An Exploration of the Individual-Level Post-Adoption Choice
Decision
Andrew Schwarz
Information Systems and Decision Sciences Department
Louisiana State University
[email protected]
Colleen Schwarz
Department of Management
University of Louisiana
[email protected]
The research investigating technology adoption has assumed that the individual accepting the technology has only
one technology in mind when making their adoption decision. Models such as the UTAUT, TAM, and PCI assume
that the decision regarding whether or not to adopt a technology occurs without an understanding of the impact of
additional technology choices on the adoption decision and that a user does not consider additional acceptance
perceptions towards other comparable technologies. Moreover, the few studies that investigate technology choice
have not examined the changes in the dominant drivers of choice before an individual uses a technology and after
they have had experience with a technology. Drawing on prior work on choice in the marketing literature, we
propose three theoretical approaches to conceptualizing choice and two mathematical approaches to measuring the
choice comparison. We report on a study of technology choice among 173 MBA students who were given an open
source technology option and an online application option to use for one month. We conclude by discussing the
implications of our findings and recommend new avenues for research on technology choice.
Keywords: Technology Choice Behavior, Perceived Characteristics of Innovations, Technology Choice
Comparison.
Volume 15, Issue 3, pp. 5-30, September 2014
Mark Srite acted as the Senior Editor for this paper.
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An Exploration of the Individual-Level Post-Adoption Choice Decision
INTRODUCTION
End users today have more choices than ever before when selecting which technology to use when confronted with
a task to complete. Consider a scenario where a salesperson must track their sales leads. In the absence of a
customer relationship management (CRM) solution from the organization, will the salesperson use a spreadsheet
solution or a database system, assuming that both are available? Alternatively, for the mobile worker of today, what
is the option that this individual will select to write a proposal or work document on a tablet or laptop, assuming that
both are available? In each of these scenarios, a user has multiple technological options to complete a given task.
This user has adopted both solutions, yet which will the user choose?
Research investigating technology adoption has historically examined each of the aforementioned technologies
without considering alternative options. However, practical experience demonstrates that all individuals going
through the process of deciding which technology to use are either implicitly or explicitly comparing the candidate
technology to others for a given task. Yet, to this point, scant research has examined the behavior of post-adoption
choice, which we define as the act of selecting a technology for a task, given that a user has adopted multiple,
alternative technologies that can each accomplish the same outcome.
From a research perspective, traditional approaches to understanding technology usage focus our attention on the
behavior of the frequency, duration, or extent of usage and not on the behavior of choice. These two decisions
represent related outcomes because choosing to use one technology will result in not using another. Yet, how are
we to theorize and understand the behavior of choice?
We argue that we need a new theoretical framework is to understand the process that a user goes through when
selecting which technology to use in a post-adoption context. In this new framework, we examine the role that the
perceptions towards these technologies exert on users’ decision making process when they are considering multiple
technology options. Specifically, we examine whether individuals judge a technology in and of itself or whether they
compare two technologies vis-à-vis one another when choosing a technology to use, assuming that both fit the task
at hand.
Extending this argument, we are not simply deciding on a task’s appropriateness to a technology (or a tasktechnology fit perspective); rather, we assume that all of the technologies fit the task at hand and that they represent
competing and equivalent alternatives. Thus, in this paper, we incorporate choice into current models of technology
adoption through a new theoretical framework. In Section 2, we discuss current approaches to adoption regarding
choice and argue that current models as they are constructed do not account for any types of comparisons. We then
introduce a theoretical framework of choice that will facilitate our understanding of the types of comparisons that a
user makes and then integrate the perceived characteristics of innovation (PCI) model into our new framework. In
Section 5, we present the results from an empirical study. Finally, in Section 6, we conclude the paper and discuss
its implications.
CONTRIBUTION
This paper contributes to information systems research in three ways. First, it introduces the concept of
technology choice between multiple technology options as a distinct behavioral outcome that is different from ITenabled performance, use, or intention to use a technology of one specific technology. Next, the paper
examines how our traditional antecedents of IT adoption influence the behavior of choice. Its findings suggest
that relative advantage is a significant driver of pre-experience choice, that communicability and measurability
impact the user after they have used a technology, and that communicability and measurability have a longerterm impact in post-experience choice. Finally, the paper illustrates the need for future researchers to examine
the phenomenon of choice when users are confronted with multiple options that are available to accomplish the
same task.
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LITERATURE REVIEW
Current Approaches to Understanding Technology Choice
The traditional explanations that researchers have used to understand the process of post-adoption technology use
originate in the “proxy view” of technology (Orlikowski & Iacono, 2001). This perspective argues that the perceptions
that an individual possesses toward technology explains the degree to which the technology is used. The proxy view
encapsulates a broad range of theories (UTAUT, TAM, PCI, TTF, etc.), with each theory attempting to articulate the
salient perceptions that a user possesses towards a particular technology and the impact of these perceptions on
usage.
Given the broad range of technology choices available to knowledge workers, however, we propose that a new
alternative theoretical lens is needed to understand how, in a post-adoption context, a user decides which
technology option to use. In the current espoused view of adoption, the extent to which an individual either uses or
intends to use a technology is dependent on their perceptions regarding the specified technology while ignoring all
other alternative substitutes. To our knowledge, no current model of technology adoption examines external
technologies outside the scope of the technology under investigation.
However, in practice, knowledge workers are faced with a myriad of technology options for each task that they
encounter. How can we explain a manager choosing to keep track of customer data in a spreadsheet versus a
database when both are available? A “proxy view” of IT would argue that the extent of usage of each of these
products was dependent on either: (1) the individual’s perceptions toward the IT (c.f. UTAUT, TAM, PCI) or (2) the fit
of the technology to the task (c.f. TTF). However, the “proxy view” cannot explain why the manager in the example
above chose one technology in lieu of another.
Therefore, our first critique of the current approaches to understanding technology adoption is that the proxy view
focuses on a single technology and perceptions towards that IT. We propose that assessments of other technologies
also exert influence on an individual’s selection of one technology versus another. Although we agree that these
perceptions and attitudes play a role in the behavior of choice, the impact of multiple candidate technologies on the
choice decision is unknown. The proxy view as it is currently conceptualized focuses on views toward only one
technology and does not address the specific standards that are employed as the basis of comparison. We posit that
these proxy views are aspects of the choice behavior, but the way in which they operate cannot be derived from the
single-technology, usage-centric views in the traditional adoption literature.
Extending the selection argument further, our second critique is that past studies have focused on a narrow set of
dependent variables (e.g. continuance, usage, or intention). We propose to broaden this set to examine alternative
outcomes, including choice. By focusing on choice, we shift our attention away from a singular focus on which
technology to adopt and move toward also understanding the post-adoption choice decision in a context with
multiple technology options. Extant studies have now demonstrated the importance of examining the time after a
technology has been received (Schwarz & Chin, 2007) to understand whether it continues to be used (Sun &
Jeyaraj, 2013; Bhattacherjee et al., 2001). By examining individuals’ decision to choose one technology over
another, we extend this stream of research to examine their post-adoption technology choice.
Therefore, we seek to understand post-adoption choice when a user has multiple technologies available to them to
complete a certain task. In the table below, we highlight each of the current adoption models, the number of
technologies specified in the approach, the dependent variable studied, whether the focus is pre- or post-adoption,
and the limitations in the context of focusing on choice.
Model
TTF
TAM/
UTAUT/
UTAU2/
PCI
Continuance
Table 1. Comparison of Previous IS Adoption Theories
Technology Dependent
Post/preLimitations within a choice context
foci
variable
adoption
TTF examines whether a single technology fits a
Posttask and does not examine if an alternative is
1
Performance
adoption
better.
Perception based models are focused on
Intention or
Post- and
understanding how an individual perceives a
1
use
pre-adoption technology for the purpose of explaining usage or
intention to use.
EDT-based models examine whether an individual
Continue to
Post1
selects to continue using a single technology and
use
adoption
ignores alternatives.
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Our analysis of the table is that no extant approach, as currently espoused, is well suited to study technology choice
in a post-adoption context. However, given a high-level framework that begins with the outcome of choice in mind,
we posit that the perceptions and attitudes examined in our traditional models can be subsumed and assimilated into
a new approach. Based on this review, we now propose a theoretical framework to better understand this
phenomenon.
An Alternative Approach to Understanding Technology Choice
Given the limitations in current views of technology adoption in understanding post-adoption technology choice, a
framework is necessary to guide the development of our new approach. We theorize that an individual, when
deciding which technology to use in a particular context, will directly assess their perceptions of the individual
technologies themselves in addition to comparing the attributes of the technologies vis-à-vis one another. We posit
that an individual makes a two-step assessment. In step 1, the individual assesses their perceptions of the individual
technology. In step 2, the individual assesses the perceptions for each technology relative to one another for each of
the attributes. While extant studies have examined step 1 for one technology, they do not account for comparisons
of multiple technologies. It is the salience of the perceptions that will dictate the selection of the individual
technology. We have depicted this graphically in Figure 1 below.
Theoretically, the aforementioned approach to choice assumes an information-processing view of human cognition;
that is, that an individual will simplify their decision making by making comparisons based on dimensions that are
deemed salient to that individual and that an individual will seek the comparison that will mitigate cognitive load.
Taking the perceptions of the individual technologies as input, the individual then processes these perceptions
relative to one another to produce the choice as output.
In reviewing literature, we found two previous studies that examined technology choice. First, Aguirre-Urreta and
Marakas (2012) provided 54 subjects with modified versions of two product websites and asked the individuals to
evaluate the technologies based on the information they gleaned from the website. In a similar vein, Szajna (1994)
demonstrated six database management systems (DBMS) to 47 MBA students and asked them which DBMS they
would select for their database work. While these approaches demonstrated the importance of choice (and further
motivation for our work), their work was limited in two important aspects. First, in the experiments, the subjects never
have direct experience with the technology; instead, their choice was based on a review of an informational website.
In other words, the studies focused on pre-adoption. Second, the experiments were cross-sectional. Our study is
longitudinal and examines how pre-adoption perceptions account for a post-adoption decision. Furthermore, while
the previous studies indicated instances where individuals chose their adoption decision based on collecting
information, we postulate that a usage period with the technology is necessary in order to more effectively emulate
the process a user would go through when making a post-adoption technology choice when there are multiple
options available.
The Dimensions of Technology Choice
In proposing a post-adoption view of technology choice, we assume that, through using the multiple technologies,
the user has formed perceptions and attitudes about each of them. This assumption derives from the proxy view of
IT and espoused adoption theory (e.g. UTAUT, TAM, PCI, etc.), which has articulated various aspects of the IT that
a user assesses when making an adoption decision.
As such, we argue that the technology attributes that are compared in a post-adoption technology choice decision
align with those used in a traditional adoption decision, albeit in a mode of comparison. Specifically, we focus on the
comparison of perceptions toward the technologies, the attitudes toward the technologies, and the intention toward
selecting one technology over another. We derive our support for these three comparisons from the mental
comparison model (Dabholkar, 1994), a marketing model that has previously been proposed and tested as a model
to understand consumer choice.
According to the mental comparison model, individuals attempting to choose between alternative products (or
services) compares their beliefs, expectancy, attitudes, and intentions toward the products vis-à-vis one another.
