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Emory University School of Law. Public Law & Legal Theory Research Paper Series. Research Paper No. 10-96. Task-Technology Fit and Process Virtualization ...

Emory University School of Law Public Law & Legal Theory Research Paper Series Research Paper No. 10-96

Task-Technology Fit and Process Virtualization Theory: An Integrated Model and Empirical Test Eric Overby Georgia Tech Benn Konsynski Emory

This paper can be downloaded without charge from: The Social Science Research Network Electronic Paper Collection: http://ssrn.com/abstract=1567097

Task-Technology Fit and Process Virtualization Theory: An Integrated Model and Empirical Test Eric Overby, Georgia Institute of Technology ([email protected]) Benn Konsynski, Emory University ([email protected]) Abstract: Task-technology fit and process virtualization theory are two theories developed by IS scholars to advance our understanding of the role and impact of information systems in business and society. Task-technology fit seeks to explain the use and associated outcomes of technologies designed to complete tasks. Process virtualization theory seeks to explain whether processes are suitable for migration into virtual environments such as those enabled by information technology. We integrate these two theories and test the integrated model by assessing the fit of electronic channels to the process of purchasing vehicles in the wholesale automotive market. The research makes three contributions. First, the integrated model addresses a gap in the task-technology fit literature by replacing the generic “task / technology characteristics” antecedents of task-technology fit with the constructs from process virtualization theory. This improves the prescriptive power of task-technology fit without sacrificing its generalizability. Second, the paper represents the first empirical test of the propositions of process virtualization theory, thereby helping determine how the theory operates in practice. Third, integrating the two theories deepens the IS discipline’s theoretical foundation by combining their strengths to improve our understanding of IS phenomena.

Keywords: task-technology fit, process virtualization theory, Information Systems theory, survey data, archival data, partial least squares, structural equation modeling, automotive.

1   Electronic copy available at: http://ssrn.com/abstract=1567097

Task-Technology Fit and Process Virtualization Theory: An Integrated Model and Empirical Test 1.0 INTRODUCTION As the information systems (“IS”) discipline has matured, IS scholars have formulated several theories to advance our understanding of the role of information systems in business and society. Two of these theories are task-technology fit theory (Goodhue and Thompson 1995) and process virtualization theory (Overby 2008.) Task-technology fit theory seeks to explain and predict the use of and outcomes associated with technologies designed to accomplish tasks. Task-technology fit theory posits that a technology which is well-suited for a given task will be used more often and will yield better outcomes than a technology which is poorly-suited for the task. Process virtualization theory seeks to explain and predict whether processes that have traditionally been conducted in physical environments can be migrated to virtual environments, particularly those based on information technology. Process virtualization theory posits that process characteristics (sensory requirements, relationship requirements, synchronism requirements, and identification and control requirements) and information technology characteristics (representation, reach, and monitoring capability) influence how suitable a process is to being conducted virtually. The purpose of this paper is to propose and test an integrated model of these two theories. This resolves open issues with both theories and yields three main contributions. First, the integrated model fills a gap in task-technology fit theory with respect to the antecedents of fit. While the consequents of fit (use and outcomes) are relatively straightforward, the antecedents of fit are less clear. Task-technology fit theory posits that “task characteristics” and “technology characteristics” influence fit, which is reasonable but too vague to provide generalizable, prescriptive guidance on how to predict or improve fit. As a result, applications of 2   Electronic copy available at: http://ssrn.com/abstract=1567097

task-technology fit have defined task and technology characteristics specific to the empirical context. This has created a gap in understanding regarding the antecedents of fit. On one hand, the “task characteristics” and “technology characteristics” antecedents proposed in the original formulation of the theory are too general; on the other hand, antecedents defined for specific contexts are too specific. We propose that integrating task-technology fit with process virtualization theory creates the necessary middle ground to fill this gap. The integration is achieved by replacing the generic “task characteristics” and “technology characteristics” antecedents of task-technology fit with the specific process characteristics and information technology characteristics proposed in process virtualization theory. Because process virtualization theory can be applied to any process, this integration maintains the broad applicability of task-technology fit but gives analysts specific constructs to consider when seeking to predict or improve fit. Second, the test of the integrated model represents the first test of many of the propositions of process virtualization theory.1 Process virtualization theory consolidates and codifies much of the knowledge about process virtualization phenomena that the IS discipline has gained. This knowledge has been generated by studying the transition of physical processes to virtual environments across multiple IS research streams. These include: a) electronic commerce studies that investigate the transition of shopping processes from physical stores to virtual storefronts on the Internet (Ba and Pavlou 2002; Bakos 1997), b) distance learning studies that investigate the transition of educational processes from collocated classroom environments to distributed, computer-mediated learning environments (Alavi and Leidner 2001; Piccoli et al. 2001), c)                                                              1

As discussed later in the paper, we do not test the full set of propositions posited in process

virtualization theory. 3  

online community studies that investigate the transition of human interaction processes from physical environments to virtual environments such as Usenet groups, social networking sites, and metaverses (Ma and Agarwal 2007; Wasko and Faraj 2005), and d) virtual team and group decision support studies that investigate the transition of team processes from physical, collocated settings to virtual settings (Majchrzak et al. 2000; Zigurs and Buckland 1998). The next step for the IS discipline after the construction of process virtualization theory is to begin testing its propositions. We do so in the context of the wholesale automotive market. We test whether the process virtualization theory variables can explain the fit between electronic purchasing channels available in the market and the process of purchasing vehicles. We find support for many of the hypothesized relationships and conclude that the model has good explanatory power. This contributes to process virtualization theory by developing measures of its constructs, empirically validating many of its propositions, and otherwise examining how the theory operates in practice. Third, integrating task-technology fit and process virtualization theory contributes to the IS discipline by enhancing its underlying theory base. The separate construction of the theories has enhanced the theoretical breadth of the discipline. The integration of the two theories enhances the theoretical depth of the discipline by combining the strengths of the theories to improve our understanding of the role of technology in the conduct of tasks and/or processes. The paper is structured as follows. Section 2 describes task-technology fit theory, process virtualization theory, and how the two theories can be integrated. Section 3 describes the empirical context used to test the integrated model. Section 4 presents the research model and hypotheses, and Section 5 discusses the data and measurement strategy. Section 6 presents the analysis approach and empirical results. Section 7 discusses implications for research and

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practice, along with limitations and opportunities for future research. A key part of Section 7 is a discussion of the integrated model’s utility across different contexts. Section 8 concludes with a summary of the paper’s intended contributions. 2.0 TASK-TECHNOLOGY FIT AND PROCESS VIRTUALIZATION THEORY 2.1 Task-Technology Fit 2.1.1 Premise Goodhue and Thompson (1995: p. 216) defined task-technology fit as “the degree to which a technology assists an individual in performing his or her portfolio of tasks.” They defined tasks as “the actions carried out by individuals that turn inputs into outputs” and technologies as “tools used by individuals in carrying out their tasks.” At its core, task-technology fit theory predicts that technologies that are well-suited to the tasks they are used to accomplish will be used more and will generate better performance than technologies that are poorly suited to the tasks. For example, consider the task of inserting nails into a board and a hammer and a screwdriver as technologies to complete this task. Task-technology fit theory posits that because the hammer has a higher fit to the task than does the screwdriver, the hammer will be used more than the screwdriver, and people who use the hammer will insert nails more effectively than people who use the screwdriver (Goodhue 2007). Both task characteristics and technology characteristics influence task-technology fit. If either the task or the technology changes, then so will the task-technology fit. For example, if the task is changed to inserting a screw (instead of a nail) into a board, then the fit between the task and hammer decreases while the fit between the task and screwdriver increases. However, if the hammer is changed to include a screwdriver shaft at the end of the handle, then the task / hammer fit remains high. Task characteristics, when operationalized as task requirements, are generally expected to be negatively related to task-technology fit. As task requirements increase, 5  

task-technology fit is harder to achieve irrespective of the technology’s characteristics. Conversely, technology characteristics, when operationalized as technology functions, are expected to be positively related to task-technology fit. As technology gains functionality, tasktechnology fit is easier to achieve because the technology can do more and/or is more flexible (Dishaw and Strong 1999; Goodhue 1995). Although often omitted, characteristics of the individual conducting the task also influence task-technology fit. Goodhue and Thompson (1995) highlighted the importance of individual characteristics by acknowledging that a more accurate label for the task-technology fit construct would be task-individual-technology fit. The rationale for considering individual characteristics is that a task-technology combination may represent a good fit for one individual but a poor fit for another. For example, many tasks have a high fit with software packages (e.g., letter writing and word processing), but only for individuals who know how to use a computer. A stylized nomological network of task-technology fit appears as Figure 1. 2.1.2 Antecedents of task-technology fit As shown in Figure 1, “task characteristics” and “technology characteristics” influence tasktechnology fit. Although these antecedents are reasonable, they are essentially tautological and are too vague to provide generalizable guidance on how to improve or predict fit. For example, consider a grocery store manager considering implementing a new online shopping system for his/her customers. Task-technology fit theory predicts that the system will be useful to customers if it has a high fit to their shopping tasks. However, the theory offers little guidance to the manager on how to assess that. Or, consider a therapist considering opening an online clinic for treating patients. Task-technology fit posits that the features of the online clinic should have a high fit to the therapy tasks, but offers minimal guidance to the therapist on how to assess that.

