TOPCO 崇越論文大賞
論文題目：
A Study on the Determinants of Continuance
Usage of Pervasive BIS
泛用型商業智慧系統的持續使用因素探討
報名編號：
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AI0013
摘要
To cope with the huge data flow in today’s uncertain economic environment, many
organizations have adopted pervasive business intelligence systems (BIS) to provide internal
and external information to stakeholders at all working levels for their decision-making
scenarios. However, the continued pervasive usage of BIS has become a practical challenge.
The aim of this study is to build a model to explain the pervasive BIS user’s continued usage.
In addition to Bhattacherjee’s original continued usage model and Limayem et al.’s model,
we also include the concept of empowerment and causal attribution theory through
individual intrinsic task motivation in the pervasive BIS context.
The model was empirically tested using data from 133 respondents obtained from a
two-stage questionnaire process asking questions designed to illuminate the situation of
continued voluntary pervasive usage of BIS. The results support Bhattacherjee’s original
continuance usage model and demonstrate that habit has a direct effect on pervasive BIS
usage and acts as a moderator between continuance intention and usage. We also found that
psychological empowerment has a direct effect on continuance intention and that causal
attribution acts as a pure moderator between pervasive continuance BIS intention and
continued usage behavior. This result clarifies the relationship between pervasive BIS
continuance intention and continuance usage. Following by our survey, important theoretical
and practical implications of these finding are discussed for researchers, system developers
and managers.
Keywords：Continued usage, Empowerment, Causal Attribution Theory, Habit, Pervasive
BIS
1. Introduction
1.1 Research Background and Motivation
In an era of increasingly large amounts of data, managers must continuously strive to
make the right decisions in a dynamic and uncertain environment. Many organizations have
already adopted enterprise resource planning ERP in their organizational processes. The
business intelligence system (BIS) has become a widely used and powerful tool to facilitate
the user’s decision-making process, and has become a major factor in a company’s judgment
process (Caserio, 2011). According to the annual report prepared by Gartner in 2013, there is
an increasing focus and scaling up to support the analysis of large amounts of data (Gartner,
2013). From 2013 through 2017 (IDC, 2013), the BIS global market is expected to grow at a
compound annual growth rate (CAGR) of 9.7%. Organizations have started to utilize BIS to
provide prediction, optimization and even more decision-making flexibility at the same time
in order to obtain competitive capabilities (Bergeron et al., 1995; Gartner, 2011; Li, 2011), a
strategy designed to provide integration of internal and external information to
decision-makers.
BIS has been developed and designed to enhance the decision-making process. It can
be used by middle-managers and front-line personnel, as well as senior-managers (Watson &
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Wixom, 2007). BIS functions at all working levels, so employees can use pervasive BIS to
clarify their goal setting and facilitate communication throughout the organization. Every
person can be empowered, to use their own initiative and work process and adopt
pervasive BIS tools. With adoption of pervasive BIS in an organization, individual
contributors can have a greater impact on the organization’s strategic and
operational decision-making capabilities.
Primarily focusing on the pervasive property, pervasive BIS solutions can help
stakeholders make good decisions based on how many employees are willing to continue to
use it. This is different from the mandatory quality of an ERP system (Youngberg et al.,
2009). When more internal BIS usage occurs across departments and business functions, the
BI technology becomes more pervasive and greater process adoption occurs (IDC, 2008). In
the past, the successful adoption of information technology was primarily evaluated based on
the initial acceptance of the theory of reasoned action (TRA), theory of planned behavior
(TPB), and the technology acceptance model (TAM) (Ajzen, 1991; Davis, 1989; Davis et al.,
1989; Limayem et al., 2001; Limayem et al., 2007). Prior studies have focused on using an
intention-based model to determine user behavior with more attention paid to the initial
adoption. Although the initial acceptance of the system is an important step to successful
incorporation of the information system (IS), however, a better understanding of what is
necessary for success, will be noticed after long-term usage. Therefore, others have gone on
to show that the “long-term viability of an IS and its eventual success depend on its
continued use rather than first-time use” (Bhattacherjee, 2001a; Limayem et al., 2007). We
can then look at how to promote the continued usage of pervasive BIS by adopters.
Going beyond past research, we follow Bhattercheree’s IS continuance model that
seeks to determine the discretionary pervasive BIS user’s continued usage based on
expectation-confirmation theory. It is known that the people's response in common situations
is not only governed by their behavior intention, but their actions are also affected by
automatically repeated behavior. Therefore, Limayem et al. extended the model to include
the effect of the unconscious on behavior (Limayem et al., 2007). In addition to the
conscious and unconscious aspects, motivation is at the psychological root explaining human
behavior. In our research, we take the aspect of intrinsic human motivation into
consideration when discussing pervasive continued BIS usage. Within intrinsic motivation,
we include two aspects – empowerment and causal attribution. First, it is known that the
work environment influences employees’ feelings of individual empowerment and leads
users to want to engage in work related tasks or wish to obtain more innovative results (Knol
& Van Linge, 2009), which in turn has an impact on the commitment to reuse pervasive BIS.
Second, In general, user’s perceptions of whether something feels good or not affects how
they will interpret the success or failure of their current efforts. These personal causal
attributions will in turn be expended on activities in the future (B. Weiner, 1985; B. Weiner
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et al., 1987). Hence, in recent studies, causal attributions are considered to contribute to
explaining why user behavior is affected by intrinsic motivation (Karsten, 2002). In this
study, it is assumed that different types of causal attribution will have different effects on
continued BIS usage behaviors.
In this study, pervasive BIS is assumed to be a kind of optional system, which is
different from the mandatory nature of ERP usage (Amoako-Gyampah & Salam, 2004). In
other words, employees have more flexibility in their use of pervasive BIS (Joshi & Pant,
2008) and the extent of continued usage behavior is largely a personal and voluntary choice.
Thus, there is a need to explore what determines whether users will continue to use pervasive
BIS. Hence, the proposed model focuses on what will assist system managers and developers
in planning how to improve BIS continued usage behavior after the organization has gone to
the considerable expense of obtaining organizational decisional competitiveness. In addition,
our conceptual model combines general conscious and unconscious behavioral factors as
well as considers the motivational determination affecting empowerment and causal
attribution, seeking to encompass the core, that is those fundamental factors that specifically
focus on continued pervasive BIS usage. Therefore, the primary objective here is provide a
model that aggregates the conscious, unconscious and intrinsic motivational factors and
provides insight into continued usage of the system that will influence a successful
continuance usage strategy.
2. Literature Review
2.1 Pervasive BIS
The BIS (Business Intelligence System) is comprised of a set of technologies and
processes which, combined together, provides integrated information which top management
can draw upon in decision-making scenarios. BIS can be implemented at the divisional level
and is likely to have an impact on top management by assisting in the collection of internal
and external information. It also assists users in planning, tracking, and analyzing data,
assisting top-management to align their strategies, intelligence, and decision-making skills in
today’s competitive and uncertain environment (Bergeron et al., 1995; Rainer Jr & Watson,
1995). Recent investigation has indicated that BIS has become a widely used and powerful
tool (Gartner, 2013). From 2013 to 2017, the BIS global market is expected to grow at a
compound annual growth rate (CAGR) of 9.7% (IDC, 2013). It appears that BIS will
become one of the key tools in this new information age and the development of new
technologies and service vendors are making it more functional. According to International
Data Corporation (IDC) research regarding BIS, this new wave of technology and process
adoption continues to move on (IDC, 2008). Beside of BIS functions, data/content, and
internal developers are growing. We can see the number of BIS users, but this does not mean
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that the number of “top-managers” is growing. It can be seen that BIS adoption has
“pervaded” every business unit.
When organizations make BIS functionality more pervasively available to all
stakeholders it strengthens the work initiative (IDC, 2008). BIS users are pervasive
throughout the user population based on diversified mechanisms. Non-traditional BIS users
include front-line workers, partners, and others (Markarian et al., 2008). Traditional BIS
users use tools such as summary reports from ad hoc inquiries, on-line analytical processing
(OLAP) and data warehousing. The new growth of pervasive BIS technology is based on
using various types of historical and real time data to give more diversified predictive
analysis, alerts, and event monitoring. When BIS becomes more widely used, it allows for
the individual to have a greater impact on organizational objectives in the process of making
the thousands upon thousands of small decisions that comprise their daily work lives
(Markarian et al., 2008). It also gives employees the power to enhance their Key
Performance Indicator (KPI) and to help their organization obtain a competitive advantage.
