Why Does It Fail? A Systematic Review of End-Users’ Challenges in Business Intelligence

Why Does It Fail? A Systematic Review of End-Users’ Challenges in Business Intelligence

Mohd Mustafa Alfariz¹, Ariff Md Ab Malik^2*, Anitawati Mohd Lokman³

^{1, 2} Faculty of Business and Management, Universiti Teknologi MARA, Shah Alam, Malaysia

³ College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Malaysia

* Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8110025

Received: 16 October 2024; Accepted: 23 October 2024; Published: 27 November 2024

ABSTRACT

Business Intelligence (BI) has become an essential tool for modern organisations, facilitating strategic decision-making through data analysis and actionable insights. Despite extensive research on organisational challenges, there is a gap in understanding the adoption and usage barriers at the individual level. Following the PRISMA 2020 protocol, this systematic literature review includes empirical studies from Scopus and Web of Science, published between 2021 and September 2024. Using automation and qualitative methods, 18 documents were selected, identifying 12 themes across three domains: technology, individual, and organisation. The study reveals the interconnected nature of these determinants, with some acting as antecedents or precedents to others. Subsequent research should delve into external influences on users and other understudied factors. This review offers comprehensive insights into the multifaceted challenges faced by end-users, offering valuable guidance for researchers and practitioners in successful BI implementation.

Keywords: Business intelligence, systematic literature review, challenges, individual level.

INTRODUCTION

BI has become a digital transformation catalyst for modern organisations, driven by the necessity to leverage data for strategic decision-making. It is a technological tool that analyses, transforms and presents fragmented data into meaningful information. It allows users to access and explore data from multiple sources, benefiting them by providing insights into trends, patterns, and opportunities. Despite its growing importance and the substantial investments made by many organisations, research reveals that numerous BI projects failed to achieve the desired results. According to a report by analyst firm Gartner, between 70% to 80% of BI implementation efforts fail (Goundar et al., 2021, p.112), with many initiatives falling short of expectations or entirely being abandoned (Williams et al., 2024; Williams & Sheikh, 2022).

Several studies have systematically examined the causes of this phenomenon. For instance, Poba-Nzaou et al. (2019) identified 23 obstacles to BI adoption, revealing factors such as adequacy of resources, difficulties in data integration and legitimacy concerns. A review by Mashayekh et al. (2023) in consolidating the critical success factors of BI disclosed that managerial elements such as clear vision, support and teamwork are essential in ensuring BI success. Further, Trincanato and Vagnoni (2024) highlighted that data access restrictions due to sensitivity, heavily regulated practice, and imbalanced participation from various stakeholders are some of the obstacles to BI implementation in the healthcare sector. However, these studies primarily focused on the organisational level, with little attention given to the motivation and resistance at the ground level of the applications. Scholars contend that while implementing BI is a managerial strategy, the success or failure of BI is ultimately contingent upon the end-users (Alfariz et al., 2024; Kapo et al., 2021; Trieu et al., 2022). This underscores the critical need to assess the challenges at the individual level. Nonetheless, review studies in this area remain scarce (Ain et al., 2019).

Therefore, the study aims to fill the gap by systematically reviewing the challenges and barriers to the adoption and utilisation of BI at the individual level. The study will contribute to a deeper understanding of obstacles experienced by employees in adopting and optimising BI. It will also illuminate future research on the determinants of BI success by offering insights from end-user perspectives. Consequently, organisations and system developers can develop more effective approaches and strategies to maximise the benefits of BI and mitigate the likelihood of BI failure. The structure of the study begins with the methodology section that explains the protocol and procedure of the systematic review, followed by the results. Next, findings and subsequent discussion of the topic will be presented. Finally, limitations and recommendations are provided in the conclusion section.

METHODOLOGY

A systematic literature review (SLR) is a structured approach to gathering, organising, and evaluating all available evidence on a specific research question using consistent, transparent, and rigorous methods to reduce bias. It involves the process of identifying, collecting, synthesising, and assessing existing evidence within a research domain (Paul & Barari, 2022). This methodology offers several advantages, such as providing a comprehensive overview of the specific topic, replicable and guiding the directions for new knowledge (Paul et al., 2021). There are several protocols and techniques available for conducting SLR. In this study, the updated Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA 2020) protocol established by Page et al. (2021) is adhered to guide the researchers. This protocol offers more explicit guidance and the capability to report both quantitative and qualitative studies.

