An empirical assessment of technology adoption as a choice between alternatives

Information Resources Management Journal, 22(4), 23-44, October-December 2009 23 An Empirical Assessment of Technology Adoption as a Choice between Alternatives Ernst Bekkering, Northeastern State University, USA Allen C. Johnston, University of Alabama Birmingham, USA Merrill Warkentin, Mississippi State University, USA Mark B. Schmidt, St. Cloud State University, USA ABSTRACT Technology adoption by individuals has traditionally been regarded by information systems researchers as a choice between adoption and non-adoption of a single technology. With the current diversity of technology DOWHUQDWLYHVWKHDGRSWLRQGHFLVLRQPD\EHPRUHDFFXUDWHO\VSHFL¿HGDVDFKRLFHEHWZHHQFRPSHWLQJDOWHUQDWLYH technologies. The research question may no longer be simply whether technology is adopted, but rather which WHFKQRORJ\LVDGRSWHG7KHDXWKRUVLOOXVWUDWHWKLVZLWKDVLPSOL¿HGPRGHORIFKRLFHEHWZHHQWZRFRPSHWLQJ WHFKQRORJLHVZKHUHWKHVHFRQGWHFKQRORJ\LVDQHQKDQFHGYHUVLRQRIWKH¿UVW7KHLUWKHRUHWLFDOPRGHOLVEDVHG RQ([SHFWDQF\7KHRU\ (7 5HVXOWVLQGLFDWHWKDWV\VWHPFKDUDFWHULVWLFVFDQEHVXFFHVVIXOO\FDSWXUHGLQWKH 9DOHQFH0RGHORI(7DQGHIIRUWH[SHFWDQF\LQWKH)RUFH0RGHO)XWXUHUHVHDUFKFDQH[SDQGRQWKHVHUHVXOWV by including more factors in the Valence Model, and by comparing more than two alternative technologies. .H\ZRUGV &RPSHWLQJ7HFKQRORJLHV([SHFWDQF\7KHRU\+XPDQ&RPSXWHU,QWHUDFWLRQ0RELOH&RPSXWLQJ Tablet PC, System Acceptance, Information Technology Adoption INTRODUCTION Technology adoption research has been one of the main topics in Information Systems research. As Information Systems and their position in society have changed, the focus and methods of research studying their adoption have changed. Understanding these decisions has become paramount. Previous technology DOI: 10.4018/irmj.2009061902 adoption research has focused on identification of factors that influence individuals’ decisions to adopt a technology or not. This has resulted in an impressive body of literature that describes influences of system factors, social factors, facilitating factors, and personal factors. With the current pervasiveness of technology, the adoption decision process may no longer be a choice between adopting a technology or not, but a decision of which technology should be selected. This is illustrated by the shift Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 24 Information Resources Management Journal, 22(4), 23-44, October-December 2009 from desktop computers to laptop computers. In recent years, mobile professionals have adopted laptop and handheld computers for record keeping, billing, research, reference, reporting, documentation, collaboration, and countless other activities. In June 2005, the sale of laptops surpassed the sale of desktop computers for the first time (CBS Broadcasting Inc., 2005). Indeed, feature-laden models are now termed “desktop replacements.” A second example is the development of the Tablet PC, which presented a unique opportunity for mobile use (Coursey, 2003; Einhorn, Greene, & Kunii, 2004). Laptops require support on a work surface (such as a desk, table, or lap) to enable input with the keyboard or mouse, whereas Tablet PCs and other devices do not. Users may consider using these technologies as alternatives to traditional laptop computers because of their relative advantages. Using a computer in mobile work environments requires a combination of sufficient screen size, instant availability, sufficient processing power, proper software, connectivity, and the capability of use regardless of body position. This was the promise of the Tablet PC (Howard, 2005). Personal Digital Assistants (PDAs) and web-enabled cell phones can be used while standing and are instantly available, but lack processing power, business software, sufficient screen size, and keyboards. Tablet PCs have seen adoption in selected areas, such as healthcare and education, which indicates that the special features of Tablet PCs are compelling for some users, but not for all mobile computer users. In summary, users today are likely to choose among four major alternatives in mobile computing: regular notebooks, Tablet PCs, PDAs, and webenabled cell phones. In our research we present an empirical study in which pre-professionals with a high need for mobile computing compare two alternatives in a relatively simple model. This study illustrates the principle of technology adoption as a comparison between alternatives, as opposed to an adoption decision. We compare positive differences between two alternatives, the regular notebook and the Tablet PC, with the Tablet PC possessing a set of enhancements that may make them more attractive to mobile professionals. Applicants to a veterinary college in the southeastern United States, required to use laptop computers or Tablet PCs in their program of study, provided answers to survey questions designed to investigate the research question. First, they viewed a live demonstration of three sets of selected Tablet PC features. After familiarization with the potential benefits, they reported their perceived attraction to all possible combinations of the three feature sets. The results of this study are relevant to decision makers within businesses or institutions contemplating adoption of Tablet PCs, to mobile computer users who are considering a choice between Tablet PCs and traditional laptop computers, and most of all, IS researchers who may wish to study technology adoption as a choice between competing technology alternatives rather than isolated adoption decisions for single technologies. The next section presents a discussion of the development of technology adoption based on seven sentinel publications. We follow with a discussion of Expectancy Theory as a vehicle for research and a description of the special capabilities of Tablet PCs. The next sections present our methodology as well as Analysis and Results. The final section describes our Discussion and Conclusions. LITERATURE REVIEW AND RESEARCH HYPOTHESES Technology Adoption Theories and Models Over the years, Information Systems adoption research has changed in focus and methodology. We will discuss seven major publications as representatives of this development, and discuss how Expectancy Theory can inform further developments in IS technology adoption research. One of the earliest technology adoption theories used in IS research is the Diffusion of Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Information Resources Management Journal, 22(4), 23-44, October-December 2009 25 Innovations Theory (Rogers, 1962). In his book, Diffusion of Innovation, Rogers leveraged more than 500 publications to synthesize several commonalities in the adoption process. The adoption process needs four crucial elements and moves through several stages. For an innovation to be adopted, not only is the innovation necessary (element 1), but the innovation must also be communicated between individuals (element 2) in a social system (element 3) over time (element 4). In this social process, adopters go through several stages. First they become aware of the innovation, interest is generated through communication with others, the potential benefits are evaluated, the innovation can be tried, and when sufficiently positive, adopted. This favorable decision to adopt can be based on five characteristics of the innovation. Relative advantage denotes the superiority of the new technology over its predecessor, but is not sufficient in and of itself. The innovation also has to be sufficiently compatible with the values and experiences of the adopter, the FRPSOH[LW\ of the new solution should not be overly great, trial should be possible on a sufficiently limited basis, and the results should be sufficiently easy to communicate. Elements of the diffusion process, the stages that