It can help transform front-line employees within an organization so that they are able to
make the right decision at the right time (Manojlovich, 2005).
As we know, this new growth of BIS technology is based on taking advantage of
historical and real time data to help users make the right decisions. It can be described in a
variety of ways, such as pervasive BIS, ambient BI, democratization of BI and operational
BI (IDC, 2008). In our research, we use the term “pervasive BIS” to express this new
concept.
Information system continuance usage varies with different contexts, rules, and target
users. Pervasive BIS is not mandatory in nature like the usage of an ERP system is
(Youngberg et al., 2009), so the user has more discretion in its application (Wright & Wright,
2002). In pervasive BIS, users have some control over the outcome on their efforts
(Amoako-Gyampah & Salam, 2004), and they have more flexibility (Joshi & Pant, 2008).
Therefore, although pervasive BIS acts to reduce latency between events and provides better
decision-making service, its adoption is also an organizational challenge that must be
overcome in order to influence employees to continue to use it. This is important from a
practical standpoint in a computing environment that is increasingly driven by voluntary
users (Bergeron et al., 1995). Therefore, in this study we follow and modify the continued
usage model as it relates to appropriate utilization of pervasive BIS.
2.2 IS Continuance Model – On the Conscious Side
One of the most common topics in IS research is identification of the determinants of
the individual user’s continued usage of information technology (Bhattacherjee, 2001a;
Limayem et al., 2001; Limayem et al., 2007). The study of continued IS usage has appeared
under a variety of labels in the literature in recent years, including IS continuance
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(Bhattacherjee, 2001a, 2001b; Kim & Malhotra, 2005; Limayem et al., 2007), post-adoptive
IT usage (Jasperson et al., 2005), and continued IS usage behavior (Guinea & Markus, 2009).
In the past, most research on the adoption of information technology has focused on the
theory of reasoned action (TRA), the theory of planned behavior (TPB), and the technology
acceptance model (TAM) (Ajzen, 1991; Davis, 1989; Davis et al., 1989; Kim & Malhotra,
2005; Limayem et al., 2007). All these theories focus on using an intention-based model to
determine user adoptive behavior toward overall IS success. In continued IS usage, the first
adoption stage is not the central issue, but rather post-adoption becomes the key factor for
success.
Research on continued IS usage shows that post-adoption usage is not just an extension
of adoption behavior. Bhattacherjee developed a post-acceptance model of IT continued
usage (2001b) based on expectation-confirmation theory(Oliver, 1980). Following this line
of thought, in our study, we focus on the continued pervasive usage of BIS that gives every
employee the power to enhance their decision making through efficient analysis and
experience. To do this, individuals can obtain information from various sources such as
prepared reports, past experience and so on, as well as having the use of advanced data
processing tools, such as data mining and statistics in their decision making. People receive
useful experience when they use a pervasive tools/functions which in turn increases their
willingness to continue using pervasive BIS. Here, we adopt Bhattacherjee’s model to
discuss personally conscious continued pervasive BIS usage.
Bhattacherjee’s IS continuance model is modeled upon how the conscious human mind
acts and is designed to know the user’s intention towards continued IS usage (Bhattacherjee,
2001b). Although the model is well-built, the linkage between rational decisions and action
control is still under discussion. The implication is that the relationship between stimulus and
action is not fully developed (Kim & Malhotra, 2005; Limayem et al., 2007). Clearly, an
advance in conception shows that we need to be aware of the relation of the unconscious side
towards the continued usage of IS.
2.3 IS Continuance Model – On the Unconscious Side
Limayem et al. (2007) built a model that posited that habit plays a role between the
conscious and unconscious in the continued use of IS. People’s response to their task
environment is not only determined by personal intention. Frequently, their actions and
behavior are governed by habit and thus become automatic. When habit is incorporated into
the IS continuance model, rational argument ceases to have as much influence on the
individual’s intention to use IS. Habitual IT use is often a repeated behavior that arises
automatically from the unconscious environment.
Limayem et al. (2007) found that personal habit influences continued IS use without
conscious intention and also has an interactive effect, changing the variance between
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intention and IS usage behavior (Limayem et al., 2001; Limayem et al., 2007). According to
Lymayem’s model, habits are constructed and have a direct effect on continued behavior
acting as a moderator between continued intention and continued behavior. First, habits
based on past repeated behavior affect future behavior directly, rather than going through
behavioral intention (Charng et al., 1988). Such as a direct effect implies that habit will
affect continued usage behavior. Second, in traditional continued usage theory the focus has
always been on examining the conscious side to explain IS continued usage behavior, but
habit is considered as related to the unconscious side in Lymayem’s model and has a
moderating effect (suppressor) between IS continuance intention and IS continuance usage.
Modeling it this way means that the stronger the habit, the less the power of intention on the
continued behavior.
As we know, both the conscious and unconscious have an influence on the continued
usage of pervasive BIS. On the unconscious side, a person’s continuous usage can be
attributed to habitual or automatic behavior triggered by their personal task environment.
This is similar to Limayem et al.’s model. We also argue that the continued use of pervasive
BIS often arises due to habitual and automatic behavior.
Due to the fact that pervasive BIS is a kind of discretionary system, it is assumed that
continued use of pervasive BIS will be influenced by a mixture of conscious and
unconscious factors which include satisfaction, continued intention, continued usage and
habit. In addition to the conscious and unconscious, we also consider user motivation.
Motivation is the inner drive to behave or act in a certain manner empowering the user in
their working environment. It can stimulate the desire to use or to continue to use BIS to
attain a goal. In the next two sections, we will discuss the two intrinsic factors motivating
continued pervasive BIS usage.
2.4 Psychological Empowerment
The word “empowerment” comes from the word “empower”, which means to give
people power, or to enable them to do something. Employee empowerment is basically the
transfer of power and authority from top managers to employees. But, empowerment can be
viewed through different theoretical lenses. The different empowerment concepts can be
divided into three approaches (Spreitzer & Doneson, 2005). The first approach is a kind of
community empowerment. It is based on critical social theory and emphasizes that people
use self-reflection and have a basic need to act independently. Most situations can be
described by the concept of the awakening of the consciousness of oppressed groups. This
type of empowerment as a method is associated with feminism, the deterring of community
threats to improve quality of life. It functions within the community. The second approach,
which we call structural empowerment, is comprised of a set of management practices and
manager behaviors reacting to and arising from organization and management theory. This
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approach involves the delegation of power, autonomy, and responsibility to employees in an
organizational structure. In past research, structural empowerment has manifested in six
dimensions (Kanter, 1993; Wagner et al., 2010). The final approach, called psychological
empowerment, arises from social psychological theory. It is based on the individual
viewpoint and considers the psychological state of employees due to the implementation of
empowerment. It is argued that empowerment provides greater individual motivation and
results in greater effectiveness (Conger & Kanungo, 1988). More precisely, it is fair to say
that psychological empowerment is a form of intrinsic motivation to perform the job well, to
act independently (Thomas & Velthouse, 1990). Therefore, in the nontraditional paradigm,
the employees’ perception of the actual work environment will be impacted by their intrinsic
task motivation in the context of pervasive BIS. So, we discuss psychological empowerment
in more detail in the next paragraph.
Psychological empowerment can be elaborated as an intrinsic motivational construct
where the employee has the ability to accomplish their work well in their own mind, instead
of through the managerial practice of the delegation of responsibilities (Conger & Kanungo,
1988; Spreitzer, 1995; Thomas & Velthouse, 1990). Tomas and Velthouse (1990) argued that
psychological empowerment is multifaceted and cannot be captured by a single concept. It
has four components which reflect individual intrinsic task motivations: meaning,
competence, self-determination (which is synonymous with Thomas and Velthouse’s choice),
and impact. Meaning entails the value of the goal to the employees, and it is judged by the
individual’s intrinsic caring about a given task. The second dimension is competence,
meaning that an employee has confidence and is able to perform their job skillfully. The
third dimension is self-determination, which refers to an employee having an individual
perception of autonomy and the feeling of control in the work environment. The fourth
dimension is impact, referring to the level at which people can use their ideas to influence
the organization. Based on these four dimensions and from statistical analyses, Spreitzer
proposed the Psychological Empowerment Scale (PES) to reflect why people feel
empowered (Spreitzer, 1995). In our study, we use the PES to analyze employee’s feelings of
psychological empowerment as a reflection of the individual’s intrinsic motivation..