However, using the mental comparison model requires a researcher to understand the product and the context of
choice and that, depending on the comparison, the researcher must choose whether a belief, expectancy, attitude,
or intention comparison model would be most appropriate. In other words, these are four independent, nonintegrated models of choice. Furthermore, the focus in the marketing model is on which product to purchase (or, in
our context, pre-adoption), while we focus on a post-adoption IT choice made by a user after having experience with
multiple technologies.
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THEORIZING THE COMPARISON
Based on the previous discussion, we posit that an individual, when given a choice between alternative
technologies, compares their perceptions of technology A versus technology B. As an exploratory study, we seek to
understand whether the salience of the comparison factors is consistent with those of the direct intention-based
perceptions; nonetheless, we must explore how to theorize the comparison. To theorize this comparison, we draw
on and extend the work of Dabholkar‘s (1994) mental comparison model. Dabholkar (1994) highlights four potential
models of choice: the belief comparison model, the expectancy comparison model, the attitude comparison model,
and the intention comparison model. We use three of these four (i.e., the belief comparison model, the expectancy
comparison model, and the attitude comparison model).
The difference between the three models has to do with when the individual makes the comparison. Do users first
evaluate their perceptions towards each technology without reference to the other, or do they compare the
perceptions relative to one another? How do these either separate or joint perceptions influence their attitudes?
Furthermore, is attitude a function of a comparison or separate beliefs? How and when individuals assess the
technology separates our three models of choice. We now review each of these models.
Attitude Comparison Model
According to the attitude comparison model of choice, users first assess perceptions of each technology separately.
These independent assessments lead to the creation of attitudes with separate attitudes about each technology
alternative and separate intentions for each technology. Based on each of these separate attitudes and attitudes, the
user formulates an intention to select one technology over another.
Expectancy Comparison Model
According to the expectancy comparison model of choice, users first assess perceptions of each technology
separately. Based on these two separate perceptions, they then compare the options and develop a comparison
attitude. This joint attitude then shapes users’ intention to select one technology over the other. In contrast to the
attitude comparison model, users holds separate perceptions of the technologies, but they possess a single
comparison attitude of the technologies.
Belief Comparison Model
According to the belief comparison model of choice, users do not hold separate perceptions; instead, everything is a
comparison of one technology versus another. First, users compare perceptions for each of the options to one
another. Then, having formulated these perceptions of one technology over another, they form an attitude about one
technology over another and outline an intention to select the technology corresponding to this attitude.
Comparing the Three Approaches
Table 2 outlines the three approaches to technology choice. Each approach differs regarding when the user makes
the choice decision—whether it is during the perception stage, the attitude stage, or only when the intention is
formed. These three competing approaches offer three lenses through which to understand the formulation of
technology choice.
Approach
Attitude comparison
model
Expectancy
comparison model
Belief comparison
model
Table 2. Approaches to Technology Choice
Perceptions
Attitude
Intention
PerceptionsTECH A
AttitudeTECH A
IntentionTECH A IntentionCHOICE
PerceptionsTECH B
AttitudeTECH B
IntentionTECH B
PerceptionsTECH A
AttitudeCOMPARISON
IntentionCHOICE
PerceptionsTECH B
PerceptionsCOMPARISON
AttitudeCOMPARISON
IntentionCHOICE
THE TECHNOLOGY COMPARISON
The diffusion of innovation approach (based on diffusion research) (Rogers, 1995a) claims that there are
fundamental characteristics of a new technology that promote its usage and “adoption”. This approach argues that
innovations have eight characteristics that influence acceptance: the relative advantage of the system over its
precursor, the compatibility of the innovation with users’ work patterns, the ability to try out an innovation, the ease of
use of the innovation, the visibility of the innovation, the demonstrated results from using the innovation, the image
associated with using the innovation, and the voluntariness of using the innovation. Moore & Benbasat (1991) takes
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these characteristics and argues that they are not absolute but perceptional; they term the characteristics the
perceived characteristics of innovations. The diffusion view specifies “that adopters should have more positive
perceptions of using the (innovation) than non-adopters and thus score higher on the scales developed” (Moore &
Benbasat, 1991, p. 208).
While the TAM and its’ constructs have been widely used in the past, the Moore and Benbasat (1991) PCI scales
have been rather neglected in their actual implementation: “Despite its’ theoretically rich development and fairly
rigorous initial testing, the full set of PCI belief constructs has received relatively little empirical attention” (Plouffe,
Hulland, & Vandenbosch, 2001, p. 210). Further, while Moore and Benbasat attempted to uncover the differences
between adopters and non-adopters without using deterministic models of human behavior, subsequent research
has studied the phenomenon by relying on studies that employ causal models to determine the ability of the PCI
scales to predict user acceptance. In subsequent work on the development of the PCI scales, Moore and Benbasat
tested the ability of the constructs to predict individuals’ usage behavior (Moore & Benbasat, 1996). They concluded
that the most significant perceptions that had an effect on degree of use were ease of use, relative advantage, and
compatibility (a finding that Gagliardi & Compeau (1995) also support). Relative advantage and compatibility were
also found to be significant predictors of intention to adopt a group support system (Chin & Gopal, 1995).
While early research validated the predictiveness of the characteristics in isolated studies, research has since
sought to compare the TAM to the PCI. Studies have found (Plouffe et al., 2001) that the PCI belief constructs
explain more variance in adoption intent than the TAM suggests and indicate that ease of use is not as significant as
the TAM suggests.
There is a tradition in PCI research to examine the acceptance process longitudinally. Agarwal and Prasad found
that initial use was shaped by the characteristics of compatibility, visibility, trialability, and voluntariness and that this
initial use enables the development of feelings of relative advantage and result demonstrability, which facilitates
long-term usage (Agarwal & Prasad, 1997). Karahanna, Straub, and Chervany (1999) suggest that the initial use is
shaped by social factors (such as visibility) and subsequent usage is dependent more on attitude.
The original model of the PCI constructs assumes that each perception independently contributes to intention. This
view has recently been to account for the emergence of perceptions (Compeau, Meister, & Higgins, 2007) and, in
the current paper, we adopt a modified PCI model to understand technology choice. While the proposed model
outlines how each of the PCI constructs is inter-related, we instead opt for a reduced set of PCI factors. Specifically,
for the sake of parsimony and for our context under investigation, we focus on only four perceptions that directly
influence the intention choice:
• Communicability, or the ease with which the results of using the innovation can be easily described to others
(Rogers, 1995b; Tornatzky & Klein, 1982; Holak & Lehmann, 1990).
• Ease of use, or the degree to which an individual believes that using a particular system will be free of
physical and mental effort (Chen, Gillenson, & Sherrell, 2002, Igbaria, Zinatelli, Cragg, & Cavaye, 1997,
Plouffe et al., 2001, Venkatesh & Davis, 2000; Agarwal & Karahanna, 2000).
• Relative advantage, or the degree to which an innovation is perceived as being better than its precursor
(Shin, Lee, Shin, & Lee, 2010, Udeh, 2008, Van Slyke, Johnson, Hightower, & Elgarah, 2008).
• Measurability, or the degree to which the impact of an innovation can be assessed (Venkatesh, 2000;
Venkatesh & Davis, 2000).
While we acknowledge this as a potential limitation (which we expand on later), we posit that the variables outlined
below are the most likely to impact our dependent variable due to their theoretical proximity to the behavior that we
are seeking to understand: choice. Figure 2 outlines our proposed research model. In Section 5, we discuss how we
will integrate choice into our nomological network.
RESEARCH CONTEXT
Over the course of three years, MBA students who were enrolled in an IS course at a university in the Southeastern
United States were required as part of the course to participate in a research study designed to understand
technology choice. Each year, the first author was the instructor for two sections of the course; thus, six sections of
MBA students distributed across three years participated in the study. The research context that we selected to use
was the choice of which spreadsheet application the student intended to use for future spreadsheet needs.
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Communicability
Ease of
Use
Intention to
choose
Attitude
Relative
Advantage
Measurability
Figure 2. Research Model
Each student used two spreadsheet solutions for a period of one month each. For the first month, half of the
students used Zoho (www.zoho.com), which is an online provider of a spreadsheet application (Zoho Sheets). Zoho
Sheets is an online spreadsheet application that allows a user to create, edit, and share spreadsheets using the
Zoho platform. The functionality of the application is similar to other spreadsheets, with the advantage of the
approach being the collaborative nature of the solution. However, the downside of such an application, from the
perspective a user, might be the need to constantly maintain Internet connectivity in order to use the application.
Simultaneously, the other half of the students were required to download, install, and use Open Office Calc
(http://www.openoffice.org/), an open source spreadsheet application. In contrast, Open Office Calc is a free,
downloadable spreadsheet solution that does not require online access, and its functionality is similar, but not
necessarily equivalent to, either Zoho or other non-open source solutions. The potential limitations of an application,
from the perspective of a user, might be the lack of collaborative tools and a decrease in functionality. We selected
these two platforms to offer two competing alternatives with different functionality that were still capable of
accomplishing a similar task. After a month of using one or the other, the students switched and used the other
application, which gave them experience with both applications.
At the beginning of the month, the instructor provided training on the application that each student was required to
use; 30 minutes of in-class time was devoted to training students about the software. This training allowed the
instructor to provide the student with the relevant functionality of the technology necessary to complete the
assignments (detailed next) while using the application. Following the training, each student completed an “initial
impressions” survey of each technology.
The students were then given a weekly assignment to complete using the spreadsheet application, with each
student completing a total of eight assignments (four using Zoho and four using Open Office Calc). Each assignment
required the student to complete a business analysis on data with the student using the application for approximately
one hour each week. After completing the weekly assignments, the students completed a “final impressions” survey
for each technology. Moreover, after using both applications, the students completed a “comparison” survey in which
they were asked to assess the differences between the two technologies. To receive full credit for completing the
class assignment, students were required to: (1) complete the “initial impressions” survey for each technology, (2)
complete the “final impressions” survey for each technology, (3) complete the “comparison” survey, and (4) complete
all eight assignments. One hundred and seventy-three (173) students fulfilled these criteria and were included in our
data set. To eliminate variation between sections and between years, we standardized our data in each section and
combined the data for our final data set.
Measurement
With the proposed research model developed (Figure 2), we clarified the measurement of the constructs. To do so,
we generated items that corresponded to the proposed theoretical model (items were based on Compeau et al.,
2007). For each construct, we selected items previously validated in the literature and changed only the wording to
reflect our research context. Regardless of the three approaches to understanding choice, our dependent variable
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(intention to choose) remained the same. To measure intention, we selected a Likert scale in which we asked the
respondent to assess their intention to select Zoho over Open Office Calc. In all questions of choice, Zoho was
always the option that was used as the basis of comparison. Appendix A displays the full instrument.
Measuring the Comparisons
Previous work on understanding comparisons between perceptions has proposed competing approaches to
modeling these differences; namely: (1) direct perceptions and (2) subtraction. Dabholkar (1994) used these
approaches, and we use them as well for our investigation. Each approach differs theoretically and mathematically,
which provides us with additional insight into our understanding of user choice.
