End-User Business Intelligence Tools Adoption in a Higher 1 Education Institution 2

7 This study examines how business intelligence (BI) tools are adopted in a higher education 8 setting. It made use of the theories of Diffusion of Innovation (DOI), Technology Readiness 9 Index, and Technology Acceptance Model (TAM). The psychometric features of the intended 10 end-users were defined using TAM. The technology-readiness of the users was assessed using 11 TRI. The DOI was used to describe the innovation itself. The important factors for the 12 adoption of the technology in this particular environment were identified through the 13 examination of both quantitative and qualitative data. To gain a better understanding of the 14 socio-technical system as a whole, a systems dynamics tool is presented to model the 15 interaction of these elements along with the recommended interventions.


Introduction
19 management dashboard, similar to a car or airplane dashboard, offers information regarding the working 20 conditions of what or where the user is viewing. It is a system in which multiple components are constructed so 21 that data can be processed and displayed. The back end of such a dashboard is a technical system that enables 22 it to function as intended. The dashboard and the user are components of an additional system known as the 23 human-computer interaction system. According to Brian Whitworth [11], social-technical systems emerge when 24 cognitive and social interaction is mediated by information technology rather than the natural world.

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The technical system automates business intelligence data retrieval, analysis, transformation, and reporting. In 27 addition, it includes data cleansing and, extracting and loading technologies. Such a technical solution was 28 developed in a higher education institution (HEI), where large amounts of data are presumed to exist to support 29 sound decision-making. 30 Its administrators are the end-users in this study. They monitor metrics that are crucial to the achievement 31 of institutional goals and objectives. Prior to the availability of these business intelligence (BI) Interaction refers 32 to the communication between the end-user and the computer, as well as the manner in which the user and 33 computer collaborate. This interaction occurs via the interface of the BI tools, in which the computer visualizes 34 the data and the user draws insight from the information and formulates a plan of action based on whether 35 they are working and contributing to the university's ultimate objective. This institution's deployment of a BI 36 tool was primarily motivated by the need to facilitate such activities in an efficient manner. However, regardless 37 of how information technology intends to aid organizations, technology adoption issues may arise and impede 38 any potential benefits [9]. In order to optimize its intended benefits, an examination of the degree of end-user 39 adoption of this business intelligence product is being conducted. Model-based psychometric profile of this HEI's intended users, as measured by their behavioral intent to adopt 51 technology. tools are adopted in a higher education setting. It made use of the theories of Diffusion of Innovation 52 (DOI), Technology Readiness Index, and Technology Acceptance Model (TAM).

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The psychometric features of the intended end-users were defined using TAM. The technology readiness of 54 the users was assessed using TRI. The DOI was used to describe the innovation itself. The important factors 55 for the adoption of the technology in this particular environment were identified through the examination of 56 both quantitative and qualitative data. To gain a better understanding of the socio-technical system as a whole, 57 a systems dynamics tool is presented to model the interaction of these elements along with the recommended 58 interventions. 59 tools, they were required to obtain foundational data from sources that maintain such data in an unstandard-60 ized format and varying structure. It took so much time to collect data that, if decisions had to be made, there 61 would be little time for analysis. Thus, leading to decisions that may not be particularly sound.

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? To model an effective means of increasing the adoption of BI tools in HEIs II.

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Methodology E.M. Rogers created the DOI Theory in 1962, making it one of the oldest social science ideas.

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It originates in communication to describe how an idea or product gets momentum and spreads within a 65 certain demographic or social system over time. This theory considers the innovation itself to be the primary 66 element affecting adoption, which neither the TRI Model nor the TAM Model account for. It investigates the 67 perceived characteristics of the innovation in terms of relative advantage, compatibility, complexity, trialability, 68 and observability.

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The Technology Readiness Index (TRI) examines four dimensions of technological beliefs that influence a user's  The theories are then used to process these inputs to produce a model that will serve as the basis for strategies 83 in increasing the adoption of the BI tools. The initial questionnaire was distributed to 10 respondents for data standardization reliability analysis. Following 87 the standardization and reliability analysis, the Cronbach's alpha coefficient was calculated to demonstrate the 88 interrelationships between each factor and to assess its validity and internal reliability. The Cronbach analysis 89 indicates a satisfactory level, hence all constructs were retained.