2.5 Attribution Theory – Causal Attribution
Throughout our life, we always seek to explain the world we live in and the
experiences we undergo. Most people get used to explaining both to ourselves and other
people. When something happens, we usually become an “observer” and try to explain the
“actor’s” or “entity’s” behaviors. In social psychology this process is called the “attribution
process” (Heider, 1958). Attribution theory addresses how people arrive at causal inferences,
what sort of inferences people make, and what the consequences of these inferences are
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(Folkes, 1988). Attribution theory has been applied in many fields, such as consumer
behavior, IT, sales management, and so on. Attribution theory helps to explain the post-initial
outcome decision making process (B. Weiner, 2001). It can also play a central role in
explaining continued IS usage after the initial choice.
In our daily life, we find relationships and reasons to make sense of the world around
us. In particular, we realize that our life actions have consequences. For example, we get a
high grade on our exams, get a promotion at our job, make a lot of money, win a game and
so on. We arrive at some conclusions as to what has caused these consequences regarding
success or failure. In the theory of attribution this is formulated in an achievement context.
Weiner proposed a causal attribution theory meaning that people evaluated the perceived
cause of an outcome that they experienced as positive or negative (B. Weiner, 1985, 2001).
The original concept is based on Heder’s viewpoint (the personal factor versus the
situational factor), but Weiner elaborated on and precisely defined causal attribution
dimensions (Bernard Weiner et al., 1971).
Weiner (1985) incorporated three primary dimensions into the individual’s suspected or
inferred cause. First, the Locus of causality, meaning that the success or failure was caused
by something about one’s internal or outside/external situation. The second dimension,
Stability, is the perceived degree of variability or stability of the internal or external cause in
the future. Third, Controllability refers to the degree that people feel they can make efforts to
improve their own ability, or how much they can be held accountable for the outcome.
3. Research Hypotheses
3.1 Research Hypotheses
3.1.1
Satisfaction, Continuance Intention, and Continued Usage
In Bhattacherjee’s model, satisfaction is a strong predictor of consumers’ intention to
continue using IS services (Bhattacherjee, 2001a, 2001b). As we know, satisfaction is n
important factor of IS success. Several studies have already suggested that user satisfaction
is a factor for successful implementation of IS (Wixom & Todd, 2005; Zhao et al., 2012).
From a cognitive perspective, satisfaction is defined as ex-post evaluation of a user’s
experience, and the user’s experience is evaluated by perceived usefulness and confirmation
in the Bhattacherjee model (Bhattacherjee, 2001a, 2001b). Perceived usefulness and
confirmation constructs are not included in our model because we consider users to be
continually in a pervasive BIS environment. Perceived usefulness and confirmation should
be attributed to satisfaction from the user’s perspective with special focus on the relationship
between satisfaction, continued usage intention, and continued usage behavior.
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In the ECT and Bhattacherjee’s model, satisfaction is the primary motivation for
continued use (Bhattacherjee, 2001a, 2001b; Oliver, 1980). It has already been pointed out in
many empirical studies that satisfaction is an attitude construct that is positively influenced
by continuance intention (Kuo et al., 2009). Users tend to use pervasive BIS more and more
frequently if they are satisfied with their past experience. The relation between the
past-experience construct and post-adoption intention construct is examined. Therefore,
referring to Bhattacherjee’s model we propose the following hypothesis:
H1: User’s satisfaction with pervasive BIS use is positively associated with their
pervasive BIS continuance intention.
According to the TRA, TPB, and TAM models, user behavior can be affected by
intention (Ajzen, 1991; Davis, 1989; Davis et al., 1989). The link between continuance
intention and continued behavior is also clearly implied in IS (Bhattacherjee, 2001b). Thus,
we also argue the continuance intention has an effect on the behavior of the user of pervasive
BIS user.
H2: User’s pervasive BIS continuance intention is positively associated with their
pervasive BIS continued usage.
3.1.2
Habit
Habit reflects a behavioral tendency to repeat responses developed in a stable
supporting context (Ouellette & Wood, 1998). In many disciplines, habits are understood as
automatic responses to specific situations (Orbell et al., 2001), for example: fastening our
seatbelt when we get into the car. These are an individual’s learned responses to some kind
of condition, carried out without conscious thought (Mittal, 1988). Some studies have found
that habit directly affects IT use (Kim & Malhotra, 2005; Lindbladh & Lyttkens, 2002); other
studies have found that habit moderates the impact of intention of IT use (Kim & Malhotra,
2005; Limayem et al., 2007; Lindbladh & Lyttkens, 2002). In particular, Limayem et al.’s
model includes the antecedents of the IS habit and how habit impacts IS continuance. There
is an interactive effect between intention and usage (Limayem et al., 2007).
In IS habit, we want to know if the user tends to perform their usage behavior
automatically. According to the literature, the more often a person performs a behavior, the
more likely it is that the behavior will become a habit (Charng et al., 1988). To sum up, a
habit is repeated as learned without conscious intention in a triggering environment (Ortiz de
& Markus, 2009). Past repeated behavior (used as a substitute for habit), is always one of the
factors directly affecting future behavior, in addition to the effect of the conscious
decision-making process (Charng et al., 1988). Past research in many disciplines has shown
that habit directly affects behavior; for example, in seat belt usage (Mittal, 1988), blood
donations (Charng et al., 1988), and food consumption (Tuorila & Pangborn, 1988).
Therefore, from past results, it can be assumed that the constructs of habit and intention act
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as independent predictors of actual behavior. We assume that the habits of pervasive BIS
users will directly affect their continued usage behavior.
H3: User’s pervasive BIS usage habit is positively associated with their pervasive BIS
continued usage.
It should be noted that although Ajzen (1991) suggests that the user’s intent is the main
factor determining their behavior, habit diminishes the role of conscious thought in our
behavior. This is because when the user repeatedly and satisfactorily performs a particular
behavior it becomes a habit in a stable context, so that cognitive processing is no longer
needed for the task (B. Verplanken et al., 1997). This means that for individuals habitually
carrying out some particular behavior, the predictive power of intention is diminished
(Limayem et al., 2001; Limayem et al., 2007). This does not refute the existence of a
relationship between intention and actual continued behavior (Limayem et al., 2007).
Therefore, our research model follows Lyimayem’s model to posit that habit exerts a
moderating effect on the relationship between the pervasive BIS continued intention and
continued usage.
H4: The effect of pervasive BIS continuance intention on continued usage is
suppressed for higher usage habits than for lower usage habits.
3.1.3
Psychological Empowerment
We examine the users' pervasive BIS behavior in relation to the issue of continuing
usage. In the nontraditional paradigm, the employees’ perception of the actual work
environment will be impacted by their intrinsic task motivation in the context of pervasive
BIS. Intrinsic task motivation is a part of psychological empowerment and focuses more on
the psychological state of the employee who experiences empowerment (Conger & Kanungo,
1988; Spreitzer, 1995). If employees are not empowered, they cannot cope with changes in
the company. Employees that experience psychological empowerment are assumed to feel
four psychological dimensions (meaningful work, competence, self-determination, and
impact) which are conducive towards higher intrinsic task motivation and commitment
(Conger & Kanungo, 1988; Hochwälder, 2007; Laschinger et al., 1997; Pieterse et al., 2010;
Thomas & Velthouse, 1990). Heightened intrinsic task motivation is able to influence the
employee’s behavior on the job (Howell & Avolio, 1993). The adoption of a BIS by an
organization has a well-known effect on organizational performance (Bergeron et al., 1995;
Rainer Jr & Watson, 1995). Therefore, we posit that employee’s psychological empowerment
inspires pervasive BIS continuance intention. Users have the use of intelligence and
competitive information regarding their work initiative. In contrast, when employees have
low psychological empowerment, this actually acts as a hindrance and they do not take the
initiative at work, which leads to less pervasive BIS continuance intention.