The direct perception approach theorizes that, if a user makes a comparison, then the best approach is to directly
ask about this comparison. From a measurement perspective, this calls on a user to compare one technology to
another. To model this approach, in all of our perceptions and attitude questions, we asked users about their views
of Zoho compared to Open Office Calc.
Model
1
2
3
4
5
6
7
8
9
Table 3. Comparison of Research Models
Independent variables
Mediator
Initial baseline models
Initial perception towards Calc
Initial attitude toward Calc
Initial perception towards Zoho
Initial attitude toward Zoho
Final baseline models
Final perception towards Calc
Final attitude toward Calc
Final perception towards Zoho
Final attitude toward Zoho
Attitude comparison model
Final attitude toward Calc
Final perception towards Calc
Final attitude toward Zoho
Final perception towards Zoho
Final intention toward Calc
Final intention toward Zoho
Expectancy comparison model
Final perception towards Calc
Perceived attitude difference
Final perception towards Zoho
between Zoho and Calc
Final perception towards Calc
Subtracted attitude difference
Final perception towards Zoho
between Zoho and Calc
Belief comparison model
Perceived differences between Zoho
Perceived attitude difference
and Calc
between Zoho and Calc
Subtracted differences between Zoho
Subtracted attitude difference
and Calc
between Zoho and Calc
Dependent variable
Initial intention to use Calc
Initial intention to use Zoho
Final intention to use Calc
Final intention to use Zoho
Intention to choose Zoho
over Calc
Intention to choose Zoho
over Calc
Intention to choose Zoho
over Calc
Intention to choose Zoho
over Calc
Intention to choose Zoho
over Calc
The subtraction approach theorizes that individuals look at each option as “not being as good as” another or that
individuals mentally subtract a set of features when they compare between various options. Mathematically, this
would require a researcher to ascertain perceptions of technology A and perceptions of technology B and then
subtract one from another to arrive at a difference score. To model this approach, we subtracted the perception of
each item for Open Office Calc from the Zoho score to arrive at individual perception subtraction items.
We ran a total of nine models for our analysis (Table 3 summarizes), including:
• Initial baseline models: we first ran baseline models that examined participants’ initial intention to use the
individual software. Participants completed the initial perception, attitude, and intention survey directly after
the initial training and prior to any experience with the software. We ran a total of two models: one baseline
model for Calc and one baseline model for Zoho.
• Final models: we next ran models that examined the final intention to use the individual software.
Participants completed the final perception, attitude, and intention survey after a month of using the
software. We ran a total of two models: one final model for Calc and one final model for Zoho.
• Attitude comparison model: according to the attitude comparison model, a user holds a perception of Calc
and a separate perception of Zoho. These perceptions lead to an attitude and an intention toward each
technology. These attitudes and intentions culminate in a participant’s choice.
• Expectancy comparison model: we ran two separate models as expectancy comparison models. In both
models, the independent variable was the final perception towards Calc and Zoho. However, the difference
was the mediator and dependent variable. In the direct perception model, the perception of the attitude
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between the two technologies was asked directly, while, in the subtraction model, the attitude difference
between Calc and Zoho was calculated. Both models employed the same dependent variable.
• Belief comparison model: we ran two separate models as belief comparison models. In the direct perception
model, users assessed their perceptual and attitudinal difference between the technologies, while, in the
subtraction model, the difference between the final perception towards Zoho and Calc and the final attitude
towards Zoho and Calc was calculated in explaining choice.
With the baseline models, the three choice models, and the two difference approaches, we formulated nine models
to analyze. We modeled each of the three choice approaches under the condition for each of the three difference
approaches, which would provide insight into how a user makes a choice. Next, we discuss our data analysis.
Data Analysis
We analyzed the data using structural equation modeling. Given our sample size (n = 173), we were unable to use a
covariance-based approach (MacCallum & Browne, 1993) and, thus, selected the partial least squares (PLS)
approach and used Smart PLS software. This approach enabled us to understand each of the nine models of
technology choice.
Measurement Model
We first analyzed the measurement (or outer) model. We completed the analysis by first examining the adequacy of
the measures to ensure that the items measured the constructs as they were designed. As a guideline, Chin (1998,
p. 325) states that “Standardized loadings should be greater than 0.707”. We wanted the measurement to be
consistent across all nine models. Using this criterion as an assessment, we eliminated eight items (two from
1
attitude, two from communicability, two from ease of use, one from measurability, and one from relative advantage) .
Second, to determine whether the items loaded on other constructs and on their theorized construct, we computed
cross-loadings. For cross-validated items to be included in the finalized data set, the loading needed to be greater
on the intended construct than on any other construct. Consequently, on determining that none of the items loaded
higher on any construct other than the intended construct, we included all the items (Appendix B shows the crossloadings, weights, and loadings).
Using the loadings from the constructs in the model, we created composite reliabilities for the constructs in the
model (Table 4), along with the average variance extracted and the correlations between the constructs (Table 5
through 13). We compared the square root of the average variance extracted with the correlations among constructs
to ensure that, on average, each construct was more highly related to its own measures than to other constructs
(Chin, 1998). The analysis revealed that the constructs were reliable and demonstrated discriminant validity.
Table 4. Composite Reliabilities
Calc
Attitude
Zoho
Attitude
comparison
Expectancy
comparison
model
(perceptual)
Initial
Final
Initial
Final
Calc
Zoho
Calc
0.943
0.965
0.953
0.963
0.965
0.963
0.962
Zoho
Expectancy
Belief
comparison model comparison Belief comparison
model
(subtracted)
model
(subtracted)
(perceptual)
Calc
Zoho
0.956
0.962
0.956
Communicability
0.940
0.871
0.881
0.879
0.871
0.873
0.856
0.889
0.866
0.784
0.906
0.862
EOU
0.944
0.962
0.942
0.944
0.962
0.944
0.962
0.944
0.962
0.944
0.974
0.930
Intention
0.993
0.990
0.992
0.995
0.990
0.995
Measurability
Relative
advantage
0.930
0.946
0.920
0.942
0.945
0.942
0.946
0.938
0.945
0.899
0.933
0.913
0.965
0.978
0.968
0.972
0.978
0.972
0.978
0.972
0.978
0.972
0.984
0.953
0.991
0.989
Choice
0.991
0.991
0.991
1
As a test of Common Method Bias, we assessed each of the measurement models using Harman’s One Factor Model Test. One factor
accounted for the following percentage of variance: Initial Zoho: 54.85%; Initial Calc: 54.73%; Final Zoho: 60.73%; Final Calc: 61.18%; Attitude
Comparison Model: 36.73%; Expectancy Comparison Model (Perceptual): 31.42%; Expectancy Comparison Model (Subtraction): 32.20%; Belief
Comparison Model (Perceptual): 60.46%; Belief Comparison Model (Subtraction): 55.95%. Based upon these results, we can therefore conclude
that CMB is not a source of concern.
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Table 5. Initial Baseline (Calc) Discriminant Validity Analysis
AVE
Attitude
Communicability
EOU
Intention
Measurability
Attitude
0.768
0.877
Communicability
0.888
0.574
0.942
EOU
0.810
0.756
0.674
0.900
Intention
0.980
0.545
0.372
0.481
0.990
Measurability
0.870
0.422
0.450
0.331
0.428
0.933
Relative advantage
0.796
0.767
0.485
0.628
0.534
0.418
Relative
advantage
0.892
Table 6. Initial Baseline (Zoho) Discriminant Validity Analysis
AVE
Attitude
Communicability
EOU
Intention
Measurability
Attitude
0.801
0.895
Communicability
0.787
0.344
0.887
EOU
0.801
0.692
0.533
0.895
Intention
0.975
0.539
0.199
0.457
0.987
Measurability
0.852
0.332
0.384
0.320
0.317
0.923
Relative advantage
0.812
0.720
0.266
0.628
0.689
0.360
Relative
Advantage
0.901
Table 7. Final Baseline (Calc) Discriminant Validity Analysis
AVE
Attitude
Communicability
EOU
Attitude
0.846
0.920
Communicability
0.773
0.540
0.879
EOU
0.863
0.734
0.679
0.929
Intention
0.970
0.558
0.317
0.404
Intention
Measurability
Relative
advantage
0.985
Measurability
0.897
0.408
0.535
0.371
0.481
0.947
Relative advantage
0.863
0.816
0.478
0.650
0.654
0.510
0.929
Table 8. Final Baseline (Zoho) Discriminant Validity Analysis
14
AVE
Attitude
Attitude
0.838
0.915
Communicability
0.786
0.157
0.887
EOU
0.809
0.793
0.312
0.900
Intention
0.985
0.691
0.199
0.612
0.993
Measurability
0.890
0.010
0.522
0.166
0.151
0.944
Relative advantage
0.833
0.847
0.240
0.742
0.742
0.117
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Communicability
EOU
Intention
Measurability
Relative
advantage
0.913
0.541
0.879
0.863
0.734
0.681
0.929
0.970
0.558
0.318
0.403
0.985
0.897
0.408
0.533
0.371
0.481
0.947
0.863
0.816
0.479
0.650
0.654
0.510
0.929
0.974 -0.282 -0.164 -0.220 -0.074 -0.131
-0.238
0.987
0.838
0.194
0.158
0.202
0.227
0.075
0.235
0.568
0.915
0.777
0.060
0.379
0.146
0.088
0.317
0.075
0.072
0.161 0.882
0.809
0.233
0.329
0.369
0.249
0.200
0.297
0.444
0.793 0.315 0.900
0.985
0.006
0.067
0.050
0.269
0.108
0.102
0.602
0.691 0.203 0.612 0.993
0.890
0.039
0.172
0.042
0.041
0.393
0.080
-0.007 0.010 0.514 0.165 0.151 0.944
0.833
0.140
0.131
0.179
0.266
0.155
0.283
0.517
Zoho
ttitude
Zoho
EOU
0.772
Choice
0.920
0.847 0.243 0.742 0.743 0.117
Zoho
relative
advantage
Zoho
measurability
Zoho
intention
Zoho
communicability
Calc
relative
advantage
Calc
measurability
Calc
intention
0.846
Calc
EOU
Calc
attitude
Choice
Zoho
attitude
Zoho
comm.
Zoho
EOU
Zoho
intention
Zoho
measure
Zoho
relative
AVE
Calc
attitude
Calc
comm.
Calc
EOU
Calc
intention
Calc
measure
Calc
relative
advantage
Calc
communicability
Table 9. Attitude Comparison Discriminant Validity Analysis
0.913
Table 10. Expectancy Comparison (Perceptual) Discriminant Validity Analysis
Calc
comm.
Calc
EOU
Calc
measurability
Calc
RA
Comparison
attitude
Intention
Zoho
Com
Zoho
EOU
Zoho
measurability
Zoho
RA
AVE
Calc
comm.
Calc
EOU
Calc
measurability
Calc
RA
Comparison
attitude
0.751
0.867
0.863
0.700
0.929
0.897
0.504
0.363
0.947
0.863
0.491
0.648
0.504
0.929
0.837
-0.043
0.014
-0.083
-0.004
0.915
0.974
-0.174
-0.220
-0.127
-0.239
-0.296
0.987
0.801
0.360
0.142
0.333
0.072
-0.105
0.058
0.895
0.809
0.329
0.370
0.197
0.295
-0.251
0.444
0.306
0.900
0.883
0.164
0.051
0.406
0.088
-0.092
-0.003
0.547
0.169
0.940
0.833
0.127
0.177
0.154
0.282
-0.323
0.517
0.232
0.741
0.117
Intention
Zoho
comm.
