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Adoption of Business Intelligence - Technological, Individual and Supply Chain Efficiency

Nasir Abdul Jalil Department of Business Analytics

Sunway University Business School, Sunway University Bandar Sunway, Selangor, Malaysia

nasira@sunway.edu.my

Pichit Prapinit Loei Rajabhat University

234, Loei-Chieng Kan, Loei, 42000, Thailand

pichitprapinit@gmail.com

Mustakim Melan Technology Management and Logistics

UUM College of Business, Universiti Utara Malaysia Sintok, Kedah Darul Aman, Malaysia

mustakim@uum.edu.my

Abaidullah bin Mustaffa Technology Management and Logistics

UUM College of Business, Universiti Utara Malaysia Sintok, Kedah Darul Aman, Malaysia

abaidullah.mustaffa@uum.edu.my

Abstract - Making strategic decisions in a vigorous business setting is a challenge encountered by many organizations nowadays. Today, information is gathered all over the place and is rapidly expanding. Organization required powerful application and systematic system that could run in real time, provide insightful tracking for supply chains, logistics and operations that closely related to applications for sales tracking, hourly, daily to monthly production, financial, and many other sources of business data for purposes that consist of business performance management. Business Intelligence has a critical role in terms of organizational development as Business Intelligence (BI) be able to provide a competitive advantage in the context of achieving positive information asymmetry, that is, unifying and making useful heterogeneous data. However, the impact of BI and the relative importance of its insight on business performance have not yet been investigated. For this study, data were collected from a survey questionnaire of IT managers in 162 multinational companies in Malaysia and analyzed using the partial least squares (PLS) with the SmartPLS software. This research recommends that although BI and its insight contribute to management practices, the information requirements are diverse according on the level of uncertainty versus ambiguity characteristic of the organizations practice.

Keywords: Business Intelligence adoption ; Attitude ; Technology Characteristics; Management support ; Technology anxiety

I. INTRODUCTION BI in the operational level is required to be an information

system that is made up of three significant components that include a technological component that collects raw data, stores the data, transform data into information, a human component that accelerates the human competencies urging humans to retrieve data better and deliver it as processed information, and then generate knowledge and decision accordingly, and the last component is that used in supporting organizational business

process the requires the transformation of information into useful knowledge to give organizations more business values and profits [1]. Ancveire [2] mentioned that BI systems are required to be much different than only a type of IT infrastructure. BI systems need IT infrastructure to operate such as hardware and shared services identical database shared services, and security services. BI, as such it is relative to organizational efficiency and what are the required organizational capabilities that needed to support the assimilation of BI[3].

Generally, BI provide different benefits for different organizations [4]. Research confirms that the benefits including improved performance, efficiency, productivity, decision making, business growth, resource planning and supplier buyer relationship and reductions in costs from BI implementation and can ultimately lead to competitive advantage [5]; [6]; [7]; [8]. The reviewed literature gives sufficient background regarding the level of study in the context of users’ acceptability within the technology acceptance field, and provides grounds to select the baseline replica used to verify the significant main factors affecting the adoption of BI systems in multinational companies.

II. LITERATURE REVIEW

A. Attitude Towards Business Intelligence Fishbein and Ajzen [9] defined attitude as how “an

individual’s degree of evaluation affects the target behaviour”. According to Brown and Town [10], attitude towards BI system has no significant effects on the intention to use BI. However, the findings of different research studies [11]; [12] confirm that attitude toward BI system has a positive influence with a direct effect on the intention to use BI. BI were developed to simplify the flow of information and to integrate an organization’s procedures so as to promote synergy. With

2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

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BI, the information systems department is liberated from the duty of integrating tasks and duties because users can access all requisite information from the system [13]. In order for a software package to be regarded as BI system, it should have a number of particular attributes such as the ability to integrate information, to function in real time and to enable the access of all applications by one database repository so as to avoid data redundancy and duplications in data definitions [14]. According to the Gartner Research Group (1992), BI are software packages that wield highly integrated abilities and are sufficiently flexible to address the unique needs and requirements of an organization.