it traverses, and the innovation characteristics combine to describe a social process. However, it is predominantly the categorization of adopters that forms the enduring legacy of Roger’s work. Adopters are classified in groups according to sequence in distinct percentages. Innovators, the very first adopters who are the most risk-taking, form the first group with 2.5% of total users. Early adopters follow with 13.5%, early and late majority each with 34%, closing with 16% of laggards. Each group has its own characteristics, and stimulation of adoption (e.g., marketing efforts or communication to employees by managers) should be tailored to each specific group. The next influential technology adoption theory was the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975). Similar to Rogers, Fishbein and Ajzen synthesize existing works to form a conceptual framework. Their focus was on the adoption decision as a cognitive event rather than on the adopters or the adoption process. Two factors determine the decision – the individual’s attitude towards the outcome and the opinions of the adopter’s social environment. Mathematically, this relationship can be expressed asB~I = (Aact) w1+ (SN) w2where B = Behavior, I = Intention, Aact = adopter’s attitude towards the behavior, SN = influence of subjective norms, and W = empirically derived weights. Over time, criticism of the TRA included reliance on self reported intention (common methods bias), the need for congruity between intention and attitude, and most of all, the assumption that the decision is rational and conscious. These criticisms of TRA led Ajzen to modify the theory by adding the Perceived Behavioral Control construct. The Theory of Planned Behavior (TPB) (Ajzen, 1991) considers motivation and intention to also be influenced by perceived difficulty of the task and the perceived probability of success. Again, as in TRA, the theory is a conceptual framework based on a synthesis of previous work. Neither of the foundation theories was immediately empirically tested as part of a validation process. This changed with the first technology adoption theory developed specifically for Information Systems, the Technology Acceptance Model (TAM) (Davis, 1989). Based on TRA and TPB, TAM presented immediate validation with early information technologies and became one of the most influential models in IS research. The Attitude measures from TRA/TPB were replaced with the constructs Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) (Figure 1). In doing so, the focus of attention completely shifted from adopters and adoption process to the technology itself, albeit as characteristics of the technology as perceived by the adopter. Rather than a social process, the adoption process was viewed solely as an individual decision. Individual factors like age and gender were later added, but social influences took more than a decade to be added. In the meantime, TAM consistently explains approximately 40% of variance in behavioral Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 26 Information Resources Management Journal, 22(4), 23-44, October-December 2009 Figure 1. Technology Acceptance Model (TAM) (Source: Davis, 1989) Perceived Usefulness Behavioral Intention Actual System Use Perceived Ease of Use intent, shows a stronger influence of PU than of PEOU, and is criticized for lack of actionable guidance. Shortly after the turn of the century, Venkatesh and Davis (2000) presented the Technology Acceptance Model 2 (TAM2). Social influences indirectly influence adoption decisions through the influence of the opinions of people significant to the adopter (Subjective Norm) and status (Image) on Perceived Usefulness. As such, the social process is re-introduced, albeit in an indirect way. Other non-social additions include applicability to work (Job Relevance), quality of results (Output Quality), and visible results (Result Demonstrability) (Figure 2). Three years later, a group including the same authors integrates eight main adoption theories into a single unified theory. The Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) combines technology factors (Performance Expectance, Effort Expectance), social factors (Social Influence, Facilitating Conditions, Voluntariness of Use), and personal factors (Age, Gender, Experience) (Figure 3). Again, the theory is immediately validated by the results of studies in multiple settings. The expanded model accounts for approximately 70% of variance in usage intention, but lack of practical relevance leads Venkatesh and Bala (2008) to present their Technology Acceptance Model 3 (TAM3), which explicitly presents determinants of perceived usefulness and perceived ease of use in groups of individual differences, system characteristics, social influences, and facilitating conditions (Figure 4). The model uses the same data as TAM2, but the enhanced model explains behavioral intent to a greater degree. A summary of characteristics of these salient publications is presented in Table 1. Despite the progress in predictive strength of the improved models, current technology acceptance models focus only on the adoption or non-adoption of technologies. This singular focus reflects the past practice where technology usage was an option, and does not acknowledge the current common practice of selecting from competing technologies. In other words, the question is no longer simply whether technology is adopted, but rather which technology is adopted. Practitioners face choices between wired and wireless communication channels, proprietary and open-source software, local software installations and distributed or networked applications, physical and virtual servers, and many others. To choose effectively from among competing technologies, adopters must compare the relative advantages and disadvantages of the alternatives. We present how Expectancy Theory can be used to analyze the decision process, and we apply this to the selection between two alternative technologies – in this Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Information Resources Management Journal, 22(4), 23-44, October-December 2009 27 Figure 2. Technology Acceptance Model 2 (TAM2) (Source: Venkatesh and Davis, 2000) Predictors: ‡6XEMHFWLYH1RUP ‡,PDJH ‡-RE5HOHYDQFH ‡5HVXOW Demonstability ‡([SHULHQFH ‡9oluntariness Perceived Usefulness Behavioral Intention Actual System Use Perceived Ease of Use case between regular notebook computers and Tablet PCs. Expectancy Theory For the purposes of the present study, Expectancy Theory (ET) (Vroom, 1964) serves as a general framework from which to examine the attractiveness and pervasiveness of the Tablet PC as an alternative to more traditional forms of mobile computing such as the laptop computer. As early as the 1980s, DeSanctis (1983) and Lovata (1987) suggested that ET could be used as a theoretical framework to study individual acceptance and intention to use IS. Expectancy Theory dictates that individuals, when faced with choices, will assess the expected value of each alternative and choose the one that provides the greatest benefit (Wolf & Connolly, 1981). As such, ET clearly differs from theories and models used traditionally in IS by comparing alternatives rather than adoption/non-adoption decisions as described previously. The choice is dependent not only on a cognitive evaluation of relative benefits, but also on the subjective expectation that the benefits will actually be realized. The level of motivation to pursue the alternative or choice in turn is based on this combination of expected benefits and the probability that they will actually be realized. ET uses two different models, the Valence Model and the Force Model, to quantify the expected benefits and the willingness to expend effort, respectively. The theory has been successfully applied within a