ABSTRACTIn the Business Intelligence Systems (BIS) use context, the well-researched dimension of intensity of use is not broad enough to capture its meaningful use. The authors therefore investigate the whole spectrum of its use that encompasses effective utilization. The post-adoption research of Information System (IS) use is still under-researched; the study therefore brings novel insights to this area. The authors have conceptualized three dimensions of BIS use that capture the intensity of BIS use, the extent of BIS use, and BIS embeddedness. Confirmatory Factor Analysis (CFA) was used to determine convergent and discriminant validities proving construct validities, based on survey data gathered from BIS users in medium and large sized organizations. The results of our study provide sound evidence for apprehending these three constructs as different concep-tualizations. This work adds to the literature by emphasizing the role of post-adoptive behavior that can lead to greater and effective utilization in the studied BIS environment, and potentially to a better organizational performance.
Three Dimensions of Business Intelligence
Systems Use BehaviorTanja Grublješič, Academic Unit for Business Informatics and Logistics, University of
Ljubljana, Ljubljana, Slovenia
Jurij Jaklič, Academic Unit for Business Informatics and Logistics, University of Ljubljana, Ljubljana, Slovenia
Keywords: Business Intelligence Systems (BIS), BIS Use, Embeddedness of Use, Extent of Use, Intensity of Use
INTRODUCTION
The research stream examining adoption and acceptance is one of the richest and most mature streams in the IS field (e.g. Davis, 1989, Ven-katesh & Davis, 2000; Venkatesh, Morris, Davis & Davis, 2003; Venkatesh & Bala, 2008). But, in contrast, post-adoptive use behavior is still under-researched, and often conceptualized as increasing intensity or greater frequency of use (Jasperson, Carter & Zmud, 2005). Some efforts
have already been made and show that post-adoptive use behavior may also diminish over time with gained experience (Bhattacherjee, 2001), or on the other hand become habitualized and routinized in individuals’ work routines (Jasperson et al., 2005). Undoubtedly, research on technology acceptance and initial use can enrich our understanding of post-adoptive use behaviors (Hernandez, Jimenez & Martin, 2008), but distinctions between pre- and post-adoptive beliefs and behaviors have already
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been observed (Agarwal & Karahanna, 2000). Understanding the three different dimensions of effective use in a specific BIS utilization context is central to this work. Deng and Chi (2013) argue that the Business Intelligence (BI) application context offers an ideal opportunity to examine a variety of post-adoptive system uses.
In the IS literature, the positive impact of the information provided by Business Intelligence Systems (BIS) on decision-making has been emphasized, particularly when organizations operate in highly competitive environments (Popovič, Hackney, Coelho, & Jaklič, 2012; Harison, 2012). These technological innova-tions are one of the main sources of competitive advantage for the long-term survival of orga-nizations (Jourdan, Rainer & Marshal, 2008), although their benefits can only be fully realized in situations where these promising innova-tions are completely accepted and effectively used. User acceptance is also crucial to BIS success, but in this context, in contrast with operational information systems (IS), there is a particularly pronounced difference between using or accepting the system and long-term routinization (Bhattacherjee, 2001) of the use of information provided by the system as part of management of the organization and business processes (Popovič et al., 2012).
There are two motivations for this study. First, previous research in the field of IT ac-ceptance has mainly focused on general IS and thereby only considered the narrower aspect of use, merely the frequency of use and not how IS is used (Davis, Bagozzi & Warshaw, 1989; Venkatesh & Davis, 2000; Venkatesh & Bala, 2008), treating system use simply and solely as a measure of the relevant behavior consequent to technology acceptance. This work represents a step in the direction advocated by Jasperson et al. (2005) who call for richer conceptualizations of system use by also treating system use as a theoretical construct. Second, it also responds to the call by Burton-Jones and Straub (2006) to develop the conceptualization of usage and select usage constructs and measures for specific contexts. The specific features of BIS use as compared to operational IS motivate the three
conceptualizations of BIS use. For BIS it is important if BIS are deeply embedded within the business (Shanks et al., 2012). Therefore, BIS use behavior should be measured by several dimensions to yield a rich understanding of its utilization. In addition to quantitative measures of how much a BIS is used (frequency or dura-tion) (Davis et al., 1989), we are also interested in a qualitative measure of how a BIS is used in the organization. The aims of this study are therefore to:
• Conceptualize and develop relevant mea-sures for the three constructs of BIS use behavior, namely intensity, extent, and embeddedness; and
• Empirically test and evaluate the construct reliability and validity of these three con-ceptualizations of BIS use.