Research Question

Formulating a research question will serve as a guiding framework for the study. PICo technique, which stands for population (P), phenomenon of interest (I) and context (Co), is used to develop the research question qualitatively (Stern et al., 2014). The population under investigation is the BI users; the interest focuses on identifying the challenges or barriers associated with the adoption and utilisation of the system, while the context is precisely at the individual level, without restricting it to any geographic or sectoral setting. Therefore, the research question for this study is: What are the challenges or barriers faced by users in adopting or using BI?

Eligibility Criteria

Eligibility refers to the inclusion and exclusion elements that define the scope of the study. First, the inclusion period is limited to five years (2020 – 2024) to ensure that only the most recent and relevant studies are included. Considering BI is a fast-emerging technology (Purnomo et al., 2021), focusing on the recent evidence and interconnections between the latest findings will offer more quality for informing future research directions (Kraus et al., 2020). Second, while BI is often used interchangeably with other terms such as business analytics, decision-support system or big-data analytics, BI is distinct from the related concepts in terms of process and how the technology is utilised (Ain et al., 2019). Therefore, this review focuses explicitly on BI. Table 1 shows the inclusion and exclusion for the study:

Table 1: Inclusion and Exclusion Criteria

Information Sources

Two academic online databases, namely Scopus and Web of Science, are used to find the relevant studies. These databases are selected due to their comprehensive coverage, interdisciplinary reach, and provide high-quality sources (Zhu & Liu, 2020). Employing dual databases also minimises the risk of single-source bias (Wanyama et al., 2022). The last date the information was retrieved from the databases is 30th September 2024.

Search Strategy

The authors used keywords such as end-user, employee, manager, practitioner, worker, executive and professional to capture all empirical evidence of BI studies that focus on the individual level. By using specific search strings combined with Boolean operators to refine the search results, the input phrases used are as follows:

Table 2: The Search Strings

Selection Process

The Scopus database yielded 415 records, and the Web of Science database produced 393 records, totalling 808. These records were saved as BibTeX files and imported into JabRef, an open-source reference management software (www.jabref.org). Using JabRef, duplicate records between the two databases were identified and merged, resulting in 236 consolidated records. Next, the remaining 572 records were screened for ineligibility using the software’s identification features. As a result, 487 records are excluded based on the parameters: 1) Studies within the domain of algorithm development, programming, modelling or computing of BI systems, 2) Studies discussing the methodological, technical or operational applications of BI, 3) Studies conducted at the organisational level (i.e. governments, enterprises, companies), 4) Studies initially registered as research article but subsequently identified otherwise. While relevant, this research falls outside the objective of this review study. An additional two records were excluded due to retraction notices indicated by one of the databases, resulting in 83 records for screenings.

Screening and Retrieval

Screening is a crucial qualitative process to ensure that only applicable and significant studies are included in the review. The remaining records were organised into a spreadsheet and distributed equally among the authors. Through rigorous manual assessments, the authors scrutinise each record’s titles, keywords, and abstracts to exclude articles that fall into the exclusion criteria. Consequently, 52 records were excluded, keeping 31 records eligible for retrieval. Further, full-text documents were retrieved from publishers’ websites or researchers’ collaboration platforms. Despite diligent efforts, three documents remained inaccessible. In addition, all authors agreed to dismiss another ten articles due to several ineligibility factors. Figure 1 illustrates the flow diagram of the methodology processes for this study, adapted from the PRISMA 2020 protocol (Page et al., 2021).

Data Collection

The authors reviewed the final 18 articles and tabulated them in a matrix format, detailing the research design, the country of data collection, and the industry of the studies. Next, thematic analysis, a structured qualitative method for identifying, analysing and registering patterns within textual data, is employed (Abdullah et al., 2022; Clarke & Braun, 2017). Using this method, phrases and statements that indicate the challenge, barrier or obstacle faced by users toward BI were identified and organised into a specific theme consented upon by all the authors.

Figure 1: Flow diagram of the methodology processes, adapted from Page et al. (2021).