number of fields, including IS, as a basis for the Theory of Planned Behavior (Ajzen & Fishbein, 1980), the Technology Acceptance Model (Davis, 1989), and the use of Decision Support Systems (DeSanctis, 1983). More recently, it has been used to explain user acceptance of ERP systems (Lim, Pan, & Tan, 2005), employee suggestion management systems (Fairbanks et al, 2003), and use of groupware applications (Chen & Lou, 2002). Outside the field of IS, examples of use of the theory include studying student perceptions of peer evaluation and teaching effectiveness (Chen, Gupta, & Hoshower, 2004; Chen & Hoshower, 2003; Chen & Lou, 2004), using cooperative learning (Abrami et Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 28 Information Resources Management Journal, 22(4), 23-44, October-December 2009 Figure 3. Unified Theory of the Acceptance and Use of Technology (UTAUT) (Source: Venkatesh et al., 2003) ‡3HUIRUPDQFH Expectancy ‡(ffort Expectancy ‡6RFLDO,QIOXHQFH Behavioral Intention Actual Use Facilitation Conditions ‡*HQGHU ‡$Je ‡([SHULHQFH ‡9oluntariness al., 2004), and research productivity (Chen, Gupta, & Hoshower, 2006; Tien, 2000). Valence Model in ET In the first model, the Valence Model, overall attractiveness can be calculated as the summation of products of individual benefits and their respective expected probabilities of realization. Mathematically, this can be expressed in the formula Vj Ȉ nk=1 (Vk Ijk), where Vj is overall attractiveness, Vk is the attractiveness of outcome k, and Ijk is the probability of realization. Multiple linear regression can be used to determine the relative strength and importance of individual benefits as Beta coefficients in the regression equation. Differences between values of the Beta coefficients indicate that some benefits are more important than others. Figure 4. TAM 3 (theoretical framework) (Source: Venkatesh and Bala, 2008) Individual Differences Perceived Usefulness System Characteristics Behavioral Intention Actual System Use Social Influence Perceived Ease of Use Facilitating Conditions Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Definition of Core Constructs Technology Studied Voluntary or Mandatory Adoption Methodology Results of Analysis Diffusion of Innovations Characteristics of individual adopters Categorizes adopters as Innovators, Early Adopters, Early and Late Majority, and Laggards None. Review of more than 500 publications on from anthropology, sociology, education, industry, and others Not explicitly mentioned, voluntary assumed Literature review, synthesis of results, and proposal of model Bell-shaped curve with specific distributions Theory of Reasoned Action (TRA) Individual adoption decision process Attitude: “an individual’s positive or negative feelings (evaluative affect) about performing the target behavior” Subjective Norm: “the person’s perception that most people who are important to him think he should or should not perform the behavior in question” None. Used Psychology literature Not explicitly mentioned, voluntary assumed Review of empirical research, followed by formulation of conceptual framework Conceptual framework Theory of Planned Behavior (TPB) Individual adoption decision process As in TRA, with added Behavioral Control: “the perceived ease or difficulty of performing the behavior” None. Used Psychology literature Not explicitly mentioned, voluntary assumed Review of empirical research, followed by formulation of conceptual framework Conceptual framework Technology Acceptance Model (TAM) Individual adoption decisions Perceived Usefulness: the degree to which a person believes that using a particular system would enhance his or her job performance” Perceived Ease of Use: “the degree to which a person believes that using a particular system would be free from effort” Study 1: Electronic Mail (E-Mail) and a file editor (XEdit). Study 2: two graphics packages Not explicitly mentioned, voluntary assumed Study 1: Scale development, survey of existing users. Study 2: Hands-on practice, questionnaire Scales with high Cronbach Alpha, significant correlations between PEOU/ PU and Behavioral Intent Information Resources Management Journal, 22(4), 23-44, October-December 2009 29 Focus Table 1. Significant theories and models that inform the present study Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Theory Definition of Core Constructs Technology Studied Voluntary or Mandatory Adoption Methodology Technology Acceptance Model 2 (TAM2) Individual adoption decision making TAM constructs with determinants of Perceived Usefulness added: Subjective Norm: “the degree to which an individual perceives that most people who are important to him think he should or should not use the system” Image: “the degree to which an individual perceives that use of an innovation will enhance his or her status in his or her social system” Job Relevance: “the degree to which an individual believes that the target system is applicable to his or her job” Output Quality: “the degree to which an individual believes that the system performs his or her job tasks well” Result Demonstrability: “the degree to which and individual believes that the results of using a system are tangible, observable, and communicable” Scheduling system, financial analysis, customer account management system, portfolio analysis system Both voluntary and mandatory Longitudinal field study in four organizations. All users received handson training. Analysis with Structural Equation Modeling Unified Theory of Acceptance and Use of Technology (UTAUT) Individual adoption decisions Performance Expectancy: “the degree to which an individual believes sthat using the system will help him or her to attain gains in job performance” Effort Expectancy: “the degree of ease associated with the use of the system” Social Influence: “the degree to which an individual perceives that important others believe he or she should use the new system” Facilitating Conditions: “the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system” Financial analysis software, Customer Service system Both voluntary and mandatory Synthesis of eight models into a unified model, validated with two samples (voluntary and mandatory). Analysis with Structural Equation Modeling Results of Analysis Good predictor of behavioral intention 30 Information Resources Management Journal, 22(4), 23-44, October-December 2009 Focus Table 1. Significant theories and models that inform the present study Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Theory Definition of Core Constructs Technology Studied Voluntary or Mandatory Adoption Methodology Results of Analysis Technology Acceptance Model 3 (TAM3) Individual adoption decision making, with intent to strengthen managerial support TAM2 constructs with determinants of Perceived Ease of Use added: Computer Self-Efficacy: “the degree to which an individual believes that her or she has the ability to perform a specific task/job using the computer” Perception of External Control: “the degree to which and individual believes that organizational and technical resources exist to support the use of the system” Computer Anxiety: “the degree of ‘an individual’s apprehension, or even fear, when she/he is faced with the possibility of using computers’” Computer Playfulness: “ the degree of cognitive spontaneity in microcomputer interactions” Perceived Enjoyment: “the extent to which the activity of using a specific system is perceived to be enjoyable in its own right, aside from any performance consequences resulting from system use” Objective Usability: “a comparison of systems based on the actual level (rather than perceptions) of effort required to completing specific tasks” Scheduling system, financial analysis, customer account management system, portfolio analysis system Both voluntary and mandatory Longitudinal field study