Electronic Commerce Research and Applications 10 (2011) 6–16

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Electronic Commerce Research and Applications

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Law enforcement officers’ acceptance of advanced e-governmenttechnology: A survey study of COPLINK Mobile

Paul Jen-Hwa Hu a,*, Hsinchun Chen b, Han-fen Hu a, Cathy Larson b, Cynthia Butierez c

a Department of Operations and Information Systems, David Eccles School of Business, University of Utah, United Statesb Department of Management Information Systems, Eller School of Management, University of Arizona, United Statesc Tucson Police Department, City of Tucson, AZ, United States

a r t i c l e i n f o a b s t r a c t

Article history:Available online 1 July 2010

Keywords:COPLINK MobileUser technology acceptance in lawenforcementE-government technology adoption by lawenforcement officers

1567-4223/$ - see front matter � 2010 Elsevier B.V. Adoi:10.1016/j.elerap.2010.06.002

* Corresponding author. Address: 1645 East CampuUT 84112-9301, United States. Tel.: +1 801 587 7785

E-mail address: actph@business.utah.edu (P. Jen-H

Timely information access and knowledge support is critical for law enforcement, because officersrequire convenient and timely access to accurate data, relevant information, and integrated knowledgein their crime investigation and fighting activities. As an integrated system that provides such support,COPLINK can improve collaboration within and across agency boundaries. This study examines field offi-cers’ acceptance and actual use of COPLINK Mobile, a critical technology that offers COPLINK core queryfunctionalities through a lightweight, handheld device or mobile applications running on a small band-width. We propose and empirically test a factor model explaining the focal technology acceptance withsurvey data collected from 40 field officers. The data support our model and most of the hypotheses,which can reasonably explain an officer’s acceptance and actual use of COPLINK Mobile. Among the deter-minants investigated, perceived usefulness has the greatest impact and depends on both efficiency gainand social influence. Our findings have important implications for both research and practice.

� 2010 Elsevier B.V. All rights reserved.

1. Introduction

Advancements in information and telecommunication technol-ogy have changed the way governments provide services tocitizens and constituencies, as well as improved agencies’ opera-tions within incumbent administration structures (Layne and Lee2002, Terpsiadou and Economides 2009). Information access andknowledge support is particularly critical for law enforcement, be-cause officers must have convenient and timely access to accuratedata, relevant information, and integrated knowledge support iftheir crime investigation and fighting activities are to be effective(Easton 2002, Lin et al. 2004). Despite the essential need to deployadvanced information technologies among law enforcementagencies, there is no guarantee of their effective use by officers.User acceptance represents a central challenge to the proliferationof advanced information systems in various contexts (e.g.,Terpsiadou and Economides 2009), including law enforcement(Lin et al. 2004). This challenge demands a fuller examination ofuser acceptance in such specialized work settings (Chau and Hu2002, Yi et al. 2006).

An expanding array of e-government initiatives involve theimplementation and use of advanced information technologies,

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s Center Drive, Salt Lake City,.wa Hu).

such as COPLINK, an integrated system funded by the NationalInstitute of Justice and the National Science Foundation’s DigitalGovernment Initiative, that provides law enforcement officersaccess to data and information stored in multiple, dispersed,autonomous sources and thus helps them monitor and analyzecrime-related information (Hu et al. 2005). Its overarching goalis to improve regional law enforcement information sharingand knowledge support (Lin et al. 2004). Of particular impor-tance is COPLINK Mobile, a critical technology that providesfield officers with a core set of COPLINK query functionalitiesin the form of a lightweight, handheld device or mobile applica-tions that can run on minimal bandwidth. COPLINK Mobile canbe easily implemented on personal digital assistants, mobilephones, or tablet devices; field officers, supported by this tech-nology, thus are not restricted by screen space, geographic lim-its, or temporal constraints in their use of COPLINK. Officers canalso use general packet radio service communications to queryand access, nearly real-time, important information about sus-pects, locations, weapons, vehicles, or crime events. That is,COPLINK Mobile provides anytime, anywhere information accessand integrated knowledge support and is particularly importantas law enforcement grows more networked and mobile (Chenet al. 2002).

Despite all its promise though, COPLINK Mobile has no effectunless field officers use it in their routine tasks and operations.Therefore, we examine the following questions:

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1. What are the key factors that influence field officers’ acceptanceand actual use of COPLINK Mobile?

2. How do these factors, individually or jointly, affect field officers’acceptance and actual use of COPLINK Mobile?

Our analysis and empirical examination is premised on estab-lished technology acceptance and adoption theories. Specifically,we analyze the important characteristics of the target officersand their work context and develop a factor model that draws fromthe theory of planned behavior (TPB; Ajzen 1991), the technologyacceptance model (TAM; Davis 1989), and the unified theory ofacceptance and use of technology (UTAUT; Venkatesh et al.2003). Our proposed model integrates key acceptance drivers per-taining to the technology and the organizational work context; itincludes efficiency gain, usefulness, and ease of use as essentialtechnology factors, as well as timely assistance, facilitating condi-tions, and social influence, which are important organizationalcontext factors. We empirically test the model and its associatedhypotheses using survey data collected from 40 field officers.

Overall, the data support our model and most of the hypothesesit suggests. According to our results, the proposed model can ex-plain an officer’s acceptance and actual use of COPLINK Mobile.Among the determinants we study, perceived usefulness emergesas the most important driver of acceptance, which itself is affectedby both efficiency gain and social influence. Our findings thus sug-gest that field officers exhibit a prominent utilitarian emphasis intheir acceptance and use decisions and place a lesser emphasison the technology’s ease of use and the organizational, facilitatingconditions.

The remainder of this article is organized as follows: In Section2, we review relevant research and highlight our motivation. InSection 3, we analyze the focal technology acceptance phenome-non, followed by the proposed model and the associated hypothe-ses in Section 4. We detail our study design and data collection inSection 5 and describe our data analyses, key results, and theirimplications in Section 6. We conclude with a summary in Section7, together with discussions of the study’s contributions and limi-tations and several potential research directions.