RESULTS

The descriptive analysis of the data provides a comprehensive overview of the study, highlighting trends and patterns and enhancing the transparency of the review process (Hiebl, 2023). The analysis of publication trends over the period reveals a significant peak in 2023, indicating a surge of academic interest during that year, as shown in Figure 2. The study indicates a diverse geographical representation of studies, encompasses countries from Africa (South Africa), Asia (Jordan, United Arab Emirates, China, Thailand), North America (USA) and Europe (Sweden, Bosnia and Herzegovina, Portugal, Germany), along with one cross-regional study. Notably, there is a regional concentration of publications from Jordan (n=7), underscoring a focused research interest in this region. In addition, the review found that the studies span a wide range of professions, such as accountants, human resource managers, government employees and bank executives, reflecting the broad applicability and relevance of the research across different fields. From the research design perspective, 13 studies employed quantitative methods, four qualitative approaches, and one adopted mixed-method design, as illustrated in Figure 3. The varied research designs are advantageous as they offer a comprehensive understanding of the research topic (Hiebl, 2023).

Figure 2: Research Articles Distribution by Year
(*Note: Until September 2024)

Figure 3: Distribution of Articles by Research Design

FINDINGS

The systematic review identifies 12 key challenges and barriers encountered by users in adopting and utilising BI. These themes, some of which are recurrently mentioned across multiple articles, encompass a range of issues. Table 3 summarises the findings, and further explanation is provided below.

Table 3: Summary of Individuals’ Challenges in Adopting and Utilising BI.

Notes: (A) System Complexity, (B) Poor Data Quality, (C) Integration Difficulties, (D) Privacy and Security Concerns, (E) Lack of Skills, (F) Self-Resistance, (G) Limited Evidence, (H) Resource Constraints, (I) Insufficient Training, (J) Unsupportive Culture, (K) Job Mismatch and (L) Restricted Access.

A. System Complexity

System complexity appears to be the most significant challenge in BI, frequently addressed across multiple studies. It refers to poor usability and system unfriendliness, such as in navigating (Al-Dwairi et al., 2024; Jaradat et al., 2022; Sousa & Dias, 2020), the intricacy of processes involved in the operation (Al-Okaily et al., 2023) and difficulties in understanding the output generated by the system (Alkhwaldi, 2024). The studies emphasised that users, particularly those with little technological or analytical know-how, often struggle with the complicated features, leading to frustration (Alkhwaldi, 2024), confusion (Reinking et al., 2020), inefficient use (Trieu, 2023) and even discontinuance of usage (Mudau et al., 2024).

B. Poor Data Quality

The studies show that poor data quality, described as inaccuracy, inconsistency, unreliable and outdated data, has led to a decline in user engagement with BI. Since the information generated by BI relies heavily on the data input, substandard data quality can lead to flawed analysis (Awad & Mahmoud, 2024), unreliable insights (Sousa & Dias, 2020), uninformed decision-making (Khrisat & Darwazeh, 2023) and even potential risk to the organisation (Raed et al., 2023). Specifically, Mudau et al. (2024) highlighted that poor data quality can lead to mistrust and hinder the users from fully optimising the system.

C. Integration and Migration Difficulties

The studies indicate that integrating existing work systems and processes into BI can be very complex and resource-intensive (Al-Dwairi et al., 2024; Raed et al., 2023; Sousa & Dias, 2020). Moreover, users often encounter significant technical challenges when migrating essential functions into BI that may result in work disruptions (Reinking et al., 2020), especially when the data formats are incompatible (Al-Okaily et al., 2023). Consequently, these issues contribute to a reluctance among users to adopt and utilise BI.

D. Privacy and Security Concerns

Two studies have highlighted the issues with privacy and security concerns. In examining BI’s impact on human resource practices, Awad and Mahmoud (2024) indicate that the risk of data breaches and unauthorised access to sensitive information might pose significant threats to employee confidentiality. Maaitah (2023) also raises concerns about storing BI data on cloud-based platforms. These circumstances can lead to a lack of trust, hinder data sharing, and ultimately result in user retention toward BI.

E. Lack of Skills

Several studies highlighted the literacy and proficiency gap in using BI. Inexperienced users, particularly those with limited technological and analytical skills, often find BI systems overly complex, leading to users’ avoidance (Raed et al., 2023; Ritbumroong, 2023; Trieu, 2023). Consequently, insufficiently skilled employees can result in suboptimal use of BI, impacting the whole organisational performance (Khrisat & Darwazeh, 2023; Maaitah, 2023).