in four organizations. All users received handson training. Analysis with Structural Equation Modeling Good predictor of behavioral intent Information Resources Management Journal, 22(4), 23-44, October-December 2009 31 Focus Table 1. Significant theories and models that inform the present study Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Theory 32 Information Resources Management Journal, 22(4), 23-44, October-December 2009 Force Model in ET The second model, the Force Model, measures motivation to use. Overall attractiveness from the Valence Model is multiplied by a probability that effort will result in successful use and realization of the expected benefits. Mathematically, this can be expressed as Fi Ȉnj=1 (Eij Vj), where Fi represents the motivational force, Eij is a probability of success, and Vj is the overall attractiveness from the Valence Model. Simple linear regression is used with reported level of effort as the dependent variable. The product of attractiveness and probability of success is used as the independent variable. To minimize the number of required measurements for each respondent, the probability of success can be stated at two extremes such as 0% vs. 100% or 10% vs. 90%. The Beta coefficient in the regression equation indicates the strength of the motivation to expend effort in the adoption process, and the sign (positive or negative) indicates whether increased probability of success tends to increase or decrease effort. Tablet PCs Tablet PCs represent a relatively new form of technology that, for some prospective users, has potential advantages over regular notebooks. Tablet PCs have not experienced widespread use as prominent tools in most computing environments, but have gained acceptance in selected environments, such as education and healthcare. Potential adopters must typically choose a technology to support their information requirements. This decision is frequently a choice between a Tablet PC and an alternative technology such as a laptop computer, PDA, or web-enabled cell phone. The decision process becomes a comparison of competing technologies, where the relative advantages are considered in the decision-making process. Based on the preceding discussion of Expectancy Theory, we expect that in forming intentions to use, potential adopters will weigh the advantages provided by the Tablet PC features against (1) those provided by alternative technologies and (2) the expected ease of using those features. Benefit Categories To determine which features would most likely be considered in forming a decision to adopt a Tablet PC, we performed a content analysis of the case studies of early adopters of the technology, as presented on the Microsoft website for Tablet PCs (Microsoft Corporation, 2003). The benefits of Tablet PCs, as listed in these case studies, were coded and grouped. During this process, it became apparent that not all benefits, as they were presented, were exclusive to Tablet PCs. For instance, the uploading of information by field consultants and the immediate availability of this information to others in the company, as presented in the 7-Eleven case study (Microsoft Corporation, 2002), really depends on the use of wireless technology or connectivity through wired networks. These capabilities are currently available on all new laptop computers and on many PDAs and web-enabled cell phones. In contrast, immediate data entry without re-typing or re-keying handwritten information, as in the case study of the home health agency Gentiva Health Services (Microsoft Corporation, 2005), is a benefit exclusive to Tablet PCs. Less obvious is the example of the ability to use the Tablet PC in any position, whether sitting, standing, or walking. (This can also be performed with PDAs and cell phones, but screen limitations provide a significant constraint on the volume of information accessible at one time.) Though both laptops and Tablet PCs can be used to access the Internet, only the Tablet PC can realistically be used to quickly check e-mail while standing and walking. In this example, the Tablet PC should still be considered as having expanded information access capabilities compared with laptop PCs. Another complicating factor is that the method of typing differs significantly between notebooks and Tablet PCs on one hand and PDAs and cell phones on the other hand. The lack of full-size keyboards and the significantly reduced screen sizes place these latter Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Information Resources Management Journal, 22(4), 23-44, October-December 2009 33 two technologies in another category. In order to make relevant and valid comparisons, we decided to limit ourselves to a choice between regular notebooks and convertible Tablet PCs, where the Tablet PCs have all functionality of regular notebooks but with addition of special features. Focusing on capabilities specific to Tablet PCs and based on a careful content analysis of the aforementioned case studies, we formulated a three-category array of benefits or feature sets. The first benefit category identified from review of the case studies relates to flexibility of input. Tablet PCs allow users to sit, stand, or walk when working with the computer. Forms and reports can be completed by typing on the keyboard or by handwriting with the electromagnetic pen. Users may cradle the Tablet PC in one arm, while entering data with the other, thereby eliminating the need to sit at a desk or table. In addition to using keyboard and mouse, users can capture handwriting, record voice messages, make drawings, and use built-in speech recognition. Users have the option to select the input method most suitable to the circumstances. In general, because data entry can be performed on the spot, Tablet PCs enable users to stay more current with their paperwork. The second benefit category relates to availability of information. Not only can users acquire information in any position, from the office or the Internet, at any time, and from any place by using a wireless network, but Tablet PCs have special search capabilities related to finding information on the local hard drive. Users of competing technologies can only find typed text and indexed information, such as file names and file types, whereas Tablet PC users can search within handwritten notes that have earlier been entered using the electronic pen. As a result, all work-related information can either be stored locally on the Tablet PC, or accessed over a network. Taken together, this creates the potential to make all work-related information available anywhere and anytime. The third benefit category concerns collaboration features going beyond those available on other devices. In meetings with colleagues, users can quickly exchange documents over a wireless connection, or work on shared screens with other team members who also have a Tablet PC. Afterwards, there is no need to transcribe or file notes, and notes can be distributed immediately. Again, some of these features are available on other devices, but it is the ubiquitous availability that distinguishes the Tablet PC. For instance, desktop sharing has long been available in the NetMeeting application, but can only be used when a user happens to be at his desk. In summary, data can be entered anywhere and anytime, information can be available anywhere and anytime, and users can collaborate electronically anywhere and anytime. Each feature set might positively influence the attractiveness of Tablet PCs to mobile computer users. Therefore, a research model was developed with three independent variables (Flexibility of Input, Availability of Information, and Collaboration Features) and one dependent variable (Attractiveness) in the Valence Model. For the Force Model, Attractiveness and Probability of Success functioned as the