assurance, and Economics of Information. He serves as Co-Editor-in-Chief of Information Systems Frontiers, guest senior editor of MISQ and AE of IEEE SMC, DSS, ACM Trans on MIS, etc. Prof. Rao has published over 120 archival journal papers in MISQ, ISR, IEEE SMC, DSS, Management Science, etc. He has received best paper and best paper runner-up awards from ISR, ICIS, AMCIS and other conferences.

APPENDIX Table A1. Measurement instrument for telephone interview Information-gathering behaviour Orientation scripts (for Information gathering) Please assume the following: You just heard that an unknown disease is spreading over the US homeland, and some experts suspect that the disease may be caused by a biological or chemical attack launched by a terrorist group. From now on, please assume that you decided to search for more information about biochemical events.

• •



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Table A1. cont. Intention to use (FBI Web site for information gathering) What is the probability that you would search the FBI Web site for more information about biochemical events? Please give your answer as a percentage (%). Measures for TPB-based model (FBI Web site for information gathering) How much do you agree or disagree with the following statements? (1: Strongly disagree, 3: Neutral, 5: Strongly agree) Attitude towards the behaviour: Relying on the Web site of the FBI for information about biochemical events is a good idea in this situation. Subjective norm: Most Americans will rely on the Web site of the FBI for information about the biochemical events. Perceived behavioural control: If I want, I will actually be able to search the FBI Web site for the information in this situation. Measures for two-factor preference model (FBI Web site for information gathering) Source preference: How much do you prefer to contact the following organizations to get more information about biochemical events? (1: Least preferred, 10: Most preferred) – FBI* Channel preference: How much do you prefer to use the following channels to get more information about biochemical events? (1: Least preferred, 10: Most preferred) – Web* Information reporting behaviour Orientation scripts (For information reporting) Now, please assume the following: You found a reasonable cause to suspect that someone you know may be involved in a terrorism activity. If you contact a government authority or news media to provide this information, they will try to verify your identity. From now on, please assume that you decided to report the information to someone else. Intention to use (FBI Web site for information reporting) What is the probability that you would report the information to the FBI Web site? Please give your answer as a percentage (%). Measures for TPB-based model (FBI Web site for information reporting) How much do you agree or disagree with the following statements? (1: Strongly Disagree, 3: Neutral, 5: Strongly Agree) Attitude towards the behaviour: Using the Web site of the FBI to report the information is a good idea in this situation. Subjective norm: Most Americans will use the Web site of the FBI to report the information.

• • • • • •

• • • • • •

Perceived behavioural control: If I want, I will actually be able to report the information to the FBI through its Web site. Measures for two-factor preference model (FBI Web site for information reporting) Source preference: How much do you prefer to contact the following organizations to report the information? (1: Least preferred, 10: Most preferred) – FBI* Channel preference: How much do you prefer to use the following channels to report the information? (1: Least preferred, 10: Most preferred) – Web*

• •

*The sequences of multiple options were rotated.


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