The structure of the paper is as follows. In the next section, the specifics of BIS and the three conceptualizations of its use are elaborated. Further on the research design, methodology, and results of the estimations are given. This is followed by a discussion of the results, including the implications for research and practice, and a conclusion.
SPECIFICS OF BIS USE
There are many definitions of BIS, but all of them state that BIS provide the ability to analyze business information in order to support and improve management decision-making across a broad range of business activities (Elbashir, Collier & Davern, 2008; Yeoh, Koronios & Gao, 2008). Their growing strategic importance is seen from the rising volume of investments in BIS (Gartner Research, 2013) which empha-sizes the need to give them greater attention in research studies.
To study the use of BIS, one must un-derstand the specific characteristics of BIS as compared to operational or transactional IS, particularly in the post-technology adoption phase. BIS are different from operational IS
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from the use point view in several respects. The use of BIS is mostly voluntary, whereas for operational IS the use is mostly manda-tory or necessary for carrying out the business processes. Soltani, Elkhani & Bardsiri (2014) already identified the importance of volun-tariness of use and user’s willingness to use when studying continued IS usage behavior. In general, compared to operational IS the benefits of BIS are much more indirect and long-term (Gibson, Arnot & Jagielska, 2004) which may negatively affect their perceived usefulness and consequently their acceptance and use. The structuredness of the information needs and processes within which an IS is used, and the structuredness of instructions for using the BIS is much lower since the use is usually more research-oriented and innovative in nature based on performance management (Popovič et al., 2012; Mouakket, 2010). Concerning the problems of the quality of information with information provided by operational IS, the focus is primarily on traditional problems of data quality (accuracy, completeness, etc.), while with BIS it is primarily a question of the relevance of the information provided by a BIS because a key problem of information quality in unstructured business processes is providing relevant information to managers (Delone & McLean, 2003; Eppler, 2006; Mandal, 2006). All these specific characteristics of BIS sug-gest the need for an expanded definition of its use in order to capture the whole spectrum of effective BIS utilization.
CONCEPTUALIZATION OF THE THREE CONSTRUCTS OF BIS USE
To ensure the better performance of the or-ganization and the value added of BIS users should accept, use and take full advantage of their capabilities. Understanding the effective use (Burton-Jones & Grange, 2013) of BIS is thus “critically important” (Straub & Guidice, 2012, p. iii) for achieving their designed goals and benefits. According to Burton-Jones and
Grange (2013, p. 636), effective use at the individual level is defined as “using a system in a way that helps attain the goals for using a system”.
As the objects of use investigation in accep-tance models have mostly been operational IS, where using an IS is seamlessly integrated with execution of the business process, the use of IS has primarily been measured by the frequency, intensity, and duration of use (e.g. Davis et al., 1989). However, for BIS use these measures are not enough since, for BIS to be effectively used, mere intensity does not reflect the desired and promised outcomes of these systems as it is central for them to be deeply embedded within the business (Shanks et al., 2012). The issue of the intensity of use not capturing the breadth and depth of BIS use leading to their success was also raised by practitioners in BIS use case study research (Grublješič & Jaklič, 2014). Frequency and duration of use do not capture the difference between successful or meaningful use and unsuccessful time spent using the system (Doll & Torkzadeh, 1989; Deng & Chi, 2013).
Moving from the use to the effective use of BIS requires an understanding of the nature and purpose of BIS. Burton-Jones and Straub (2006) elaborate that in IS for the decision-making domain researchers typically study IS characteristics that improve user decision-making. Thus, informed action is necessary for effective BIS use in the sense of acting upon the information obtained from BIS (See-Pui Ng, 2012). We therefore employ Burton-Jones and Straub’s (2006) dimensions of system us-age measured as the use of information from BIS, which are the frequency or intensity of use, the extent of use, and the nature of use. The embeddedness of BIS encompasses the preferred or desired nature of use.