independent variables, and Effort as the dependent variable. The model is illustrated in Figure 5. Consistent with prior research studies based on this methodology, for the Valence Model, we formulated the following hypotheses: H1,QFUHDVHGIOH[LELOLW\RILQSXWSRVLWLYHO\LQIOXences overall attractiveness of Tablet PCs. H 2:Increased availability of information positively influences overall attractiveness of Tablet PCs. H3: Increased collaboration features positively influence overall attractiveness of Tablet PCs. Each feature set is unique and would exert a different influence on the individual decision maker’s perception of the alternative technologies. Accordingly, it would be expected that Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 34 Information Resources Management Journal, 22(4), 23-44, October-December 2009 Figure 5. Research model Not included in the model: Individual Differences, Facilitation Conditions, Social Influence System Characteristics Flexibility of Input Availability of Information H1 H2 Effort Attractiveness H5 H3 Collaboration Features the previous three influences would differ in strength, we also posited: Probability of Success METHODOLOGY OF THE STUDY H45HVSRQGHQWVZLOOYDOXHIOH[LELOLW\RILQSXW Expectancy Theory addresses the relative availability of information, and collaboration levels of individual decision makers’ perceptions of the comparative strengths of decision features differently. alternatives, along with the probability (weight) that they will be realized. To analyze the indiFinally, to test the Force Model, we forvidual adoption decisions in this context, we mulated: established a research design which captured the essential characteristics and featured reH5: Increased probability of successful use posi- spondents uniquely motivated to consider the tively influences intention to use Tablet PCs. choices. The process by which we tested the hypotheses involved a demonstration of the In order to test these hypotheses, a rigorous Tablet PC features to veterinary college apresearch methodology was established in which plicants of a large southeastern university and a survey of applicants to a veterinary college a subsequent survey of their perceptions. This was conducted. The methodology of the survey, sample is appropriate due to the nature of their results, and analysis of the data are described studies and future profession. If selected into the in the following sections. college, the students are expected to maintain accurate records, to provide detailed reports, to collaborate with peers, and to present their work at a moment’s notice in an environment where they must walk from room to room throughout the day. These activities represent quintessential mobile professional situational Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Information Resources Management Journal, 22(4), 23-44, October-December 2009 35 factors. Furthermore, the respondents were told that they would be expected to own and utilize a portable computing device to fulfill their informational needs. Over the course of three days, as groups of applicants toured the college, Tablet PC features and benefits were demonstrated to 137 admission finalists. These finalists were selected from the original pool of 363 applicants to physically visit the campus for second round interviews in the application process. The response rate for the survey was 100% (all 137 applicants chose to participate). The applicants ranged in age from 19 to 51 years and had a mean age of 23.5 years. The majority (65.7%) of the applicants were female. About half (43%) of the applicants had heard of Tablet PCs before the demonstration while very few (3.7%) had actually used a Tablet PC. Most of the applicants can be characterized as frequent computer users, using a desktop PC (82.2%) or laptop (54.5%) at least weekly. The applicants frequently use the World Wide Web and email; all respondents report that they participate in both activities at least weekly; while 85.9% surf the web and use email daily. With regard to software utilization, 49% of female and 66% of male applicants consider themselves “proficient” with several software packages. A slightly larger portion, 60% of females and 70% of males, feel “comfortable” with several software packages. Most applicants (90%) use Microsoft Word at least weekly, while other Microsoft packages such as Excel, PowerPoint, and Access are not used as often. Overall, about one in four (24%) applicants felt unfamiliar with computers and software beyond basic word processing and email use. Yet most of the applicants (89%) plan on bringing a computer to campus, should they gain acceptance and choose to attend. Eighty-nine percent of females plan on bringing a PC to campus with 53% planning on purchasing a new PC for the program. Ninety-one percent of males plan on bringing their own PC and 62% are planning to purchase a new PC for the program. Table 2 provides a summary of these and other descriptive factors asked at the end of the survey instrument. During a campus visit to the college, all applicants viewed a live demonstration of a Tablet PC and responded anonymously via paper questionnaire at the conclusion of the demonstration. All participants were assured that their response would not affect their acceptance into the program, that their response would be completely anonymous, and that their response could help the college to improve the program they sought to enter. The demonstration was delivered by two of the coauthors. One coauthor, who does not own or use a Tablet PC, acted as the narrator; a second, who uses a Tablet PC regularly, utilized a convertible Tablet PC to demonstrate each of the three feature sets. Both presenters stood and walked on the stage throughout the demonstration, and the Tablet PC operator carried the Tablet PC without once putting it down. The desktop view of the Tablet PC was displayed to the study participants via an overhead projector attached to its VGA port. (The narrator explained that the cable was only necessary to show the Tablet PC’s display, and that the Tablet PC was completely mobile otherwise.) To demonstrate the first benefit category, flexibility of input, the Tablet PC operator wrote a note in Microsoft Word by using the Tablet PC input panel. The built-in handwriting recognition software converted the handwritten input to typed text in the Word application. Secondly, a handwritten note complete with a simple graphic (a smiley face) was created in the Windows Journal application. Both files were saved to their default locations on the hard drive. The second set of features, related to availability of information, was demonstrated by wirelessly accessing the Cable News Network (CNN) website and pointing out the current date and time. The narrator explained that information needed for their program of study, such as retrieving medication information on the network, could just as easily be done in real-time. This was followed by a search on the local hard drive, where search terms specific Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 36 Information Resources Management Journal, 22(4), 23-44, October-December 2009 Table 2. Summary of background and demographic information Age Mean 23.43 years (19-51 years) Gender Female 65.7%, male 34.3% Percentage of work time spent away from desk 64.7% (5-100%) Self-reported typing speed 52.8 words per minute (12-150 wpm) Have heard of Tablet PCs before 43.0% Have used Tablet PCs before 3.7% Use a windows desktop at least weekly 82.2% Use a MAC desktop at least weekly 4.5% Use a windows laptop at least weekly 54.5% Never use a MAC laptop 91.8% Use a PDA at least once a week 6.6% Never use Unix / Linux 90.1% Surfing the web 100% at least weekly, 85.9% daily Using email 99.2% at least weekly, 85.9% daily Sending email attachments 83.6% at least weekly, 