2. Literature review and motivation

An expanding array of information technologies has been de-ployed to support law enforcement agencies and officers (Miller1996). For better data and information management, many agen-cies have replaced their conventional, paper-based record systemswith computerized information systems that can be easily ac-cessed, processed, searched, queried, and integrated (Chen et al.2002). Such technology-enabled data management involves dat-abases; the use of database systems improves law enforcementagencies’ effectiveness (e.g., Chen et al. 2002, Miller 1996). Dat-abases also are indispensable to advanced intelligence analysistools (Miller 1996), expert systems (Bowen 1994, Brahan et al.1998), and data mining systems (Adderley and Musgrove 2001).However, integrated data repositories and timely informationand knowledge support seem generally lacking in law enforcement(Chen et al. 2002), and officers often have difficulty accessing dataand information or receiving knowledge support in a timelyfashion.

Mobile technology may improve law enforcement officers’crime investigation and fighting capability. For example, Easton(2002) studies patrol divisions in different US states that use mo-bile devices for wireless network connections and real-time accessto central database systems. The results include improved crimeevent processing, enhanced officer productivity, greater neighbor-hood safety, better coordination among agencies and officers, and

considerable cost reductions (Easton 2002). Yet though studiesseem to converge with regard to the increasing role of mobile tech-nology and its benefits, little research considers officers’ accep-tance of such technologies.

In contrast, substantial previous research examines users’ tech-nology acceptance in corporate settings (e.g., business managers,end-users) and offers insights into technology acceptance anduse. As Chau and Hu (2002) note though, people likely exhibit sub-tle differences in their technology acceptance decision making,depending on their professional context. For example, profession-als or specialized personnel require specialized training, are accus-tomed to professional work arrangements, and normally haveconsiderable autonomy in their work practices (Chau and Hu2002), which means they may consider different factors whendeciding whether to use a new technology. Law enforcement tendsto involve diverse data and require extensive information orknowledge support, such that efficiency and timeliness likely arecrucial to officers’ task performance (Horan and Schooley 2007),because crime fighting represents a constant struggle against time.Furthermore, officers appear to have relatively strong psychologi-cal attachments to their agencies and the affiliated social system,compared with members of business organizations (Lin et al.2004). The demand for timely support and relatively strong socialinfluence thus may play important roles in officers’ technologyacceptance decision making.

Few studies consider technology acceptance by law enforce-ment officers or examine officers’ use of advanced mobile technol-ogy. From the perspective of both research and practice, it isimportant to understand the key acceptance determinants and pat-terns of their influences, independently or jointly. Therefore, westudy field officers’ acceptance of COPLINK Mobile in particular.

3. Analysis of officers’ acceptance of COPLINK Mobile

We study important factors that seem likely to affect officers’acceptance and use of COPLINK Mobile, with a focus on targetusers, technology, and the organizational context. According toChau and Hu (2002), investigations of individual-level technologyacceptance (adoption) should consider key factors pertaining tothese areas. For example, the organizational context, as character-ized or defined by essential factors or conditions, may influencehow people perceive and assess the technology, which in turnmay determine their intention to use the technology (Chau andHu 2002). Our analysis of the organizational context addressesimportant external factors that are not directly related to the tech-nology but rather represent situational enablers of human behav-iors (Ajzen 1985) that can shape officers’ perceptions andassessments of COPLINK Mobile, particularly during the technol-ogy introduction stage (Venkatesh 2000). Specifically, we notethe significance of timely assistance, facilitating conditions, and so-cial influence.

Timely assistance, which refers to the extent to which an officercan obtain assistance when needed from designated sources,whether written or verbal (Lee 1986), should be crucial. When theyuse a newly deployed technology, many people need instructionsand assistance, and timely assistance should improve their percep-tions of the conditions surrounding the new technology (Lee 1986,Thompson et al. 1991). With timely assistance, people may per-ceive fewer constraints and thus alter their behavioral intentionsand actual behaviors (Ajzen 1991). Such assistance also influencesusers’ beliefs about their control over the resources devoted toassisting them (Taylor and Todd 1995).

The facilitating conditions established by an agency generallyrefer to the degree to which a user believes that an organizationaland technical infrastructure exists to support his or her use of the

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system (Venkatesh et al. 2003). Typically, this factor encompassesperceptions of the knowledge, control, or support needed to usethe new technology effectively (Taylor and Todd 1995, Thompsonet al. 1991). In this sense, facilitating conditions address the roleof external factors and can influence behavioral performance.

Finally, social influence refers to the degree to which an officerperceives that important others believe he or she should use a newsystem (Davis 1989, Davis et al. 1989, Fishbein and Ajzen 1975,Venkatesh et al. 2003). In an organizational setting, group normsinfluence members’ decisions and behaviors; peers, superiors,and other people who are important to the focal user influencehis or her perception and actual use of a new technology (Taylorand Todd 1995, Venkatesh and Davis 1996). Social influence alsocan create social pressure, with effects that vary between volun-tary and mandatory (Venkatesh et al. 2003, 2008). Several priorstudies report that social influence might not have strong effectson voluntary acceptance, but we expect it to have importanceinfluences in mandatory settings. That is, a requirement or requestto use a technology moves through the line of supervision or orga-nizational regularities in law enforcement agencies, and organiza-tional expectations that employees use the technology mayrepresent a form of social influence. The group norm to use a cer-tain technology then would become an imminent source of socialinfluence, which is particularly crucial in early stages of technologyimplementation as people start learning to use it (Venkatesh et al.2003, 2008).

We also consider several technology-related factors, namely,efficiency gain, usefulness, and ease of use. In general, efficiencygain refers to the degree to which a person perceives that he orshe could perform tasks more efficiently by using a technology(Hu et al. 2005, Lin et al. 2004). Efficiency gain often motivatesagencies to invest in and deploy new technologies (Kumar andGupta 2006). Therefore, as Melville et al. (2004) comment, whenintroducing a new technology, organizations should analyze thepromised efficiency gain thoroughly; their members are likely todo so. Law enforcement officers in particular face constant andmounting pressures to make timely responses to safety concernsand crime-related activities.