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To ensure confidentiality of the data, the compilation of responses was stored and the questionnaire itself was 91 distributed via the university's Google Workplace account, with access restricted to university employees and the 92 researchers.

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Two questions were added in the survey to eliminate insincere answers. Forty-eight percent (48%) of the replies 94 submitted were eliminated because they were deemed "insincere." The remaining 52 percent is being analyzed.

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A review of the Diffusion of Innovation (DOI) Theory, the Technology Readiness Index (TRI), and the 96 Technology Acceptance Model (TAM) was conducted [1]. The concept gained from such a review, though 97 intended to enhance the adoption and use of intelligent waste management systems in smart cities, is utilized in 98 this investigation. The conclusion of the study was a recommendation to integrate the three adoption models, 99 as each model complements the deficiencies of the others. From the introduction and subsequent stages in the 100 deployment of the BI tool, which is the innovation referred to in this study, suitable documentation and field 101 notes were maintained along the journey and used as data sources in this study.

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The relative advantage of the technology is derived from related memos and communications thread that 103 explicitly articulate the said advantage of the BI tools. While there are project documents available, the 104 perspective of the management as one of the endusers of the tool was purposefully chosen as the source document 105 since it would characterize how the management perceived the relative advantage of the new methods over the 106 old ones.

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Learning sessions with small groups of individuals who do not necessarily hold managerial positions was offered 108 for voluntary participation. Compatibility, or the degree to which an innovation is regarded to be consistent with 109 existing values, past experiences, and the needs of potential adopters, is extracted from the notes derived from 110 these learning sessions. The purpose of the workshops then was to persuade potential adopters that their processes 111 could be streamlined with the use of such information technology.

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In the project's status reports, the progress of the deployment and interaction of the components of the the perceived complexity of the innovation was deduced.

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A workshop was arranged to document the utilization of the BI tool in its early phases. This is where 116 trialability, the extent to which the innovation was experimented, as perceived by the participants, can be 117 sourced.

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Understanding the perceived observability of the invention can be obtained from a variety of sources, such as 119 excerpts from the president's report to the board of trustees; where the innovation is utilized on an institutional 120 level; and documents from accreditation preparations and continuity planning during the pandemic.

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An analysis of these qualitative data, though primary intended to extract perceived characteristics of 122 innovation, can also provide additional context for the survey results.  Table 1 displays the composition of the study's sample population. Slightly more than half of the responses 129 are academic employees, and 24.59 percent are also academic employees or teaching staff but are handling 130 administrative functions. The remainder are administrative personnel or non-teaching staff, with 18.03% who 131 are purely administrative employees and 6.56% with teaching load. 57.38% of these employees have been at the 132 university for more than 15 years. 133 Table 2 shows the mean scores for each factor used to calculate the TRI. The mean value of optimism is 134 greater than that of innovativeness, whereas the mean value of insecurity is greater than that of discomfort. It 135 surpasses the contributing factor of innovativeness. Thus, it is evident that the predominant personality trait 136 of these end-users is optimism coupled with insecurity. Certain studies analyze TRI in conjunction with the 137 following user classifications: explorers, pioneers, skeptics, paranoid, and laggards, as shown in Table 3. Based 138 on an individual's technology readiness score and the TRI, Badri et. al [3] used cluster analysis to further 139 classify technology users further into these five technology-readiness segments. Based on the high optimism and 140 insecurity scores of end-users in this study, they may be either pioneers, as early adopters, or paranoids, as 141 the late majority in social categorization. Inferring a composition of pioneers and paranoid individuals whose 142 insecurity, when handled, may eventually join the adopters.

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It is possible to calculate a mean total technological readiness (TR) score by subtracting inhibitors from 144 contributors. A positive TRI is suggestive of a technology-ready orientation, whereas a negative TRI for an 145 orientation that was not technology-ready [7].