These software packages integrate the main functions as finance, accounting, business management and logistics required to manage and control the procedures of the organization by providing “cross-organization integration” of data through embedded business processes [13]. Samundsson and Dahlstrand [15] concisely indicated that the steadily growing competition between technology based companies has made knowledge the most important tool that can be used to capitalize on the available opportunities in contemporary businesses, as well as in other non-business organizations.

In other words, an individual can take the decision whether or not to become a user of BI systems. According to different research studies [16]; [17]; [18], intention to use demonstrates a positive influence on the actual use of the system. Moreover, according to the meta analysis by Legris [19], the majority if not all of the research that has examined the relationship between behavioural intention and actual use has found a positive relation. Studies on BI systems [20]; [21] found a positive and strong relation between behavioural intention and actual use of BI.

Given the above findings, this research suggests the following hypotheses:

H1. Attitude Towards Business Intelligence have a positive effect on adoption of Business Intelligence

B. Technology Characteristics Technology Characteristics is not entirely dependent on the

technical aspects of IT. External aspects, such as organizational and individual characteristics are also imperative in order to facilitate adoption [22]. Implementation of BI is complex and, therefore, their adoptions are prone to major problems that are related to organizational and individual issues, rather than to technical issues [23]; [24]. Thus, BI require individual perspectives coupled with organizational viewpoints. According to Gefen [25], when organizations make their BI both useful and easy to use by their employees, this helps both organizational and individual strategic issues. Therefore, a good understanding of users’ beliefs is essential. Technology characteristics apprehension various social progressions, mechanisms and support organizations that guide entities and facilitate the use of BI system [26]. Various studies have confirmed the implication of organizational factors on the assertiveness of users, precisely throughout the adoption of new BI technologies. The support from top management increases the users’ attitudes and decreases computer anxiety. Research that have investigated the effectiveness and

significance of training and education on the adoption of BI are insufficient [27]. Managerial intervention, such as user training, affects BI acceptance [28] and, according to Bradley (2008), inadequate training decreases ease of use and increases users’ resistance, which may have major consequences on ERP system success and usage.

Given the above findings, this research suggests the following hypotheses:

H2. Technology Characteristics have a positive effect on adoption of Business Intelligence

C. Management support Sabherwal [29] mentioned that it was evident from research

on information systems that management support positively influences users’ perceptions of information systems. System users who receive sufficient support from their managers or supervisors would have a better understanding regarding the relevance of the system that is related to perceived ease of use [30]. Urbach and Ahlemann [31] concluded that management support is critical in building up and determining users’ perceptions on system usefulness. In fact, according to Nwankpa and Roumani [32] and Rajan and Baral [33], management support is essential and shapes users’ perceptions regarding the usefulness of the system. Moreover, Nwankpa and Roumani [32] assert that management support helps users to understand BI usefulness.

Management support is critical in forming users’ perceptions on the system’s ease of use. Lee [34] stated that: “when an organization provides sufficient support to their employees for using a system, the employees will more easily use and access the system”. Additionally, Davis [16] asserted that management support affects the perceived ease of use of a system. Costa [35] examined the main determents of BI satisfaction and adoption. The results of their study showed that management support significantly and positively affects the perceived ease of use of BI. This was also supported by Lee [34] who examined the influence of management support on the behavioural intention of the users of BI. The findings of their study indicated that management support is positively associated with the perceived ease of use of BI. This was also supported by Rajan and Baral [33], Shih and Huang [36] and Ngai [37], who concluded that management support strongly and positively affects the perceived ease of use of BI.

Given the above findings, this research suggests the following hypotheses:

H3. Management support have a positive effect on adoption of Business Intelligence

D. Technology anxiety Gelbrich and Sattler [38] stated that technology anxiety has

a direct negative effect on intention to use, which is greater than the indirect effect through the reduction of ease of use. Moreover, Igbaria and Iivari [39] also concluded that technology anxiety has a direct and negative effect on perceived ease of use and adoption of Business Intelligence . BI systems are a complex technology and such complexity may negatively influence users’ perceived ease of use of these

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systems [39], especially users with high levels of technology anxiety. Shih and Huang [40] stated that “individuals with lower anxiety are much more likely to interact with computers than people with higher anxiety”.