Perceived usefulness and ease of use, the two core constructs ofTAM, refer to the degree to which a person believes the use of atechnology will enhance his or her task performance and the ex-tent to which the person believes his or her use of a technology willbe free of effort, respectively (Davis 1989). When deciding to use anew technology, people attempt to minimize their efforts andadopt only what benefits them (Thompson et al. 1991). Previousstudies generate abundant empirical evidence about the importantrole of perceived usefulness and perceived ease of use for technol-ogy acceptance decisions, which mirrors findings from behavioraldecision-making literature (Ajzen 1991). Perceived ease of use alsomay affect perceived usefulness, because people are more likely to‘‘see” the practical value of a new technology when it is easy to usethan otherwise. Both perceived usefulness and perceived ease ofuse might mediate the influences of key organizational context fac-tors on law enforcement officers’ intention to use and actual use ofCOPLINK Mobile. In addition, perceived efficiency gain may influ-ence their perceptions of COPLINK Mobile’s usefulness, becauseofficers should be more likely to consider the technology usefulwhen it substantially increases their efficiency.

4. Research model and hypotheses

Our model development is rooted in the TPB (Ajzen 1991), TAM(Davis 1989), and UTAUT (Venkatesh et al. 2003). The TPB extendsout of the theory of reasoned action (TRA; Ajzen and Fishbein1980) and incorporates perceived behavioral control to account

for situations in which people lack substantial control over thebehavior under study. A person’s behavior therefore can be ex-plained by an underlying intention, which is jointly determinedby attitude, subjective norms, and perceived behavioral control.The TAM is specific to new technology but has been applied tostudy a wide array of scenarios involving different user groups,technologies, and contexts. In essence, this model suggests that aperson’s perception of a technology’s usefulness and ease of use di-rectly affects his or her acceptance and use of the technology, witha prominent emphasis on perceived usefulness and perceived easeof use (Venkatesh 2000). Finally, the UTAUT synthesizes prevalenttheories and models to offer a unified view of people’s technologyacceptance and use (Venkatesh et al. 2003), with the postulate thata person’s intention to use a new technology reflects the jointinfluence of performance expectancy, effort expectancy, socialinfluence, and facilitating conditions.

Our research model in Fig. 1 suggests that an officer’s intentionand actual use of COPLINK Mobile can be explained by efficiencygain, perceived usefulness, perceived ease of use, timely assistance,facilitating conditions, and social influences. These determinantscomprise both the technology and the organizational (agency) con-text, following a general pattern by which organizational contextfactors influence perceptions of technology-related factors, and to-gether, they determine technology acceptance and use (Chau andHu 2002). In addition, our model suggests several causal links thatwe test as hypotheses.

Efficiency gains represent a critical source of utility (Hu et al.2005) and can influence people’s assessments of a new technology.In our study setting, law enforcement officers engage in constantcompetition against time, with the expectation that they respondto criminal activities in a timely and effective manner. Pragmati-cally, officers should consider COPLINK Mobile useful if this tech-nology use results in substantial savings of time or effort. Forexample, officers may view COPLINK Mobile as useful if they recog-nize considerable time or effort reductions in their queries aboutand access to crime-related information through COPLINK Mobile.Prior studies have demonstrated the importance of efficiency gaingenerated through the use of a new technology (Hu et al. 2005),especially as people form their perceptions about a technology’susefulness (Venkatesh and Davis 2000); efficiency gains can influ-ence perceived usefulness directly (Lin et al. 2004). Accordingly,we posit a positive association between efficiency gain and per-ceived usefulness:

H1: A law enforcement officer’s perception of the efficiency gainresulting from the use of COPLINK Mobile relates positively tohis or her perception of the technology’s usefulness.

Perceived ease of use also should have a positive impact on per-ceived usefulness (Davis 1989). In the early stages of the technol-ogy’s introduction, people exert effort to learn how to use it andappreciate its utility through first-hand observations and experi-mentations. If this learning barrier is too high, people likely havedifficulty understanding its functionality, which limits their useof the technology. They also may suffer difficulty, reluctance, orfrustration while exploring the technology’s functionality throughtrial-and-error experiments. In our case, COPLINK Mobile is rela-tively sophisticated, so its ease of use likely determines the fieldofficers’ assessments of its usefulness. Our observations of thetraining program for COPLINK Mobile suggest nontrivial time andeffort requirements to gain skills in using the technology. All elsebeing equal, officers are more likely to recognize the usefulnessof COPLINK Mobile if it is easy for them to use, in line with priorresearch that indicates perceived ease of use has a positive influ-ence on perceived usefulness (Karahanna and Limayem 2000, Mat-hieson 1991, McCloskey 2003, Szajna 1996, Taylor and Todd 1995).

Fig. 1. Research model.

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Therefore, we hypothesize a positive association between per-ceived ease of use and perceived usefulness:

H2: A law enforcement officer’s perception of COPLINK Mobile’sease of use relates positively to his or her perception of thetechnology’s usefulness.

Because advanced technologies can be so sophisticated, theireffective use may require in-depth instructions and reliable techni-cal assistance (e.g., troubleshooting) (Lee 1986). The speed atwhich this information becomes available appears increasinglyimportant in human–computer interactions, because timelinesshas a strong influence on people’s acceptance of a new technology(Chin and Lee 2000). Assistance provided in the form of operatinginstructions, up-to-date information, or technical support, typi-cally delivered by designated personnel, can enhance users’ per-ceptions that the conditions will facilitate their usage. Forexample, if an officer receives adequate instructions and technicalsupport in a timely manner, he or she should perceive favorableconditions for using COPLINK Mobile. In the field, officers requirerapid, nearly real-time information access and knowledge support,which means they demand timely assistance if they have questionsor encounter difficulties using COPLINK Mobile. If such assistanceis available quickly, officers are more likely to consider their tech-nology use facilitated, similar to the effects in other work environ-ments (Morris and Venkatesh 2000, Venkatesh and Morris 2000).We thus anticipate a positive association between timely assis-tance and perceptions of facilitating conditions:

H3: Timely assistance relates positively to the conditions thatfacilitate the use of COPLINK Mobile by law enforcementofficers.