F. Self-Resistance

Self-resistance towards BI is another significant challenge emphasised in the studies. It can manifest as insecurity, where users fear that BI will disrupt their accustomed way of doing work (Awad & Mahmoud, 2024; Maaitah, 2023; Raed et al., 2023). Insecurity may also arise from concerns that BI could undermine one’s authority and managerial decision-making styles (Ritbumroong, 2023). Additionally, low enthusiasm due to a lack of awareness about the benefits of BI (Alkhwaldi, 2024; Khrisat & Darwazeh, 2023; Reinking et al., 2020) or a lack of confidence in using the system (Al-Dwairi et al., 2024; Al-Okaily et al., 2023; Passlick et al., 2020) can caused self-resistance.

G. Limited Evidence

A few studies have pointed out the issues with users’ scepticism towards BI, particularly when the immediate benefits are not apparent nor aligned with their current work purposes (Al-Dwairi et al., 2024). Users are likely to discontinue using BI when there is an absence of evidence showing BI will improve their work performance (Kapo et al., 2021; Mudau et al., 2024).

H. Resource Constraints

Considering BI is an organisational system, several studies highlighted that the lack of resources, such as infrastructure, human capital, and support systems, limits users’ ability to utilise the system. The issue is evident across various sectors, such as in higher education institutions (Maaitah, 2023), the healthcare industry (Janyapoon et al., 2021), and in developing countries (Jaradat et al., 2022).

I. Insufficient Training

Several studies have indicated that insufficient training significantly contributes to lower adoption rates and ineffective utilisation of BI. The issue encompasses multiple dimensions, including an inadequate number of training sessions (Al-Okaily et al., 2023; Jaradat et al., 2022), training modules that fail to equip employees with the necessary skills (Alkhwaldi, 2024; Sousa & Dias, 2020), insufficient ongoing support (Al-Dwairi et al., 2024), and unclear organisational guidance (Reinking et al., 2020). These factors collectively hinder the adoption and effective use of BI.

J. Unsupportive Culture

An unsupportive culture, such as resistance to change and lack of institutional enthusiasm toward innovation and data-driven practices, can pose significant challenges to the users in adopting and utilising BI (Al-Dwairi et al., 2024; Alkhwaldi, 2024; Jaradat et al., 2022). In addition, Trieu (2023) emphasised that an organisation’s restriction on users’ decision-making autonomy can also restrict their ability to use BI effectively. Furthermore, cultural resistance may arise from traditional reliance on routine practices and accustomed methods, such as evidence in the healthcare sector (Janyapoon et al., 2021).

K. Job Mismatch

Two studies demonstrated that the characteristics of employees’ tasks can influence their perception and attitude toward BI. When job responsibilities do not align with BI, users may struggle to effectively integrate BI into their work processes (Trieu, 2023). Further, Passlick et al. (2020) argue that there is no universal BI solution for every employee since each work nature is unique and requires tailored approaches.

L. Restricted Access

Finally, two studies indicate that restricted access to BI hinders users’ intention to adopt or utilise the technology. This barrier can stem from limited authorisation granted to employees (Bao et al., 2023) or restricted access to specific data sources (Lennerholt et al., 2021).

DISCUSSION

This section presents a further discussion of the findings above. Based on the 12 themes synthesised from the literature, the challenges and barriers users encounter in adopting and utilising BI can be categorised into three main domains: technological, human and organisational.

Technological Barriers

The technological factor derived from the findings refers to BI’s characteristics, functionalities, operations and information input and output. System complexity, such as poor usability, difficult navigation, intricate processes, and challenges in understanding outputs, often frustrates users, particularly those with limited technological skills. Poor data quality, including inaccuracies and outdated data, leads to flawed analyses and mistrust, hindering effective use. Integration and migration difficulties, such as incompatible data formats, further complicate BI adoption. Additionally, privacy and security concerns, including risks of data breaches and unauthorised access, undermine trust and deter data sharing. Collectively, these technological barriers pose substantial challenges to the successful implementation of BI systems.

Individual Challenges

Human factors in the studies encompass users’ knowledge, skills, experience, workplace position, personality, and work responsibilities. A lack of know-how, particularly in technology and analytics, makes users find BI systems overly complex. Self-resistance is another major challenge, manifesting as insecurity about disrupting accustomed routines or undermining authority and low enthusiasm due to a lack of awareness of the benefits. Additionally, scepticism arises when users see the absence of clear evidence of its effectiveness, leading to the discontinuation of use.