Earlier research studies showed that technology anxiety facilitates the intention to use IT [41]; [18]; [42]. The perceived ease of use of IT are affected by technology anxiety [41]; [43]. This was also supported by Brown and Town [44], who assert that technology anxiety positively influenced adoption of Business Intelligence. Venkatesh [45] claimed that technology anxiety is an individual variable that affects users’ perceptions of perceived ease of use. Technology anxiety can be defined as the level of an individual’s uneasiness, or even fear, when she or he encounters the likelihood of using computers [46].

Given the above findings, this research suggests the following hypotheses:

H4. Technology anxiety have a negative effect on adoption of Business Intelligence

III. THEORETICAL FRAMEWORK Technology adoption is not entirely dependent on the

technical aspects of IT. External aspects such as organizational and individual characteristics are also important in order to facilitate adoption [22]. The implementation of BI systems is complex and, therefore, their adoption is prone to major problems that are related to organizational and individual issues, rather than to technical issues [23]; [24]. Thus, BI require individual perspectives coupled with organizational viewpoints. According to Gefen [47], when organizations make their BI systems both useful and easy to use by their employees, this helps both organizational and individual strategic issues. Therefore, a good understanding of users’ beliefs is necessary.

Different research studies such as [28]; [27]; [48]; [12]; [49]; [21] have used TAM and applied it to BI systems by incorporating new factors in order to gain a better understanding of the determinants of technology acceptance and to increase TAM’s predictive validity. Research studies that utilize TAM to understand BI adoption have considered individual and organizational factors as independent factors that may affect the usage of BI systems. Individual factors, as well as computer usage, are the main determinants of ease of use [18]. Organizational characteristics capture various social processes, mechanisms and support organizations that guide individuals and facilitate the use of an BI system.

Various studies such as [50]; [28]; [27] have confirmed the significance of organizational variables on the attitudes of users, especially during the adoption of new BI technologies. Therefore, in addition to the core determinants of TAM, this research will include other sets of factors organizational and individual that may affect the adoption of BI systems. Researchers such as Jiang [51]; Chau and Hu [52]; Horton [53] have modified TAM to suit new technologies, including internet, intranet and World Wide Web. In addition, several studies extended TAM by focusing specifically on antecedents of technology adoption [18]; [54], or added additional components to the model such as perceived self-efficacy [55]; [56]; [57] in order to add the justification for their studies’

context. Despite the limitations of the different frameworks that have been discussed in the previous sections, some may wonder why not utilize another model such as technology organization environment (TOE) or DeLone and McLean’s IS success model instead of using TAM.

Despite these models having been used to develop frameworks and conceptual models in order to understand the relationship of various factors that may affect BI adoption, it is worth noting that the previous research on some of these models such as DeLone and McLean’s IS success model have not been empirically proven [58]. Additionally, the majority of the research studies using the DeLone and McLean IS success model focus on people rather than systems [59]. However, low usage of information systems could cause low return of IS investment [60]. Thus, the usage intention of the system users can be considered an important determinant to information system success [59]. Further, this model suggests that information and system qualities are important factors for the success of information systems, since the BI system is within the framework of information systems.

IV. RESEARCH METHODOLOGY The main aim of the survey in the present study is to

explore the usage of BI systems by multinational companies, and it is based on a survey of BI users in multinational companies who are believed to have relevant experience with, and insights into, the factors affecting their adoption of BI systems. The use of questionnaires is created in the survey strategy, therefore, the main data collection technique applied in this research is questionnaires. A five-point scale are used in this study to calculate the variables. This scale is recommended within the literature to suit the validity and reliability criteria [61]; [16]; [18]. These standard scales are readily adapted to the present context by specifying the desired target. The literature was assessed for accessible scales satisfying the specified necessities validity and reliability.