Favorable conditions for the use of a new technology usuallyentail resources that can influence people’s sense of behavioralcontrol (Taylor and Todd 1995), as well as their assessments, atti-tudes, and intentions (Ajzen 1991, Thompson et al. 1994), whichmake the focal act seem easy to perform (Thompson et al. 1991).

Users consider certain resources, controls, and knowledge neces-sary for their effective technology use (Venkatesh et al. 2003), solaw enforcement agencies should be able to encourage the use ofCOPLINK Mobile by providing essential resources to officers(Thompson et al. 1991). These resources might include hardwaredevices and knowledge support. Adequate equipment enables offi-cers to use COPLINK Mobile and investigate its functionalities; suf-ficient knowledge about it decreases the difficulty of usingCOPLINK Mobile. The usage conditions also should address com-patibility between COPLINK Mobile and existing systems, becausethe handheld device loaded with COPLINK Mobile tends to runother resources as well. We test the following hypothesis:

H4: Conditions that facilitate the use of COPLINK Mobile by lawenforcement officers are positively associated with perceptionsof the technology’s ease of use.

Beyond the resources provided by the agencies, the social influ-ence they exert may affect the officers’ intentions to use the newtechnology, as theorized by the TRA (Ajzen and Fishbein 1980),TPB (Ajzen 1985), and innovation diffusion theory (Rogers 1995).Social influence signifies and reinforces the need to be compliantwith the expectations of important others (Venkatesh et al.2003), especially in mandatory settings (Hartwick and Barki1994, Venkatesh and Davis 2000), through two mechanisms: inter-nalization and identification. Internalization refers to the processby which a person changes his or her beliefs by incorporating per-ceptions of important others’ beliefs and expectations (Kelman1958, Venkatesh and Davis 2000, Warshaw 1980). The process ofidentification instead relates to people’s need to achieve member-ship in groups and thereby obtain social support (Pfeffer 1981,Venkatesh and Davis 2000). In our study context, a law enforce-ment officer may believe COPLINK Mobile is useful if his or hersuperior or senior colleagues promote its use. As a member ofthe group of law enforcement officers, the officer should internal-ize this belief about COPLINK Mobile’s usefulness, especially if theothers who promote its use are creditable experts or leaders in theorganization (Venkatesh and Davis 2000). Therefore,

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H5: Social influence in support of COPLINK Mobile relates pos-itively to a law enforcement officer’s perception of itsusefulness.

In turn, perceived usefulness directly affects a person’s inten-tion to use a new technology. As Davis (1989, p. 319) notes, ‘‘it isa fundamental determinant of user acceptance.” From a utilitarianperspective, people are more likely to accept a technology whenthey consider the use of that technology beneficial to their worktasks and performance (Lin et al. 2004). In law enforcement, offi-cers must meet demands for increased safely through effectiveand timely crime investigations and fighting, as well as continuallyimproved performance. Therefore, they should be likely to acceptCOPLINK Mobile if it appears capable of increasing their task per-formance. Prior research provides the theoretical premises thatunderlie the relationship between perceived usefulness and useracceptance (e.g., Davis et al. 1989, DeSanctis 1983, Swanson1987) and substantial empirical evidence in support of its signifi-cance (e.g., Davis 1989, Davis et al. 1989, Hu et al. 2005, Lin et al.2004, Mathieson 1991, Venkatesh and Davis 1996). We thus posita positive relationship between perceived usefulness and theintention to use COPLINK Mobile:

H6: A law enforcement officer’s perception of COPLINK Mobile’susefulness relates positively to his or her intention to use thetechnology.

According to the TAM, the perceived ease of use also influencesbehavioral intentions directly and indirectly, through influences onattitude toward the behavior and perceived usefulness. These ef-fects receive strong empirical support (e.g., Agarwal and Karahan-na 2000, Bhattacherjee 2001, Davis et al. 1989, King and He 2006,Venkatesh 2000, Venkatesh and Davis 2000). All else being equal,when a person can exert less effort and use a new technology, heor she should be more willing to experiment with and use it. Inother words, when the barrier to learning a new technology islow, people are more likely to adopt the technology. In our studycontext, field officers may be motivated to use COPLINK Mobilewhen the associated learning cost is not demanding. That is, anofficer will adopt COPLINK Mobile if he or she considers it easyto use. Accordingly, we test the following hypothesis:

H7: A law enforcement officer’s perception of COPLINK Mobile’sease of use relates positively to his or her intention to use thetechnology.

To foster wider acceptance of a new technology, organizationsmust establish conditions that mitigate external constraints andbarriers (Ajzen 1985), because these conditions shape users’ tech-nology assessments and can influence their acceptance decisions,particularly in introductory stages (Venkatesh et al. 2008). Bydesirable facilitating conditions, the agency can reduce the diffi-culty that users may encounter with the new technology; in partic-ular, it can encourage them to explore the technology’sfunctionality and harness its full benefits. The positive effects ofthese facilitating conditions also reflect a cost–benefit analysis;people should have stronger intentions to use a new technologywhen the cost associated with the technology use decreases (Davis1989, Johnson and Payne 1985). Furthermore, facilitating condi-tions pertain to the role of external factors, such as resources(Venkatesh and Morris 2000), and the UTAUT suggests an impor-tant relationship between these conditions and intentions (Venk-atesh et al. 2003). In our case, officers expect support fromnecessary resources if their agency creates favorable facilitatingconditions that enable them to experiment with COPLINK Mobileand become familiar with its functionality, which in turn should

strengthen their intention to use the technology. The conditionsalso might address officers’ worries about external barriers to theiruse of COPLINK Mobile by enabling them to engage in explorationsand experimentation through their work tasks. Accordingly, wetest:

H8: The facilitating conditions created by a law enforcementagency are positively associated with officers’ intentions toaccept COPLINK Mobile.