Intensity of BIS Use
The intensity of use is the dimension most commonly used in the literature to measure the usage of IS (Davis et al., 1989; Venkatesh & Davis, 2000; Venkatesh et al., 2003; Venkatesh
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& Bala, 2008; Venkatesh, Brown, Maruping & Bala, 2008). Ideally, the intensity of use should be understood as “cognitive absorption” which Agarwal and Karahanna (2000) use to charac-terize the “extent to which a user is absorbed when using a system”. But this dimension of use has most often been conceptualized and operationalized as the frequency or duration, based on users’ self-assessment of the time spent on using a system or the duration of their usage via system logs (Taylor & Todd, 1995). These conceptualizations only capture the quantitative aspect of how long the system is used, overlooking any unsuccessful time spent using a system or if an employee has actually completed a business task by using the applica-tion (Deng & Chi, 2013). Unproductively lost time using a system may negatively impact the task performance of an individual, certainly leading in the opposite direction of organizations reaping benefits from system implementation.
Extent of BIS Use
The extent of BIS use should capture the “extent to which the user employs the system to carry out the task” (Burton-Jones & Straub, 2006, p. 233). In the post-adoption context, more use is not always considered desirable. There is a plethora of social and economic impacts at the level of the individual, the work group, and the organization which are not only captured by the intensity of use measure (Doll & Torkzadeh, 1998). The value of BIS is not solely generated by the use of BIS, but by activities based on the information provided by BIS that are go-ing in the right direction to achieve successful business performance management (Popovič et al., 2012). Therefore, it is important for BIS use whether users are acting on the basis of the information provided by BIS.
In the BIS context there is a need for multidimensional measures of how extensively a BIS is utilized in an organizational context for different tasks. We therefore employ and operationalize this dimension by adapting Doll & Torkzadeh’s (1998) measures captur-ing the utilization of BIS for decision support (problem-solving and decision rationalization),
work integration (horizontal and vertical inte-gration), and customer service functions. The decision support function relates to improving the decision-making process, and making sense of the data, thus amplifying creative judgment in problem-solving. The horizontal and vertical in-tegration function captures the communication and coordination of work activities with others in the organization. Finally, the customer service function involves creating value for customers by improving customer service (Hirschhorn & Farduhar, 1985). This multidimensional concept of system use “identifies key performance-related usage behaviors” (Doll & Torkzadeh, 1998, p. 173) capturing the social and economic impacts of BIS in the organizational work envi-ronment. Doll & Torkzadeh (1998, p. 172) state that “how extensively IT is used to perform these functions defines how effectively it is used in the organizational context”. In relation to this, Po-An Hsieh and Wang (2007) introduce the term “extended use” that refers to using several of the complex system’s features to support an individual’s task performance.
Embeddedness of BIS
Embeddedness represents a qualitative leap in use and can be understood as the level “to which the use of BIS is an integral part of organizational activity” (Furneaux & Wade, 2011, p. 579). Embeddedness has been conceptualized and named in several different ways in the literature.
Cooper & Zmud (1990) say that, from the technology diffusion perspective in the post-adoption phase of the stage model of IT implementation, use should evolve from initial acceptance through the routinization phase to infusion. ElSawy (2003) says the fusion of BIS evolves when the BIS and business correspond to the “oneness-with-environment property”. In the context of BIS, according to Shanks et al. (2012) fusion involves deeply embedding BIS within the business to create “BI-driven decision-making routines and BI-enabled organizational processes that take managerial decision making to new levels of understand-ing and foresight”. Burton-Jones and Straub (2006, p. 236) conceptualize the dimension
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of deep structure usage as exploitive system usage, which can occur in the way a person restructures information or in any other way that evokes interesting new ways of seeing. In connection to this, Straub and Guidice (2006) also introduce the notion of transformative uses of systems which are rarer and different and occur when users utilize the IS capabilities to create something new, an invention, or a new perspective. Jasperson et al. (2005) elaborate that much post-adoptive behavior or continuing IT use is likely to reflect a “habitualization of action, where the decision to use the IT applica-tion feature occurs more or less automatically via a subconscious response to a work situation” and Ortiz de Guinea and Markus (2009) sup-port this habitual and automatic use. Limayem, Hirt and Cheung (2007) further argue that more comprehensive usage of IS is more likely to become habitual and that habitual IS usage is also less prone to discontinuance.