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The correlation values of the constructs of technology acceptance model of this study concur with other studies 161 suggesting that perceived ease of use correlates with attitude and, consequently, the intention to use the model.  Table 4 below. Additionally, computation shows 166 that perceived usefulness has mean values that are higher than perceived ease of use. Given that perceived ease 167 of use correlates more strongly with attitude towards using than perceived usefulness, it may be advantageous 168 to make steps to provide end users a more accurate perception of the ease of use. When optimism, which in 169 the technologyreadiness calculation had the highest mean value, is taken into consideration, it shows a high 170 correlation to perceived usefulness and perceived ease of use (Figure ??). Innovativeness, the other contributing 171 factor, has relatively lower correlation values than optimism but nevertheless contributed positively. On the other 172 hand, insecurity and discomfort have negative and practically zero correlation values, which consistently suggests 173 that they are also inhibiting factors in the context of this particular HEI. from the realized relative advantage as in the memo with the statement, "Please be notified that at its regular 183 meeting yesterday, the Cabinet decided to adopt a common reporting template for all academic departments.

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These instruments are also intended to support forwarding planning." help in the adoption of the innovation. between data sets, which increases the perception of the technology's complexity. They consider it excessively 189 complex when they are clarified that data sets must be addressed at the source and processed in a specific manner. 190 Consequently, this influenced the non-utilization of the technology. a result, people are developing a negative 191 attitude toward innovation because they start to believe that the system is not actually being used.

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In summary, relative advantage, compatibility, and trialability produced good reactions to the innovation, 193 whereas observability and complexity produced unfavorable ones. Demands for the inclusion of the use of these 194 BI tools in recently drafted institutional policies and requests for informational materials or orientation sessions 195 from a number of stakeholders are indicative of the need to address these two frames.

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The analysis of the gathered data from this higher education institution in the context of the three theories 199 allowed for an understanding of the factors contributing to the gaps in the adoption of the technology. The study 200 found that insecurity, which is impeding technology readiness; perceived ease of use, which is delaying technology 201 acceptance, and the complexity and observability of the technology, which are influencing the formation of 202 unfavorable attitude towards the innovation, are the areas that need to be addressed.

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Interventions will be necessary to get this sociotechnical system close to fully utilizing the BI tool as the 204 best course of action, as expected and as desired in this study. This study is not, however, merely settling on 205 recommendations addressing the causes of the specific event that this study is looking at, which is adoption 206 rate, in isolation. Rather, it is taking into account that such a system is complex in the sense that it involves 207 interacting cross-functional processes and entails strategic level considerations rather than merely operational 208 actions. Therefore, this study uses a system thinking model called a causal loop diagram to show how cause and 209 effect operate from the perspective of the system (Figure 4). to consider them, and 2 were completely unaware of them. This is suggestive of a positive attitude toward the 216 innovation, which may have been prompted by the fact that the innovation allowed them to test the system.

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End-users are not seeing the benefits of the innovation, as evidenced by comments like, "How come we are no 219 longer asked for the reports like before?," "This X office is still asking for these documents. They are not at all 220 utilizing what we have submitted to the system," and "I thought you will just extract it from the One example 221 of the many instances in which one can affirm that the end users can resonate to the innovation being deployed 222 as compatible with their values and experiences is the local phrase, "sakto ingon ana jud ang nahitabo, maong 223 dapat naa tay ingon ana nga system," which means, "exactly, that is exactly what is happening, that is why 224 we need a system like this." This is regarded to have influenced a favorable attitude towards the use of such become more abundant, complexity may also rise, as in information overload. Understanding such dynamics is 236 necessary to determine the best strategy for delivering these information or orientation sessions.

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? R3. The implementation of these business intelligence tools aims to improve the institution's capacity for 238 reflection, which will help decisionmakers make wise choices when considering measures to address problems with 239 technological readiness like insecurity and, as a result, boost adoption rates.

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? B1. The more the necessity for the innovation's observability, which may also include certain reinventions 248 of earlier versions, the better the adoption rate will be until the use of business intelligence tools becomes 249 institutionalized.

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? B2. As the cost of implementing actions or strategies to overcome barriers to technology readiness rises (i.e.    Table 4 :