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Due to the lack of prescriptive guidance regarding which task and technology characteristics influence fit, task-technology fit researchers have chosen characteristics specific to their context. For example, Zigurs et al. (1998; 1999) argued that the communication support, process structuring, and information structuring features of group decision support systems (the technology characteristics) provided different levels of fit for simple, problem, decision, judgment, and fuzzy tasks (the task characteristics.) Lin and Huang (2008) argued that technology characteristics such as knowledge mapping and task characteristics such as tacitness influence fit in the knowledge management context. Although these antecedents are useful within their domains, they do not generalize to other contexts. This has hindered the accumulation of knowledge about task-technology fit. In particular, the research on the antecedents of fit has been fragmented across different contexts, leaving researchers and practitioners with no general prescriptive model of what affects fit. We propose that integrating process virtualization theory with task-technology fit helps address this issue. Prior to discussing process virtualization theory and how the two theories can be integrated, we first review how task-technology fit has been measured. This discussion is important because it pertains to the antecedents of fit and whether they can be measured. 2.1.3 Measurement of task-technology fit 2.1.3.1: The user perception approach: One measure of task-technology fit is users’ perceptions of how well the technology fits their tasks. This is typically measured psychometrically. The most systematic psychometric instrument for measuring task-technology fit was developed by Goodhue (1998) and used in related research (Goodhue 1995; Goodhue and Thompson 1995). This measure of task-technology fit is specific to managers using corporate information systems to complete job tasks; it measures “the degree to which an organization’s

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information systems and services meet the information needs of its managers” (Goodhue 1998: p. 105). The measure is composed of multiple dimensions, including data accuracy, data accessibility, data compatibility, and systems reliability. The measure and corresponding instrument have been employed by other researchers (e.g., Lee et al. 2007), but in general have been poorly adopted (Junglas et al. 2008). A potential explanation for the low adoption is that the instrument has limited applicability outside the context for which it was constructed. Another explanation is that the instrument does not yield a single score for task-technology fit; instead it yields a score for each of the dimensions. To overcome this second issue, Staples and Seddon (2004) measured task-technology fit as a second-order construct composed of multiple first-order constructs. Other researchers have measured task-technology fit as a first-order, uni-dimensional construct using items such as “the technology meets my needs for the task,” “the technology makes sense for the task,” or “I prefer technology A over technology B for the task” (Jarupathirun and Zahedi 2007; Lin and Huang 2008). The user perception measurement approach permits testing the antecedents of tasktechnology fit. One method of testing involves estimating the correlation between measures of task and technology characteristics and a measure of task-technology fit. This is illustrated in Figure 2. The user perception measurement approach also permits measuring different dimensions of fit. However, a disadvantage to this is the risk of conflating the dimensions of fit with its antecedents. For example, “Data Accuracy” (also labeled “Information Quality”) has been considered a dimension of task-technology fit (Goodhue 1998; Lee et al. 2007; Staples and Seddon 2004). However, it could also be considered an antecedent in the sense that a technology that provides inaccurate data will negatively influence task-technology fit, regardless of task. The “Systems Reliability” dimension (Goodhue 1998) is similar. This construct arguably describes a

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technology characteristic, which is an antecedent of task-technology fit in the sense that an unreliable technology will negatively influence fit for most tasks. 2.1.3.2: The theoretical approach: Another measure of task-technology fit is the theoretical match between a task and a technology. Referring to this as a “measure” is a misnomer, because task-technology fit is not actually measured in this approach. Instead, the researcher deems tasktechnology fit to be high or low based on theoretical grounds. An example of this approach is found in Mathieson & Keil (1998). They conducted an experiment in which subjects gathered information (the tasks) from one of two databases (the technology.) Some information-gathering tasks were straightforward with one database but relatively complex with the other (e.g., requiring multiple table joins.) Thus, the researchers judged the fit of each task to be high with one database and low with the other. Similar approaches have been used to assess fit in the contexts of group decision support systems (Dennis et al. 2001; Fuller and Dennis 2009; Zigurs and Buckland 1998; Zigurs et al. 1999) and mobile technologies (Junglas et al. 2008). This conceptualization of fit is often referred to as “fit as profile deviation,” because an ideal profile of technology capabilities is defined based on task requirements (Venkatraman 1989). The closer the technology used for a task matches this profile, the higher the fit. This conceptualization has also been labeled the “facets of fit” approach (Staples and Seddon 2004). 2.1.3.3: The computed approach: A critique of both the user perception and theoretical approaches is that the measures of fit are largely context-specific (Dishaw and Strong 1998; Junglas et al. 2008). Even the relatively expansive measure of task-technology fit developed by Goodhue is limited to managerial tasks supported by an organization’s information systems, which may not generalize to other tasks for which information technology is used nor to newer generations of information technology. A solution to this is to measure task-technology fit as the

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mathematical product of a measure of task characteristics and a measure of technology characteristics, i.e., task-technology fit = task measure * technology measure. This approach mirrors the concept of “fit as moderation” (Venkatraman 1989) and has been referred to as the “computed” approach (Dishaw and Strong 1999). This constitutes a universally-applicable method to measure fit, and it is useful for assessing whether fit has an effect on a dependent variable beyond the effects of task and technology alone. For example, consider the following two regression models: y = α + β1 TaskMeasure + β2 TechnologyMeasure + ε

(1)

y = α + β1 TaskMeasure + β2 TechnologyMeasure + β3 Task-Technology Fit + ε,

(2)

where Task-Technology Fit = TaskMeasure * TechnologyMeasure. If β3 in the second equation is significantly different from zero, then task-technology fit has an influence on the dependent variable beyond that of task and technology alone. However, a disadvantage to this approach (which is particularly relevant for our purposes) is that it does not permit testing the antecedents of fit. This is because task and technology characteristics are the factors (i.e., components) of task-technology fit; they are not the antecedents of it. Task / technology characteristics and fit are related only in a mathematical, predetermined way. Figure 3 provides an illustration. 2.1.3.4 Summary: The computed approach mathematically precludes measuring the antecedents of fit, and the theoretical approach does not involve formal measurement of fit. Thus, the user perception approach is arguably best suited for investigating the antecedents of fit, and therefore is the approach we use in this study. 2.2 Process Virtualization Theory Many societal processes that have traditionally been conducted in physical environments are

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being transitioned to virtual environments such as those made possible by the Internet. For example, shopping processes are migrating from physical to online stores (e.g., Ba and Pavlou 2002; Brynjolfsson and Smith 2000), and educational processes are migrating from physical to online classrooms (e.g., Alavi and Leidner 2001; Piccoli et al. 2001). However, not all processes have proven to be equally “virtualizable,” and many processes seem stubbornly rooted to the physical world. Process virtualization theory is designed to explain this variance by explaining why some processes are more suitable to being conducted virtually than others (Overby 2008). Overby (2008, p: 278) defined a process as “a set of steps to achieve an objective,” a physical process as a “process that involves physical interaction between people or between people and objects,” and a virtual process as a “process in which physical interaction between people and/or objects has been removed.” He labeled the transition from a physical process to a virtual process “process virtualization.” For example, both the process of shopping for books and the process of shopping for groceries have traditionally been physical processes in which participants engage in physical interaction with each other and with the products. Both can be conducted via electronic commerce, which constitutes a virtualization of the process because it removes the physical interaction that process participants have with each other and with the products. The virtualization of the book shopping process has proceeded steadily, while the virtualization of the grocery shopping process has not (Ramus and Nielsen 2005). Process virtualization theory is designed to explain this variance. There are four variables in process virtualization theory that describe process characteristics: sensory requirements, relationship requirements, synchronism requirements, and identification and control requirements. Each of these is proposed to have a negative relationship with the dependent variable, “process virtualizability,” which describes how suitable a process is to being

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conducted without physical interaction between people or between people and objects. As originally proposed, process virtualizability can be measured as: a) adoption / use of the virtual process, or b) the quality of outcomes achieved via the virtual process. For example, if a process has high sensory requirements related to the need to see, touch, taste, smell, and hear other participants or objects, then the process will be less virtualizable (as measured by lower adoption and/or poorer outcomes) than if the process did not have these requirements. Like task-technology fit theory, process virtualization theory is not specific to information technology-related phenomena. Just as task-technology fit can be applied to technologies such as hammers and screwdrivers that have no IT components, process virtualization theory can be applied to processes made virtual without the use of IT. For example, mail-order catalogs allow people to engage in a virtual shopping process, i.e., one in which physical interaction with salespersons and products has been removed, without the use of IT. However, most contemporary manifestations of process virtualization, such as the migration of shopping, education, and dating processes to the Internet, have been made possible by IT. Because of the importance of IT in process virtualization phenomena, it has a central role in process virtualization theory. A key premise of process virtualization theory is that IT can be used to make a process more amenable to virtualization by helping to satisfy sensory, relationship, synchronism, and identification and control requirements. Process virtualization theory includes three variables characteristic of IT to represent this effect: representation, reach, and monitoring capability. These variables moderate the relationship between the process characteristic variables and the dependent variable. For example, a process with high sensory requirements is expected to be relatively unsuited to virtualization, unless the sensory requirements can be adequately

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represented through information technology such as visual, auditory, haptic, and/or olfactory interfaces. The general process virtualization theory model appears in Figure 4. We discuss each construct and many of the proposed relationships in more detail in Section 4; see Overby (2008) for a complete description. 2.2.1 State of process virtualization theory research: Process virtualization theory applies to several research streams in the IS discipline, ranging from electronic commerce to virtual teams to distance learning. It has the potential to be an important theory for the discipline to consolidate much of the knowledge we have developed. However, process virtualization theory is new, and its propositions have yet to be tested empirically (to our knowledge.) Empirical testing is important in order to determine if the theory holds in practice, the relative power of its explanatory variables across different contexts, and whether there are boundary conditions that limit the utility of the theory. Thus, we take the logical next step by testing many of the propositions of process virtualization theory via the integrated model developed below. 2.3 Integrating Task-Technology Fit and Process Virtualization Theory 2.3.1 Comparing the two theories We propose that despite differences between task-technology fit and process virtualization theory, the two theories can be fruitfully integrated. First, task-technology fit applies to tasks while process virtualization theory applies to processes. Although this initially appears to be a difference between the two theories, the focus on tasks vs. processes is arguably more of a similarity than a difference. Goodhue and Thompson (1995) defined tasks as “actions carried out by individuals that turn inputs into outputs,” while Overby (2008) defined a process as a “set of steps to achieve an objective.” These definitions are strikingly similar. “Turning inputs into outputs” typically results in a completed “objective,” and

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“actions” can easily take the form of a “set of steps.” It is possible to argue that a difference in level of analysis (task vs. process) renders the two theories incompatible, because a single process involves multiple tasks. However, this distinction often breaks down in practice. For example, a task often studied in the IS literature is the student admissions task (Dennis 1996), in which an individual or group selects applicants to admit to a university. This “task” is arguably multiple tasks, including identifying the relevant information to consider, weighting that information, computing a score for each applicant, and then determining which applicants to admit based on the score (Fuller and Dennis 2009). As such, this task could logically be labeled a process. Similarly, a process that consists of a single step could be thought of as a task. Thus, we propose that while the different focus of the two theories makes sense for their respective purposes, it does not render them incompatible. To the contrary, the focus on tasks and processes makes the theories more similar than different. Second, both theories can be applied to a wide range of phenomena. Task-technology fit can be applied to any case in which technology may be used to accomplish a task. Process virtualization theory can be applied to any case in which a physical process is migrated into a virtual environment. This currently includes a wide range of processes spanning from shopping to education to banking to friendship development, and it will apply to additional processes as society continues to migrate processes into virtual environments. Third, both theories involve inanimate objects (information technologies) and artifacts of human endeavor (tasks and processes.) Neither can “self-report” their characteristics; humans must evaluate them. For example, whether an information system yields good fit or provides faithful representations are subjective assessments made by humans. Similarly, whether a task or process has high sensory requirements is ultimately subjective. One human may view a process