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H5: User’s psychological empowerment is positively associated with their pervasive
BIS continuance intention.
On the opposite side, the direct use of pervasive BIS does not mean that employees
experience psychological empowerment. It may be just an organizational policy or working
need that influences their continued usage behavior. However, when employees begin to be
empowered, their intrinsic task motivation is enhanced, which will facilitate their work
behavior on the job (Conger & Kanungo, 1988; Spreitzer, 1995).
Thus, in the context of continued usage of pervasive BIS, employees’ intrinsic task
motivation will raise personal self-efficacy to promote their willingness to use pervasive BIS
for their work initiatives. Therefore, psychological empowerment enhances continued usage
intention and actually makes continued usage more likely. We hypothesize that
psychological empowerment can ameliorate the relationship between pervasive BIS
continuance intention and pervasive BIS continued usage.
H6: Employees’ psychological empowerment moderates the effect of the pervasive
BIS continuance intention on pervasive BIS continued usage. The effect of pervasive BIS
continuance intention on continued usage is stronger when psychological empowerment is
higher. Conversely, the effect of the continuance intention on continued usage is weaker
when psychological empowerment is lower.
3.1.4
Causal Attribution
In past work, causal attribution theory has played an important role in post-behavioral
analysis. Empirical evidence has shown that causal attribution arising from a negative
experience influences the consumer’s post-consumption behavioral intention (Folkes, 1988;
Poon et al., 2004; Tsiros et al., 2004). According to causal attribution theory, individuals
determine their personal preferences based on three dimensions when they do not like the
product/service. In the first case, they will not continue consumption the next time (locus of
causality) (Folkes, 1988; Poon et al., 2004). Second, their inference of the stability of the
reason for the failure of the product/service will influence repurchase intention (stability)
(Folkes, 1988). Third, negative outcomes are affect by perceived controllability, making
them less willing to purchase in the future (controllability) (Tsiros et al., 2004).
There are various causal attribution factors that influence the users’ behavioral patterns
in terms of continued. In our research, we focus on successful causal attribution and explore
how users make causal ascriptions. Then, we consider that users who have the ability and
know-how to use pervasive BIS, will have continued usage intention (locus of causality).
Users who perceive that pervasive BIS is helpful for maintaining stability, demonstrated
improved willingness to continue usage (stability). Finally, the perception that the system is
a controllable mechanism for improving work performance, will influence pervasive BIS
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continued usage (controllability). These three factors trigger particular emotions and
cognitive understandings affecting continued IS usage intention. We assume that causal
attribution will influence users’ continued usage intention of pervasive BIS.
H7: In successful pervasive BIS usage encounters, causal attributions is positively
associated with pervasive BIS continuance intention.
Besides being considered in the main effects of hypothesis 7, it has been pointed out by
various authors that causal attribution is also at work not only in base models but also the
interaction model (Mone & Baker, 1992). The three dimensions of causal attribution theory
can explain the individual’s experience of affective reaction post-adoption (Mone & Baker,
1992; B. Weiner, 2001). In successful encounters, the affective response results from
reaching or exceeding a personal goal. Internal causal attributions will then tend to
encourage the same behavior in the future, whereas external causal attribution often has a
suppressive action on responses (B. Weiner, 2001; B. Weiner et al., 1987). Secondly,
successful performance will lead to similar future behavior, especially under stable
circumstances, as well as leading to higher expectations (B. Weiner, 1985). However, if
causal attributions are perceived as unstable, then future performance will not be expected to
positive and the willingness to carry out the same behavior will be diminished, and vice
versa (Mone & Baker, 1992). Finally, if the reaction can be controlled, future behavior will
be more intensely affected. Thus, in the IS context, if the system achieves results from more
control, the relation between continuance intention and continued behavior will be stronger.
Hence, in our study, we propose the following:
H8: Employees’ causal attribution moderates the effects of pervasive BIS
continuance intention on pervasive BIS continued usage. The effect of continuance
intention on continued usage is stronger with higher internal control, stability, and
controllability causal attributions. Conversely, the effect is weaker with lower internal,
stability, and controllability causal attributions.
4. Research Methodology
4.1 Data Collection and Instrument Development
Respondents in this study actually used pervasive BIS in their work setting. The survey
method is a relatively low cost and efficient method for collecting data (Check & Schutt,
2011). Thus we used a self-administrated questionnaire to collect data for this study.
However, since it is hard to precisely select a suitable sample from a target population, we
used a snowball sampling technique to identify potential subjects for questionnaire sampling
(Biernacki & Waldorf, 1981). Thus, we first invited students from an in-service Masters
Program in Information Management to participate, and then used a snowballing technique
12
to help recruit their coworkers to participate. In addition, to make sure that the respondents
were appropriate for the sample, the first question on the questionnaire was designed to
determine which functions or tools respondents had actually used in their work environment
(questions were drawn from a consensus of three BIS consultants through the Delphi
Technique). If none of the tools of interest were chosen by a specific respondent, the
questionnaire process was stopped and that respondent was not included in our sample
group.
Data collection was carried out in two steps. First, respondents were asked to respond
to a questionnaire to assess the constructs, which included respondent’s satisfaction, habit,
pervasive BIS continuance intention, psychological empowerment, and causal attribution.
The second step was carried out following the procedure used in past studies that suggested
the duration of usage needed to obtain the necessary intensity of usage (Limayem et al., 2007;
Venkatesh et al., 2000). Therefore, seven days after the first stage, an email was sent to the
same respondents to evaluate their continued PBIS usage.
The validation measures for our constructs were developed based on existing scales
drawn from a review of the literature. We measured satisfaction, pervasive BIS continuance
intention, and continued pervasive BIS usage using instruments developed from
Bhattacheriee’s theory (Bhattacherjee, 2001a, 2001b). The concept of habit was adopted
from Limayem et al. (2007). Psychological empowerment was measured with the
Psychological Empowerment Instrument (PEI). This scale included four dimensions:
meaning, competence, self-determination, and impact (Spreitzer, 1995). Causal attribution
was measured by the causal dimension scale (locus of causality, stability, and controllability)
and provided a detailed understanding of the cause of successful pervasive BIS (Russell,
1982). In addition to the causal attribution dimension (9-scale), all measures were rated on a
five-point Likert scale ranging from strongly disagree (1) to strongly agree (5). All measures
were first sent to several experts (one professor and three post-doctoral candidates) for
validity testing who were asked to make suggestions about the wording of the sentences. All
measures were revised by incorporating their feedback.
5. Data Analysis
5.1 Descriptive Statistics
As indicated in Chapter 3, data collection was carried out in two steps. In the first step,
respondents were asked to fill our a questionnaire designed to assess several constructs
which included the respondent’s satisfaction, their habits, PBIS continuance intention,
psychological empowerment, and causal attribution. In the second step, which was carried
out seven days after the first, an email was sent to the same respondents for the purpose of
measuring their continued PBIS usage. In the first round, although 197 responses were
collected, in 32 cases there was no answer given as to what type of pervasive BIS was used.
13
So, a total of 165 useable responses were received in the first round. In the second round of
data collection, 32 respondents did not answer questions about their continued PBIS usage
condition. Therefore, a total of 133 respondents completed both steps of the data collection
process. In the final sample collection process, the 133 valid responses were collected and
analyzed. Sample demographic profiles are shown in Table 1.
Thus, our survey method may have led to a non-response bias because our data set was
collected in two stages. To test whether there is a non-response bias or not for incomplete
responses from those who have only answered the first stage and full two-stage respondents,
we make a comparison of demographic characteristics – gender (p-value=0.519), education
(p-value=0.334), job (p-value=0.834), job-level (p-value=0.702). Chi-square tests reveal no
significant differences in demographic characteristics between these two groups.
In the next stage, two-stage structural equation modeling, as recommended by Anderson and
Gerbing (Anderson & Gerbing, 1988), was applied. The measurement model was first
examined for reliability and validation, followed by examination of the structural model in
the second stage of testing and then postulation of associations.