58.2% daily Using instant messaging 36.3% daily, 49.6% at least weekly Using a digital camera 25.8% more than occasionally Using voice recognition software 3.7% more than occasionally, 82.1% never Using broadband 24.8% daily, 54.9% never Using USB flash drives 66.4% never, 8.2% daily Using MS Word 88.1% at least weekly, 48.9% daily Use other word processing packages 30.8% at least weekly Using Excel 58.5% never or occasionally Use other spreadsheet packages 93.2% never or occasionally Using PowerPoint 17.9% more than occasionally Using statistical software 71.6% never Using graphical software 89.6% never or occasionally Using databases 7.4% more than occasionally, 69.6% never to the messages written in the first part of the demonstration were easily found in both the Word document with typewritten text and the Journal note with handwritten text. To demonstrate the final feature set of Tablet PCs, the collaboration features, the operator showed how the Journal note could be sent to an email recipient with a few taps of the pen, and explained that the recipient could easily make changes and additions to the note and send it back for further collaboration. After having seen these Tablet PC features live, participants completed the anonymous questionnaire. The questionnaire presented participants with eight different configurations of Tablet PCs, where each of the three benefit categories were either “Standard” or “Enhanced” compared with laptop PCs. The descriptions of the two levels for the three benefit categories are listed in Table 3, and the combinations of Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Information Resources Management Journal, 22(4), 23-44, October-December 2009 37 benefits in Table 4. For each configuration, participants were asked to rate how attractive they considered the configuration for their work, and how much effort they would be willing to give if the probabilities of successful use were high (90%) and low (10%). The scale ranged from -5 (very unattractive) to +5 (very attractive) for attractiveness, and from 0 (no effort) to 10 (maximum effort) for effort. The questionnaire concluded with questions about familiarity with different types of hardware and software, and the participants’ comfort level using computers. DATA ANALYSIS AND RESULTS Data obtained were analyzed consistent with the previous methodology of Chen et al (2004) and Chen and Hoshower (2003). The Valence Model was used to test hypotheses H1 through H4. First, we performed a multiple regression analysis of the three feature sets, coded as dummy variables (0 = Standard, 1 = Enhanced), on the attractiveness score for all combinations and all participants. The results showed that the model explained 51% of attractiveness (average adjusted R2 = 0.508, s.d. = 0.38). All four hypotheses were supported. The Beta coefficients were positive and statistically significant for Flexibility of Input (H1, p=.000), Availability of Information (H2, p=.000), and Collaboration Features (H3, p=.000). In addition, the three Beta coefficients were clearly different with values of .329, .211, and .140 respectively (thereby supporting H4). The three feature sets had different levels of influence. These results are summarized in Table 5. Since the study used a repeated measurement (rating eight combinations for each participant), we repeated the multiple regression with not only the three feature sets, but also with all participants individually as independent variables. In doing so, the variance associated with all participants as a repeated measure was removed. The results showed that all standardized Beta coefficients and significance scores remained unchanged. The T-score did increase for all three benefits, indicating that separating the influence of the Table 3. Descriptions of benefit categories Flexibility of Input Enhanced Assume you can sit, stand or walk when working with this particular Tablet PC. All forms and reports can be completed with keyboard or electronic “pen.” In addition to using keyboard and mouse, you can capture handwriting, record voice messages, use your voice for commands (voice recognition), or make drawings. You can choose the input method most suitable to the circumstances. The Tablet PC enables you to be more current with your paperwork. Flexibility of Input Standard Assume you need to sit down to work on this Tablet PC (like you would with a laptop PC), and use some paper forms and handwritten notes. These are later entered into the Tablet PC using keyboard and mouse. Availability of Information Enhanced Assume you can get information from the office or the Internet at any time and from any place by using a wireless network. You can search information stored on this Tablet PC, even in handwritten notes. All work-related information can be stored on the Tablet PC. Availability of Information Standard Assume you must phone the office for information. You can perform simple text searches on your Tablet PC. Your daily schedules and contact information can be stored on your Tablet PC. Collaboration Features Enhanced Assume in meetings with co-workers, you can quickly exchange documents over a wireless connection, or work on shared screens with other team members who also have a Tablet PC. After meetings, there is no need to transcribe or file notes, because you can share documents wirelessly, and you can distribute meeting notes immediately. Collaboration Features Standard Assume in meetings with co-workers, you can exchange documents only on disk or paper. You use whiteboards or flipcharts for drawings. Meeting notes are transcribed from handwritten notes. Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 38 Information Resources Management Journal, 22(4), 23-44, October-December 2009 Table 4. Configurations Configuration # Flexibility of Input Availability of Information Collaboration Features 1 Standard Standard Standard 2 Enhanced Standard Standard 3 Standard Enhanced Standard 4 Standard Standard Enhanced 5 Enhanced Enhanced Standard 6 Standard Enhanced Enhanced 7 Enhanced Standard Enhanced 8 Enhanced Enhanced Enhanced 7DEOH5HJUHVVLRQRIIOH[LELOLW\DYDLODELOLW\DQGFROODERUDWLRQRQDWWUDFWLYHQHVV Unstandardized Coefficients Model Standardized Coefficients t Sig. 69.192 .000 .329 16.785 .000 .087 .211 10.755 .000 .087 .140 7.120 .000 B Std. Error (Constant) 5.993 .087 Flexibility of Input 1.454 .087 Availability of Information 0.931 Collaborative Features 0.617 Beta Dependent Variable: Attractiveness 7DEOH5HJUHVVLRQRIIOH[LELOLW\DYDLODELOLW\DQGFROODERUDWLRQRQDWWUDFWLYHQHVVDVDUHSHDWHG measure Unstandardized Coefficients Model t Sig. 18.007 .000 .329 22.782 .000 .064 .211 14.598 .000 0.617 .064 .140 9.664 .000 0.625 .524 .024 1.192 .233 B Std. Error (Constant) 6.749 .375 Flexibility of Input 1.454 .064 Availability of Information 0.931 Collaborative Features P1 P2…P136 P137 Standardized Coefficients Beta * * * * * -1.739E-13 .524 .000 .000 1.000 Dependent Variable: Attractiveness * Complete data for all individual users’ models withheld for purposes of brevity, but are available upon request. Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Information Resources Management Journal, 22(4), 23-44, October-December 2009 39 individual respondents was (marginally) effective in reducing error. The results of the second analysis of the Valence Model are summarized in Table 6. Next, the Force Model was employed to test the final hypothesis (H5). Since the research model contained a single independent variable (overall attractiveness), we used simple regression rather than multiple regression. In other respects, the same approach was used as for the Valence Model. The results showed that H5 was also supported. The Force Model explained 67% of effort (average adjusted R2 = 0.670, s.d. = .26). Without removing the influence of repeated measurement, the Beta coefficient for the regression was positive and statistically significant (+.734, p=.000). After removing the variance associated with repeated measurement again, the Beta coefficient remained positive and statistically significant (+.719, p=.000). Again, the T-score increased, indicating that separating the influence of the individual respondents was effective in reducing error. The results of the analyses of the Force Model are summarized in Tables 7 and 8. DISCUSSION AND CONCLUSION The present study has applied a rigorous methodology to investigate the application of Expectancy Theory to analyze the important technology adoption decision where an individual chooses between competing alternatives. In doing so, we investigated the relative attractiveness of Tablet PCs compared with regular notebook PCs. This effort illustrates the principle of comparing competing technologies as a useful enhancement or new opportunity for technology adoption research. Results of our research show that participants in our study Table 7. Regression of the product of attractiveness and probability on effort Unstandardized Coefficients Model B Std. Error (Constant) 3.153 .070 Product .706 .014 Standardized Coefficients t Sig. 44.888 .000 50.268 .000 Beta .734 Dependent Variable: Effort Table 8. Regression of the product of attractiveness and probability on effort as repeated measure Unstandardized Coefficients Model Standardized Coefficients t Sig. B Std. Error (Constant) 4.483 .468 9.588 .000 Product .691 .012 .719 56.098 .000 P1 -.270 .656 -.007 -.413 .680 P2…P136 P137 Beta * * * * * -1.895 .656 -.051 -2.891 .004 Dependent Variable: Effort * Complete data for all individual users’ models withheld for purposes of brevity, but are available upon request. Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 40 Information Resources Management Journal, 22(4), 23-44, October-December 2009 evaluated potential benefits differently, and differed in their intended effort to adopt based on a combination of overall attractiveness and the anticipated effort necessary. These results mimic the outcomes of traditional adopt/non-adopt models, where behavioral intent is influenced by performance factors and effort expectancy factors. Performance factors are captured in the Valence Model, and effort expectancy factors in the Force Model. Our veterinary college applicants consider the flexibility of input, in particular the electromagnetic pen, to be the most attractive feature of Tablet PCs. The next most attractive feature is availability of information - the ability to have work-related information readily available at all times, while collaborative features is the least important of the three feature sets investigated. This order of preference may be an indication of the participant’s traditional expectations of tablet applications in the academic environment. Traditionally, successful students come from academic environments that promote independent work. As team-oriented activities are promoted in the curriculum, and with the professional practice climate after graduation having shifted towards multi-partner practices and collaborative referral practices, more sophisticated strategies of information management will be required, and students may subsequently place a higher importance on the collaboration features. The results of this study are highly consistent with what can be observed in the case studies on the Microsoft website. The Microsoft case studies place the most emphasis on the flexibility of input, while the immediate transfer of information and the use of Tablet PCs for collaboration receive less emphasis. This indicates that research with other groups of mobile professionals may result in comparable results, but this will have to be demonstrated in future studies. Equally important for the potential adoption of Tablet PCs is not only that the increased capabilities of the Tablet PC are attractive to potential adopters, but also that these adopters are also willing to put forth effort to learn how to use them. McGrath (1982) describes the “three horned dilemma” to highlight the trade-offs between various research designs, and argues that all empirical designs are subject to inherent limitations. Various research designs may result in greater or less (1) generalizability to the target population, (2) precision in measurement and control of the behavioral variables, and (3) realism of context. Our experimental design slightly favored realism (actual field study with a real technology, not a contrived lab experiment) and precision (using established, previously-validated instrument items with a statistically significant sample size) over generalizability (using college student volunteers with demographic characteristics that do not perfectly match the entire population of computer users). Our findings reflect several specific limitations. One limitation of our research design is the lack of participant hands-on experience with Tablet PCs. Potential adopter perceptions were based on demonstration of the features only, given the time constraints for participant involvement. Our research is limited to comparing only two alternative technologies, using positive factors only. For instance, we did not include factors such as higher price, boot-up delays, glare on the screen in daylight, and limited battery life compared with the duration of the work day for mobile professionals. The price premium is generally considered to be a factor in the limited market penetration, with only about 2% of the laptop market consisting of Tablet PCs (Williams, 2005). At the time of data collection, there was a small price premium, though this has been largely eliminated. Additionally, tablet functionality is now integrated as part of Microsoft’s latest operating system, Vista, which further reduces the number of obstacles to adoption. The lack of discussing any negative factors may have artificially inflated the Tablet PC’s perceived attractiveness and our respondents’ willingness to expend effort. However, to the extent that any negative perceptions of Tablet PCs would have reduced their attractiveness, this would also have been captured by the study’s ET-based methodology, and does not diminish its practical value for understanding Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Information Resources Management Journal, 22(4), 23-44, October-December 2009 41 technology adoption decisions from among alternative or competing technologies. Furthermore, we excluded other handheld devices such as PDAs and web-enabled cell phones. Our focus was on presenting the principle of comparing different alternatives in the technology adoption process, and future research can expand on the number of alternatives as well as the number of factors to be considered. Researchers who want to use this approach with more than two alternatives should consider that expanding the alternatives from two to three will increase the number of comparison pairs. In our research, including PDAs would have increased comparison pairs from one (notebook – Tablet PC) to three (notebook – Tablet PC, notebook – PDA, Tablet PC – PDA). The further inclusion of web-enabled cell phones would have increased the comparison pairs to six. One of the strengths of our study is the high participation rate. All applicants present during the orientation days participated, and the data were remarkably complete for such a large sample. The research design relied on self-reported beliefs, attitudes, and intentions, which is generally considered to be an imperfect measure for actual behavior. As is the case with almost all technology adoption research, only the intended behavior is measured but not the actual behavior. Therefore, our design suffers from common methods bias. Of course, as with almost all technology adoption research, the collection