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as having high sensory requirements, while another may view it as having only moderate sensory requirements. This illustrates that the characteristics of tasks / processes and information technologies are arguably more subjective than objective, although characteristics with low variance among raters can be considered relatively objective. As a result, measures of tasks, processes, and technologies must be based on human perceptions, which can be gathered in different ways. One method is to distribute questionnaires to multiple individuals about a task / process and technology. This taps the perceptions of a wide range of individuals and is common in survey-based research. Another method is to ask a group of experts to comment on multiple tasks / processes and technologies. This taps the perceptions of a narrow range of individuals and has been used to categorize the effect of IT on different industries (Chatterjee et al. 2001) and to assess the potential of different jobs to be moved to other parts of the world (Apte and Mason 1995; Mithas and Whitaker 2007). The first approach can be thought of as providing ample depth but less breadth, while the second approach can be thought of as providing ample breadth but less depth. Fourth, the dependent variables are similar across the theories. First, task-technology fit seeks to predict whether a technology will be used; process virtualization theory seeks to predict whether a virtual process will be used. Second, task-technology fit seeks to predict whether a technology will improve performance; process virtualization theory seeks to predict whether a virtual process will lead to high quality outcomes. Fifth, the structure of the independent variables is similar across the theories. The process characteristics of process virtualization theory (e.g., sensory requirements, relationship requirements, etc.) parallel the task characteristics in task-technology fit. The IT characteristics of process virtualization theory (representation, reach, and monitoring capabilities) parallel the

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technology characteristics in task-technology fit. A key distinction is that process virtualization theory specifies individual constructs, while task-technology fit does not. 2.3.2 An integrated model We propose integrating task-technology fit and process virtualization theory by replacing the generic “task characteristics” and “technology characteristics” in task-technology fit with the more specific process characteristics (e.g., sensory requirements, relationship requirements, etc.) and IT characteristics (e.g., representation, reach, etc.), respectively, from process virtualization theory. This is similar to many empirical applications of task-technology fit in which the generic task and technology characteristics are replaced with variables specific to a particular context (e.g., Chang 2008). What distinguishes this approach is that the process virtualization theory variables are applicable to any task/process and technology combination. Thus, this integration maintains the generalizability of task-technology fit theory while improving its prescriptive power by giving analysts specific constructs to consider when predicting fit. In this way, the integration creates a middle ground for task-technology fit theory between the imprecise “task and technology characteristics” constructs and overly precise operationalizations of these constructs specific to a particular context. We illustrate the integrated model below and provide other examples of the utility of the integrated model in Section 7.2. Integrating the two theories in this manner represents an extension not only to tasktechnology fit but also to process virtualization theory. This is because process virtualization theory was not originally designed to explain fit, despite its utility for doing so. Extensions of this nature are useful, as it is important for the IS discipline to integrate theories where possible to prevent the proliferation of rival theories that may overlap. 3.0 EMPIRICAL CONTEXT

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We tested an integrated model of task-technology fit and process virtualization theory in the context of the wholesale automotive market. This market is a multi-billion dollar market in which commercial buyers and sellers exchange used vehicles. Buyers in the market are automobile dealers (e.g, Carmax, your local Ford dealer, etc.) who purchase used vehicles in the wholesale market to resell to retail customers. Sellers in the market include rental car firms (e.g., Avis), the financial affiliates of automotive manufacturers (e.g, GMAC), and other fleet operators who wish to sell large quantities of used vehicles in the wholesale market. Traditionally, the wholesale automotive market in the U.S. has operated as a physical market.2 Buyers, sellers, and vehicles are all collocated at physical market facilities operated by market intermediaries. The price mechanism used in the market is an ascending auction, which is the reason the intermediaries are often referred to as automotive auction companies. Over the last 10-15 years, several intermediaries, both incumbent to the industry and start-up firms, have launched electronic channels based on Internet technology. These channels are designed either to complement or to compete with the traditional physical market. This has provided buyers with the ability to purchase vehicles using the physical channel or the electronic channels.3 Prior research has used this market as the empirical context to examine questions related to adverse selection (Genesove 1993), sellers’ search for buyers (Genesove 1995), the effect of electronic trading on prices (Lee 1998; Lee et al. 1999), and how buyers and sellers use the electronic and physical channels (Overby and Jap 2009). To our knowledge, the present study is                                                              2

For an examination of the wholesale automotive market in Japan, in which electronic trading

has been more common, see Konsynski, Warbelow, and Kokuryo (1989) and Lee (1998). 3

Buyers typically refer to the physical channel as the “physical auction” and the electronic

channels as “electronic auctions.” We adopted this terminology for our measurement items. 17  

the first to assess buyers’ perceptions of the electronic and physical channels, including how well they fit the vehicle purchasing process. 4.0 RESEARCH MODEL AND HYPOTHESES The basic research model appears as Figure 5. We used archival transaction data and a survey of buyers in the market (described below) to test how well our model could explain the fit between the electronic channels and the wholesale vehicle purchasing process. The research model is similar to the model shown in Figure 1, except that it integrates the process virtualization theory constructs and omits the “Task Performance” dependent variable. Task Performance is not an appropriate dependent variable in this context because it is unclear what it would mean for a buyer to do a “good” or “bad” job purchasing vehicles using either the physical or electronic channels. As discussed in Section 2.3.1, we were faced with the decision of testing our model using: a) a single process analyzed by a large number of raters (capturing depth), or b) multiple processes analyzed by a small number of raters (capturing breadth.) We chose the former for two reasons. First, the process we studied is suitable for testing many of the relationships proposed in process virtualization theory, as discussed below. Second, we were able to gather the perceptions of a large number of highly qualified raters, namely the used car dealers who engage in the process as an integral part of their business. This enhances the validity of our measures. A common approach in studies that analyze two or more theories is to compare their explanatory power (e.g., Venkatesh et al. 2003) and/or show that an integrated model explains more than each individual theory (e.g., Dishaw and Strong 1999). This “horse race” approach is not appropriate in our context, because a stand-alone task-technology fit model in this context would not differ from the integrated model shown in Figure 5. This is because our method of

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integrating the two theories embeds the process virtualization constructs into the task-technology fit model, as opposed to making them incremental to it. 4.1 Perceived Fit Perceived Fit (PFiti) represents a buyer’s perception of how well the electronic channels fit the process of purchasing vehicles compared to the physical channel. Perceived fit is likely to differ among buyers in the market, yielding variance in this measure. We propose three groups of antecedents to explain this variance: a) buyers’ perceptions of the characteristics of the purchasing process, b) buyers’ perceptions of the characteristics of the technology used to virtualize the purchasing process (i.e., electronic channels), and c) buyers’ perception of their own characteristics. We also tested whether perceived fit is correlated with buyers’ actual use of the electronic channels. 4.2 Perceived Process Characteristics We used the four process characteristic variables proposed in process virtualization theory as antecedents of perceived fit. These are sensory requirements, relationship requirements, synchronism requirements, and identification and control requirements. First, we describe each of these variables in general terms, using definitions and proposed relationships drawn from Overby (2008.) Second, we formulate hypotheses specific to the empirical context. 4.2.1 Perceived Sensory Requirements In general, sensory requirements refer to the need for process participants to be able to enjoy a full sensory experience of the process and the other process participants and objects. Although virtual processes may simulate the sensory experiences inherent to the physical process, they may fall short of capturing them in their full essence. For example, the sensory aspects associating with touching, feeling, and smelling products have been identified as a barrier to the

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virtualization of many shopping processes, particularly those involving food and other agricultural products (Ramus & Nielsen, 2005). In our empirical context, perceived sensory requirements (PSensi) refer to a buyer’s perceived need to see, touch, smell, and hear the vehicles offered in the market. Because the vehicles are used, there is heterogeneity in their condition. This may lead to quality uncertainty that requires physical inspection to resolve. For example, the topology of vehicle dents and scratches and the smell of a vehicle’s interior are straightforward to assess physically but more difficult to assess electronically, where vehicle information is provided via photographs and text. This suggests that as a buyer’s perception of the sensory requirements of the purchasing process increases, his/her perception of the fit between the electronic channels and the purchasing process will decrease. H1: Perceived sensory requirements are negatively related to perceived fit. 4.2.2 Perceived Relationship Requirements In general, relationship requirements refer to the need for process participants to interact with each other in a social or professional context. Although it is possible for processes with high relationship requirements to be virtualized (there are several examples of relationships forged in virtual environments), research suggests that purely virtual relationships tend to be weaker and/or less developed than corresponding relationships developed in physical environments (Mesch and Talmud 2006; Parks and Roberts 1998). In our empirical context, perceived relationship requirements (PReli) refer to a buyer’s perceived need to interact with other buyers in the market. These interactions may help buyers nurture existing relationships, gather market intelligence related to pricing and supply/demand, and/or learn about overall market trends. The physical channel facilitates this interaction, as

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buyers are gathered at a common location that provides multiple venues for social interaction, such as the cafeteria and registration area. By contrast, the electronic channels are designed primarily as transaction platforms and lack features to promote social interaction among buyers. This suggests that as a buyer’s perception of the relationship requirements increases, his/her perception of the fit between the electronic channels and the purchasing process will decrease. H2: Perceived relationship requirements are negatively related to perceived fit. 4.2.3 Perceived Synchronism Requirements In general, synchronism requirements refer to the degree to which the activities that make up a process need to occur quickly with minimal delay. Because virtual processes abstract participants away from the physical context, they are often conducted asynchronously, which can create delays in the process. For example, a key reason why many shopping processes continue to be conducted physically is the need for the shopper to take immediate ownership of the product, rather than wait for it to be shipped (Alba et al. 1997). This is of particular concern for perishable products or those that otherwise diminish in value over time (Kamarainen and Punakivi 2004). In our empirical context, perceived synchronism requirements (PSynci) refer to a buyer’s perceived need to take possession of purchased vehicles immediately. The physical channel permits buyers to purchase vehicles and then return with them to their dealerships the same morning. This is important for used car dealers because they need physical possession of vehicles to market them optimally to retail customers. When a buyer purchases a vehicle via the electronic channels, there is usually a delay between purchasing the vehicle and receiving it, because the vehicle must be transported from a remote location. This suggests that as a buyer’s perception of the synchronism requirements increases, his/her perception of the fit between the