All these conceptualizations imply that after having successfully integrated IS within the organizational environment, the breadth and depth of IS use can create synergies,
leading to gained competence, the creation of a competitive advantage and ultimately an organization’s better performance. Since BIS provide innovative and competitive information (Elbashir, Collier & Sutton, 2011) usage that is deeply embedded into the routines of workers can enhance these organizational goals.
Figure 1 presents a conceptualization of the three dimensions of BIS use capturing the effective use of BIS.
RESEARCH DESIGN AND METHODOLOGY
In the following we describe how the data and results were obtained by explaining the research design, the data collection, and the methodology used for analyzing the data.
Research Instrument
Our questionnaire was developed by building on previous theoretical bases to assure content validity. To ensure face validity, pre-testing was conducted using a focus group involving
Figure 1. Conceptualization of the three constructs of BIS use
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selected university staff and IS academics from the field who were not included in the subsequent research. Minor changes were made based on their suggestions, mostly involving adaptation of the questionnaire items to suit the specific context of BIS use. We used a structured ques-tionnaire with seven-point Likert scales, with anchors ranging from totally disagree (1) to totally agree (7) for the items used to measure the intensity of use and embeddedness of BIS, while the extent of BIS use was measured with anchors from not at all (1) to a great deal (7).
Measurement Items
The measurement items were developed based on the literature review and supported by expert opinions. Use behavior was measured as a reflective composite index of three di-mensions capturing the intensity, extent, and embeddedness of BIS use, following Burton-Jones and Straub’s (2006) classification. The three intensity of use items were adapted from Wixom and Todd (1995), capturing the increas-ing intensity of BIS use. Extent of BIS use was measured by adapting Doll and Torkzadeh’s (1998) measures. Each concept was measured with three item scales having the highest fac-tor loadings (Doll and Torkzadeh, 1998). No established measures were specifically available for measuring embedded use. Nevertheless, Shanks et al. (2012) provide three dimensions for embedding BIS into the organization, which were used to operationalize embedded BIS use, capturing whether the use of BIS is seamlessly integrated with business process execution, whether the BIS is embedded into the decision-making routines of decision-makers across the organization, and whether the importance and use of insights from the BIS are embedded within the business strategy formulation pro-cess, leading to the alignment of the BIS and the business strategy (Shanks et al., 2012). All three manifest items had large factor loadings in the confirmatory factor analysis, proving the reliability and validity of these measures for the BIS embeddedness construct. Table 1 provides a detailed list of all the indicators used in the measurement model.
Data Collection
The empirical data were collected through a survey of 2,173 medium- and large-sized busi-ness organizations in an EU country, namely Slovenia. Organizations were selected from an official database published by the national Agency for Public Legal Records and Related Services. The questionnaires were addressed to a wide range of employees, that is, all BIS users (top management, heads of departments and divisions, IS managers, etc.). The question-naires were sent to the contact persons available in the database, with a request to distribute the questionnaires to relevant users of BIS. A total of 195 completed surveys was collected after a follow-up round. The final response rate was 9.3%.
Data Analysis
The measurement model specifies the re-lationships between the indicators and the latent variables. Our measurement model is reflective, which means that “the direction of causality is from the construct to the indicators; thus observed measures are assumed to reflect the variation in the latent variable” (Henseler, Ringle & Sinkovics, 2009, p. 289). Each in-dicator therefore represents an error-afflicted measurement of the latent variable. Changes in the constructs are expected to be manifested in changes in all of their indicators (Nunally & Bernstein, 1994).
To conduct the data analysis, particularly the factorial validity, a Confirmatory Factor Analysis (CFA) was used where the pattern of loadings of the measurement items on the latent constructs is specified explicitly in the model (Gefen & Straub, 2005). Factorial valid-ity is important in the context of establishing the validity of latent constructs because these are research abstractions that cannot be mea-sured directly (variables such as beliefs and perceptions) (Gefen & Straub, 2005). Practice dictates that latent variables can be measured indirectly through several items in a research instrument (i.e. Anderson & Gerbing, 1988; Gefen & Straub, 2005). Each measurement
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Table 1. Indicators of the measurement model
Use of BIS
INTENSITY OF BIS USE IU1 I use BIS as a routine part of my job.