5.2 Measurement Model
5.2.1
Reliability and Validity
The reliability and validity of the measurement model and research instrument were
tested. We followed the guidelines of Henseler et al. and Chin to verify the reliability,
convergent validity and discriminate validity of the constructs in the model (Chin, 1998;
Chin, 2010; Henseler, 2010). First, it was asked whether the path loading of all items was
significant at the 1% level. As we can see from Table 2, the loading of almost all items was
significant at p<0.001 and exceeded the minimum loading criterion of 0.5. STAB1 was
dropped from the study because the loading criterion was lower than 0.5 meaning that 31
items are included in the following analysis. A synthesized table was produced that included
the Cronbach’s ɑ, composite reliability (CR), average variance extracted (AVE), mean, and
standard deviation to evaluate our first-order construct’s reliability and convergent validity.
In terms of the reliability (see Table 3) the Cronbach’s ɑ and composite reliability (CR) are
used to assess the internal consistency reliability. All constructs nearly met the 0.7 criteria for
Cronbach’s ɑ (controllability = 0.65), and all exceeded the 0.5 criteria for AVE, and were
higher than the 0.8 criteria for composite reliability (Fornell and Larcker, 1981). Thus, we
can say that all of the constructs had good internal consistency and reliability.
Convergent validity refers to the measures of the construct that are theoretically under
the construct and should be related.
The convergent validity of the scale items was
evaluated based on three criteria (Fornell and Larcker, 1981). The loadings of each item in
our research model are shown in Table 2. In order to assess the first criteria of convergent
validity, we observe all loadings from constructs for measures that are higher than 0.5
(Fornell and Larcker, 1981; Chin, 1998). All measures are reasonably high and found to be
14
significant (p < 0.01). Besides the loadings of each item, we also need to evaluate the
convergent validity which was based on the AVE and which had to be above 0.50; the value
of CR had to be 0.70 or more (Fornell and Larcker, 1981; Chin, 1998). In our analysis results,
both AVE and CR attained a satisfactory level.
Table 1 Demographic Profiles of the Respondents
Gender
Male
Freq.
95
%
71.4
Vendor
IBM/SPSS
Freq.
20
%
9.85%
Vendor
SAP
Freq.
24
%
11.82%
Female
38
28.6
Microsoft
55
27.09%
DataSystem
(Taiwan’s Co.)
13
6.40%
Education
Freq.
%
Microstrategy
0
0.00%
Developed by
themselves*
52
25.62%
High School
BA
1
35
0.8%
26.3%
NCR/Teradata
Oracle
5
24
2.46%
11.82%
Others**
Total
10
203
4.93%
100%
Masters
97
72.9%
Job
Manufacturing
Business
Services
Others
Freq.
50
1
76
6
%
37.6%
0.8%
57.1%
4.5%
*Companies that adopted BIS Solution software and developed their own tools at the
same time.
**Others includes: Analyzer (1), Google (1), SAS (1), SonarQube (1), Splunk (2), Tableau
(2), Tibco (1), Weka (1).
Others includes: Government officials (3),
Education (2), Healthcare (1)
Tools
Freq.
%
KPI Flow
26
6.81%
Scorecard
15
3.93%
Dimension
Modeling/Cube
Viewer
19
4.97%
Reporting
Tools/Standard
Reporting/
Report generator
60
15.71%
OLAP/Pivot Table
41
10.73%
Ad-hoc analysis
18
4.71%
Data
Mining/Simulation
Tools
40
10.47%
Statistical Tools
45
11.78%
Intelligent
Mapping
15
4.19%
Visualizing
Interaction
20
5.50%
Digital Dashboard
41
10.99%
Proactive Alerting
38
10.21%
Total
378
100%
Count
%Tools
%Level
Count
%Tools
%Level
Count
%Tools
%Level
Count
%Tools
%Level
Count
%Tools
%Level
Count
%Tools
%Level
Count
%Tools
%Level
Count
%Tools
%Level
Count
%Tools
%Level
Count
%Tools
%Level
Count
%Tools
%Level
Count
%Tools
%Level
Count
%Total
Characteristics
Sales/Clerk
Staff
Middle Manager
Manager
1
4
15.4
6.5
4
26.7
6.5
11
57.9
17.7
30
50
48.4
18
43.9
29
9
50
14.5
16
40
25.8
17
37.8
27.4
6
40
9.7
10
50
16.1
17
41.5
27.4
17
44.7
27.4
62
46.6
15
Job Level
Freq.
62
27
32
13
Job Level
2
3
6
11
23.1
42.3
22.2
34.4
4
5
26.7
33.3
14.8
15.6
5
2
26.3
10.5
18.5
6.3
11
13
18.3
21.7
40.7
40.6
10
9
24.4
22
37
28.1
3
3
16.7
16.7
11.1
9.4
10
10
25
25
37
31.3
10
10
22.2
22.2
37
31.3
4
4
26.7
26.7
14.8
12.5
4
5
20
25
14.8
15.6
10
11
24.4
26.8
37
34.4
7
11
18.4
28.9
25.9
34.4
27
32
20.3
24.1
%
46.6%
20.3%
24.1%
9%
4
5
19.2
41.7
2
13.3
16.7
1
5.3
8.3
6
10
50
4
9.8
33.3
3
16.7
25
4
10
33.3
8
17.8
66.7
1
6.7
8.3
1
5
8.3
3
7.3
25
3
7.9
25
12
9
Total
26
15
19
60
41
18
40
45
15
20
41
38
133
100
Psychological empowerment and causal attribution were modeled by a second-order
construct and evaluated following the guidelines for specifying hierarchical latent variables
provided by Wetzels et al. (Wetzels et al., 2009). As shown in Table 3, we also utilized the
loading, CR and AVE to diagnose the reliability and validity of psychological empowerment.
The results show that the AVE did not exceed the cut-off value of 0.50 proposed by Fornell
and Larcker (Fornell and Larcker, 1981). The CR was acceptable, and the loading of the
first-order constructs was almost significant at 1%. Therefore convergent validity is
established for the these two second-order constructs.
The discriminant validity was tested with two constructs that are not related each other.
The validity of our instrument was verified by extracting the square root of the average
variance of each construct to find whether it was higher than the correlation between it and
the other constructs (Chin et al., 2003; Chin, 2010). As shown in Table 3, all square roots of
the AVE for each construct were greater than the correlation between that construct and other
constructs, which means that our constructs expressed adequate discriminant validity.
5.3 Structural Model
In this step, we examine the path coefficients and the significance in our structural
model using the SmartPLS software to test the strength of each hypothesis and a
bootstrapping method to generate 500 samples from which to determine the significance of
the paths (Chin et al., 2003). The mean-centering approach is used to test the moderating
effects (Hair et al., 2013). The results of structural modelling are shown in Figure 1.
Our model demonstrated good results, with R-squared values ranging from 43% to 60%.
The R-squared value for pervasive BIS continued intention is 60%. with the value for
pervasive BIS continued usage being 43%. The R-squared value was also a criterion for
model prediction. The sample reuse technique is also applied to determine the
cross-validated redundancy of the Q-squared for model prediction. A cross-validated
communality Q-squared represents a measure of how well-observed values are reconstructed
by the model (Esposito et al., 2010). When the Q-squared is larger than zero, it implies that
the model has predictive relevance; when Q-squared is less than zero, it represents a lack of
predictive relevance (Chin, 2010; Esposito et al., 2010). In our research model, it can be seen
that the Q-squared values are both larger than zero, which indicates that the exogenous
constructs used in our research model have predictive relevance.
More detailed results of structural model testing are shown in Figure 1. In addition to
the two second-order constructs shown, all first-order constructs have significant path
coefficients. In our model, good results are obtained for most of the hypotheses. Hypothesis
1 predicts a positive relationship between satisfaction and pervasive continuance intention.
The results indicate that the Hypothesis 1 is significantly supported (β=0.734, p<0.001).