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EOU
Zoho
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Zoho
RA
0.913
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Table 11. Expectancy Comparison (Subtracted) Discriminant Validity Analysis
AVE
Calc
comm.
Calc
EOU
Calc
measurability
Calc
RA
Intention
Subtraction Zoho
attitude
comm.
Zoho
EOU
Calc Comm.
0.765
0.875
Calc EOU
0.863
0.688
0.929
Calc measurability
0.896
0.523
0.371
0.946
Calc RA
0.863
0.485
0.649
0.511
0.929
Intention
0.974
-0.168
-0.219
-0.132
-0.238
0.987
Subtraction attitude
0.813
-0.286
-0.396
-0.245
-0.421
0.654
0.902
Zoho Com
0.660
0.365
0.148
0.270
0.075
0.109
0.113
0.813
Zoho EOU
0.810
0.331
0.370
0.200
0.295
0.443
0.455
0.326
0.900
Zoho
measurability
Zoho measurability
0.818
0.211
0.070
0.404
0.105
0.006
-0.035
0.454
0.173
0.904
Zoho RA
0.833
0.130
0.177
0.155
0.282
0.517
0.570
0.257
0.740
0.114
Zoho
RA
0.913
Table 12. Belief Comparison (Perceptual) Discriminant Validity Analysis
AVE
Attitude
Communicability
EOU
Intention
Measurability
Attitude
0.836
0.914
Communicability
0.828
-0.205
0.910
EOU
0.902
-0.329
0.467
0.950
Intention
0.974
-0.298
0.408
0.863
0.987
Measurability
0.875
-0.149
0.631
0.389
0.358
0.935
Relative advantage
0.900
-0.307
0.434
0.904
0.844
0.365
Relative
advantage
0.949
Table 13. Belief Comparison (Perceptual) Discriminant Validity Analysis
AVE
Attitude
Communicability
EOU
Intention
Measurability
Attitude
0.813
0.902
Communicability
0.759
0.309
0.871
EOU
0.770
0.767
0.369
0.877
Intention
0.969
0.633
0.254
0.483
0.984
Measurability
0.840
0.173
0.469
0.198
0.335
0.917
Relative advantage
0.804
0.813
0.371
0.652
0.687
0.264
Relative
advantage
0.896
Structural model
Tables 14 through 17 present the results of the data analysis using Smart PLS. To determine the statistical
significance of the paths, we used the bootstrapping procedure with 400 samples. To understand the nature of
technology choice, we first analyze the results in choice (comparing the comparisons), in comparisons (comparing
the choices), and across choice and across comparison.
Table 14. Baseline Model Structural Model Results
Calc initial
Attitude
Calc final
Intention
Attitude
ns
Attitude
0.079
ns
ns
0.220**
ns
-0.055
0.064
-0.090
-0.003
-0.026
-0.056
-0.022ns
EOU
0.423*
0.162*
0.316*
-0.050ns
0.392*
0.019ns
0.399*
0.045ns
Measurability
0.077ns
0.228*
-0.053ns
0.239*
0.042ns
0.080ns
-0.094ns
0.093ns
Relative advantage
0.452*
0.216*
0.607*
0.485*
0.460*
0.602*
0.575*
0.517*
0.719
0.375
0.740
0.468
0.615
0.483
0.794
0.573
The designation refers to the significance of the path. ns means non-significant; ** p < 0.10; * p < 0.05
16
Intention
0.036
r
ns
Attitude
ns
0.150
ns
Zoho final
Intention
Communicability
2
ns
Attitude
ns
0.192
ns
Zoho initial
Intention
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Table 15. Attitude Comparison Structural Model Results
Calc attitude
Calc attitude
Calc intention
Choice
0.150ns
-0.104ns
Calc communicability
0.063ns
-0.088ns
-0.053ns
Calc EOU
0.317*
-0.051ns
-0.107ns
Zoho attitude
Zoho intention
-0.043ns
Calc intention
Calc measurability
-0.053*
0.238ns
0.039ns
Calc relative advantage
0.607*
0.486*
-0.197*
Zoho attitude
0.360*
0.221**
ns
Zoho communicability
-0.013
-0.056
-0.018ns
Zoho EOU
0.117*
0.399*
0.044**
Zoho intention
0.344*
Zoho measurability
-0.054ns
-0.094*
0.091ns
Zoho relative advantage
-0.020 ns
0.576*
0.517*
0.558
0.794
0.573
2
r
0.740
0.468
ns
Note: The designation refers to the significance of the path. ns means non-significant; ** p < 0.10; * p < 0.05
Table 16. Expectancy Comparison Structural Model Results
Perceptual
Attitude
ns
Subtraction
Choice
Calc communicability
-0.076
Calc EOU
0.086 ns
-0.170**
ns
ns
Calc measurability
-0.074
Calc relative advantage
0.120 ns
Attitude
ns
Zoho communicability
0.036
Zoho EOU
-0.045 ns
ns
Zoho measurability
-0.039
Zoho relative advantage
-0.321*
-0.042
ns
0.023
-0.317*
-0.100
ns
-0.064
ns
0.327*
-0.044
Attitude
-0.020
ns
-0.298*
-0.011
ns
-0.450*
0.384*
-0.039 ns
-0.096**
0.012 ns
-0.197*
0.262*
-0.011
ns
0.348*
ns
Choice
-0.071
-0.021 ns
0.229*
ns
0.507*
-0.016 ns
0.281*
2
r
0.125
0.487
0.764
0.489
Note: The designation refers to the significance of the path. ns means non-significant; ** p < 0.10; * p <
0.05
Table 17. Belief Comparison Structural Model Results
Perceptual
Attitude
Attitude
ns
Subtraction
Choice
-0.013
ns
-0.016
ns
Attitude
Choice
0.279*
-0.090 ns
Communicability
-0.073
EOU
-0.256 ns
0.544*
0.421*
-0.044 ns
Measurability
0.014 ns
0.029 ns
-0.043 ns
0.215*
0.344*
0.563*
0.465*
Relative advantage
-0.048
ns
-0.035
ns
2
r
0.112
0.768
0.763
0.524
Note: The designation refers to the significance of the path. ns means non-significant; ** p < 0.10; * p <
0.05
On participants’ first exposure to Calc, relative advantage (0.4521) and ease of use (0.4229) were the only
significant drivers of attitude, while measurability (0.2281), relative advantage (0.2164), and EOU (0.1619) were not
2
significant. The r for attitude was 0.7192, while for intention it was 0.375. After a month of using Calc, relative
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2
advantage increased in predicting attitude (0.6067) while ease of use dropped slightly (0.3161) and the overall r for
attitude remained similar (0.7396). Intention was driven by relative advantage (0.4854) and measurability (0.2391)
2
with an increase in r (0.4677).
On participants’ exposure to Zoho, relative advantage (0.4601) and ease of use (0.3917) were the significant drivers
2
of attitude, while relative advantage (0.6018) was the only driver of intention. Overall, the r for the initial Zoho
2
attitude was lower than that of Calc (0.6153), but the intention r was higher (0.4829). After a month of exposure to
Zoho, relative advantage (0.5753) and ease of use (0.3988) predicted attitude, while intention was explained by
2
relative advantage (0.5168) and attitude (0.2204). The final r for both attitude (0.7938) and intention (0.5732)
increased after the month of exposure.
From this initial set of baseline models, we can conclude that the formulation of the overall attitude towards Calc and
Zoho were relatively similar: relative advantage and ease of use were important for both, while Calc also included a
driver of measurability after a month of use. We hypothesize that the inclusion of measurability for Calc was that the
open source solution was more similar to spreadsheets used by our students during the time of the study, while the
web-based application model was only emerging during this period and the benefits were more difficult for the
students to calculate. The final models were also similar: the drivers of the intention to use the technology were a
function of measurability and relative advantage (for Calc) and relative advantage and attitude (for Zoho). This
finding further illustrates the time period in which the study was conducted—that the students were not able to
determine the measureable benefits that Zoho offered. However, did these same drivers explain the choice of one
technology over another?
The attitude comparison model argues that users has perceptions of, attitudes about, and intentions for each
technology separately and that these individual perceptions, attitudes, and intentions determine their choice. While
2
the overall r for choice was high (0.5576), the decision was driven by the views of the relative advantage of Calc (0.1965) and the attitude (0.3595), intention of using (0.3443), and EOU (0.1167) of Zoho. The drivers for each of the
individual technologies remain similar to the final baseline models.
The expectancy comparison model argues that users have perceptions of each technology, but when deciding on
one technology over another, the comparison is made based on the attitude of one technology versus another. The
2
overall r for choice was lower than the attitude comparison model and was similar for viewing the attitude difference
perceptually (0.4868) versus subtraction (0.4887). According to the expectancy comparison model, the drivers of the
choice derived from the relative advantage (-0.3167 for perceptual and -0.1966 for subtraction) and ease of use (0.1695 for perceptual and -0.1966 for subtraction) of Calc and from the relative advantage (-0.3836 for direct and
0.2814 for subtraction) and ease of use (0.3269 for direct and 0.2291 for subtraction) of Zoho.
The belief comparison model argues that the perceptions and attitudes are all comparisons in explaining the choice
2
of a technology. This model exhibited the highest overall r for choice (0.7675 for perceptual and 0.5242 for
subtraction). According to the belief comparison model, the decision was driven by ease of use (0.5441 for direct
and 0.2149 for subtraction) and relative advantage (0.3439 for direct and 0.4649 for subtraction).
DISCUSSION
Our findings demonstrate that the belief comparison model had the highest explanatory power in predicting users’
choice when comparing two different technologies to determine which option to select. Therefore, we can conclude
that users, when making a selection decision about a technology, maintain their individual views toward the
technology in their intentionality in using a specific IT, but that the choice is a function of a comparison. Interestingly,
in our work, the two antecedents for the choice were the ease of use of the technology and the relative advantage.
We postulate that the rationale for this was due to the ease of cognitive load required for the comparison.
Relative advantage appears to be well-suited to explain choice given the focus on the relative nature of the
construct. The concept of comparison is inherent in the construct because it asks for an individual to compare
advantages of the given technology over its predecessor or, in this case, the opposing innovation. It is, therefore, not
surprising that relative advantage was significant. Similarly, a comparison of the ease of use of one technology also
required less cognitive load. When asked about a feature set of one technology over another, it appears that users
can easily assess this comparison.
We found, however, that, when making their technology choice decision, users were more focused on their
experience while using the technology rather than what happened after they selected and used a particular
technology. While ease of use and relative advantage represent concepts that will impact the user’s temporal
interaction with the technology, communicability and measurability impact the user after they have used the
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technology, and they have a longer-term impact. Thus, when comparing technologies to determine which option to
use, users are more focused on short-term advantages rather than on the long-term benefits of selecting one
technology over another.
Implications For Research
We recommend that more work needs to be conducted regarding comparisons that users employ when making a
technology choice. As a guiding principle, we recommend that work focus on assessments that do not require a high
degree of cognitive load and are suited specifically to comparisons rather than those that directly assess the
innovations. We further urge researchers to use the belief comparison model as the starting point for additional
work.