Due to the complex nature of BI systems, this study necessitates conducting empirical investigation with various BI users. This study is conducted with 162 multinational companies that have implemented BI systems, but does not differentiate between mature and less mature adopters. This approach is required not only to improve the response rate, but also to provide opportunities to expand the range and diversity of approaches to BI adoption. The existence of such an expanded range of approaches to BI provides a comprehensive and holistic view of BI and its adoption.

The model assessment was done using the software package SMART PLS-SEM, version 3.0. There are two main stages to analyze a path model in PLS-SEM; namely, analyzing the measurement model and the structural model. The former refers to the relationships between constructs and its associated indicators. The latter refers to the relationships between constructs of the path model [62]. In this study, we followed the PLS-SEM evaluation procedure given in Figure 1.

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Figure 1. PLS Algorithm Path Diagram for the Research Model

A. Convergent Validity The first stage of analyzing results in the PLS-SEM is

examining the measurement model. This stage is about evaluating the quality of measurements prior to assessing the structural relationships. Once the measurement model evaluation indicates a satisfactory level of quality, the researcher can proceed to the second stage of examining the structural model and testing hypotheses [62]. Applying the PLS-SEM evaluation procedure given in Figure 1, the evaluation criteria for reflective models include indicators’ reliability, internal consistency reliability (composite reliability), convergent validity, and discriminant validity. To carry out measurements’ model analysis, each evaluation criterion needs to fulfil certain threshold as given in Table 1 [63].

The composite reliability coefficients measure is used to test the construct reliability, meaning how relevant the participants’ responses are in tackling the construct. Although the traditional internal consistency reliability measure is Cronbach’s alpha, it acts as a “conservative measure” of internal consistency reliability by assuming equal loadings for all items. Thus, an additional internal consistency reliability measure of composite reliability can be used [64]; [65]. The threshold of 0.70 indicates high internal consistency among items associated with its construct. As given in Table 1, the composite reliability and Cronbach’s alpha for all constructs are higher than 0.70, indicating high internal consistency of measures. Furthermore, as shown in Figure 1, all factor loadings are greater than 0.7 and the AVE values for all the constructs are greater than 0.5, showing evidence of convergent validity.

TABLE I. SUMMARY OF CRONBACH’S ALPHAS, RHO_A, COMPOSITE RELIABILITY AND AVERAGE VARIANCE EXTRACTED (AVE)

Cronbach's Alpha rho_A

Composite Reliability

Average Variance Extracted

(AVE)

ATT 0.813 0.83 0.869 0.572 BI 0.918 0.928 0.946 0.818 MS 0.916 0.923 0.937 0.750

TECH 0.968 0.971 0.975 0.887 TA 0.960 0.962 0.969 0.862

B. Discriminant Validity Analysis The principle of discriminant validity assumes that there is

a divergence among items of different constructs [23]. This indicates that each construct is unique representing different theoretical concepts. Indicators of a construct should not be strongly correlated with items of other constructs. Thus, items across constructs should be discriminant and divergent rather than convergent. There are two approaches for evaluating discriminant validity; these are cross-loadings of items and the Fornell-Larcker criterion [62]. The former evaluates validity at the indicators’ level whereas the latter evaluates validity at the constructs’ level. As far as the cross-loadings approach is concerned, it entails that items should load highest with the associated construct compared to other constructs. The Fornell- Larcker criterion requires that “each construct’s square root of the AVE should be higher than its correlation with any other construct” [62]; [66] as illustrated in table 2 and table 3.