Through internalization, identification, and/or compliance(Karahanna et al. 1999, Kelman 1961, Venkatesh et al. 2003), peo-ple allow social influences to alter their intentions, which suggestsa positive association between social influences and behavioralintentions. Previous research indicates that social influences helpdetermine user acceptance (e.g., Davis et al. 1989, Fishbein and Aj-zen 1975, Taylor and Todd 1995, Thompson et al. 1991, Venkateshand Davis 1996, Venkatesh et al. 2003). To conform with the per-ceived norms of their agency, law enforcement officers should at-tempt to adjust their decisions or behaviors to match others’opinions or suggestions. When significant referents express andemphasize the value of COPLINK Mobile, for example, officersshould perceive the technology use as more acceptable and formfavorable intentions toward using it. We postulate a positive asso-ciation, as follows:

H9: The social influence perceived by a law enforcement officerrelates positively to his or her intention to use COPLINK Mobile.

Finally, the TAM posits that a person’s actual use of a new tech-nology depends on his or her intentions (Davis et al. 1989), suchthat behavioral intentions fully mediate the impacts of other vari-ables on the actual use of technology. Prior research suggestsbehavioral intentions explain 40–60% of the variance in actualtechnology use (Venkatesh and Davis 2000); we similarly expectthat the factors that influence officers’ intentions to use COPLINKMobile also affect their actual use of the technology, as mediatedby their intentions. We test the following hypothesis:H10: A lawenforcement officer’s intention to use COPLINK Mobile relates pos-itively to his or her actual use of the technology.

5. Study design and data collection

We conducted a survey study to test our model and hypotheses;in this section, we describe our study design (e.g., measurements,pretest, target officers) and data collection procedure.

5.1. Measurements and pretest

We used multiple items to measure each construct (see theAppendix). We operationalized efficiency gain using three itemsadapted from Lin et al. (2004); timely assistance was measuredby four items adapted from Chin and Lee (2000); the measures ofperceived usefulness and perceived ease of use employed itemsfrom Davis (1989); and facilitating conditions and social influenceused items from Venkatesh et al. (2003). We measured intentionwith items adapted from Hu et al. (1999). Actual technology use,our dependent variable, featured three self-reported items thatevaluated the extent of the officers’ use of COPLINK Mobile.According to Burton-Jones and Straub (2006), these items repre-sent ‘‘lean measures of use” that can reveal the content of the activ-ity through an omnibus measure (e.g., use versus nonuse, durationof use, extent of use), which makes them more appropriate for theintended objective than are alternative measures that focus oncognitive absorption or deep structure use. All of our question

Table 1Summary of factor loadings.

Measurement item Loading Standard deviation t-Statistics

Efficiency gainEG-1 0.93 0.03 26.51

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items employed seven-point Likert scales, on which 1 indicated‘‘strongly disagree” and 7 is ‘‘strongly agree.” An expert panel ofthree highly experienced officers assessed our measurement itemsfor face value and indicated their adequacy for the target officersand context.

5.2. Target officers

We targeted all 153 field officers who had received a custom-made, handheld device for using COPLINK Mobile. These officersworked for different agencies and had completed the mandatoryuser training program to enable their use of COPLINK Mobile. Atthe end of the training program, we obtained from each officersome important demographic data.

5.3. Data collection

We performed the survey study three months after the manda-tory user training program, which gave the field officers a reason-able amount of time to learn about and experiment with thetechnology and actually use it in their work tasks. The timing ofour data collection thus was consistent with prior studies thatinvestigate users’ acceptance, several months after they receivedinitial training (e.g., Amoako-Gyampah and Salam 2004, Karahannaet al. 2006). With the assistance of agency administrators, we dis-tributed the survey to each officer with a request that they returnthe completed versions within two weeks. We provided additionaltime to officers who did not return the completed survey withinthe normal response window.

EG-2 0.79 0.19 4.12EG-3 0.94 0.02 39.53

Timely assistanceTA-1 0.86 0.05 17.04TA-2 0.70 0.12 5.90TA-3 0.64 0.12 5.67TA-4 0.82 0.07 12.69

Perceived usefulnessPU-1 0.90 0.05 18.45PU-2 0.86 0.08 11.45PU-3 0.93 0.03 30.67PU-4 0.94 0.03 29.54PU-5 0.94 0.03 29.99PU-6 0.89 0.04 21.29

Perceived ease of usePEOU-1 0.80 0.07 11.58PEOU-2 0.76 0.08 9.06PEOU-3 0.90 0.03 27.43PEOU-4 0.89 0.04 24.05PEOU-5 0.86 0.05 17.28PEOU-6 0.92 0.04 23.94

Facilitating conditionsFC-1 0.78 0.06 12.16FC-2 0.88 0.04 21.64FC-3 0.88 0.04 21.60FC-4 0.75 0.06 11.78

Social influencesSI-1 0.85 0.09 9.88SI-2 0.91 0.05 19.55SI-3 0.84 0.11 8.09SI-4 0.77 0.08 9.69

Intention to useBI-1 0.95 0.02 50.18BI-2 0.95 0.02 54.26BI-3 0.90 0.05 17.05BI-4 0.94 0.04 22.14BI-5 0.98 0.01 79.79

Technology usageUSE-1 0.95 0.03 36.84USE-2 0.96 0.01 67.34USE-3 0.92 0.04 23.90

6. Data analyses and results

Among the 153 field officers who received the survey, 41 ofthem completed and returned it, though 1 officer’s response wasincomplete and thus removed from our subsequent data analyses.As a result, our sample includes 40 officers, for an effective re-sponse rate of 26.14%. Our sample is representative of the overallfield officer pool, as suggested by the insignificant between-groupdifferences in age, number of years in law enforcement, and com-puter competence (p > .10). We assessed nonresponse bias by com-paring early and late respondents, that is, those who returnedcompleted surveys within two weeks and those who needed addi-tional time to do so. According to our analysis, these groups arehighly comparable in age, number of years in law enforcement,and computer competence (p > .10). These comparative resultssuggest that nonresponse bias is not a major threat.