With this extension, we argue that the proxy view needs to be expanded to include comparisons rather than direct
perceptions. For example, the assumption in technology adoption research has been that perceptions drive the
adoption decision. This assumption is a pro-innovation bias in that it implicitly assumes that the technology is the
only option (as opposed to not using technology at all). If we explicate the scenario we originally proposed, what if a
salesperson used index cards and post-it notes rather than a CRM to track sales leads? Even outside of the context
of choice, we argue that the findings regarding the importance of comparisons is interesting for all adoption
researchers.
These comparisons are becoming increasingly important given the commoditization of IT. Indeed, with the bringyour-own-device (BYOD) movement, managers and executives have an increasing array of innovations to select to
complete a given task. This emergent phenomenon means that researchers must begin to understand the
relationship between task, choice, and performance in a more meaningful way. In our research, we assumed that
the task could equally be completed by two alternative technologies, yet what if this assumption was relaxed? What
if one technology was better suited to complete a task? Traditional TTF research would answer that the individual
would underperform if there was not a fit (and argue that there was either under or over fit). Yet, the focus in TTF is
on performance and usage, not choice. A user might choose a technology that does not fit a given task, yet our
focus on the direct perceptions and beliefs instead of comparison perceptions and beliefs would not adequately
address this question.
Furthermore, given the recent trends toward the customization of IT, users are increasingly selecting from a broader
set of alternatives to create customized IT solutions that are tailor-made for every individual. If we consider two
iPads from two users, each user will customize their device and engage in a series of choice behaviors that will
result in two distinct paths. Each of these choice behaviors carries with it a heuristic that needs further examination
as individuals are provided with more technology options that empower them with more IT choices to complete their
tasks.
Implications For Practice
For our colleagues in practice, the heuristic for which technology to select for a given task depends on the
availability of IT at the moment that the task is required. Nonetheless, our work demonstrates that users have
multiple choices and that IT departments should enable and empower their users to move further towards BYOD.
We argue that these devices and software solutions put pressure on the internal IT department to make internal
systems that have advantages over the consumer products and will, therefore, increase adoption and use of the
internal corporate systems. Our findings elucidate that users are already making the comparison between internal IT
and the external IT solutions that are being used outside of the corporate world. And, given our findings that it is
relative advantage that is driving the choice behavior, we argue that there needs to be a stronger advantage for
using the internal solution than the device or software solution that exists outside of the boundaries of the firm.
The specific advantage that users use when comparing internal IT solutions to the external options is the ease of
use of the systems. This finding replicates previous work in the adoption research community, but our work shows
that ease of use is important in not only intention to use but also intention to select. In other words, while choice and
use are not the same, ease of use is a driver of both and is, therefore, critical to ensure that a user will chose the ITpreferred solution.
Limitations
Our work does come with some limitations. First, we conducted our study in a university in the Southeastern United
States. We urge other researchers to investigate the mechanisms of technology choice in other contexts. Next, we
limited our work to a sub-set of the PCI constructs. While we made this choice for the sake of parsimony and to
demonstrate how technology choice behavior can be studied, we recognize that a broader set of constructs could
yield more theoretical coverage of the domain space. Third, we selected two technologies that are productivity
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based. Other technologies such as hedonic systems might yield different results, and we urge other researchers to
expand on our findings to discover how differing technologies alter the technology choice behavior.
CONCLUSION
Research investigating technology adoption has assumed that an individual selecting a technology to use has only
one technology option when making their adoption decision. We overcame this limitation by providing three choice
approaches and two comparison approaches to understand how individuals make their selection. If we continue to
study technology choice, we will gain more insight into the mechanisms behind how individuals assess alternatives
with technology.
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20
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Article 2
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APPENDIX A
RESEARCH ITEMS USED
Label
Direct measurement
Perceived difference
ATT1
Pleasant – unpleasant*
More pleasant - more unpleasant
ATT2
Good – bad*
Better – worse
ATT3
Likable – dislikable*
More likable - more dislikable
ATT4
Harmful – beneficial*
More harmful - more beneficial
ATT5
Wise – foolish*
Wiser - more foolish
ATT6
Negative – positive*
More negative - more positive
ATT7
More Valuable - More Worthless
For my future tasks that require a spreadsheet, I am more likely to
select Zoho Sheet than Open Office Calc
My intentions are to continue using Zoho Sheet instead of Open
Office Calc for future tasks that require a spreadsheet
For my future tasks that require a spreadsheet, I would more likely
plan to use Zoho Sheet than Open Office Calc
I would find it easier to tell others about the results of using Zoho
Sheet than Open Office Calc
Explaining the advantages and disadvantages of Zoho Sheet would
be more difficult than explaining those of Open Office Calc
I think that I could more easily describe the effects of using Zoho
Sheet than Open Office Calc
I believe I could better communicate to others the consequences of
using Zoho Sheet than Open Office Calc
It is harder to measure the results of using Zoho Sheet than Open
Office Calc
The effects of using Zoho Sheet can be assessed more precisely
than Open Office Calc
It is easier to determine the impact of Zoho Sheet than Open Office
Calc
I believe that Zoho Sheet was more cumbersome to use than Open
Office Calc
EOU7
Valuable – worthless*
For my future tasks that require a spreadsheet, I
intend to use (Calc/Zoho Sheet)*
My intentions are to continue using (Calc/Zoho
Sheet) for future tasks that require a spreadsheet*
For my future tasks that require a spreadsheet, I
plan to use (Calc/Zoho Sheet)*
I will/would find it easy to tell others about the
results of using (Calc/Zoho Sheet)
Explaining the advantages and disadvantages of
(Calc/Zoho Sheet) will/would be difficult
I think that I will/could very easily describe the
effects of using (Calc/Zoho Sheet)
I believe I could communicate to others the
consequences of using (Calc/Zoho Sheet)*
It will be/is hard to measure the results of using
(Calc/Zoho Sheet)
The effects of using (Calc/Zoho Sheet) can be
assessed precisely*
It is easy to determine the impact of (Calc/Zoho
Sheet)*
I believe that (Calc/Zoho Sheet) will be/was
cumbersome to use
It will be/was easy for me to remember how to
perform tasks associated with using (Calc/Zoho
Sheet)
I believe that it will be/was easy to get (Calc/Zoho
Sheet) to do what I want it to do
Overall, I believe that (Calc/Zoho Sheet) will
be/was easy to use
Learning to operate (Calc/Zoho Sheet) will be/was
easy for me
EOU8
(Calc/Zoho Sheet) will be/was user friendly
Zoho Sheet was more user friendly than Open Office Calc
It will be hard to measure the results of using
(Calc/Zoho Sheet)*
The effects of using (Calc/Zoho Sheet) can be
assessed precisely*
It is harder to measure the results of using Zoho Sheet than Open
Office Calc
The effects of using Zoho Sheet can be assessed more precisely
than Open Office Calc
INT1
INT2
INT3
COM1
COM2
COM3
COM5
MEAS1
MEAS4
MEAS5
EOU1
EOU2
EOU5
EOU6
MEAS1
MEAS4
It was easier for me to remember how to perform tasks associated
with using Zoho Sheet than Open Office Calc
I believe that it was easier to get Zoho Sheet to do what I want it to
do than Open Office Calc
Overall, I believe that Zoho Sheet was easier to use than Open
Office Calc
Learning to operate Zoho Sheet was easier for me than Open Office
Calc
Volume 15
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Article 2
21
It is easy to determine the impact of (Calc/Zoho
It is easier to determine the impact of Zoho Sheet than Open Office
Sheet)
Calc
Using (Calc/Zoho Sheet) will enable/enabled me to
Using Zoho Sheet enabled me to accomplish my tasks more quickly
RA1
accomplish my tasks more quickly
than if I used Open Office Calc
Using (Calc/Zoho Sheet) will improve/improved the
Using Zoho Sheet improved the quality of work I do more than if I
RA2
quality of work I do
used Open Office Calc
Using (Calc/Zoho Sheet) will simplify/simplified my
Using Zoho Sheet simplified my tasks more than if I used Open
RA3
tasks
Office Calc
Using (Calc/Zoho Sheet) will improve/improved my
Zoho Sheet improved my performance on the assignments more
RA4
performance on the assignments
than when I used Open Office Calc
Overall, I will find/found using (Calc/Zoho Sheet) to
Overall, found using Zoho Sheet to be more advantageous than
RA5
be advantageous in completing my assignments
Open Office Calc in completing my assignments
Using (Calc/Zoho Sheet) will reduce/reduced my
Using Zoho Sheet improved my effectiveness on my assignments
RA6
effectiveness on my assignments
more than Open Office Calc
Using (Calc/Zoho Sheet) will give/gave me greater
Using Zoho Sheet gave me greater control over my work than Open
RA7
control over my work
Office Calc
Using (Calc/Zoho Sheet) will make/made me more
Using Zoho Sheet made me more productive than when I used
RA8
productive
Open Office Calc
Note: 1. The * designates that the research items were exactly the same prior to use and one month after use.
2. If there is not an * following the item, then the item was phrased differently prior to use and after one month of use. For the non
* items, the verb prior to the “/” was used prior to use and the verb after the “/” was used after one month of use. For example,
COM1 prior to use was I will find it easy to tell others about the results of using (Calc/Zoho Sheet) and after one month of use
was I would find it easy to tell others about the results of using (Calc/Zoho Sheet).