21  

electronic channels and the purchasing process will decrease. H3: Perceived synchronism requirements are negatively related to perceived fit. 4.2.4 Perceived Identification and Control Requirements In general, identification and control requirements refer to the degree to which process participants require: 1) unique identification of other participants, and 2) the ability to exert control over / influence their behavior. Virtual processes are susceptible to identity spoofing and control problems because participants cannot physically inspect others to confirm their identity. Identification and control problems have hindered the virtualization of several processes, including shopping processes where buyers may have difficulty identifying the seller as a legitimate provider (Ba and Pavlou 2002; Friedman and Resnick 2001). This construct as originally proposed is multi-dimensional, consisting of both an identification dimension and a control dimension. This is not a problem in and of itself, but it does necessitate different measurement than the other constructs in the theory. It is also possible that both dimensions may not operate in a given context. For example, in our context, buyers may perceive a need to identify others bidding on a vehicle but would have little control over their bidding behavior. Thus, we focus on the identification dimension of the identification and control requirements construct. In our empirical context, perceived identification requirements (PIDi) refer to a buyer’s perceived need to identify who else is bidding on a vehicle to inform his own bidding. This may be important for two reasons. First, buyers may want to observe others bidding on a vehicle to verify that bids are genuine, i.e., that there no “shill” bids (Chakraborty and Kosmopoulou 2004). Shilling is of particular concern in virtual environments, because buyers cannot visually confirm that others are bidding (Lucking-Reiley 2000). Second, buyers may infer vehicle quality based

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on who else is bidding. Because many buyers in electronic auctions withhold their bids until the end of the auction (a practice known as sniping (Roth and Ockenfels 2002)), buyers who want to use other buyers’ bids as quality cues are likely to prefer the physical channel, where the identity of other buyers is observable throughout the process, rather than only at the end. This suggests that as a buyer’s perception of the identification requirements increases, his/her perception of the fit between the electronic channels and the purchasing process will decrease. H4: Perceived identification requirements are negatively related to perceived fit. 4.3 Perceived Information Technology Characteristics In addition to the process characteristic variables, process virtualization theory also includes IT characteristic variables: representation, reach, and monitoring capability. 4.3.1 Perceived Representation In general, representation refers to IT’s capacity to present information relevant to a process, including simulations of actors and objects within the physical world, their properties and characteristics, and how we interact with them. For example, representation allows many sensory requirements such as sight and sound (and to a lesser degree, touch, taste, and smell) to be replicated in IT-based virtual processes (Jiang and Benbasat 2007; Steuer 1992). High representation capabilities make a process more suitable to virtualization and also have a positive moderating effect on the relationship between sensory requirements and process virtualizability. In our empirical context, perceived representation (PRepi) refers to the degree to which a buyer believes that the electronic channels can represent vehicle characteristics that s/he could otherwise learn about through physical vehicle inspection. For example, most vehicles traded electronically have condition reports that include vehicle photographs and descriptions of damage. This provides buyers with information about vehicle condition in the absence of

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physical inspection. Thus, as a buyer’s perception of the representation capability of the electronic channels increases, his/her perception of the fit between the electronic channels and the purchasing process should increase. In addition to this main effect, perceived representation is also likely to moderate the relationship between perceived sensory requirements and perceived fit. This is because a buyer may perceive the process to have high sensory requirements, but may still perceive a high fit between the purchasing process and the electronic channels if s/he believes that the electronic channels have sufficient representation capability. H5a (main effect): Perceived representation is positively related to perceived fit. H5b (moderating effect): Perceived representation positively moderates the relationship between perceived sensory requirements and perceived fit. 4.3.2 Perceived Reach In general, reach refers to IT’s capacity to allow process participation across both space and time. For example, IT has extended the reach of many processes such as formal education to all locations and participants with Internet connectivity. This allows people to participate in processes to which they otherwise would not have access, which may lead to beneficial relationships which otherwise would not exist (McKenna and Bargh 2000). This makes a process more suitable for virtualization and creates a positive moderating effect on the relationship between relationship requirements and process virtualizability. In our empirical context, perceived reach (PReachi) refers to the degree to which a buyer believes that the electronic channels enable him/her to buy vehicles to which s/he would not otherwise have access. For example, some buyers may need to buy vehicles from across the country to fulfill their inventory needs. If a buyer perceives that the electronic channels help him/her accomplish this, then s/he is likely to perceive a higher fit between the purchasing

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process and the electronic channels. This suggests that as a buyer’s perception of the reach afforded by the electronic channels increases, his/her perception of the fit between the electronic channels and the purchasing process will increase. H6: Perceived reach is positively related to perceived fit. Process virtualization theory proposes a moderating effect for reach. Based on the original arguments proposed (Overby 2008), this moderating relationship applies to relational exchange, such as building new or nurturing existing interpersonal relationships in a social or professional context. The reach of the electronic channels in our context pertains more to transactional exchange, such as trading with a seller in a different part of the country. Because reach in our context pertains more to transactional than to relational exchange, we do not hypothesize a moderating relationship for reach. 4.3.3 Perceived Monitoring Capability In general, monitoring capability is IT’s capacity to authenticate process participants and track activity. Participants in virtual processes based on IT are typically required to authenticate themselves via a log-in or similar method, which allows participants to be uniquely identified and their actions to be tracked (Zuboff 1988). This has a positive moderating effect on the relationship between identification and control requirements and process virtualizability. In our empirical context, we did not consider monitoring capability because this variable does not vary appreciably between the physical and electronic channels. Buyers are required to swipe an access card through an automated kiosk to access the physical market, which is similar to entering a user ID and password to participate via the electronic channels. The kiosk also issues each buyer a number which is recorded along with each vehicle they purchase. Thus, monitoring capability is similar in both the physical and electronic channels, and is therefore not

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useful as an explanatory variable in our context. 4.4 Perceived Individual Characteristics An important individual characteristic that may affect perceived fit is perceived computer anxiety, which refers to an individual’s perception of his/her own level of discomfort using computers (Compeau and Higgins 1995; Heinssen et al. 1987; Thatcher and Perrewe 2002). For example, task / process characteristics and technology characteristics may suggest a high level of perceived fit, but not for users who are uncomfortable with computers. Computer anxiety and related constructs such as computer self-efficacy and computer literacy are commonly considered in fit studies (e.g., Goodhue 1995; Strong et al. 2006). Evidence suggests individuals who are uncomfortable using computers report low levels of fit (Lee et al. 2007). Thus, we except that perceived computer anxiety (PAnxi) will be negatively related to perceived fit. H7: Perceived computer anxiety is negatively related to perceived fit. We used two other individual characteristics as control variables. RuralBuyer is a (selfreported) dummy variable set to 1 if the buyer’s dealership is located in a rural area. Because most physical market facilities are located near metropolitan areas, buyers located in rural locations may have difficulty attending them, which could increase both perceived fit and their use of the electronic channels. LargeBuyer is a (self-reported) dummy variable set to 1 if the buyer’s dealership is large. Large buyers will purchase more vehicles than small buyers. This could affect both perceived fit and use of the electronic channels. 4.5 Use of the Electronic Channels A key premise of task-technology fit theory is that fit should be positively correlated to use (Goodhue and Thompson 1995). Put simply, if a technology is a good fit for a task, then a user will use it more than if the technology were a poor fit for the task. This suggests that as a buyer’s

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perception of the fit between the electronic channels and the purchasing process increases, his/her use of the electronic channels (ElecUsei) will increase. H8: Perceived fit is positively related to use of the electronic channels. 5.0 DATA COLLECTION AND MEASUREMENT We collected data from two sources: a) archival transaction data, and b) a survey of buyers in the market. 5.1 Archival Transaction Data We conducted this research with the support of the National Auto Auction Association (“NAAA”), which is an industry group representing the automotive auction companies. The NAAA provided financial support for the study and also provided access to a mailing list of over 10,000 automobile dealers in the U.S. The list included each dealer’s name and address, along with archival data showing the number of vehicles purchased physically and electronically per dealer. Each dealer on the list had bought vehicles both physically and electronically, with an average of 88.01 physical purchases (st. dev. 110.99) and 18.07 electronic purchases (st. dev. 28.79).4 We used the archival data to create the “Use of the Electronic Channels” construct that appears in Figure 5. We created two measures for this construct. First, we calculated the percentage of vehicles that each dealer i purchased via the electronic channels (PctElecPurchasesi.) Second, we calculated the ratio of vehicles each dealer purchased electronically to those s/he purchased physically (RatioElecPhysi.) We took the natural logarithm to alleviate skewness and so that its magnitude was similar to PctElecPurchasesi. We modeled the “Use of the Electronic Channels” construct (ElecUsei) as a reflective construct with these two measures as indicators. Using both measures helps address the shortcomings of each individual                                                              4

This high level of activity indicates that the dealers were competent to respond to our survey. 27

 

measure, although results are not affected if only one measure is used. 5.2 Survey Data We used a survey instrument to measure the other constructs shown in Figure 5. A survey instrument was necessary for these measures because they are perceptual in nature as discussed in Section 2.3.1. We developed new measurement items for the process virtualization theory constructs because: a) process virtualization theory is new, and its constructs are not widely used in the literature, and b) the items had to be sufficiently specific to the wholesale automotive market to be meaningful to the respondents. We created the new items following the methodology proposed by Moore and Benbasat (1991); see the appendix for details. We adapted existing measurement items for computer anxiety (Heinssen et al. 1987; Thatcher and Perrewe 2002) and perceived fit (Jarupathirun and Zahedi 2007; Lin and Huang 2008). Recall that perceived fit represents a buyer’s belief of how well the electronic channels fit the process of purchasing vehicles. Because this essentially involves a comparative evaluation of whether the physical or electronic channels provide a better fit to the purchasing process, we measured perceived fit with items such as “electronic auctions meet my needs better than the physical auction” and “I prefer to purchase vehicles online as opposed to at the auction.” Our focus on measuring whether the technology meets users’ needs mirrors that of Goodhue (1998).5 Further, we measured perceived fit as a uni-dimensional, first-order construct to avoid conflating dimensions of perceived fit with antecedents of it (see related discussion in Section 2.1.3.1.) Each survey recipient was asked to indicate his level of agreement with the survey’s statements on a 1-7 scale. The end points of the scale were labeled “Disagree” and “Agree.” The                                                              5