We tested the model and the associated hypotheses using par-tial least squares (PLS), which allows for factor analysis with linearregressions and requires minimal distribution assumptions (Gefenet al. 2000). Furthermore, PLS supports the test of both measure-ment and structural models and offers advantages over other pre-valent data analysis techniques (e.g., LISREL), especially for ourrelatively small sample, in which the data distribution may notconfirm to multivariate normality. In general, the sample size re-quired for PLS analysis is 10 times the dependent latent variablewith the largest number of independent variables influencing it;i.e., the largest structural equation (Chin and Newsted 1999). Inour case, the largest structural equation includes the four variablesthat affect intention, and therefore, our sample meets the require-ment at the lower margin.

6.1. Instrument revalidation

We first examined our instrument in terms of its reliability andconvergent and discriminant validity. We evaluated the loading of

each item on its corresponding construct; Nunnally (1978) sug-gests items that load greater than .7 are generally reliable, whereasthose with a loading lower than .5 should be considered for re-moval. Almost all of our measurement items achieve factor load-ings greater than .7. As we summarize in Table 1, the loadings ofthe remaining items are statistically significant at the .001 level.

We examined the construct reliability in terms of internal con-sistency and composite construct reliability. We used Cronbach’s ato assess the internal consistency, with a common threshold of .7(Nunnally 1978). As we show in Table 2, each construct reachesa Cronbach’s a value of greater than .7, which suggests adequateinternal consistency. The composite reliability of each constructalso exceeds .7, in support of adequate construct reliability (Fornelland Larcker 1981). Overall, our results suggest that our instrumentexhibits appropriate construct reliability.

We next examined convergent validity, using the average vari-ance extracted (AVE) to denote the variance captured by the indi-cators. An AVE in excess of .5 generally signifies appropriateconvergent validity (Fornell and Larcker 1981). As we summarizein Table 2, each construct provides an AVE greater than .5, in sup-port of our instrument’s adequate convergent validity.

Table 2Analysis of reliabilities and variance extracted.

Compositereliability

Cronbach’sa

Varianceextracted

Efficiency gain 0.92 0.87 0.79Timely assistance 0.85 0.76 0.59Perceived usefulness 0.97 0.96 0.85Perceived ease of

use0.94 0.93 0.73

Facilitatingconditions

0.90 0.85 0.69

Social influence 0.91 0.87 0.72Intention to use 0.98 0.97 0.90Technology usage 0.96 0.93 0.88

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Finally, we examined the convergent and discriminant validityof our instrument on the basis of the cross-loadings, which wecomputed from the correlation between each construct’s compo-nent score and the manifest indicators of the other constructs(Chin 1998). All items load substantially higher on their own con-struct than on any other constructs, such that our instrumentexhibits satisfactory convergent and discriminant validity. As wesummarize in Table 3, the square roots of the AVEs are also greaterthan the correlation among any pair of latent constructs (Chin1998). Taken together, our results suggest appropriate convergentand discriminant validity.

6.2. Model testing results

We tested the proposed model by examining the R2 value ofeach non-endogenous variable. As we show in Fig. 2, the model ex-plains a substantial portion of the variance in perceived usefulness(R2 = .60), perceived ease of use (R2 = .71), facilitating conditions(R2 = .48), intention to use (R2 = .66), and technology usage(R2 = .64).

6.3. Hypothesis testing results

We tested each hypothesis by examining the statistical signifi-cance and the coefficient of the corresponding path in the model.For increased statistical rigor and validity (Chin 1998), we em-ployed a bootstrap resampling procedure with resamples of 250.The results indicate that efficiency gain has a significant, positiveassociation with perceived usefulness (path coefficient = .51,p < .05), in support of H1. Perceived ease of use is not significantlycorrelated with perceived usefulness though, so our data do notsupport H2. The association between timely assistance and facili-tating conditions is significant and positive (path coefficient = .70,p < .01), in support of H3. Facilitating conditions also are positivelyassociated with perceived ease of use (path coefficient = .84), at astatistically significant level (p < .01), in support of H4. We findsupport for H5, because the association between social influence

Table 3Latent variable correlations.

Square root of AVE 1 2

1. Efficiency gain 0.892. Timely assistance 0.77 0.503. Perceived usefulness 0.92 0.71 0.54. Perceived ease of use 0.85 0.69 0.75. Facilitating conditions 0.83 0.69 0.76. Social influence 0.85 0.39 0.47. Intention to use 0.96 0.69 0.58. Technology usage 0.94 0.73 0.4

and perceived usefulness is statistically significant (path coeffi-cient = .34, p < .01). The significant, positive association betweenperceived usefulness and intention to use (path coefficient = .61,p < .01) also confirms H6. However, we cannot confirm H7–H9, be-cause perceived ease of use, facilitating conditions, and social influ-ences, despite their positive associations with intention to use, arenot statistically significant. Finally, the association between useracceptance intention and actual technology use is strong and sig-nificant (path coefficient = .80, p < .01), in support of H10.

Furthermore, we calculated the effect size for each path, assummarized in Table 4. According to Cohen’s (1988) delineationsof strong (.35), medium (.15), and weak (.02) effect sizes, the ef-fects of H2, H7, H8, and H9 are relatively weak. Weak effects tendto be difficult to detect with any existing statistical methods,including PLS (Goodhue et al. 2006). Therefore, though our samplesize is reasonably sufficient for detecting medium and strong ef-fects, the hypothesized relationships in H2, H7, H8, and H9 maynot be apparent in our analyses because of their weak effects.