IU2 I use BIS at every opportunity.
IU3 I have been increasingly using BIS.
EXTENT OF BIS USE
DECISION SUPPORT:
Problem-solving PS1 I use BIS to help me think through problems.
PS2 I use BIS to make sure the data matches my analysis of problems.
PS3 I use BIS to check my thinking against the data.
Decision rationalization:
Explaining decisions ED1 I use BIS to help me justify my decisions.
ED2 I use BIS to help me make explicit the reasons for my decisions.
ED3 I use BIS to rationalize my decisions.
Improving decisions ID1 I use BIS to control or shape the decision process.
ID2 I use BIS to improve the effectiveness and efficiency of the decision process.
ID3 I use BIS to make the decision process more rational.
WORK INTEGRATION:
Horizontal integration HI1 My work group and I use BIS to coordinate our activities.
HI2 I use BIS to coordinate activities with others in my work group.
HI3 I use BIS to exchange information with people in my work group.
Vertical integration:
Work planning WP1 I use BIS to help me manage my work.
WP2 I use BIS to monitor my own performance.
WP3 I use BIS to get feedback on job performance.
Vertical communication VC1 I use BIS to communicate with people who report to me.
VC2 I use BIS to communicate with people I report to.
VC3 I use BIS to keep my supervisor informed.
CUSTOMER SERVICE CS1 I use BIS to serve internal and/or external customers.
CS2 I use BIS to improve the quality of customer service.
CS3 I use BIS to more creatively serve customers.
BIS EMBEDDEDNESS EMB1 Use of BIS is seamlessly integrated with business process execution. Processes in which there is an information need for analytical decision-making are carried out without interruption due to the use of BIS.
EMB2 BIS is embedded into the decision-making routines of decision-makers across the organization.
EMB3 The importance and use of insights from BIS are embedded within the business strategy formulation process, leading to alignment of the BIS and the business strategy.
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item is thus assumed to reflect one and only one latent variable.
The fit of this pre-specified model is examined to determine its convergent and dis-criminant validities since these are components of the larger scientific measurement concept of construct validity. This factorial validity deals with whether the pattern of loadings of the mea-surement items corresponds to the theoretically anticipated factors (Gefen & Straub, 2005). The estimations were performed using Smart PLS (Ringle, Wende & Will, 2005).
RESULTS
We examined the reliability and validity mea-sures for our reflective measurement model.
Measurement of Reliability and Convergent Validity
The results of the reliability and convergent validity test can be seen in Table 2. First, the loadings of the items were tested against the value 0.7 (Hulland, 1999) on the construct being measured and, as can be seen from the results, all the loadings were satisfactory. Further, all of Cronbach’s alphas by far exceeded the 0.7 threshold (Nunnally & Bernstein, 1994). With-out exception, the latent variables’ composite reliabilities are higher than 0.8 and in all cases even higher than 0.9 (which are the thresholds for more advanced stages of research), showing the high internal consistency of the indicators measuring each construct and thus confirming construct reliability (Nunally & Bernstein, 1994). As suggested by Fornell and Larcker (1981), the Average Variance Extracted (AVE) was used as a criterion of convergent validity. The AVE is higher than 0.6, thus exceeding the threshold of 0.5 (Fornell & Larcker, 1981), demonstrating the convergent validity of the constructs. The reliability and convergent valid-ity of our model were also confirmed by com-puting standardized loadings for the indicators and Bootstrap t-statistics for their significance (also see Table 3). All standardized loadings of
the indicators exceeded (or were marginal to) the 0.7 threshold and without exception were found to be significant at the 0.001 significance level, thus confirming the high indicator reli-ability and convergent validity.
Measurement of Discriminant Validity
The discriminant validity is shown by the following two procedures: (1) a comparison of the item cross-loadings with the construct correlations (Gefen & Straub, 2005); and (2) a determination of whether each latent variable shares more variance with its own measurement variables or with other constructs (Fornell & Larcker, 1981; Chin, 1998).