Organisational Issues

The organisational factor applies to the ability, culture, leadership, and collective behaviour within an organisation that may influence BI adoption and usage. Resource constraints, such as inadequate infrastructure, human capital, and support systems, limit users’ ability to use BI effectively. Insufficient and inadequate training, including a lack of ongoing support and guidance, further establishes BI adoption and utilisation challenges. An unsupportive culture, characterised by organisational resistance to change and low enthusiasm for innovation, poses additional challenges. Restrictions on decision-making autonomy and reliance on traditional methods exacerbate these issues. Job mismatch, where employees’ tasks do not align with BI capabilities, complicates integration into work processes. Lastly, restricted access, due to limited authorisation or access to data sources, deters users from adopting and utilising BI effectively.

The Interrelation of Factors

From the various factors identified, it is evident that these factors are not isolated, nor should they act as stand-alone. The studies show these factors are interrelated, with one factor potentially influencing or exacerbating another. This interconnectedness underscores the complexity of end-users challenges in adopting and utilising BI. Some factors have multiple origins. For example, system complexity can arise from human factors, such as a lack of skills and experience (Raed et al., 2023; Trieu, 2023). It can also stem from technological traits like poor design and usability, making BI challenging even for experienced users (Mudau et al., 2024). In addition, the findings show that some factors act as antecedents or precedents to others. For instance, insufficient training often results from resource constraints like a limited budget (Janyapoon et al., 2021). This lack of training, unfortunately, leaves employees unprepared to use the system effectively (Al-Dwairi et al., 2024). Consequently, without the necessary skills, users find the BI system complex and challenging, leading to low system utilisation (Mudau et al., 2024; Ritbumroong, 2023). Figure 4 illustrates an example of these interrelations and the cascading effects of factors realised from the studies.

Figure 4: An example of the interrelations of multifaceted factors that lead to BI failure (developed by authors).

RECOMMENDATION

Future studies investigating the impact of BI at the individual level should consider incorporating the key determinants identified in this study. However, it is crucial to acknowledge that BI is an organisational system that operates within a regulated context. Therefore, while this study emphasises individual-level factors, it is imperative not to overlook external elements influencing employees’ behaviour and attitudes toward the technology, particularly the dynamic of organisational influence. A similar recommendation has also been highlighted by Trieu (2023) and several other studies (Paradza & Daramola, 2021; Zhang et al., 2021). In addition, although the review focuses explicitly on BI, the authors believe the factors apply to other technology systems with similar characteristics, such as Enterprise Resource Planning (ERP) systems.

Secondly, the findings indicate three factors under investigation: privacy and security concerns, job mismatch, and restricted access. With the growing issues of privacy and security concerns of technology, especially involving big data such as BI (Al-Zahrani & Al-Hebbi, 2022), this element becomes imperative for future study. Further, there is a tendency for organisations to adopt new technologies to keep up with industry trends without thoroughly evaluating whether their workforce genuinely needs the technology or if it aligns with their job requirements, thus creating job mismatch issues (Griep et al., 2021; Trieu, 2023). This instance can lead to underutilisation and misallocation of resources. Conversely, there are also situations where employees are interested, or their position necessitates using BI to assist them in making decisions. However, their desire is restrained due to access limitations (Bao et al., 2023; Szukits & Móricz, 2024), resulting in missed opportunities for BI success. Future research should consider these arguments.

LIMITATION

The findings of this study should be interpreted with certain limitations. Firstly, selecting and assessing research that only focuses on the individual level is challenging, particularly in ensuring the accuracy of the research’s level of analysis. This process may introduce potential biases and confusion during the identification and screening stages. Future research should consider using alternative methodological approaches or techniques that are more robust, efficient and unbiased. Second, the review is constrained by the timeframe of studies published within the last five years, up to September 2024. Considering BI is a ubiquitous technology with ongoing implementation challenges, the findings and discussions may not capture the most recent developments. Third, the source of studies is limited by the scope of only two databases. Future research should consider incorporating a broader range of databases and registers, including grey literature, ensuring more comprehensive perspectives.

CONCLUSION

This study provides a comprehensive and unique review of the challenges and barriers to the adoption and utilisation of BI at the individual level. The review identifies 12 themes and highlights three main domains of obstacles: technological, individual, and organisational. Furthermore, the study emphasises that these determinants are interrelated, with some factors acting as antecedents or precedents to others. The study proposes that future research integrate external factors, such as organisational influences, that affect individual behaviour and attitudes toward BI adoption and usage. By offering insights from end-user perspectives, this study aims to illuminate the determinants of BI success for future researchers and practitioners. Consequently, it also addresses the knowledge gap in understanding why BI implementations often fail.

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