MEAS5
APPENDIX B
CROSS-LOADING ANALYSIS
Initial Baseline Model (Calc)
Weight
22
Attitude
Communicability
EOU
Intention
Measurability
Relative advantage
CIATT1
0.242
0.908
0.581
0.736
0.485
0.375
0.704
CIATT2
0.238
0.891
0.533
0.709
0.504
0.373
0.679
CIATT3
0.245
0.920
0.509
0.73
0.496
0.360
0.730
CIATT5
0.206
0.802
0.367
0.481
0.500
0.342
0.637
CIATT7
0.207
0.857
0.509
0.626
0.404
0.403
0.606
CICOM1
0.583
0.588
0.954
0.677
0.383
0.392
0.501
CICOM3
0.477
0.483
0.930
0.585
0.310
0.464
0.404
CIEOU5
0.290
0.705
0.571
0.927
0.448
0.319
0.614
CIEOU6
0.299
0.718
0.625
0.951
0.477
0.331
0.630
CIEOU7
0.223
0.538
0.668
0.84
0.354
0.291
0.414
CIEOU8
0.295
0.730
0.583
0.877
0.439
0.254
0.570
CIINT1
0.332
0.535
0.346
0.474
0.987
0.403
0.525
CIINT2
0.339
0.545
0.384
0.476
0.991
0.433
0.530
CIINT3
0.340
0.541
0.373
0.478
0.992
0.435
0.530
CIMEAS4
0.522
0.354
0.368
0.253
0.417
0.929
0.361
CIMEAS5
0.550
0.431
0.468
0.362
0.383
0.936
0.418
CIRA1
0.153
0.645
0.333
0.532
0.467
0.277
0.885
CIRA2
0.168
0.696
0.409
0.585
0.530
0.367
0.922
CIRA3
0.159
0.701
0.482
0.594
0.441
0.368
0.910
CIRA4
0.157
0.683
0.422
0.499
0.448
0.396
0.910
CIRA5
0.170
0.732
0.490
0.608
0.494
0.333
0.882
CIRA7
0.156
0.658
0.468
0.583
0.471
0.417
0.876
CIRA8
0.158
0.671
0.421
0.511
0.477
0.455
0.860
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Article 2
Initial Baseline Model (Zoho)
Weight
Attitude
Communicability
EOU
Intention
Measurability
Relative advantage
ZIATT1
0.226
0.928
0.341
0.646
0.465
0.283
0.661
ZIATT2
0.229
0.928
0.350
0.690
0.451
0.254
0.662
ZIATT3
0.244
0.934
0.317
0.665
0.541
0.324
0.693
ZIATT5
0.206
0.859
0.239
0.528
0.479
0.308
0.580
ZIATT7
0.212
0.821
0.285
0.554
0.473
0.319
0.619
ZICOM1
0.629
0.328
0.915
0.507
0.214
0.351
0.272
ZICOM3
0.495
0.279
0.859
0.433
0.131
0.329
0.193
ZIEOU5
0.301
0.620
0.442
0.888
0.502
0.295
0.633
ZIEOU6
0.296
0.668
0.507
0.942
0.404
0.263
0.563
ZIEOU7
0.226
0.524
0.536
0.864
0.287
0.327
0.422
ZIEOU8
0.292
0.647
0.440
0.885
0.415
0.273
0.599
ZIINT1
0.344
0.551
0.208
0.466
0.981
0.329
0.691
ZIINT2
0.336
0.522
0.183
0.434
0.991
0.312
0.676
ZIINT3
0.333
0.523
0.199
0.453
0.991
0.298
0.672
ZIMEAS4
0.537
0.318
0.319
0.318
0.276
0.922
0.323
ZIMEAS5
0.546
0.296
0.389
0.274
0.310
0.925
0.342
ZIRA1
0.164
0.676
0.223
0.582
0.634
0.344
0.920
ZIRA2
0.161
0.666
0.237
0.532
0.623
0.335
0.929
ZIRA3
0.161
0.654
0.231
0.553
0.636
0.354
0.921
ZIRA4
0.165
0.654
0.281
0.556
0.670
0.316
0.914
ZIRA5
0.158
0.656
0.225
0.591
0.607
0.300
0.913
ZIRA7
0.140
0.594
0.271
0.576
0.523
0.315
0.827
ZIRA8
0.160
0.641
0.216
0.575
0.639
0.310
0.882
Final Baseline Model (Calc)
Weight
Attitude
Communicability
EOU
Intention
Measurability
Relative advantage
CFATT1
0.226
0.951
0.530
0.741
0.503
0.390
0.784
CFATT2
0.228
0.952
0.515
0.758
0.511
0.371
0.787
CFATT3
0.227
0.963
0.501
0.723
0.534
0.368
0.778
CFATT5
0.202
0.877
0.410
0.544
0.521
0.339
0.701
CFATT7
0.202
0.852
0.521
0.589
0.502
0.409
0.695
CFCOM1
0.701
0.558
0.938
0.716
0.347
0.415
0.499
CFCOM3
0.420
0.354
0.816
0.421
0.175
0.580
0.307
CFEOU5
0.294
0.721
0.637
0.948
0.444
0.386
0.647
CFEOU6
0.272
0.687
0.593
0.932
0.370
0.329
0.622
CFEOU7
0.229
0.603
0.633
0.905
0.272
0.299
0.535
CFEOU8
0.280
0.702
0.661
0.931
0.393
0.356
0.602
CFINT1
0.341
0.563
0.336
0.416
0.984
0.483
0.647
CFINT2
0.338
0.551
0.314
0.394
0.987
0.460
0.650
CFINT3
0.337
0.535
0.287
0.382
0.983
0.478
0.635
CFMEAS4
0.473
0.348
0.507
0.314
0.401
0.936
0.449
CFMEAS5
0.582
0.418
0.507
0.383
0.501
0.958
0.511
CFRA1
0.154
0.764
0.460
0.607
0.605
0.490
0.919
Volume 15
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Article 2
23
CFRA2
0.154
0.772
0.409
0.578
0.594
0.440
0.946
CFRA3
0.155
0.772
0.473
0.612
0.598
0.468
0.953
CFRA4
0.153
0.749
0.443
0.574
0.607
0.502
0.949
CFRA5
0.153
0.788
0.442
0.622
0.565
0.449
0.928
CFRA7
0.155
0.723
0.444
0.616
0.662
0.496
0.890
CFRA8
0.152
0.734
0.438
0.619
0.623
0.469
0.917
Final Baseline Model (Zoho)
Weight
Attitude
Communicability
EOU
Intention
Measurability
Relative advantage
ZFATT1
0.235
0.943
0.105
0.797
0.669
0.017
0.834
ZFATT2
0.227
0.941
0.114
0.765
0.653
-0.013
0.800
ZFATT3
0.231
0.951
0.156
0.731
0.712
0.027
0.818
ZFATT5
0.199
0.867
0.177
0.676
0.555
0.019
0.697
ZFATT7
0.199
0.870
0.179
0.649
0.560
-0.005
0.717
ZFCOM1
0.783
0.176
0.972
0.325
0.221
0.455
0.256
ZFCOM3
0.301
0.065
0.792
0.190
0.087
0.551
0.131
ZFEOU5
0.295
0.720
0.294
0.919
0.619
0.168
0.709
ZFEOU6
0.311
0.791
0.261
0.953
0.609
0.143
0.741
ZFEOU7
0.195
0.540
0.313
0.782
0.328
0.139
0.468
ZFEOU8
0.300
0.765
0.276
0.934
0.588
0.148
0.706
ZFINT1
0.336
0.682
0.197
0.610
0.989
0.171
0.733
ZFINT2
0.337
0.687
0.202
0.610
0.996
0.147
0.739
ZFINT3
0.335
0.691
0.195
0.602
0.993
0.133
0.738
ZFMEAS4
0.487
0.006
0.432
0.137
0.131
0.934
0.106
ZFMEAS5
0.572
0.013
0.546
0.173
0.153
0.953
0.113
ZFRA1
0.165
0.801
0.210
0.687
0.727
0.097
0.940
ZFRA2
0.161
0.789
0.231
0.710
0.703
0.116
0.944
ZFRA3
0.161
0.787
0.242
0.737
0.709
0.131
0.944
ZFRA4
0.159
0.795
0.197
0.712
0.682
0.093
0.937
ZFRA5
0.168
0.831
0.191
0.715
0.730
0.074
0.932
ZFRA7
0.139
0.714
0.239
0.597
0.570
0.078
0.843
ZFRA8
0.139
0.686
0.230
0.569
0.606
0.164
0.844
24
Weight
Calc
attitude
Calc
comm
Calc
EOU
Calc
intention
Calc
measure
Calc
RA
Choice
Zoho
attitude
Zoho
comm
Zoho
EOU
Zoho
intention
Zoho
measure
Zoho
RA
Attitude Comparison Model
CFATT1
0.207
0.952
0.531
0.741
0.503
0.390
0.784
-0.301
0.177
0.070
0.242
-0.011
0.065
0.128
CFATT2
0.207
0.952
0.516
0.758
0.511
0.371
0.787
-0.286
0.188
0.053
0.251
-0.008
0.025
0.124
CFATT3
0.210
0.963
0.502
0.722
0.535
0.368
0.778
-0.301
0.161
0.056
0.212
-0.007
0.033
0.125
CFATT5
0.190
0.874
0.410
0.543
0.521
0.339
0.701
-0.179
0.211
0.070
0.200
0.076
0.015
0.173
CFATT7
0.186
0.852
0.521
0.589
0.503
0.409
0.695
-0.217
0.162
0.028
0.158
-0.012
0.036
0.098
CFCOM1
0.537
0.559
0.941
0.716
0.348
0.415
0.499
-0.192
0.148
0.316
0.312
0.084
0.089
0.112
CFCOM3
0.463
0.354
0.812
0.422
0.175
0.580
0.307
-0.068
0.129
0.376
0.264
0.018
0.265
0.126
CFEOU5
0.255
0.722
0.639
0.948
0.444
0.386
0.647
-0.202
0.194
0.168
0.353
0.075
0.068
0.193
Volume 15
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Article 2
CFEOU6
0.251
0.688
0.594
0.932
0.370
0.330
0.622
-0.205
0.167
0.067
0.318
0.026
-0.017
0.148
CFEOU7
0.244
0.604
0.635
0.905
0.272
0.299
0.535
-0.194
0.151
0.177
0.354
0.034
0.086
0.134
CFEOU8
0.251
0.703
0.663
0.931
0.393
0.356
0.602
-0.215
0.234
0.134
0.347
0.046
0.024
0.182
CFINT1
0.333
0.563
0.337
0.415
0.985
0.483
0.647
-0.068
0.235
0.089
0.256
0.277
0.023
0.261
CFINT2
0.334
0.550
0.315
0.394
0.987
0.460
0.650
-0.095
0.204
0.079
0.224
0.230
0.035
0.238
CFINT3
0.333
0.535
0.288
0.382
0.982
0.479
0.635
-0.055
0.230
0.093
0.257
0.288
0.063
0.287
CFMEAS4
0.494
0.348
0.505
0.314
0.401
0.935
0.449
-0.099
0.077
0.319
0.171
0.086
0.410
0.143
CFMEAS5
0.506
0.418
0.506
0.383
0.501
0.958
0.511
-0.144
0.067
0.286
0.204
0.116
0.342
0.149
CFRA1
0.141
0.764
0.461
0.607
0.605
0.490
0.919
-0.225
0.202
0.104
0.270
0.062
0.125
0.223
CFRA2
0.146
0.773
0.410
0.577
0.594
0.440
0.946
-0.211
0.253
0.081
0.285
0.090
0.076
0.282
CFRA3
0.147
0.772
0.474
0.612
0.598
0.468
0.953
-0.200
0.256
0.083
0.314
0.089
0.030
0.271
CFRA4
0.146
0.749
0.444
0.574
0.607
0.502
0.949
-0.225
0.200
0.051
0.262
0.095
0.089
0.268
CFRA5
0.143
0.788
0.443
0.622
0.565
0.449
0.928
-0.237
0.211
0.017
0.250
0.044
0.029
0.231
CFRA7
0.137
0.724
0.444
0.616
0.662
0.496
0.889
-0.202
0.192
0.088
0.286
0.162
0.085
0.266
CFRA8
0.141
0.734
0.440
0.619
0.623
0.469
0.917
-0.246
0.215
0.063
0.268
0.123
0.087
0.298
ZCFINT1
0.332
-0.283
-0.159
-0.236
-0.069
-0.130
-0.232
0.983
0.574
0.058
0.439
0.607
-0.010
0.529
ZCFINT2
0.333
-0.268
-0.162
-0.201
-0.076
-0.127
-0.226
0.985
0.550
0.072
0.437
0.573
-0.009
0.500
ZCFINT3
0.335
-0.284
-0.164
-0.213
-0.075
-0.130
-0.245
0.993
0.558
0.084
0.438
0.601
-0.002
0.501
ZFATT1
0.206
0.187
0.176
0.222
0.230
0.106
0.248
0.533
0.943
0.107
0.797
0.669
0.018