TABLE II. DISCRIMINANT VALIDITY FORNELL & LARCKER CRITERION

ATT BI MS TECH TA

ATT 0.7560 BI 0.6390 0.9040 MS 0.6480 0.6350 0.8660

TECH 0.6660 0.7370 0.5890 0.9420 TA 0.7140 0.6990 0.6230 0.8470 0.9290

TABLE III. DISCRIMINANT VALIDITY: HETEROTRAIT-MONOTRAIT RATIO

ATT BI MS TECH TA ATT

BI 0.730 MS 0.740 0.690

TECH 0.743 0.785 0.621 TA 0.803 0.748 0.662 0.878

C. Analysis of the Constructs The structural model consists of relationships among

constructs. These relationships reflect the suggested hypotheses in this research. The structural model with latent variables reflects the theories and concepts behind the path model. Hence, it is crucial to assess how strong and significant these hypothesized relationships are. According to Sarstedt [67], the structural model analysis focuses on testing hypotheses through relationships between constructs. Thus, it indicates the degree to which these relationships are meaningful and significant. Ultimately, the assessment of relationships among constructs indicates the prediction quality of the model. PLS bootstrapping with 5000 re-samples procedure was conducted to obtain stable estimates. As reported in Figure 2 and Table 4, the T-Values with each path coefficient were produced via the bootstrapping method, and P-Values subsequently were produced.

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Figure 2. Bootstrapping result

TABLE IV. BOOTSTRAPPING RESULT: HYPOTHESIS TESTING

Hypotheses Original Sample

(O)

Sampl e

Mean (M)

Standard Deviatio n (STDEV)

T Statistics (|O/STDEV|)

Decision

H1: ATT- > BI

0.1320 0.1280 0.0720 1.8280 Supported

H2: MS - > BI

0.2390 0.2370 0.0690 3.4600 Supported

H3: TEC- > BI

0.4340 0.4460 0.0960 4.5200 Supported

H4: TA-> BI

0.0880 0.0760 0.1090 0.8090 Not Supported

IV. DISCUSSION AND MANAGERIAL IMPLICATION Business intelligence is a proficient application where the

system is responsible for analyzing data that is used by a business and organization. Data used in BI largely help in decision-making. The BI system works with useful of data to maximize its utility. BI system was developed to provide new business intelligence solutions. BI encompasses an extensive variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against that data and create reports, dashboards and data visualizations.

Attitude can be defined as an “individual’s perception that most people who are important to them thinks they should or should not perform the behaviour in question” [9]. Attitude was found to have significant effects on the adoption of BI systems.

The findings of the current research study clarify that the Technology Characteristics is a powerful construct that can be utilized to understand users’ adoption of BI system. Furthermore, this will comfort users understand the new system, decrease anxiety, enhance their interaction with systems, get rid of any doubts about technology and ultimately develop adequate perceptions with regard to the use of the system and consequently their adoption.

Management support has an influence on the BI adoption and this finding is consistent with prior research. For instance, Davis [16], Lee [68], Rajan and Baral [33] and Costa [35] all indicated that management support strongly and positively affects BI system adoption.

Technology anxiety was found not supported on the adoption of BI systems, which is consistent with the research of Igbaria and Iivari [39]. A possible explanation for this relationship is that users with low CA levels were looking for more facilities and benefits from the BI systems. Technology anxiety was found to have no influence on the adoption of BI systems, which is inconsistent with the research of Venkatesh [56] and Brown and Town [44]. However, the findings of the current study are consistent with prior research studies as Venkatesh and Davis [56], and Thompson [69] that argue that due to a user’s experience of technology and information technology is less affected by individual factors and more linked to particular characteristics of the software.

V. RESEARCH CONTRIBITION This study developed a coherent model that combined

factors that have been validated in different empirical studies and have strong support in the literature. It identifies those factors, verifies the variables, illustrates the differences between variables and tests the significance on the adoption of BI systems. The findings of this study confirm the significant role that both organizational and individual factors play in influencing users’ perceptions and acceptance of BI adoption. The empirical validation of the study measure for the factors examined in the current and passed study adds further theoretical contribution by highlighting the measurement and conceptual issues related to the development of BI theories. Therefore, this study provides further theoretical understanding in how BI systems can be adopted.

This will help organization to make more precise decisions regarding the adoption process, as well as facilitating them to get rid of the productivity paradox. In the long run, information system users’ attention will grow dramatically with regard to the usefulness of the systems. Consequently, organization adoption teams and technology developers need to consistently improve their systems to meet organizational objectives. This will help users to build realistic expectations of the system that are more likely to be met and will therefore increase the usage of the BI system.

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