Of the 36 measurement items Goodhue (1998) listed in Appendix A, 16 refer to the technology

as meeting a user’s “needs” and/or “purposes.” 28  

items for each construct along with the construct definitions presented in Section 4 are shown in Table 1. All constructs were modeled as reflective. We placed an ID number on each survey so that each response could be linked to that dealer’s purchasing activity as recorded in the archival data. Items were distributed randomly on the survey instrument. We mailed the survey to 1,000 buyers drawn randomly from the mailing list provided by the NAAA. 53 surveys were returned as undeliverable. Of the remaining 947, we received 136 responses. We eliminated 6 responses due to missing data, leaving a final sample of 130 completed responses. We judged our response rate to be reasonable given the nature and demographics of our population (user car dealers surveyed randomly), which differ substantially from those of populations more commonly used in IS research (e.g., employees at a firm sponsoring the research.) Of greater importance than the raw response rate is whether the respondents are representative. To test this, we used a t-test to compare the RatioElecPhysi variable for the buyers in the mailing list who responded to the survey to that of those who did not. The resulting t-statistic was not statistically different from zero (p = 0.16), indicating that the respondents are representative and that non-response bias is unlikely to be an issue. 6.0 ANALYSIS AND RESULTS 6.1 Appropriateness of a Partial Least Squares Analysis Partial least squares (“PLS”) analysis permits testing relationships between latent constructs such as those in our model. A critical consideration when determining the appropriateness of PLS as the analytical technique is sample size and statistical power (Marcoulides and Saunders 2006). Although PLS is often touted as being suitable for small sample sizes, PLS is no different from other methods in that small samples result in low statistical power (Goodhue et al. 2006). Accordingly, we estimated the power associated with a PLS analysis for our sample (n=130) via

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the following steps (Marcoulides et al. 2009). First, we calculated the sample size required to detect small (0.02), medium (0.13), and large (0.26) effect sizes for the most complex relationship in our model, which is the seven independent variables predicting PFiti (Chin and Newsted 1999; Cohen 1988).6 Using a 0.05 significance level and a desired power of 0.80, we determined the following sample sizes were required for each effect size: a) small effect size, n = 396, b) medium effect size, n = 59, c) large effect size, n = 30 (Cohen 1988; Green 1991). This suggests that we have enough power to detect effect sizes between small and medium. However, Marcoulides and Saunders (2006) cautioned that other factors influence power in PLS analysis, including the quantity, quality, and normality of the measures. To estimate the importance of these factors in our case, we first noted that although the number of measures per construct is relatively low (usually 3 per construct,) the quality of each measure is high. Of the 24 measures shown in Table 1, 3 had loadings between 0.70 and 0.80, 11 had loadings between 0.80 and 0.90, and 10 had loadings greater than 0.90 (see Table 3.) The high loadings indicate that we are unlikely to lose power due to mismeasurement. As stated by Chin et al. in their discussion of power (2003: Appendix D, article supplement, p. 10), “…a couple of good quality measures are as good as many less reliable measures.” Goodhue et al. (2006) found similar power results as ours in their Monte Carlo simulations involving constructs measured by three items with loadings of 0.70, 0.80, and 0.90, which provides further assurance that our power estimates are reasonable. We then examined the normality of our                                                              6

This is the preferred approach advocated by Chin and Newsted (1999). A less optimal (but

more commonly used) approach they offered is the “10x rule” whereby sample size must be 10 times the number of relationships in the most complex portion of the model (see Marcoulides et al. 2009 for a clarification of Chin and Newsted's recommendation). 30  

measures and concluded that the measures for the PSensi and PAnxi constructs were skewed. This might reduce our power for finding significant relationships for these constructs, but it causes no other problems in the analysis, as normality is not a requirement of PLS (Lohmoller 1989), although it does affect power. In summary, we concluded that a PLS analysis of our data had adequate power to detect effect sizes between small (0.02) and medium (0.15), with the possible exception of PSensi and PAnxi, for which we might only be able to detect a larger effect size. 6.2 Partial Least Squares Analysis7 The paths in the PLS model reflect the hypotheses. We also allowed paths from RuralBuyeri and LargeBuyeri to both PFiti and ElecUsei. Each variable in the study, other than RuralBuyeri, LargeBuyeri, and ElecUsei, was modeled as a latent construct reflected by the survey items shown in Table 1. RuralBuyeri and LargeBuyeri were drawn from individual survey questions (e.g., is your dealership in a rural location? Would you characterize your dealership as small or large?) and coded as dummy variables. As discussed above, ElecUsei is also a latent construct, but its items are drawn from the archival transaction data rather than from the survey instrument. We first evaluated the measurement model and concluded that it was valid. Descriptive statistics for the latent constructs and control variables are provided in Table 2, including average variance extracted (“AVE”), composite reliability, and Cronbach’s alpha for the latent constructs. The statistics indicate acceptable reliability and convergent validity. Factor loadings are shown in Table 3. The items load cleanly on the factors, providing                                                              7

We used SmartPLS for the analysis. Use also analyzed the data using covariance-based

structural equation modeling (via LISREL.) Those results are similar and are reported in the appendix. 31  

evidence of both convergent and discriminant validity. The correlation matrix is shown in Table 4. The square root of the average variance extracted for each latent construct is greater than the inter-construct correlations, providing additional evidence of discriminant validity (Fornell and Larcker 1981). An additional test supporting discriminant validity is reported in the appendix. We then examined the structural model. Path coefficients and R2 statistics for the model without interaction terms are shown in Figure 6 and summarized in Tables 5 and 6. Table 5 shows the coefficients of the variables hypothesized to influence PFiti, while Table 6 shows the same for ElecUsei. Table 5 also shows the coefficients when the interaction term between PSensi and PRepi (H5b) is included. The interaction term was calculated using the product indicator approach (Chin et al. 2003). The model explains 66% of the variance of PFiti, indicating a relatively high level of explanatory power for this variable. The model explains 21% of the variance of ElecUsei. 6.3 Regression Analysis We also used ordinary least squares regression to analyze the relationships between the antecedents and PFiti. We used regression because of the PSensi * PRepi interaction term and the associated discussion about whether interaction effects are best modeled using product indicators in PLS or via regression (Goodhue et al. 2007). In the regression analysis, we calculated each of the latent constructs as the mean of their items. To calculate the interaction term, we demeaned PSensi and PRepi and calculated their product. The regression results are virtually identical to the PLS results and are shown in Table 7. 6.4 Results of Hypothesis Tests 6.4.1 Perceived Process Characteristics and Perceived Fit Three of the process characteristics from process virtualization theory are negatively related

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to the perceived fit between the electronic channels and the purchasing process, as hypothesized. These are the Perceived Sensory Requirements associated with seeing, touching, and smelling vehicles (H1); the Perceived Synchronism Requirements associated with being able to take immediate possession of purchased vehicles (H3); and the Perceived Identification Requirements associated with identifying other bidders (H4.) Perceived Sensory Requirements has the strongest effect, as evidenced by the higher magnitude and significance level of its coefficient. We found no support for H2, as the coefficient for Perceived Relationship Requirements was insignificant. Perceived Relationship Requirements is negatively correlated with Perceived Fit (see Table 4.) This suggests that a negative relationship exists, but that it adds no explanatory beyond that of the other independent variables. 6.4.2 Perceived Information Technology Characteristics and Perceived Fit The relationship between Perceived Representation and Perceived Fit is positive and significant, providing support for H5a. However, the coefficient for the PSensi * PRepi interaction term is not significant, providing no support for H5b. The relationship between Perceived Reach and Perceived Fit is also insignificant, providing no support for H6. Similar to Perceived Representation Requirements, Perceived Reach is positively correlated with Perceived Fit (see Table 4.) This suggests that a positive relationship exists, but that it adds no explanatory power beyond that of the other variables. 6.4.3 Perceived Individual Characteristics and Perceived Fit None of the individual characteristics are significantly associated with Perceived Fit. Perceived Computer Anxiety has a negative correlation with Perceived Fit as expected (see Table 4), but this correlation is not significant after accounting for the effects of the other variables. Thus, there is no support for H7. This may be due to insufficient power as discussed in

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Section 6.1. Neither of the control variables, RuralBuyeri and LargeBuyeri, are significantly associated with Perceived Fit. 6.4.4 Perceived Fit and Use of the Electronic Channels Perceived Fit is positively and significantly associated with Use of the Electronic Channels, providing support for H8. The RuralBuyer control variable is also significant, indicating that rural buyers purchase more vehicles electronically than do non-rural buyers, perhaps due to the distance required to buy vehicles through the physical channel. 6.5 Robustness Check: Common Method Bias Because Perceived Fit and its antecedents were measured via the same survey instrument, the relationships among these constructs may be subject to common method bias.8 We tested for common method bias by adding a latent construct representing the method to the PLS model. This “method” construct is reflected by each of the measurement items. Thus, each item is used as a reflector of two constructs: the method construct and the substantive construct it is designed to measure (see Podsakoff et al. 2003, p. 894 for a discussion). Statistically significant and large item loadings on the method construct would indicate that respondents were responding to all items similarly, providing evidence of common method bias (Williams et al. 2003). This procedure has been applied to PLS modeling by Iacovou et al. (2009), Klein et al. (2007), Liang et al. (2007). Table 8 shows the loadings of each item on the construct it is designed to measure (“the substantive loading”) and on the method construct (“the method loading.”) Table 8 also shows the squared loadings, which can be interpreted as the amount of each item’s variance explained                                                              8

Common method bias is not an issue for the relationships involving Use of the Electronic