0.834
ZFATT2
0.206
0.154
0.156
0.226
0.190
0.065
0.197
0.557
0.941
0.117
0.765
0.653
-0.013
0.800
ZFATT3
0.208
0.155
0.138
0.164
0.227
0.069
0.203
0.574
0.951
0.160
0.731
0.711
0.027
0.818
ZFATT5
0.190
0.202
0.121
0.165
0.233
0.053
0.231
0.456
0.867
0.179
0.676
0.555
0.019
0.697
ZFATT7
0.190
0.198
0.127
0.144
0.155
0.046
0.200
0.470
0.871
0.183
0.648
0.560
-0.005
0.717
ZFCOM1
0.559
0.057
0.373
0.149
0.097
0.285
0.076
0.100
0.176
0.979
0.325
0.221
0.455
0.256
ZFCOM3
0.441
0.055
0.285
0.091
0.035
0.327
0.049
-0.036
0.065
0.771
0.190
0.087
0.551
0.131
ZFEOU5
0.256
0.230
0.305
0.315
0.267
0.211
0.276
0.432
0.720
0.296
0.920
0.619
0.168
0.709
ZFEOU6
0.265
0.235
0.299
0.355
0.252
0.187
0.286
0.444
0.791
0.265
0.952
0.609
0.143
0.741
ZFEOU7
0.218
0.132
0.314
0.325
0.055
0.129
0.176
0.297
0.540
0.313
0.783
0.328
0.139
0.468
ZFEOU8
0.260
0.221
0.283
0.341
0.274
0.180
0.309
0.402
0.765
0.279
0.933
0.588
0.148
0.706
ZFINT1
0.332
0.014
0.074
0.053
0.279
0.119
0.107
0.578
0.682
0.200
0.610
0.989
0.171
0.733
ZFINT2
0.335
-0.004
0.064
0.052
0.263
0.106
0.091
0.609
0.686
0.206
0.610
0.996
0.147
0.740
ZFINT3
0.334
0.009
0.062
0.044
0.259
0.097
0.106
0.606
0.691
0.198
0.602
0.993
0.133
0.739
ZFMEAS4
0.495
0.010
0.092
0.002
-0.006
0.331
0.040
-0.023
0.006
0.425
0.137
0.131
0.935
0.106
ZFMEAS5
0.505
0.059
0.223
0.072
0.076
0.405
0.107
0.007
0.013
0.538
0.173
0.153
0.952
0.114
ZFRA1
0.147
0.102
0.104
0.131
0.255
0.148
0.237
0.513
0.801
0.214
0.687
0.727
0.097
0.940
ZFRA2
0.148
0.150
0.150
0.206
0.198
0.153
0.289
0.471
0.789
0.234
0.709
0.703
0.116
0.944
ZFRA3
0.148
0.125
0.156
0.184
0.241
0.180
0.296
0.481
0.787
0.246
0.737
0.709
0.131
0.944
ZFRA4
0.147
0.115
0.118
0.168
0.236
0.137
0.252
0.453
0.794
0.200
0.712
0.682
0.093
0.937
ZFRA5
0.146
0.118
0.146
0.156
0.302
0.149
0.257
0.520
0.830
0.194
0.715
0.730
0.074
0.932
ZFRA7
0.132
0.157
0.098
0.173
0.243
0.092
0.259
0.406
0.714
0.241
0.597
0.570
0.078
0.843
ZFRA8
0.132
0.133
0.054
0.123
0.220
0.122
0.214
0.448
0.686
0.232
0.569
0.606
0.164
0.844
Volume 15
Issue 3
Article 2
25
Expectancy Comparison Model (Perceptual)
26
Weight
Calc
comm
Calc
EOU
Calc
measurability
Calc
RA
Comparison
attitude
Intention
Zoho
Com
Zoho
EOU
Zoho
measurability
Zoho
RA
CFCOM1
0.803
0.970
0.717
0.413
0.499
-0.044
-0.192
0.305
0.312
0.101
0.112
CFCOM3
0.296
0.749
0.423
0.584
0.306
-0.025
-0.068
0.387
0.266
0.279
0.126
CFEOU5
0.264
0.658
0.943
0.381
0.648
-0.025
-0.202
0.162
0.353
0.076
0.192
CFEOU6
0.269
0.615
0.930
0.322
0.622
-0.004
-0.205
0.064
0.319
-0.012
0.147
CFEOU7
0.260
0.650
0.912
0.292
0.534
0.060
-0.194
0.176
0.356
0.094
0.133
CFEOU8
0.284
0.679
0.931
0.353
0.602
0.020
-0.215
0.128
0.347
0.032
0.182
CFMEAS4
0.565
0.475
0.312
0.955
0.448
-0.128
-0.099
0.335
0.172
0.415
0.144
CFMEAS5
0.490
0.482
0.381
0.939
0.512
-0.021
-0.144
0.292
0.204
0.349
0.149
CFRA1
0.157
0.469
0.605
0.487
0.919
-0.001
-0.225
0.108
0.270
0.132
0.223
CFRA2
0.147
0.423
0.575
0.436
0.945
0.003
-0.211
0.079
0.284
0.085
0.281
CFRA3
0.140
0.485
0.610
0.466
0.952
-0.047
-0.200
0.080
0.313
0.036
0.271
CFRA4
0.157
0.453
0.572
0.499
0.949
-0.033
-0.225
0.053
0.260
0.096
0.268
CFRA5
0.165
0.453
0.619
0.441
0.930
0.003
-0.237
0.014
0.249
0.036
0.230
CFRA7
0.140
0.454
0.614
0.489
0.888
0.011
-0.202
0.082
0.284
0.091
0.265
CFRA8
0.171
0.455
0.616
0.461
0.919
0.033
-0.246
0.059
0.266
0.093
0.297
ZCFATT1
0.174
-0.040
0.029
-0.052
0.031
0.891
-0.232
-0.077
-0.177
-0.035
-0.226
ZCFATT2
0.253
-0.060
0.007
-0.093
-0.027
0.946
-0.294
-0.120
-0.273
-0.109
-0.334
ZCFATT3
0.219
-0.042
0.009
-0.071
-0.006
0.947
-0.255
-0.097
-0.224
-0.085
-0.309
ZCFATT5
0.225
-0.038
0.024
-0.067
0.019
0.890
-0.323
-0.081
-0.201
-0.106
-0.287
ZCFATT7
0.221
-0.013
-0.003
-0.087
-0.024
0.898
-0.241
-0.099
-0.259
-0.072
-0.302
ZCFINT1
0.342
-0.170
-0.236
-0.125
-0.234
-0.281
0.982
0.042
0.439
-0.007
0.529
ZCFINT2
0.334
-0.172
-0.201
-0.123
-0.228
-0.299
0.985
0.058
0.438
-0.004
0.500
ZCFINT3
0.338
-0.173
-0.213
-0.127
-0.246
-0.297
0.993
0.070
0.438
0.001
0.501
ZFCOM1
0.713
0.364
0.149
0.286
0.075
-0.093
0.100
0.954
0.326
0.465
0.255
ZFCOM3
0.384
0.260
0.093
0.336
0.048
-0.099
-0.036
0.831
0.191
0.559
0.131
ZFEOU5
0.303
0.304
0.314
0.210
0.275
-0.244
0.432
0.287
0.921
0.169
0.709
ZFEOU6
0.319
0.297
0.355
0.180
0.286
-0.277
0.444
0.254
0.953
0.147
0.741
ZFEOU7
0.206
0.311
0.327
0.135
0.174
-0.160
0.297
0.311
0.786
0.141
0.468
ZFEOU8
0.274
0.285
0.341
0.174
0.308
-0.203
0.402
0.268
0.929
0.152
0.706
ZFMEAS4
0.371
0.074
0.003
0.337
0.040
-0.056
-0.023
0.445
0.137
0.905
0.106
ZFMEAS5
0.683
0.199
0.072
0.411
0.107
-0.105
0.008
0.559
0.173
0.973
0.113
ZFRA1
0.173
0.100
0.130
0.151
0.237
-0.337
0.513
0.202
0.686
0.096
0.941
ZFRA2
0.154
0.150
0.205
0.152
0.289
-0.274
0.471
0.223
0.709
0.114
0.943
ZFRA3
0.160
0.155
0.183
0.176
0.295
-0.299
0.481
0.235
0.735
0.131
0.943
ZFRA4
0.159
0.114
0.167
0.133
0.252
-0.336
0.453
0.192
0.711
0.093
0.937
ZFRA5
0.172
0.145
0.155
0.148
0.257
-0.319
0.520
0.184
0.713
0.076
0.933
ZFRA7
0.137
0.090
0.172
0.094
0.260
-0.269
0.406
0.235
0.597
0.081
0.842
ZFRA8
0.139
0.047
0.121
0.124
0.214
-0.215
0.448
0.225
0.569
0.162
0.844
Volume 15
Issue 3
Article 2
Expectancy Comparison Model (Subtraction)
Weight
Calc
comm
Calc
EOU
Calc
measurability
Calc
RA
Subtraction
attitude
Intention
Zoho
Com
Zoho
EOU
Zoho
measurability
Zoho
RA
CFCOM1
0.745
0.953
0.716
0.416
0.499
-0.303
-0.192
0.329
0.313
0.130
0.112
CFCOM3
0.368
0.789
0.421
0.579
0.307
-0.165
-0.068
0.327
0.266
0.311
0.126
CFEOU5
0.280
0.647
0.946
0.387
0.648
-0.388
-0.202
0.175
0.354
0.094
0.193
CFEOU6
0.284
0.603
0.934
0.331
0.622
-0.392
-0.205
0.072
0.319
0.002
0.148
CFEOU7
0.253
0.641
0.910
0.301
0.535
-0.344
-0.194
0.168
0.357
0.113
0.133
CFEOU8
0.259
0.670
0.927
0.357
0.602
-0.342
-0.215
0.141
0.348
0.055
0.182
CFMEAS4
0.450
0.494
0.312
0.931
0.449
-0.197
-0.099
0.257
0.171
0.417
0.144
CFMEAS5
0.604
0.497
0.382
0.962
0.512
-0.259
-0.144
0.256
0.203
0.359
0.149
CFRA1
0.159
0.465
0.606
0.491
0.919
-0.404
-0.225
0.087
0.270
0.147
0.223
CFRA2
0.146
0.416
0.576
0.440
0.945
-0.370
-0.211
0.081
0.284
0.107
0.281
CFRA3
0.145
0.479
0.611
0.469
0.952
-0.371
-0.200
0.086
0.313
0.050
0.271
CFRA4
0.157
0.448
0.573
0.502
0.949
-0.398
-0.225
0.043
0.260
0.114
0.268
CFRA5
0.164
0.447
0.621
0.451
0.930
-0.416
-0.237
0.025
0.248
0.052
0.230
CFRA7
0.152
0.449
0.614
0.497
0.889
-0.396
-0.202
0.099
0.285
0.104
0.265
CFRA8
0.154
0.446
0.617
0.471
0.917
-0.377
-0.245
0.069
0.266
0.107
0.297
DIFFZCFATT1
0.231
-0.271
-0.401
-0.198
-0.402
0.932
0.616
0.072
0.417
-0.042
0.533
DIFFZCFATT2
0.235
-0.280
-0.400
-0.220
-0.431
0.950
0.633
0.083
0.405
-0.034
0.530
DIFFZCFATT3
0.235
-0.283
-0.427
-0.228
-0.428
0.955
0.643
0.097
0.378
-0.017
0.514
DIFFZCFATT5
0.192
-0.194
-0.256
-0.222
-0.325
0.816
0.500
0.101
0.419
-0.041
0.442
DIFFZCFATT7
0.213
-0.255
-0.280
-0.242
-0.300
0.848
0.544
0.163
0.443
-0.024
0.546
ZCFINT1
0.346
-0.163
-0.236
-0.131
-0.233
0.663
0.983
0.099
0.437
0.002
0.529
ZCFINT2
0.330
-0.166
-0.200
-0.128
-0.227
0.625
0.985
0.106
0.436
0.009
0.500
ZCFINT3
0.338
-0.168
-0.212
-0.131
-0.245
0.646
0.993
0.116
0.437
0.008
0.501
ZFCOM1
1.054
0.370
0.149
0.284
0.075
0.109
0.100
0.998
0.326
0.479
0.255
ZFCOM3
-0.090
0.276
0.092
0.325
0.049
0.017
-0.036
0.571
0.192
0.565
0.131
ZFEOU5
0.288
0.305
0.314
0.212
0.276
0.403
0.432
0.306
0.916
0.166
0.709
ZFEOU6
0.307
0.298
0.355
0.188
0.286
0.446
0.444
0.281
0.951
0.154
0.741
ZFEOU7
0.216
0.314
0.326
0.128
0.175
0.329
0.297
0.303
0.792
0.145
0.468
ZFEOU8
0.293
0.285
0.340
0.182
0.309
0.447
0.402
0.295
0.932
0.159
0.706
ZFMEAS4
0.042
0.085
0.003
0.329
0.040
-0.006
-0.023
0.348
0.137
0.798
0.106
ZFMEAS5
0.967
0.215
0.072
0.404
0.107
-0.035
0.007
0.455
0.173
1.000
0.113
ZFRA1
0.170
0.102
0.130
0.147
0.237
0.560
0.513
0.235
0.684
0.090
0.940
ZFRA2
0.157
0.150
0.206
0.153
0.288
0.520
0.471
0.253
0.707
0.108
0.943
ZFRA3
0.160
0.156
0.183
0.181
0.295
0.532
0.481
0.263
0.734
0.127
0.943