The assessment of the indicator loadings on their corresponding constructs is the first procedure for testing the discriminant validity. In Table 3, the figures shown in bold indicate manifest variable correlations with their theo-retically assigned latent variables that are an order of magnitude larger than other loadings on other constructs (Gefen & Straub, 2005). Since the loadings in bold are larger than the other values in the same rows, that is the cross-loadings (Henseler et al., 2009), all the item loadings met the criteria of the first procedure for assessing discriminant validity.
In the second procedure we compared the square root of the AVE for each construct with the correlations with all other constructs in the model (see Table 4). Theory suggests that the square root of the AVE of each construct should be much larger than the correlation of the spe-cific construct with any of the other constructs in the model (Chin, 1998) and should be at least 0.50 (Fornell & Larcker, 1981). It can be observed that all of the square roots of the AVE are significantly higher (and also substantially larger than the threshold) than the correlations between the constructs, thus confirming that they are sufficiently discriminable. Based on the results of both tests, we conclude that all the constructs show evidence of acceptable validity.
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DISCUSSION WITH IMPLICATIONS FOR RESEARCH AND PRACTICE
Understanding the three conceptualizations of BIS use (intensity, extent, and embeddedness) responds to the calls of Burton-Jones and Straub
(2006) and Burton-Jones and Grange (2013) to develop the conceptualization of usage and select usage measures for specific contexts. The rich measurement of different types of usage reacts to the observation of Jasperson et al. (2005, p. 544) that researchers have examined use “quite simplistically in terms
Table 2. Reliability and validity measures of the measurement model
Constructs Indicator Initial model Estimates
Loadings t-Values Cronbach’s Alpha
Composite reliability
Average Variance Extracted
Intensity of BIS use IU1 0.8970 32.8202 0.8879 0.9304 0.8168
IU2 0.9304 92.7760
IU3 0.8833 26.1379
Extent of BIS use PS1 0.7569 17.2411 0.9738 0.9757 0.6577
PS2 0.8435 30.3366
PS3 0.8473 32.7674
ED1 0.8261 23.2312
ED2 0.8764 38.4005
ED3 0.8713 36.4255
ID1 0.8779 41.0467
ID2 0.8758 37.6814
ID3 0.8853 42.8322
HI1 0.8205 25.8931
HI2 0.8212 23.4496
HI3 0.8210 22.6901
WP1 0.7749 14.3216
WP2 0.7397 18.5075
WP3 0.7387 17.5119
VC1 0.7316 15.0580
VC2 0.7485 14.4860
VC3 0.7616 15.1343
CS1 0.7800 19.5023
CS2 0.8044 21.4949
CS3 0.7955 21.1395
BIS embeddedness EMB1 0.8891 35.3388 0.9123 0.9450 0.8514
EMB2 0.9513 83.1864
EMB3 0.9266 54.0618
Note: t-statistics values above 3.29 show statistical significance at p<0.0001
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of the frequency, or level, of use of the whole technology rather than capturing users’ patterns of use regarding the technology’s features”, therefore they encourage researchers to move beyond such simplistic views of use in order to “expose the sufficiently rich depictions of use history required to surface, study, model, and understand the path-dependent episodes of use leading to routinized or habitual use of an IT application”.
Our work also builds on Venkatesh et al. (2008) who have also developed different con-ceptualizations of use that go “beyond simply treating system use as a measure of the relevant behavior consequent to technology adoption to treating it as a theoretical construct”. Their measurement of use still captures only the fre-quency, intensity, and duration of use; therefore, our study provides an extension with more profound conceptualizations of effective use. Further, a clearer and deeper understanding of BIS use gives insights into post-adoptive use behavior and other downstream consequences (Jasperson et al., 2005; Burton-Jones & Straub, 2006; Venkatesh et al., 2003; Venkatesh et al., 2008).
Our findings provide proof supporting the conceptualizing of the three different dimen-sions of BIS use. We particularly introduce the dimension of the embeddedness of BIS that has not previously been included and studied as a use construct. This significantly adds to under-standing of the deep structural use of BIS that contributes to the success of BIS. The literature suggests that IS are increasingly being used in “new ways” that hold substantial implications for the nature of work, productivity and eco-nomic growth (Doll & Torkzadeh, 1998). The embeddedness of BIS into workers’ routines should induce a fusion of doing and learning, trigger improvements in business processes, improve firm performance and create competi-tive advantage (Shanks et al., 2012).