16
Table 2 Outer Model Loadings and Cross Loadings
1. Satisfaction
SAT1
SAT2
SAT3
2.Continuance Intention
INT1
INT2
INT3
3. Continued Usage
USE1
USE2
4. Habit
HAB1
HAB2
HAB3
5. Meaning
MEA1
MEA2
MEA3
6. Compentence
COM1
COM2
COM3
7. Self-determination
SDE1
SDE2
SDE3
8. Impact
IMP1
IMP2
IMP3
9. Locus of causality
LOC1
LOC2
LOC3
10. Stability
*SATB1
STAB2
STAB3
11. Controllability
CTRL1
CTRL2
CTRL3
1
2
3
4
5
6
7
8
9
10
11
0.88
0.82
0.80
0.66
0.55
0.66
0.41
0.29
0.43
0.50
0.39
0.43
0.26
0.29
0.41
0.06
0.09
0.00
0.03
0.04
0.01
0.11
0.09
0.03
0.46
0.43
0.27
0.02
0.03
0.04
0.16
0.08
0.17
0.74
0.69
0.63
0.93
0.92
0.87
0.53
0.48
0.48
0.52
0.49
0.53
0.42
0.40
0.30
0.13
0.13
0.14
0.10
0.20
0.10
0.07
0.14
0.17
0.35
0.27
0.29
0.06
0.10
0.14
0.17
0.17
0.11
0.41
0.43
0.51
0.49
0.92
0.92
0.37
0.43
0.15
0.18
0.13
0.13
0.04
0.06
0.14
0.01
0.13
0.11
0.14
0.08
0.10
0.15
0.55
0.41
0.44
0.61
0.44
0.45
0.35
0.34
0.45
0.81
0.88
0.92
0.18
0.22
0.18
0.12
0.13
0.12
0.04
0.12
0.07
0.04
0.05
0.05
0.25
0.17
0.13
0.22
0.01
0.12
0.08
0.08
0.05
0.48
0.25
0.29
0.46
0.25
0.36
0.26
0.04
0.17
0.33
0.13
0.14
0.78
0.87
0.88
0.20
0.15
0.27
0.22
0.42
0.37
0.13
0.25
0.21
0.07
0.01
0.08
0.05
0.02
0.06
0.09
0.03
0.09
0.10
0.03
0.01
0.25
0.11
0.03
0.18
0.12
0.09
0.18
0.14
0.05
0.24
0.23
0.20
0.92
0.93
0.89
0.50
0.53
0.40
0.27
0.26
0.24
0.09
0.06
0.14
0.05
0.03
0.05
0.08
0.06
0.03
0.06
0.01
0.01
0.14
0.12
0.14
0.07
0.07
0.01
0.07
0.06
0.10
0.39
0.34
0.37
0.52
0.44
0.45
0.91
0.90
0.89
0.35
0.34
0.45
0.04
0.03
0.06
0.04
0.03
0.03
0.07
0.10
0.03
0.06
0.07
0.13
0.18
0.12
0.09
0.09
0.10
0.03
0.02
0.05
0.05
0.27
0.22
0.16
0.22
0.30
0.26
0.42
0.41
0.35
0.91
0.94
0.93
0.21
0.30
0.19
0.02
0.07
0.04
0.01
0.06
0.02
0.37
0.27
0.45
0.26
0.24
0.32
0.09
0.14
0.11
0.17
0.07
0.22
0.06
0.03
0.04
0.01
0.10
0.04
0.05
0.05
0.02
0.14
0.21
0.28
0.89
0.62
0.88
0.16
0.08
0.05
0.19
0.03
0.29
0.26
0.08
0.02
0.28
0.05
0.14
0.23
0.11
0.10
0.22
0.14
0.10
0.07
0.04
0.01
0.07
0.02
0.10
0.02
0.02
0.01
0.06
0.09
0.00
0.27
0.09
0.05
0.09
0.87
0.89
0.05
0.21
0.30
0.18
0.07
0.16
0.14
0.10
0.16
0.05
0.17
0.09
0.03
0.04
0.16
0.12
0.02
0.12
0.10
0.05
0.05
0.12
0.01
0.02
0.11
0.08
0.16
0.22
0.22
0.08
0.33
0.27
0.05
0.82
0.85
0.64
Note:


SAT = satisfaction; INT = pervasive BIS continuance intention; USE = pervasive BIS continued usage; HAB =
habit; MEA = meaning; COM = competence; SDE = self-determination; IMP = impact; LOC = locus of causality;
STAB = stability; CTRL = controllability
*STAB1 were dropped due to lower loading for further analysis.
17
Table 3 Descriptive Statistics for Constructs
SAT
INT
USE
HAB
MEA
COM
SDE
IMP
LOC
STAB
CTRL
Second-order
Construct
Items
3
3
2
3
3
3
3
3
3
2
3
CR
α
0.77
0.89
0.81
0.84
0.80
0.90
0.88
0.92
0.73
0.71
0.65
CR.
0.87
0.93
0.91
0.90
0.88
0.94
0.93
0.95
0.84
0.87
0.80
AVE
AVE
0.70
0.83
0.84
0.76
0.71
0.83
0.81
0.86
0.65
0.78
0.59
First-order
constructs
Meaning
Psychological
Empowerment
0.89
0.43
Competence
Selfdetermination
Mean
3.86
3.93
3.47
3.60
4.0
4.01
3.93
3.23
4.24
4.98
4.50
SAT
(0.84)
0.75
0.46
0.53
0.39
0.05
0.02
0.09
0.46
0.03
0.17
Loadings
0.611***
(t=5.873)
0.735***
(t=10.872)
0.846***
(t=25.344)
INT
USE
HAB
MEA
COM
SDE
IMP
LOC
STAB
CTRL
(0.91)
0.55
0.56
0.41
0.14
0.15
0.14
0.34
0.11
0.16
(0.91)
0.44
0.18
0.14
0.06
0.08
0.13
0.12
0.13
(0.87)
0.22
0.14
0.08
0.01
0.20
0.13
0.08
(0.84)
0.25
0.41
0.23
0.01
0.02
0.08
(0.91)
0.53
0.28
0.04
0.05
0.06
(0.90)
0.42
0.05
0.02
0.05
(0.93)
0.26
0.05
0.03
(0.81)
0.08
0.24
(0.88)
0.29
(0.77)
R2
Second-order
Construct
CR
AVE
0.373
0.540
Causal
Attribution
0.716
0.60
0.32
First-order
constructs
Loadings
Locus of
Causality
0.701***
(t=5.453)
0.544**
(t=2.991)
0.785***
(t=8.672)
Stability
Controllability
R2
0.492
0.296
0.616
NOTE:
 Diagonal elements are the square roots of average variance extracted (AVE)
 α=Cronbach’sα; SAT = satisfaction; INT = pervasive BIS continuance intention; USE = pervasive BIS continued usage; HAB = habit; MEA = meaning; COM
= competence; SDE = self-determination; IMP = impact; LOC = locus of causality; STAB = stability; CTRL = controllability
18
Figure 1 Research Model
Hypothesis 2 predicts a positive relationship between pervasive BIS continuance
intention and pervasive BIS continued usage. The results indicate significant support
(β=0.396, p<0.001). Then, Hypothesis 3 predicts a positive relation between habit and
pervasive BIS continued usage. The results show that this path is significant (β=0.179,
p<0.05). Next, although the results indicate a significant moderating effect for Hypothesis 4,
the path coefficient shows the opposite (β=0.240, p<0.05). A positive relation is found
between psychological empowerment and pervasive BIS continued intention for Hypothesis
5 (β=0.195, p<0.001), but the result indicates that this is an insignificant moderating effect.
Neither Hypothesis 6 nor Hypothesis 7 are supported. Finally, Hypothesis 8 is supported,
since causal attribution has a significant moderating effect between pervasive BIS continued
intention and usage.
6. Data Analysis
6.1 Satisfaction, Continuance Intention and Usage
Obviously, the results obtained for the first two hypotheses are significant, H1 – user
satisfaction affects pervasive BIS continuance intention; H2 – user’s pervasive BIS
continuance intention affects continued usage. However, the relationship among these three
19
constructs is not fully developed (Kim & Malhotra, 2005; Limayem et al., 2007), but our
results show strong support for these two hypotheses which are also strongly supported by
ECM and Bhattacherjee’s IS continuance model (Bhattacherjee, 2001a, 2001b; Oliver, 1980).