Channels because it was measured via archival data, not via the survey (Podsakoff et al. 2003.) 34  

by the latent construct. All substantive loadings are significant (p ≤ 0.01); no method loadings are significant. Also, the average squared substantive loading is 0.745; the average squared method loading is 0.006. This indicates that the common method factor has little systematic effect on the item responses, indicating that common method bias is unlikely to be a concern. We also conducted Harman’s single factor test (Harman 1967; Podsakoff and Organ 1986) to examine the potential for common method bias. To execute this test, we followed Mossholder et al. (1998) and compared the fit of two confirmatory factor analysis models: one in which the measurement items loaded onto their substantive constructs (excluding the ElecUsei construct as per footnote 8) and one in which all items loaded onto a single method construct. Table 9 shows that the first model fit the data well while the second model did not. We also conducted an exploratory factor analysis of all items and found that the first factor explained only 32.5% of the variance. This provides additional evidence that common method bias is unlikely to be a significant issue. We suspect that randomizing the items on the survey and including reversescored items helped limit the potential of common method bias. 7.0 DISCUSSION 7.1 Implications for Research The study has implications for task-technology fit theory, process virtualization theory, and the IS field overall. First, it contributes to task-technology fit theory by providing additional insight into the antecedents of fit. Task-technology fit theory posits that “task characteristics” and “technology characteristics” influence task-technology fit, which is essentially tautological. The theory does not specify which characteristics influence fit, requiring researchers to define them on a case-by-case basis. We contribute to task-technology fit theory by showing that the constructs from process virtualization theory can be used in place of the generic “task

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characteristics” and “technology characteristics.” This gives the theory additional specificity and prescriptive power while still ensuring its broad applicability across multiple domains. Second, the study contributes to process virtualization theory by being the first (to our knowledge) to measure its constructs and to test several of its propositions. Like task-technology fit, process virtualization theory applies broadly to a range of IS research topics. It has the potential to serve as a lens through which much of the knowledge generated by the IS field can be interpreted. Thus, it is important to conduct empirical tests to refine and extend process virtualization theory. Our results show that three of the four process characteristic constructs (sensory requirements, synchronism requirements, and identification requirements) had effects consistent with the propositions of process virtualization theory, with sensory requirements having the strongest effect. The fourth process characteristic, relationship requirements, did not add explanatory power beyond the other characteristics, although its correlation with fit was negative, as expected. This represents a first step in understanding which of the process characteristics has the most power for explaining process virtualization in different contexts. One of the two information technology constructs we measured (representation) had a significant effect, while the other (reach) did not. Also, the interaction term between sensory requirements and representation was insignificant. The insignificance of the interaction term suggests that although the moderating effects of the IT characteristics proposed in process virtualization theory seem plausible, their effects may be difficult to detect after accounting for main effects. Third, the study contributes to the IS field in general by contributing to the growing theory base that underlies the field. The separate construction of task-technology fit theory and process virtualization theory has broadened the theoretical base of the field. The integration of the two theories deepens the theoretical base by showing how the two theories complement each other to

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strengthen our understanding of how individuals use technologies in the conduct of tasks and/or processes. 7.2 Implications for Practice A key managerial implication of the paper is to improve the utility of task-technology fit theory for managers. For example, managers are tasked with providing technologies which have good fit to the tasks/processes they are designed to support. However, there has been little generalizable guidance about which characteristics of tasks and technologies lead to good or bad fit. The integrated model developed herein provides guidance in this area. 7.2.1: Illustrating the utility of the integrated model: The following examples illustrate the utility of the integrated model. The examples were chosen to be starkly different in order to illustrate the applicability of the model to a broad range of contexts. First, consider a forensics examiner interested in whether a new information system for managing evidence will help him/her solve crimes. Task-technology fit theory predicts that the system will be helpful if it has a high fit to the tasks that the examiner conducts. However, the theory offers little guidance to the examiner of how to assess that, which is the gap the integrated model is designed to fill. For example, the integrated model prompts the examiner to consider whether the tasks have high sensory requirements, which may be the case for evidentiary materials in which texture and odor are important. This will help the examiner predict that the fit of the information system will be poor for certain types of evidence, helping him/her manage expectations for the system and potentially divert resources to other areas. However, the integrated model also prompts the examiner to consider whether the system can be designed to provide adequate representation of the sensory requirements (e.g., via haptic and olfactory interfaces.) If so, then the examiner may conclude that the system will provide acceptable fit.

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This example mirrors our empirical context in which the sensory requirements associated with seeing, smelling, and touching a vehicle harmed fit but the representation capabilities associated with digital images and electronic condition reports improved fit. As a second example, consider a provost at a university considering whether to offer degree programs online with no expectation of student collocation. The provost must determine which degrees to offer and what online learning system to use. Task-technology fit posits that the learning system should have a high fit to the degree programs, but offers minimal guidance to the provost on how to assess that. By contrast, the integrated model prompts the provost to consider factors such as the relationship requirements of the degree program. Degree programs in which the development of interpersonal relationships among students are an integral of the educational experience (as is often the case for professional degrees such as business administration and law) may be poorer online candidates than degree programs that do not have similar relationship requirements. However, the integrated model would also prompt the provost to consider whether the online learning systems being considered have the representation capabilities to provide rich social encounters and the reach to facilitate the development of relationships that might not otherwise have occurred. These capabilities would increase the feasibility of all online programs. As a third example, consider a conservationist implementing an information system to track wildlife in wildlife preserves. Task-technology theory posits that the conservationist should select a system with high fit to the tracking tasks, but provides no guidance beyond that. By contrast, the integrated model prompts the conservationist to consider the identification and control requirements associated with knowing which animals are authorized to be where, and to select an information system with the appropriate monitoring capability. 7.3 Limitations and Future Research

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The proposed integrated model applies to a wide range of tasks/processes that individuals may use technology to help them conduct, including those for managerial decision-making, education, relationship development, shopping and procurement, and banking. We focused on a specific process (purchasing used vehicles in the wholesale market) in order to get a broad range of perceptions about the characteristics of the process and the supporting technology, thereby improving the validity of our measures. As a result, the specific empirical results may not generalize to other contexts. A possible extension to this study would be to test the model in other contexts. As mentioned above, the accumulation of research in this area will shed light on which process and information technology characteristics have the most explanatory power for process virtualization phenomena, and whether (and how) this depends on the context. Although we tested several of the relationships proposed in process virtualization theory, we did not test all of them. This is because process virtualization theory is designed to apply to a breadth of contexts. A consequence of this breadth is that not all of the variables and relationships of process virtualization theory will apply equally to all contexts. For example, some of the moderating relationships proposed in the theory did not apply to our context. Neither did the monitoring capability construct, as this construct did not vary appreciably between the physical and electronic channels we examined. A possible extension to this study would be to identify other contexts well-suited for testing other propositions of process virtualization theory. Another opportunity for future research is to expand the nomological network by studying the antecedents of the process characteristics proposed in process virtualization theory. For example, it is plausible in our context that the quality uncertainty associated with used vehicles causes the purchasing process to exhibit high sensory requirements. Also, the nature of the used car business and the need to hold vehicles in inventory as short a time as possible may cause the

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process to exhibit high synchronism requirements. It is not clear to what extent these antecedents generalize, highlighting the opportunity to study this in a systematic fashion. 8.0 CONCLUSION In this paper, we proposed an integrated model of task-technology fit theory and process virtualization theory by replacing the generic “task and technology characteristics” antecedents of task-technology fit with the variables proposed in process virtualization theory. We tested the model in the context of the wholesale automotive market. We used transaction data and survey data from buyers in this market to: a) assess how well the electronic channels in this market fit the vehicle purchasing process, b) test whether variables from process virtualization theory predicted fit (i.e., the antecedents of fit), and c) test whether fit predicted actual use of the electronic channels to purchase vehicles (i.e., the consequents of fit.) We found support for many of the hypothesized relationships and concluded that the model had good explanatory power. The paper makes three contributions. First, integrating task-technology fit and process virtualization theory improves the prescriptive value of task-technology fit theory without sacrificing its generalizability. This has value for researchers who study task-technology fit and for practitioners who apply it in order to maximize the returns from their technology investments. Second, the paper represents the first empirical application of process virtualization theory (to our knowledge.) This contributes to process virtualization theory by validating several of its propositions and by helping identify which of its variables have the most explanatory power in different contexts. Third, integrating task-technology fit and process virtualization theory contributes to the IS field in general. It is important for the field not only to broaden our theoretical foundation by developing individual theories, but also to deepen the foundation by integrating these theories where appropriate.

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TABLES AND FIGURES Perceived Sensory Requirements (a buyer’s perceived need to see, touch, smell, and hear the vehicles offered in the market.) Sens1: I like to see the vehicle, hear the engine, etc. Sens2: I like to physically inspect vehicles before I purchase them. Sens3: I like to touch / see / hear / smell the vehicles that I bid on. Perceived Relationship Requirements (a buyer’s perceived need to interact with other buyers in the market.) Rel1: I enjoy the social aspects of being at the auction. Rel2: It is important for me to mingle with other dealers at the auction. Rel3: I enjoy seeing and talking to other dealers at the auction. Perceived Synchronism Requirements (a buyer’s perceived need to take possession of purchased vehicles immediately.) Sync1: After I purchase a vehicle, I need to get it to my dealership that day or the next. Sync2: I don’t mind if a few days pass between when I purchase a vehicle and when I receive it. (reverse-coded) Perceived Identification Requirements (a buyer’s perceived need to identify who else is bidding on a vehicle to inform his/her own bidding.) ID1: I take note of who else is bidding on a vehicle. ID2: I adjust my bidding strategy based on who the other bidders are. ID3: I am interested in who else besides me is bidding on a vehicle. Perceived Representation (the degree to which a buyer believes that the electronic channels can represent vehicle characteristics that s/he could otherwise learn about through physical vehicle inspection.) Rep1: I can get the vehicle information I need when I’m online. Rep2: The online condition reports provide me with everything I need to know about a vehicle. Rep3: I don’t need to personally see a vehicle because I can get enough information online. Rep4: Electronic auctions provide me with everything I need to know about vehicle characteristics. Perceived Reach (the degree to which a buyer believes that the electronic channels enable him/her to buy vehicles to which s/he would not otherwise have access.) Reach1: The Internet allows me to buy vehicles I couldn’t otherwise get. Reach2: The Internet gives me access to vehicles that I need but can’t get locally. Reach3: The Internet allows me to access potential inventory from all over the country. Perceived Computer Anxiety (a buyer’s perception of his/her own level of discomfort using computers.) ANX1: I find computers difficult to use. ANX2: I am not very comfortable using computers in general. ANX3: Given a choice, I would prefer not to use a computer in my job. Perceived Fit (a buyer’s perception of how well the electronic channels fit the process of purchasing vehicles compared to the physical channel.) Fit1: The physical auction meets my needs better than electronic auctions. (reverse-coded) Fit2: I prefer to purchase vehicles online as opposed to at the auction. Fit3: Electronic auctions meet my needs better than the physical auction.