of actual behavior would be nearly impossible in this context. The analysis of our data is limited to system factors only. Demographic data and user background information are provided to describe the relevance of our sample, but the main focus of our study is the technology adoption process as a choice between alternatives. Future studies can use factors such as gender, age, and experience in more complex models and compare the results with the results of other IS technology adoption research. Due to data collection in a single context, we also did not address the issue of voluntariness of adoption. In the college where we collected our data, the choice between notebooks and Tablet PCs was left up to the applicants. The choice was mandatory in the sense that one of the two had to be selected, but the choice between the alternatives was completely open. This underscores our contention that technology adoption may now be a choice between alternatives, but that selection itself is mandatory. Other contexts may differ in their degree of voluntariness, which may influence the adopters’ perceptions and motivation. Another research limitation is the use of only three sets of features, each of which was presumed to be either standard or enhanced (yielding eight combinations of feature sets). Tablet PCs may be attractive for other reasons, such as size, form factor, or even prestige. Further, the features selected were based on vendor expectations, not on initial qualitative data from users. However, investigating more than three feature sets would imply far more than eight possible combinations, and could possibly bias the results due to cognitive complexity and/or respondent fatigue. The use of balanced incomplete designs may allow future studies to expand the number of factors in consideration, but this will result in reduced statistical predictive power. Future studies can also use comparison of features based on user feedback instead of vendor information. In this case, the trade-off will be between increased relevance (actual user perceptions) and accuracy (lack of user familiarity with technology, especially for innovators). Perceptions can change after initial use of a new technology (Karahanna, Straub, & Chervany, 1999). For example, when mobile phones were first introduced, initial motivations for adoption were mostly security-related or jobrelated, but quickly expanded to more “social” use (Palen, Salzman, & Youngs, 2000). In a similar fashion, the ultimate level of choice and diffusion of technologies may change over time. This presents an opportunity for longitudinal research to evaluate technology perceptions over time. Other studies, such as Venkatesh et al. (2003) and Venkatesh and Bala (2008), use repeated measurement over time. Due to the limited availability of our participants, we were Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 42 Information Resources Management Journal, 22(4), 23-44, October-December 2009 not able to pursue a longitudinal data collection design at this time. Finally, the use of prospective students in a veterinary program limited the generalizability of the results to other age groups and to working professionals with years of experience to the extent that those variables may influence technology perceptions. Future studies with different groups can address this issue. However, the purpose of this research was not specifically to investigate Tablet PC adoption, per se, but rather to demonstrate the efficacy of this research approach to broaden our understanding of technology adoption, especially when such adoption decisions are characterized by a choice between competing alternatives, and in that regard, the research design succeeded. The results of this investigation provide implications for researchers and for professional users and managers. First, this study answers the call to investigate the IT artifact (Orlikowski & Iacono, 2001) and lends knowledge that may aid future researchers investigating technology adoption, Expectancy Theory, and user interface designs. Researchers can leverage this knowledge in further investigations of emerging technologies, especially those with unique and innovative characteristics. Users or potential adopters can scrutinize their decision process through the lens of this investigation. 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University Business, 6(7), 13–15. Vroom, V. C. (1964). Work and motivation. New York: John Wiley & Sons. Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 44 Information Resources Management Journal, 22(4), 23-44, October-December 2009 Williams, M. (2005, June 27). Gates still roots for Tablet PCs. Retrieved September 20, 2006, from http://www.pcworld.com/article/id,121612-page,1/ article.html?RSS=RSS Wolf, G., & Connolly, T. (1981). Between-subject designs in testing expectancy models: a methodological note. Decision Sciences, 12(1), 39–45. doi:10.1111/j.1540-5915.1981.tb00058.x Ernst Bekkering is an associate professor in the Department of IS and Technology at Northeastern State University in Tahlequah, OK. Dr. Bekkering obtained his BS in physical therapy in his native Holland, and his MS and PhD in information systems at Mississippi State University. His research has been published in Communications of the ACM, Journal of Organizational and End User Computing, and the Journal of Advancement in Marketing Education. Allen C. Johnston is an assistant professor in the School of Business at the University of Alabama Birmingham. He holds a BS from Louisiana State University in Electrical Engineering as well as an MSIS and PhD in information systems from Mississippi State University. His works can be found in such outlets as Communications of the ACM, Journal of Global Information Management, Journal of Information Privacy and Security, and the Journal of Internet Commerce. The primary focus of his research has been in the area of information assurance and security, with a specific concentration on the behavioral aspects of information security and privacy. Merrill Warkentin is a Professor of MIS at Mississippi State University. His research, primarily in computer security management, eCommerce, and virtual teams, has been published in journals such as MIS Quarterly, Decision Sciences, Decision Support Systems, Communications of the ACM, Communications of the AIS, Information Systems Journal, Journal of Organizational and End User Computing, Journal of Global Information Management, and others. Professor Warkentin is the co-author or editor of four books, and is currently an associate editor of Information Resources Management Journal, Journal of Information Systems Security, and the special issue of MIS Quarterly on computer security, and is the co-guest editor for the special issue of the European Journal of Information Systems on computer security. His PhD is from the University of Nebraska. Mark B. Schmidt is an associate professor of business computer information systems at St. Cloud State University in St. Cloud, Minnesota. He holds a BS from Southwest State University in business and agri-business, an MBA from St. Cloud State University, and MSIS and PhD, degrees from Mississippi State University. He has works published in the Communications of the ACM, Journal of Computer Information Systems, Journal of End User Computing, Journal of Global Information Management, Journal of Internet Commerce, Mountain Plains Journal of Business and Economics, International Journal of Information Security and Privacy, Information System Frontiers, International Journal of Information Systems and Change Management, and in Information Systems Security: A Global Perspective. His research focuses on information security, end-user computing, and innovative information technologies. Copyright © 2009, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.