Much existing IS continuance research centers around these two hypotheses. The results for
these two hypotheses are not only consistent with recent post-adoption studies (Roca et al.,
2006), but also are still on the centric of the conscious side of continuance usage behaivor.
More importantly, IS continuance may be considered the situation of current IS. This
study found that an environment of pervasive BIS usage still follows the results of
Bhattacherjee’s IS continuance model. This means that if developers or managers want
employees to continue pervasive BIS usage, it first needs to satisfy employee needs,
regardless of the data quality, BIS quality, service quality or other issues related to user
satisfaction. While we find significant support for H1 and H2 in our study results, a
systematic way to demonstrate the value of continued IS usage is a basic and indispensable
requirement in the organizational environment.
6.2 Effect of Habit on Pervasive Continuance Usage
In the second part of hypothesis testing, we examine the effect of habit on pervasive
continuance usage (H3). We also assume habit to have a suppressive moderating effect
between pervasive BIS continuance intention and continued usage (H4). On the unconscious
side, related to the habit of pervasive BIS usage, Hypothesis 3 shows significant results. This
indicates that habit is also an important antecedent for continued usage of pervasive BIS.
The findings are compatible with Lymayem et al.’s model (Limayem et al., 2001; Limayem
et al., 2007). The habits built up by the employee have a positive influence on continued
usage. Therefore, in this study, the employee’s pervasive BIS usage habit reflects a
behavioral tendency in the system context. Managers and system developers must cultivate
usage habits to attract people to use pervasive BIS unconsciously.
In relation to the other hypothesis about habit examined in our study, although Figure 1
shows a significant result for Hypothesis 4, the path coefficient expresses the opposite result.
The result indicates that the user’s habit is a positive moderator between pervasive BIS
continuance intention and continued usage, meaning that once the user’s habits have been
build up, although they belong to the unconscious side, they will still affect the relationship
between pervasive BIS continuance intention and continued usage. In the original
Hypothesis 4, we follow the conclusions reached in the literature to argue that habit
diminishes the role of the conscious mind, that the relationship between continuance
intention and continued usage is suppressed by habit (Limayem et al., 2001; Limayem et al.,
2007). As illustrated in our result, when the habit is strong, regardless of whether pervasive
BIS continuance intention is low or high, continued usage still remains at a low level. That is
because when individuals have a habit of continued usage behavior, habit diminishes the role
20
of conscious thought, thereby generating lower levels of continuous usage compared with
weaker continuance usage habits. In our study results, the user’s habit is a positive
moderator between pervasive BIS continuance intention and continued usage.
In many other studies it is stated that the habit positively affects intention and
moderates intention and continuance behavior (H. Aarts et al., 1997; B. Verplanken et al.,
1997; V. Verplanken & Aarts, 1999). This corresponds to the result obtained in our research.
Following the past studies, in addition to repeating a behavior, habit is also developed by the
systematic experience of the task consequences, therefore habit becomes intentional in the
sense of being goal directed (H Aarts & Dijksterhuis, 2000) and this goal directed automatic
behavior is mentally represented. Therefore, for Hypothesis 4, when the user has the habit of
continued usage of pervasive BIS, it will accelerate the relationship between continuance
intention and continued usage. However, since pervasive BIS is more complicated to use
than an email system or media player software, for example, even users who have the habit
of using pervasive BIS still need to think about how or what tools/functions they need to
utilize. The result is that habit will trigger the relationship between continuance intention and
continued usage.
6.3 Psychological Empowerment and Pervasive Continuance Usage
As expected, psychological empowerment has a significant and positive effect on
pervasive BIS continuance intention (H5). In the context of pervasive BIS, when
employees perceive intrinsic task motivation, they will be willing to use pervasive
BIS because they experience psychological empowerment. There are four dimensions
to this - work meaning, competence, self-determination, and impact - which are
conducive towards higher intrinsic motivation and commitment (Hochwälder, 2007;
Laschinger et al., 1997; Pieterse et al., 2010). When psychological empowerment is
conducive to higher intrinsic motivation, it will also influence the employee’s
behavior on the job (Howell & Avolio, 1993; Pieterse et al., 2010). In contrast,
pervasive BIS users who are not empowered, cannot cope with the dynamic pervasive
BIS context and, in this situation, will not have the intention to continue to use it.
As shown in Figure 1, the result for Hypothesis 6 is insignificant. Psychological
empowerment does not have a moderating effect between pervasive continuance
intention and continued usage (path coefficient=0.292, t-value= 1.512). Although past
studies indicate that psychological empowerment does have a moderating effect
between conscious factors and behavior (Abdelrazek et al., 2010), findings supporting
the argument are still vague. One plausible explanation is that even if psychological
empowerment increases personal self-efficacy, this can transfer the user’s focus to
other things that can solve their task problems. Thus, it does not have a significantly
moderating effect in our research findings. In fact, despite the fact that the moderating
21
effect is not significant, our analysis still shows that psychological empowerment has
a direct effect on pervasive continuance intention.
6.4 Causal Attribution and Pervasive Continuance Usage
Significant results are not obtained for causal attribution (Hypothesis 7) in our study. In
the original hypothesis, we argued that causal attribution would have a direct effect on
pervasive continuance intention because of the user’s assumption of their own internal ability,
and that IS performance is stable and controllable, depending on successful causal attribution.
However, the results show this to be insignificant. This can be explained by users being
willing to continue to use BIS because they are satisfied with it or feel empowered. They are
not focusing on the success of their last use. Although successful results are important,
perceived satisfaction or perceived psychological empowerment makes users more willing to
continue using pervasive BIS, mitigating causal attribution based on their last successful
experience.
The last result obtained in this study shows causal attribution to have a positive
moderating effect between pervasive continuance intention and usage. As we can see in the
results for H7 and H8, causal attribution does not have a significant effect on continuance
intention, but, it is interesting that it does show a moderating effect between pervasive BIS
continuance intention and usage. Causal attribution is defined as a pure moderator in our
study. Accordingly, a pure moderator variable is a variable that interacts with the predictor to
modify the form of the relationship, but is not related either to the predictor or the criterion
(Sharma et al., 1981). It is implied that causal attribution ameliorates the relationship,
because the user attributes the success of the experience as due to themselves, feeling
pervasive BIS is stable and controllable.
7. Implications and Conclusions
The goal of this study was to develop and explore a model that shows employees’
continued usage of pervasive BIS. Toward that goal, besides examination of the conscious
and unconscious side, we also consider psychological empowerment and causal attribution in
the model. The theoretical implications, suggestions for future research and practical
applications and limitations are discussed next.
7.1 Theoretical Implications
The results suggest that most of the expectations formulated based on Bhattacherjee’s
original IS continued usage model are met. Results validated that satisfaction is still a strong
predictor of continued usage intention. Previous studies have mostly focused on explaining
continuance intention only (Legris et al., 2003). We used a two-stage process to collect
limited data for examination of the relationship between continuance intention and continued
22
usage. In our study, it is shown that people’s behavior is not just influenced by their intention;
the relationship is more complex than previously thought (Ajzen, 1991; Limayem et al.,
2007). Thus, we move further to clarify this relationship, using a two-stage process to avoid
common method variance and provide further actual evidence of behavior evidence. We find
the relationship between continuance intention and usage to be influenced by other factors.
We adapt Limayem et al’s perspective to include the influence of the unconscious mind in
the formation of the habit construct. We also consider another viewpoint as the intrinsic
motivation for continued usage, including psychological empowerment and causal
attribution. Due to the fact that we consider these different factors (habit, psychological
empowerment, causal attribution), we get clearer evidence demonstrating the nature of the
relationship between continuance intention and actual behavior. This helps us to understand
that the relationship between intention and usage is not just a simple causal relationship.
The antecedent (on the unconscious side) of continued usage, is tested. Analysis of the
collected data shows that habit has a significant result. This is consistent with previous
studies (Limayem et al., 2001; Limayem et al., 2007), and means that past repeated behavior
does have a direct effect on the user’s future behavior, rather than affecting behavior by
continuance intention. Habit is the main driver of continued usage. Such a direct effect
would mean that habit automatically has an effect on the behavior and a relation beside the
conscious decision-process processes (Limayem et al., 2007).