6.4. Post-survey interview results

After administering the survey, we interviewed five field offi-cers, randomly selected from different agencies, to gather their(qualitative) assessments and usage descriptions of COPLINK Mo-bile. All these officers considered their use of COPLINK Mobileinstrumental in their tasks and exhibited great intentions to con-tinue using the technology in the near future. One officer com-mented that ‘‘COPLINK Mobile is not only helpful, but in myopinion has become indispensible”; another noted, ‘‘I use COPLINKMobile about as much as I use my cell-phone.” Common technol-ogy usages included confirming a person’s identity if he or shedid not have an ID, obtaining photos and information about peoplein the field during surveillance or en route to a call, confirming sus-pected associates and locations, and searching for subjects usingidentifying or citation information. As one officer described, ‘‘onmore than one occasion, we have identified persons with misde-meanor and even felony warrants who have lied about their iden-tity in order to avoid detection; being able to pull up a bookingphoto in mere seconds and compare it to the person standing infront of you is just fantastic!” These users also employed COPLINKMobile in some dangerous scenarios, such as tracking down anarmed robbery suspect on the day of the robbery, hunting downfugitives wanted for homicide, confirming the identity of a murdervictim, figuring out whether an individual had been arrested underfalse names and re-arresting him or her under the correct name,and searching for people’s names and finding narcotics and weap-ons violations while this information was not in the field interviewfiles. One officer described multiple occasions on which dispatch-ers received 911 calls but were cut off prematurely, such that theyobtained only an address, not an apartment or other identifyinginformation. COPLINK Mobile enabled the officers to find the apart-ment numbers quickly. Another officer expressly noted the bene-fits of the technology:

3 4 5 6 7

31 0.591 0.66 0.847 0.57 0.43 0.474 0.79 0.56 0.63 0.554 0.66 0.63 0.66 0.50 0.80

Fig. 2. Model testing results.

Table 4Effect size of hypothesized paths.

Hypothesis R2 (including predictor) R2 (excluding predictor) Effect size

H1: Efficiency gain on perceived usefulness .60 .47 .33H2: Perceived ease of use on perceived usefulness .60 .60 .00H3: Timely assistance on facilitating conditions (Only one predictor) .48H4: Facilitating conditions on perceived ease of use (Only one predictor) .71H5: Social influence on perceived usefulness .60 .51 .23H6: Perceived usefulness on intention to use .66 .48 .53H7: Perceived ease of use on intention to use .66 .66 .00H8: Facilitating conditions on intention to use .66 .65 .03H9: Social influence on intention to use .66 .65 .03H10: Intention to use on technology usage (Only one predictor) .64

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. . . fugitive work is very fluid and fast-breaking, and we areoften required to brief an operational plan over the radio andmove into an arrest situation, without the benefit of havinghad a full briefing with photographs being shown; on multipleoccasions, I used COPLINK Mobile to access booking photo-graphs of targeted fugitives while en route to a location; thismade the situations manageable.

6.5. Discussion

Our findings have several important implications for researchand practices. First, we depict the prominent influence path fromefficiency gain to perceived usefulness, and then to intention touse. We further assess the path from intention to technology usageand note a utilitarian emphasis in officers’ technology acceptancedecision making. Therefore, in a professional working environment,people decide whether to accept a new technology by assessinghow useful that technology is for their work tasks and performance.

Second, in the particular setting of a law enforcement agency,we find that an officer’s perception of the technology’s ease ofuse has some, perhaps insignificant, influences on his or her per-ception of its usefulness and intention to use that technology. Re-sults from other studies examining end-users’ acceptance of a newtechnology (e.g., Karahanna et al. 1999, Wu and Wang 2005) indi-

cate that perceived ease of use has significant and direct effects onperceived usefulness and intention to use, but these effects maynot exist in professional contexts (e.g., Chau and Hu 2002). Ourfinding is consistent Lin et al.’s (2004) finding that law enforce-ment officers do not consider a technology useful simply becauseit is easy to use; they also echo discussions that indicate peoplein professional contexts evaluate technology acceptance differ-ently than do ordinary end-users (Chau and Hu 2002).

Third, we find that organizations can create favorable condi-tions for user acceptance by providing timely assistance, even ifthe influences of these conditions are not significant. Timely assis-tance and facilitating conditions affect people’s perceptions of anew technology’s ease of use, but they do not seem to be signifi-cant acceptance determinants in professional or specialized worksettings. Nor do external factors appear to determine officers’ deci-sions to use COPLINK Mobile. This observation suggests a utilitar-ian focus by most officers, who appear to place greater emphasison the technology’s usefulness than on its ease of use (Venkateshand Morris 2000). Facilitating conditions may enable or restrainofficers’ intended or actual use of COPLINK Mobile, yet their effectson user intentions appear insignificant.

Fourth, the insignificant direct effect of social influence suggestsofficers do not accept a new technology simply because of groupnorms or social pressures to do so. Rather, the positive effect of so-

14 P.J.-H. Hu et al. / Electronic Commerce Research and Applications 10 (2011) 6–16

cial influence seems mediated by perceived usefulness. Therefore,peer influence has no discernible, significant, direct effects on anofficer’s intention to use COPLINK Mobile, in line with Chau andHu’s (2002) conclusion about technology acceptance decisions byprofessionals. Law enforcement officers are professionals whowork closely with colleagues, so we posited that group norms orpeer opinions would have more important effects on their beliefs,but our results show that these social influences are importantonly when they align with the officer’s assessment of the technol-ogy’s usefulness. In other words, social influences can enhance theimpacts of perceived usefulness, but alone, they do not appear todrive user intentions or actual technology usage.

7. Conclusion

The use of advanced information technology is critical to lawenforcement officers’ crime investigation and fighting activities.This highly specialized professional context warrants the specificinvestigation of key factors that influence technology acceptancedecisions. Many agencies have deployed advanced technologiesthat enable e-government, without any guarantee of desirabletechnology use by the targeted officers. We propose and empiri-cally test a factor model to explain acceptance of COPLINK Mobile,using data obtained from 40 law enforcement officers. According toour results, the model generally explains their acceptance. Overall,we find that perceived usefulness is determined by social influenceand efficiency gain and has a significant, positive influence on offi-cers’ intention and actual use of COPLINK Mobile. Timely assistancepositively affects the facilitating conditions, which have importantinfluences on perceived ease of use. The facilitating conditions,perceived ease of use, and social influences have positive, direct ef-fects on officers’ acceptance of COPLINK Mobile, but none of theseeffects appears to be significant statistically.