Table 1: Measurement items and definitions for latent constructs.

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St. Composite Cronbach’s AVE Reliability Alpha Meana Dev.a Perceived Sensory Requirements 5.35 1.69 0.83 0.94 0.90 Perceived Relationship Requirements 3.85 1.69 0.77 0.91 0.85 Perceived Synchronism Requirements 4.79 1.67 0.81 0.89 0.76 Perceived Identification Requirements 3.45 1.47 0.58 0.80 0.64 Perceived Representation 3.68 1.53 0.75 0.92 0.89 Perceived Reach 4.92 1.50 0.65 0.85 0.73 Perceived Computer Anxiety 2.21 1.48 0.71 0.88 0.80 Perceived Fit 3.20 1.77 0.87 0.95 0.92 b Use of Electronic Channels --0.98 0.99 0.98 PctElec 0.26 0.30 n/a n/a n/a c RatioElecPhys -1.64 2.24 n/a n/a n/a d RuralBuyer 0.37 n/a n/a n/a n/a LargeBuyerd 0.12 n/a n/a n/a n/a a Represents the average and standard deviation of the measurement items for the construct. b Mean and standard deviation are shown for the two items measuring this construct. c This variable is logged. The mean is negative because the ratio of vehicles purchased electronically to those purchased physically is less than 1 for 105 of the 130 responses. d Coded as dummy variables.

Table 2: Descriptive statistics.

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Sens1 Sens2 Sens3 Rel1 Rel2 Rel3 Sync1 Sync2 ID1 ID2 ID3 Rep1 Rep2 Rep3 Rep4 Reach1 Reach2 Reach3 SE1 SE2 SE3 Fit1 Fit2 Fit3 RatioElecPhys PctElec

0.90 0.91 0.93 0.47 0.41 0.46 0.34 0.24 0.18 0.06 0.21 -0.43 -0.40 -0.55 -0.48 -0.21 -0.23 -0.23 0.07 0.05 0.20 -0.72 -0.64 -0.65 -0.44 -0.42

0.42 0.44 0.53 0.91 0.86 0.85 0.15 0.16 0.28 0.23 0.28 -0.28 -0.18 -0.35 -0.28 -0.16 -0.18 -0.15 0.21 0.17 0.28 -0.39 -0.39 -0.35 -0.32 -0.30

0.40 0.20 0.27 0.16 0.10 0.20 0.90 0.90 0.23 0.06 0.11 -0.15 -0.23 -0.30 -0.15 -0.27 -0.25 -0.21 0.09 0.15 0.03 -0.38 -0.30 -0.39 -0.21 -0.20

0.16 0.22 0.18 0.31 0.39 0.17 0.15 0.14 0.71 0.71 0.85 -0.04 -0.18 -0.16 -0.10 -0.08 -0.01 -0.01 -0.01 0.05 0.01 -0.35 -0.14 -0.26 -0.13 -0.16

-0.55 -0.46 -0.48 -0.23 -0.35 -0.23 -0.19 -0.24 -0.08 -0.10 -0.13 0.86 0.84 0.84 0.91 0.41 0.37 0.28 -0.01 -0.08 -0.09 0.62 0.60 0.63 0.40 0.40

-0.26 -0.22 -0.28 -0.15 -0.25 -0.11 -0.19 -0.35 -0.07 -0.11 0.04 0.45 0.31 0.40 0.36 0.81 0.83 0.78 -0.19 -0.23 -0.15 0.33 0.32 0.40 0.15 0.20

0.12 0.04 0.22 0.28 0.21 0.23 0.17 -0.01 0.05 0.04 -0.03 -0.03 -0.04 -0.14 -0.03 -0.19 -0.09 -0.24 0.86 0.82 0.86 -0.14 -0.13 -0.14 0.06 0.05

-0.67 -0.65 -0.65 -0.35 -0.40 -0.29 -0.35 -0.35 -0.15 -0.18 -0.27 0.54 0.53 0.61 0.61 0.31 0.29 0.31 -0.12 -0.09 -0.15 0.93 0.90 0.96 0.39 0.42

-0.46 -0.40 -0.32 -0.28 -0.30 -0.24 -0.26 -0.12 -0.12 -0.04 -0.16 0.32 0.35 0.34 0.41 0.21 0.10 0.12 0.09 0.03 0.02 0.39 0.37 0.39 0.99 0.99

Table 3: Factor loadings for latent constructs.

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1 2 3 4 5 6 7 8 9 10 11 1) Sensory 0.91 2) Relationship 0.51 0.88 3) Synchronism 0.32 0.17 0.90 4) Identification 0.20 0.34 0.17 0.76 5) Representation -0.54 -0.32 -0.24 -0.14 0.87 6) Reach -0.28 -0.20 -0.30 -0.04 0.44 0.81 7) Anxiety 0.14 0.27 0.09 0.02 -0.07 -0.21 0.84 8) Fit -0.72 -0.40 -0.39 -0.28 0.66 0.38 -0.15 0.93 9) Use -0.44 -0.31 -0.21 -0.15 0.41 0.18 0.06 0.41 0.99 10) Rural -0.21 -0.11 -0.03 -0.15 0.18 0.00 0.15 0.14 0.27 n/a 11) Large 0.05 -0.10 0.17 -0.03 -0.07 -0.03 -0.16 -0.03 -0.05 -0.24 n/a Italicized entries indicate correlation is significant at 0.10 level (two-tailed test.)

Table 4: Correlation matrix (bolded entries are the square root of the AVE for each construct.) Coef (Std. Error) -0.47 (0.07) *** 0.03 (0.06) -0.12 (0.06) ** -0.12 (0.07) * 0.35 (0.07) *** 0.05 (0.08) -0.03 (0.06) -0.03 (0.05) 0.04 (0.06) --

Perceived Sensory Requirements Perceived Relationship Requirements Perceived Synchronism Requirements Perceived Identification Requirements Perceived Representation Perceived Reach Perceived Computer Anxiety Rural Buyer Large Buyer Perceived Sensory Requirements * Perceived Representation R2 *** p ≤ 0.01, ** p ≤ 0.05, * p ≤ 0.10, two-tailed test

0.66

Coef (Std. Error) -0.49 (0.09) *** 0.04 (0.05) -0.12 (0.06) ** -0.13 (0.07) * 0.35 (0.07) *** 0.05 (0.08) -0.03 (0.06) -0.03 (0.05) 0.03 (0.05) 0.04 (0.07) 0.66

Table 5: PLS coefficients and bootstrapped standard errors for variables hypothesized to affect Perceived Fit.

53  

Coef (Std. Error) 0.38 (0.08) *** 0.22 (0.09) *** 0.01 (0.06) 0.21

Perceived Fit Rural Buyer Large Buyer R2

Table 6: PLS coefficients and bootstrapped standard errors for variables hypothesized to affect Use of the Electronic Channel. Coef (Std. Error) -0.49 (0.08) *** 0.03 (0.07) -0.13 (0.06) ** -0.13 (0.07) * 0.41 (0.08) *** 0.06 (0.07) -0.03 (0.07) -0.09 (0.21) 0.18 (0.31) --

Perceived Sensory Requirements Perceived Relationship Requirements Perceived Synchronism Requirements Perceived Identification Requirements Perceived Representation Perceived Reach Perceived Computer Anxiety Rural Buyer Large Buyer Perceived Sensory Requirements * Perceived Representation R2 0.66 *** p ≤ 0.01, ** p ≤ 0.05, * p ≤ 0.10, two-tailed test

Coef (Std. Error) -0.51 (0.08) *** 0.04 (0.07) -0.13 (0.06) ** -0.13 (0.07) * 0.41 (0.08) *** 0.05 (0.07) -0.03 (0.07) -0.10 (0.21) 0.16 (0.31) 0.02 (0.04) 0.66

Table 7: Regression coefficients and standard errors for variables hypothesized to affect Perceived Fit.

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Construct Perceived Sensory Requirements Perceived Relationship Requirements Perceived Synchronism Requirements Perceived Identification Requirements Perceived Representation

Perceived Reach Perceived Computer Anxiety Perceived Fit

Measurement Item Sens1 Sens2 Sens3 Rel1 Rel2 Rel3 Sync1 Sync2 ID1 ID2 ID3 Rep1 Rep2 Rep3 Rep4 Reach1 Reach2 Reach3 ANX1 ANX2 ANX3 Fit1 Fit2 Fit3 Mean

Substantive Loading 0.899 *** 0.909 *** 0.930 *** 0.917 *** 0.829 *** 0.884 *** 0.899 *** 0.900 *** 0.779 *** 0.739 *** 0.778 *** 0.869 *** 0.847 *** 0.835 *** 0.905 *** 0.812 *** 0.843 *** 0.765 *** 0.869 *** 0.863 *** 0.811 *** 0.926 *** 0.908 *** 0.959 *** 0.861

Substantive Loading2 0.808 0.826 0.865 0.841 0.687 0.781 0.808 0.810 0.607 0.546 0.605 0.755 0.717 0.697 0.819 0.659 0.711 0.585 0.755 0.745 0.658 0.857 0.824 0.920 0.745

Method Loading -0.103 0.108 -0.005 0.031 -0.105 0.069 0.002 -0.002 0.000 0.032 -0.031 -0.070 -0.125 0.239 -0.040 0.028 -0.020 -0.007 0.035 0.015 -0.054 0.119 -0.068 -0.053 0.000

Method Loading2 0.011 0.012 0.000 0.001 0.011 0.005 0.000 0.000 0.000 0.001 0.001 0.005 0.016 0.057 0.002 0.001 0.000 0.000 0.001 0.000 0.003 0.014 0.005 0.003 0.006 *** p ≤ 0.01

Table 8: Substantive and method factor loadings per measurement item. Model

χ2

DF

RMSEA CFI

8 Factors (items load onto substantive constructs)

331.52

224

0.06

0.97 0.06

1 Factor (items load onto single method construct)

1016.83

252

0.16

0.78 0.12

SRMR

DF = Degrees of Freedom; RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index; SRMR = Standardized Root Mean Square Residual.