Finally, we find that there is a positive moderating effect on the relationship between
continuance intention and continued usage behavior. In other words, habit does not suppress
conscious intentions for continued pervasive BIS usage. It can thus be assumed that
employees have to consciously think about BIS tools and how to use them to complete their
job tasks even when pervasive BIS has already become a habit. This is different from the
safety belt of suppressed conscious intention assumed to be at work in habitual usage. This
expression shows an opposite result to that shown in past studies and indicates that the
information system category is still a challenge in the area of continued usage.
In order to think about the influence of individual intrinsic motivation on continued
pervasive BIS usage, we also consider psychological empowerment and causal attribution in
our research model. In the beginning, evidence was found that psychological empowerment
has a positive effect on continuance usage intention. A majority of previous studies has
shown the same results (Howell & Avolio, 1993; Pieterse et al., 2010). It is argued that
psychological empowerment becomes an intrinsic motivation that influences intention. In
addition, causal attribution has a significant moderating effect between continuance intention
and continuance usage behavior. However, one interesting result obtained in our research
model is that causal attribution is found to be a pure moderator. Although causal attribution
does not have a direct effect on continuance intention, it still has an influence between
intention and behavior, based on the last successful experienced ascription.
23
7.2 Managerial Implications
This study examined several relationships. Implications from our findings could be of
value for developers, managers, and other stakeholders interested in stimulating continued
usage of pervasive BIS. First, we followed Bhattacherjee’s model to show that the company
should still pay attention to the quality of pervasive BIS. This is fundamental to continuance
and should push the company to pay attention to quality. In other words, the user’s
satisfaction is important in order to increase continued use intention. Satisfaction arises from
providing useful functions, a fast and stable system, and good data and service quality.
On the other hand, the unconscious side also needs to be considered, because habit is
found to have a positive effect on continued usage behavior and a moderating effect between
continuance intention and behavior. We argue that continuing pervasive BIS usage may be
far more automatic that previously assumed. In the organization, pervasive BIS continued
usage can be made habitual by using proficient tools that become part of a familiar
environment. Therefore, we suggest that employees be trained to use just one kind of tool at
a time. The training program can be arranged to familiarize employees with the tool’s usage
enough to build a habit. When the behavior becomes habitual, it becomes more efficient and
intuitive to the user. Next, for system upgrades, the system can be designed to use a similar
function interface, or process to the old system, or even alter the system to transition into a
new design to let users get used to the new system more easily.
Besides the influence of the individual’s conscious and unconscious sides, the
organization needs to facilitate personal psychological empowerment, which impacts
intrinsic motivation and can influence an individual. Managers and developers should
recognize how to practice psychological empowerment. For example, perhaps they could set
work goals together with employees to make work more meaningful and discuss whether
their job skills fit the pervasive BIS tools to help employees with self-determination of
system usage. They could even welcome employees to discuss pervasive BIS functions
during the development process. This study suggests that managers can stimulate
psychological empowerment based upon sub-factors such as meaning, competence,
self-determination and impact. Furthermore, this can increase the employee’s confidence to
deal with different contingencies in the pervasive BIS usage environment.
So, simply stated, organizations should give greater transparency and provide different
resources. Managers should foster psychological empowerment consciously, to strengthen
intrinsic motivation among employees. By these means they can show employeess their
value and increase their self-efficacy. Employees will feel themselves more empowered
when they believe that what they do actually matters. When dealing with problems with
pervasive BIS usage, they will be able to be more pro-active, using their own knowledge and
skills to solve problems with efficiency, flexibility, and decisiveness. Through these benefits,
and through individual intrinsic motivation employees can experience psychological
empowerment to make their own choice to continue using pervasive BIS.
24
The last point of note, is that causal attribution acts as a pure moderator between
continuance intention and continued usage. Causal attribution does not have a direct effect
on continuance intention, but has a quite subtle influence on continuance intention and
behavior based on the last successful experience. So, we stress that the feeling of success in
the user’s mind is one of the factors triggering continued usage of pervasive BIS. Therefore,
managers and developers need to monitor users’ perceptions about whether they can control
pervasive BIS functions/tools well or not, and what they feel about the consistency of the
results and content of reports. During implementation, the learning process should allow
employees to feel they have the ability to continue to use pervasive BIS. In general,
providing a high level of pervasive BIS that is stable, easy to control to aid performance, that
lets the user have the mindset that they can use the system well, should be the priority of the
responsible department during implementation, so that the can have the desired effect on
continued usage. Employees’ accumulation of successful moments of experience have an
effect on pervasive BIS usage.
Finally, another implication about the causal attribution is the need to show the results
to the employees, to allow feelings of success. The implementing department can
periodically display and report examples of successful usage among employees. This should
assist employees to recall previous successful uses of pervasive BIS and help them to know
how others use it as well, to strengthen intrinsic motivation to continue using pervasive BIS.
7.3 Conclusion
The primary objective of this study is to build an integrative model of pervasive BIS
continuance usage that incorporates the conscious side, the unconscious side, and the
viewpoint of intrinsic motivation. The model was empirically tested using data from 133
respondents obtained from a two-stage online questionnaire. We verified the effect of habit,
psychological empowerment, and causal attribution based on Bhattacherjee’s original
continued usage model of pervasive BIS users. In conclusion, our research results present a
survey of real continued behavior of pervasive BIS usage. Furthermore, habit is proposed as
a positive moderator between continuance intention and continuance usage. This results
related to habit are somewhat inconsistent to those discussed in the literature. Psychological
empowerment plays a more important role in driving employees to have continuance
intention for the usage of pervasive BIS. It shows that self-efficacy is conducive the higher
motivation which influences the employee’s behavior. Then, it is shown that casual
attribution does not have a direct effect on the continuance usage intention, but rather a pure
moderating effect between the continuance intention and usage. This provides a new vision
of the continuance usage issue.
Therefore, in our results, we go further to clarify the relationship between the
continuance intention and usage. Actual continuance behavior has influences from the
25
conscious, the unconscious, and intrinsic motivation. A more refined knowledge about
continuance usage as influenced by habit, psychological empowerment and causal attribution
is gained in addition to continuance intention. These factors provide a different view point
for stakeholders to cope successfully with the challenges of encouraging information system
continuance.
7.4 Limitations and Suggestions
Some limitations and suggestions derived from the present study must be considered.
These limitations need to be addressed in future research. Frist, because our respondents
were from Taiwan, we need to be careful about generalization to other countries with
different cultures which might affect the results.
Second, although pervasive BIS is a kind of enterprise information system, it is
categorized as discretionary. Employees still have to choose to use it or not. There are other
factors that might affect the continued usage of a discretionary system, such as the
comparison of different solutions/software (e.g., spreadsheet software versus pervasive BIS),
different devices (PCs versus smartphones), different job-levels, or different tools/function.
In the future, researchers may control these variables to seek a more general model. More
focus is also needed to identify the different factors that influence continued usage.
Third, we hypothesed that habit would suppress the relationship between intention and
actual usage, but the results of data analysis showed the opposite. Although some studies
have shown that habit has a positive moderating effect (H. Aarts et al., 1997; B. Verplanken
et al., 1997; V. Verplanken & Aarts, 1999), further research is needed to understand the
precise context and explore how habit facilitates or suppresses the relation between
continuance intention and actual continuance behavior, especially in relation to the differed
types of software used by the organization.
Fourth, the pervasive BIS continuance usage construct is validated in the second stage.
However, although following the literature, we set the period at seven days, the period of the
sampling time is still a judgmental factor to explain the employee’s continuation of the use
of the system. Pervasive BIS in particular is a kind of occasional task-specific system.
Therefore, to identify the appropriate time period is an issue for continued usage assessment
depending on the different types of software (Limayem et al., 2001; Limayem et al., 2007).
Moreover, further research should be carried out to separate the job level and tools/functions
to fit the appropriate time period.
Finally, we include causal attribution to validate our research model. We suggest
comparing experiences of success and failure in further studies. This would allow us to
understand users’ tendencies when they experience good or bad attributions and could help
managers and developers to make better decisions about pushing for pervasive BIS
continuance usage.
26
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