Arvidsson, Holmstrom and Lyytinen (2014) argue that conceptualizations of stra-tegic IS use within organizations commonly rely on the assumption that, for a system to be strategically successful, the system should
just be successfully embedded in relevant practices. However, this is not enough for this assumption to be true. The “strategic chal-lenge concerns the organizational capability to enact IT-enabled change: transforming and re-configuring local practices to align both system use and related organizational practices with the strategic intent” (Arvidsson et al., 2014, p. 47; Pita, Cheong & Corbitt, 2011). In response to this concern, our conceptualization of BIS embeddedness therefore also captures the aspects of the alignment of the BIS and the business strategy, given that insights into the use of BIS are being embedded within the business strategy formulation process.
One of the challenging tasks managers face today is how to enhance BIS use (Venkatesh et al., 2008). Despite huge investments in BIS in recent years (Gartner Research, 2013), there is a concern “that the implemented systems are underutilized and that users restrict themselves only to using the basic functionalities of the sys-tem” (Venkatesh et al., 2008, p. 498; Jasperson et al., 2005). The current work enhances our ability to understand and predict different stages of BIS use. Research has already emphasized the importance of human and sociological factors for the performance success of enterprise-wide systems (Nair, Reddy & Samuel, 2014; Nour & Mouakket, 2011). The contribution to practice is that managers can now consider interventions in the post-implementation phase to positively influence an individual’s effective BIS utiliza-tion and particularly focus on the embeddedness of BIS in the workers’ routines, representing higher added value of BIS use. As Wixom and Todd state (2005), “this can help with manage-ment activities such as task prioritization and resource allocation”. Limayem et al. (2007) in this context mention offering repetitive training in order to develop knowledge for more compre-hensive use and promoting knowledge sharing among users. Another important contribution to practice is helping organizations identify and select metrics of BIS usage that can explain and lead to relevant organizational outcomes from using BIS, such as better performance, greater user satisfaction, quality of work life, and so on.
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CONCLUSION
Although this study provides new and valuable results, it also has some limitations that should encourage future related research. A limitation of this study is that it examined a cross-section of interviewees’ perceptions of different dimen-sions of use. It would be beneficial to perform a longitudinal study that would capture different understandings of BIS use as they evolve over time. Future research should also study the antecedents that drive each dimension of use in order to understand how to induce and enhance them. A study of how the embeddedness of BIS actually impacts organizational performance would also be beneficial.
Meaningful BIS use requires capturing both quantitative and particularly qualitative measures that lead to greater and effective BIS utilization. The model estimation provides sound support for the conceptualization of the three dimensions of use, namely intensity, extent and embeddedness as different constructs. Over-all, the findings enrich our understanding of the phenomena of post-adoption BIS use behavior, leading towards the embeddedness of BIS in an organization’s work system and strategy.
ACKNOWLEDGMENT
The authors would like to thank the anonymous reviewers and the editor for their insightful comments and suggestions.
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Tanja Grublješič has a B.Sc. degree in the field of Management and Organization and holds a M.Sc. in International Economics from the Faculty of Economics, University of Ljubljana. She is currently a Ph.D. student in the Information Management program at the Faculty of Economics. Her current main research and teaching interests include the topics of the adoption, acceptance, embeddedness, use and success of Business Intelligence Systems in organizations.
Jurij Jaklič is a Professor of Information Management at the Faculty of Economics of the Uni-versity of Ljubljana and visiting professor at the ISEGI-UNL. He holds a bachelor degree in ap-plied mathematics from the University of Ljubljana, an M.Sc. in Computer Science from Houston University (USA) and a PhD in Information Management from the Faculty of Economics of the University of Ljubljana. His current main research interests encompass the fields of information quality, business intelligence and business process management. He is the (co)author of around 100 papers and research reports; many of them have been published in international scientific journals such as Decision Support System, Supply Chain Management, Information Research, and Simulation. He has been involved in several research and consulting projects in the areas of business intelligence, business process renovation and IS strategic planning.