Table 9: Fit statistics for confirmatory factor analyses with different numbers of factors.

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Task Characteristics Technology Characteristics

Technology Use

TaskTechnology Fit

Task Performance

Individual Characteristics

Figure 1: Stylized task-technology fit nomological network.

Task Measure Technology Measure

Dependent Variable (e.g., Use, Performance)

Task-Technology Fit

Figure 2: Basic model when task-technology fit is measured via the user perception approach.

Task Measure Dependent Variable (e.g., Use, Performance)

Technology Measure Task-Technology Fit = Task Measure * Technology Measure

Figure 3: Basic model when task-technology fit is measured via the computed approach.

Process Characteristics • Sensory Requirements • Relationship Requirements • Synchronism Requirements • Identification and Control Requirements

(−) (+)

Process Virtualizability • Adoption / Use • Quality of Outcomes

IT Characteristics • Representation • Reach • Monitoring Capability 56  

Figure 4: General theoretical model for process virtualization theory.

Perceived Process Characteristics (from Process Virtualization Theory) Perceived Information Technology Characteristics (from Process Virtualization Theory)

Perceived Fit

Technology Use

Perceived Individual Characteristics Figure 5: Basic research model

Perceived Sensory Rqmts.

H1: −0.47 ***

Perceived Relationship Rqmts. Perceived Synchronism Rqmts. Perceived Identif ication Rqmts. Perceived Representation

Large Buyer

H2: 0.03 0.04

H3: −0.12 ** H4: −0.12 * H5a: 0.35 ***

Perceived Fit R2 = 0.66

H8: 0.38 ***

Use of the Electronic Channels R2 = 0.21

−0.03 H6: 0.05

0.22 *** Rural Buyer

Perceived Reach H7: −0.03 Perceived Computer Anxiety

0.01

*** p ≤ 0.01; ** p ≤ 0.05; * p ≤ 0.10, two-tailed test

Figure 6: Path coefficients and R2 statistics.

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APPENDIX A.1 Detail on Measurement Item Development We followed the methodology described by Moore and Benbasat (1991) to develop several new measurement items for the study. As a first step, we developed a set of items designed to measure the Perceived Sensory Requirements, Perceived Relationship Requirements, Perceived Synchronism Requirements, Perceived Identification Requirements, Perceived Representation, and Perceived Reach constructs. As a second step, we conducted two rounds of card sorting to assess construct validity. The judges who conducted the card sorting were employees of one of the major auction companies in the industry and thus knowledgeable of the empirical context. The first round of card sorting was unstructured: the judges were given a stack of the items and told to group the items into categories. The judges were not told the number of categories or what the categories should represent. The second round of card sorting was structured: a new set of judges from the same auction company was given a list of the categories and their descriptions and asked to place each item into one of the categories. Several items were reworded as a result of both rounds of card sorting. After developing the instrument, we conducted a pilot survey. We mailed a survey to 100 buyers in the market. The sample was drawn randomly from the mailing list provided by the NAAA. We received a total of 17 responses during the pilot phase. We analyzed the pilot responses to ensure that the respondents understood the survey appropriately. Based on the pilot, we revised several aspects of the survey, including rewording some items. We then proceeded with the survey described in the main text.

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A.2 Results Using Covariance-Based Structural Equation Modeling We replicated the PLS analysis (without the interaction term) using covariance-based structural equation modeling using LISREL. The model shows good fit to the data. The Root Mean Square Error of Approximation (“RMSEA”) is 0.05, the Comparative Fit Index (“CFI”) is 0.97, the Standardized Root Mean Square Residual (“SRMR”) is 0.06, and the ratio of the model’s χ2 to degrees of freedom is 1.33 (404.44 / 304 = 1.33). Hu and Bentler’s (1999) recommendations for good model fit are RMSEA ≤ 0.06, CFI ≥ 0.95, and SRMR ≤ 0.08, and Ullman (2001) recommends the χ2 to degrees of freedom ratio be less than 2. Table A1 shows the coefficients of the variables hypothesized to influence PFiti, while Table A2 shows the same for ElecUsei. Results are similar to those obtained via PLS and regression. The most notable exception is the magnitude of the coefficient for PReli is larger, although still statistically indistinguishable from zero. Coef (Std. Error) -0.53 (0.10) *** 0.15 (0.10) -0.15 (0.08) ** -0.18 (0.08) ** 0.39 (0.09) *** 0.01 (0.09) -0.07 (0.07) -0.05 (0.06) 0.06 (0.06) --

Perceived Sensory Requirements Perceived Relationship Requirements Perceived Synchronism Requirements Perceived Identification Requirements Perceived Representation Perceived Reach Perceived Computer Anxiety Rural Buyer Large Buyer Perceived Sensory Requirements * Perceived Representation Squared Multiple Correlation (similar to R2) 0.76 *** p ≤ 0.01, ** p ≤ 0.05, * p ≤ 0.10, two-tailed test

Table A1: LISREL coefficients and standard errors for variables hypothesized to affect Perceived Fit.

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Coef (Std. Error) 0.41 (0.08) *** 0.21 (0.08) *** -0.01 (0.08) 0.21

Perceived Fit Rural Buyer Large Buyer Squared Multiple Correlation (similar to R2)

Table A2: LISREL coefficients and standard errors for variables hypothesized to affect Use of the Electronic Channel. A.3 Discriminant Validity Robustness Check As an additional test of discriminant validity, we conducted a confirmatory factor analysis (“CFA”) in which the measurement items loaded as hypothesized on the 9 latent constructs in the study. We then compared the fit of this CFA model to that of a series of alternative CFA models in which pairs of latent constructs were joined. This approach has been used by several IS scholars (e.g., Gefen et al. 2003; Goodhue 1998; Pavlou and Gefen 2004). There were 36 models in which pairs of latent constructs were joined. The fit statistics for the 9 factor model and each of the 8 factor models appear in the table below. χ2 difference tests suggest that the fit of the 9 factor model is superior to any of the 8 factor models (p < 0.001 in all cases), providing evidence of discriminant validity. The best fitting alternative model (in which Relationship Requirements and Identification Requirements were joined) added 69.76 to the χ2 in exchange for an additional 8 degrees of freedom. The associated loss of fit is statistically significant at p < 0.001. Model

χ2

9 Factors (All constructs load separately)

372.79 263

0.057

0.97 0.059

8 Factors (Sensory Rqmts. and Relationship Rqmts. joined)

531.35 271

0.081

0.94 0.073

8 Factors (Sensory Rqmts. and Synchronism Rqmts. joined)

461.28 271

0.067

0.95 0.069

8 Factors (Sensory Rqmts. and Identification Rqmts. joined)

453.78 271

0.071

0.95 0.071

8 Factors (Sensory Rqmts. and Fit joined)

501.23 271

0.083

0.94 0.065

8 Factors (Sensory Rqmts. and Representation joined)

570.46 271

0.110

0.93 0.072

8 Factors (Sensory Rqmts. and Reach joined)

488.64 271

0.081

0.95 0.083

8 Factors (Sensory Rqmts. and Anxiety joined)

533.45 271

0.084

0.93 0.084

8 Factors (Sensory Rqmts. and Use joined)

688.00 271

0.083

0.90 0.072

8 Factors (Relationship Rqmts. and Synchronism Rqmts. joined)

479.67 271

0.071

0.95 0.084

DF

RMSEA CFI

SRMR

60  

8 Factors (Relationship Rqmts. and Identification Rqmts. joined)

442.55 271

0.070

0.96 0.068

8 Factors (Relationship Rqmts. and Fit joined)

574.00 271

0.093

0.92 0.081

8 Factors (Relationship Rqmts. and Representation joined)

593.45 271

0.097

0.92 0.092

8 Factors (Relationship Rqmts. and Reach joined)

505.70 271

0.084

0.94 0.100

8 Factors (Relationship Rqmts. and Anxiety joined)

518.68 271

0.080

0.94 0.079

8 Factors (Relationship Rqmts. and Use joined)

721.57 271

0.089

0.89 0.092

8 Factors (Synchronism Rqmts. and Identification Rqmts. joined)

453.99 271

0.071

0.95 0.074

8 Factors (Synchronism Rqmts. and Fit joined)

451.97 271

0.065

0.95 0.066

8 Factors (Synchronism Rqmts. and Representation joined)

473.17 271

0.069

0.95 0.074

8 Factors (Synchronism Rqmts. and Reach joined)

457.19 271

0.068

0.95 0.070

8 Factors (Synchronism Rqmts. and Anxiety joined)

534.25 271

0.084

0.93 0.084

8 Factors (Synchronism Rqmts. and Use joined)

477.79 271

0.070

0.95 0.086

8 Factors (Identification Rqmts. and Fit joined)

450.03 271

0.069

0.96 0.072

8 Factors (Identification Rqmts. and Representation joined)

462.84 271

0.073

0.95 0.079

8 Factors (Identification Rqmts. and Reach joined)

464.20 271

0.073

0.95 0.083

8 Factors (Identification Rqmts. and Anxiety joined)

466.66 271

0.074

0.95 0.088

8 Factors (Identification Rqmts. and Use joined)

462.83 271

0.073

0.95 0.081

8 Factors (Fit and Representation joined)

535.97 271

0.092

0.93 0.069

8 Factors (Fit and Reach joined)

471.94 271

0.073

0.95 0.077

8 Factors (Fit and Anxiety joined)

536.60 271

0.085

0.93 0.085

8 Factors (Fit and Use joined)

697.15 271

0.082

0.89 0.075

8 Factors (Representation and Reach joined)

458.19 271

0.070

0.95 0.075

8 Factors (Representation and Anxiety joined)

540.58 271

0.086

0.93 0.088

8 Factors (Representation and Use joined)

694.11 271

0.083

0.89 0.076

8 Factors (Reach and Anxiety joined)

496.34 271

0.083

0.94 0.100

8 Factors (Reach and Use joined)

506.83 271

0.084

0.94 0.100

8 Factors (Anxiety and Use joined)

751.46 271

0.090

0.88 0.130

DF = Degrees of Freedom; RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index; SRMR = Standardized Root Mean Square Residual.

Table A3: Fit of confirmatory factor analyses after combining factors.

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