In turn, we make several research contributions. First, this studycontributes to technology adoption literature by examining useracceptance (including actual use) of an advanced information sys-tem in the specialized law enforcement setting. We analyze fieldofficers’ technology acceptance decision making, propose a factormodel using a nomological network premised in established theo-ries, and empirically test the model using survey data collectedfrom 40 officers. Prior research instead has tended to examinetechnology acceptance by ordinary end-users, with little insightfor advanced information systems in professional or specializedcontexts. Law enforcement officers represent a particular usergroup, characterized by specialized work tasks and arrangements,extensive information technology support, and constant challengesto improve their timely and effective work performance. Theempirical results imply some notable differences in their technol-ogy acceptance, compared with business managers or users (e.g.,Chau and Hu 2002). Our findings further show that individual pro-fessionals differ subtly from typical end-users in terms of theirtechnology acceptance decision making.

Second, this study offers insights into user acceptance of an ad-vanced technology in mandatory settings and highlights the utili-tarian focus of the decision making. Specifically, we find aprominent core influence from efficiency gain to perceived useful-ness to intention to use and then ultimately to technology usage.Professionals or specialized personnel seem to focus on the useful-ness of a technology rather than on its ease of use; they tend to ac-cept the social influence only when it is aligned with theirtechnology assessment. Our findings underscore the importantrole of a utilitarian view of technology, particularly with respectto task performance and efficiency gain.

Third, this study provides empirical evidence about actual tech-nology use after the introduction of a technology. Compared withprevious studies that examine user intentions shortly after the ini-

tial user training, we investigate assessments and actual usage byfield officers beyond those initial impressions. Perceived ease ofuse and social influence appear less significant after the introduc-tion, when users have accumulated an understanding of and expe-rience with the technology. Our study also responds to the call toassess actual technology usage after implementation, which isindispensable to sustainable user acceptance and system success.

However, this study has several limitations that point to somefurther research directions. For example, our results are based on asingle study pertaining to a specific technology (i.e., COPLINK Mo-bile) that involves a relatively small sample of field officers. Otheradvanced technologies deserve research attention and probablyencompass different characteristics or organizational contexts.Some technologies in support of e-government may require differ-ent levels of management involvement and user participation,which would shape the social influence and likely perceptions ofusefulness. Therefore, continued research should examine keyacceptance determinants across different technologies, officergroups, and/or agencies. Moreover, theoretical analyses and empir-ical testing could help generalize our findings. Our theory-basedconceptualization of technology acceptance in law enforcementmay shed further light on additional acceptance drivers and theirinfluences on a professional’s intended and actual use of a new tech-nology. Toward that end, the use of longitudinal data is desirable, be-cause they can better reveal how the influences of each importantdeterminant evolve over time and thereby enable agencies to formu-late effective strategies in different stages throughout the technol-ogy implementation, routinization, and internalization processes.

Acknowledgments

We acknowledge and thank the sponsor of this research, Scien-tific Research Corporation, which provided funding under Subcon-tract No. SR20061633 (B433). We also thank Xin Li for hisinvaluable assistance in the early stages of this study.

Appendix A. Question items included in the study

A.1. Efficacy gain

EG-1: By using the handheld device, I can reduce the amount oftime it usually takes to complete my work tasks.EG-2: The handheld device allows me to accomplish tasks morequickly.EG-3: Using the handheld device saves me time.

A.2. Perceived usefulness

PU-1: Using the handheld device would enable me to completejob tasks more quickly.PU-2: Using the handheld device would improve my jobperformance.PU-3: Using the handheld device in my job would increase mywork productivity.PU-4: Using the handheld device would enhance my effective-ness at work.PU-5: Using the handheld device would make it easier to com-plete my job tasks.PU-6: Overall, I would find the handheld device useful in myjob.

A.3. Perceived ease of use

PEOU-1: Learning to use the handheld device would be easy forme.

P.J.-H. Hu et al. / Electronic Commerce Research and Applications 10 (2011) 6–16 15

PEOU-2: I would find it easy to get the handheld device to dowhat I want it to do.PEOU-3: My operations of the handheld device would be clearand understandable.PEOU-4: I would find the handheld device to be easy to operate.PEOU-5: It would be easy for me to become skillful at using thehandheld device.PEOU-6: Overall, I would find the handheld device easy to use.

A.4. Timely assistance

TA-1: I can receive the information I need for using the hand-held device in a timely manner.TA-2: I have timely access to the instructions for using thehandheld device.TA-3: A designated person or group is available for assistancewhen I have questions or difficulties using the handheld device.TA-4: The help information provided by the handheld device isup-to-date.

A.5. Facilitating conditions

FC-1: I have control over using the handheld device.FC-2: I have the resources necessary to use the handheld device.FC-3: I have the knowledge necessary to use the handhelddevice.FC-4: The handheld device is compatible with other systems Iusually use in my job.

A.6. Social influence

SI-1: People who influence my behavior at work think that Ishould use the handheld device.SI-2: People who are important to me at work think that Ishould use the handheld device.SI-3: The senior members of my department have been influen-tial in my use of the handheld device.SI-4: In general, my department supports my using of the hand-held device.

A.7. Intention to use

BI-1: I intend to use the handheld device in my daily job.BI-2: I intend to use the handheld device in routine job tasks.BI-3: Whenever possible, I intend to use the handheld device atwork.BI-4: To the extent possible, I would use the handheld device fordifferent job tasks.BI-5: To the extent possible. I would use the handheld device inmy job tasks frequently.

A.8. Technology usage

USE-1: In the past month, I relied on the handheld device toaccess the information crucial to my job tasks in the field.USE-2: In the past month, I used the handheld devicefrequently.USE-3: In the past month, I often chose to use the handhelddevice to access important information for my job tasks overother alternatives (e.g., phone calls or other systems).

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