ZFRA4
0.158
0.117
0.167
0.138
0.252
0.543
0.454
0.210
0.709
0.090
0.937
ZFRA5
0.173
0.146
0.154
0.149
0.257
0.573
0.520
0.209
0.712
0.080
0.933
ZFRA7
0.136
0.095
0.171
0.092
0.259
0.453
0.406
0.245
0.595
0.090
0.842
ZFRA8
0.140
0.051
0.120
0.121
0.214
0.442
0.448
0.238
0.567
0.153
0.844
Belief Comparison Model (Perceptual)
ZCFATT1
Weight
Attitude
Comm
EOU
Intention
Measurability
RA
0.161
0.885
-0.139
-0.221
-0.232
-0.089
-0.202
Volume 15
Issue 3
Article 2
27
ZCFATT2
0.236
0.940
-0.196
-0.303
-0.294
-0.138
-0.317
ZCFATT3
0.215
0.943
-0.194
-0.289
-0.255
-0.159
-0.251
ZCFATT5
0.257
0.898
-0.201
-0.344
-0.323
-0.175
-0.326
ZCFATT7
0.224
0.904
-0.193
-0.320
-0.241
-0.103
-0.277
ZCFCOM1
0.667
-0.244
0.950
0.471
0.424
0.528
0.439
ZCFCOM3
0.423
-0.099
0.869
0.363
0.297
0.660
0.334
ZCFEOU5
0.273
-0.323
0.402
0.954
0.850
0.366
0.875
ZCFEOU6
0.268
-0.319
0.429
0.964
0.836
0.353
0.890
ZCFEOU7
0.248
-0.303
0.489
0.928
0.769
0.411
0.827
ZCFEOU8
0.263
-0.305
0.462
0.953
0.822
0.351
0.842
ZCFINT1
0.339
-0.282
0.387
0.869
0.982
0.347
0.832
ZCFINT2
0.338
-0.301
0.410
0.844
0.985
0.353
0.837
ZCFINT3
0.337
-0.298
0.411
0.842
0.993
0.360
0.829
ZCFMEAS4
0.539
-0.129
0.592
0.382
0.342
0.936
0.355
ZCFMEAS5
0.531
-0.149
0.588
0.345
0.328
0.934
0.328
ZCFRA1
0.155
-0.331
0.369
0.824
0.814
0.323
0.948
ZCFRA2
0.155
-0.307
0.418
0.877
0.820
0.366
0.965
ZCFRA3
0.153
-0.275
0.416
0.860
0.819
0.337
0.959
ZCFRA4
0.150
-0.282
0.399
0.864
0.801
0.324
0.959
ZCFRA5
0.151
-0.289
0.423
0.868
0.801
0.329
0.958
ZCFRA7
0.144
-0.265
0.426
0.856
0.767
0.420
0.898
ZCFRA8
0.146
-0.283
0.433
0.858
0.778
0.332
0.954
Belief Comparison Model (Subtraction)
28
Volume 15
Weight
Attitude
Comm
EOU
Intention
Measurability
RA
DIFFZCFATT1
0.233
0.933
0.265
0.739
0.597
0.141
0.778
DIFFZCFATT2
0.236
0.951
0.281
0.726
0.613
0.160
0.790
DIFFZCFATT3
0.236
0.955
0.306
0.720
0.617
0.172
0.777
DIFFZCFATT5
0.186
0.814
0.224
0.613
0.454
0.159
0.597
DIFFZCFATT7
0.215
0.847
0.313
0.651
0.558
0.150
0.706
DIFFZCFCOM1
0.714
0.334
0.938
0.398
0.254
0.393
0.395
DIFFZCFCOM3
0.413
0.170
0.800
0.205
0.175
0.455
0.216
DIFFZCFEOU5
0.298
0.687
0.325
0.900
0.470
0.182
0.614
DIFFZCFEOU6
0.302
0.723
0.324
0.919
0.434
0.195
0.628
DIFFZCFEOU7
0.248
0.585
0.291
0.786
0.370
0.118
0.471
DIFFZCFEOU8
0.289
0.689
0.353
0.898
0.417
0.194
0.563
DIFFZCFINT1
0.348
0.635
0.259
0.490
0.983
0.363
0.687
DIFFZCFINT2
0.340
0.622
0.256
0.474
0.988
0.314
0.688
DIFFZCFINT3
0.328
0.612
0.233
0.461
0.982
0.311
0.652
DIFFZCFMEAS4
0.538
0.143
0.410
0.173
0.310
0.914
0.246
DIFFZCFMEAS5
0.553
0.173
0.448
0.190
0.305
0.919
0.238
DIFFZCFRA1
0.231
0.760
0.320
0.605
0.632
0.209
0.919
DIFFZCFRA2
0.235
0.752
0.300
0.586
0.668
0.211
0.925
DIFFZCFRA3
0.236
0.768
0.362
0.626
0.652
0.272
0.935
DIFFZCFRA7
0.204
0.687
0.318
0.537
0.536
0.221
0.835
DIFFZCFRA8
0.208
0.673
0.369
0.566
0.583
0.271
0.863
Issue 3
Article 2
ABOUT THE AUTHORS
Andrew Schwarz, PhD, the Milton J. Womack Developing Scholar is an Associate
Professor of Information Systems in the E. J. Ourso College of Business at Louisiana
State University. His research interests focus on the adoption of new technology, ITbusiness alignment, and IT outsourcing. His previous work has been published in MIS
Quarterly, Information Systems Research, the Journal of AIS, the European Journal of
Information Systems, and others. Prior to his career in academia, he was a consultant in
the market research industry, working on projects for Fortune 100 companies on topics
related to market segmentation, brand imaging, and brand awareness.
Colleen Schwarz, PhD, is an Assistant Professor and the Home Bank/BORSF Endowed
Professor in Management at the University of Louisiana. Her research interests include IT
outsourcing, adoption of new technology, and creativity with IT. Her work has been
published in the Journal of Information Technology, European Journal of Information
Systems, Information & Management, Communications of the AIS, Journal of
Organizational and End User Computing, Journal of Management History, and Small
Group Research. Prior to her career in academia, she was an IT professional, working as
a project manager for an IT development firm, an IT analyst for a Fortune 50 oil and gas
company, and organizational development in an IT department.
Copyright © 2014 by the Association for Information Systems. Permission to make digital or hard copies of all or part
of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for
profit or commercial advantage and that copies bear this notice and full citation on the first page. Copyright for
components of this work owned by others than the Association for Information Systems must be honored.
Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists
requires prior specific permission and/or fee. Request permission to publish from: AIS Administrative Office, P.O.
Box 2712 Atlanta, GA, 30301-2712 Attn: Reprints or via e-mail from [email protected]
Volume 15
Issue 3
Article 2
29
JOURNAL OF INFORMATION TECHNOLOGY THEORY AND APPLICATION
Editors-in-Chief
Marcus Rothenberger
University of Nevada Las Vegas
Mark Srite
University of Wisconsin – Milwaukee
Jan vom Brocke
University of Liechtenstein
Virpi Tuunainen
AIS Vice President for
Publications
Ken Peffers, Founding
Editor, Emeritus Editor-inChief
Rajiv Kishore,
Emeritus Editor-inChief
Tung Bui
Brian L. Dos Santos
Robert Kauffman
Ken Kendall
Ephraim McLean
J. Christopher Westland
Roman Beck
Kevin Crowston
Karlheinz Kautz
Peter Axel Nielsen
Sudha Ram
René Riedl
Timo Saarinen
Murugan Anandarajan
Patrick Chau
Khalil Drira
Peter Green
Peter Kueng
David Yuh Foong Law
Vijay Mookerjee
Georg Peters
Rahul Singh
Issa Traore
Jonathan D. Wareham
Case Western Reserve
University
Governing Board
Lars Mathiassen
University of Nevada Las
Vegas
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AIS President
Senior Advisory Board
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University of Hawaii
Sirkka Jarvenpaa
University of Louisville
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Singapore Management Univ.
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Rutgers University
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Georgia State University
HKUST
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University of Frankfurt
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Syracuse University
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Copenhagen Business School
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Aalborg University
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University of Arizona
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University of Linz
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Aalto University
Editorial Review Board
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Drexel University
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The University of Hong Kong
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LAAS-CNRS, Toulouse
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University of Queensland
Glenn Lowry
Credit Suisse, Zurich
Nirup M. Menon
National Univ of Singapore
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University of Texas at Dallas
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Munich Univ of Appl. Sci.
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U. of N. Carolina,Greensboro
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University of Victoria, BC
Georgia State University
ISSN: 1532-3416
Volume 15
City University of Hong Kong
State University of New York,
Buffalo
JITTA is a Publication of the Association for Information Systems
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Georgia State University
Issue 3
Article 2
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State Univ. of New York, Binghamton
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