The rapid proliferation of mobile devices and technologies, as well as MBI’s significant potential in organisational decision-making, demonstrates that MBI success requires top management support that enables decision-making in line with organisational objectives, as well as robust security and compliance standards (Jansen 2020; Ngwenya 2023). Mobile Business Intelligence helps the mobile workforce to gain business insights through information analysis utilising mobile-optimised applications (Edirisinghe 2022).

Mobile Business Intelligence is a novel study area for real-time and integrated BI (Chi & Mahmud 2020; Jaradat et al. 2024). It combines BI with Internet standards and wireless communication technologies to make BI usage easier on mobile devices such as tablets and smartphones (Edirisinghe 2022; Hendrawan et al. 2024). Mobile Business Intelligence provides improved access capabilities by enabling employees to connect anytime and anywhere to the organisation’s back-end systems (Edirisinghe 2022). Furthermore, MBI plays a critical role in cost savings, removes manual processes, enhances correctness, validity and dependability, in that end users will input data directly into the device instead of through paper.

However, while mobility provides more benefits, it can also pose a larger threat to organisations and the mobile workforce if not handled properly. For instance, some of the mobile workers may possibly feel that they are being tracked, and their privacy is compromised. The lack of trust in mobility might become a barrier for organisations looking to employ mobility to gain a competitive advantage and improve business operations.

The adoption of MBI is critical as it provides decision-makers with useful information to make informed decisions, reaching the goal of their organisation (Jaradat et al. 2024). Successful approaches to designing applications for personal computers cannot be applied to mobile devices directly, owing to limitations of the devices’ screen size and computing power, the characteristic behaviours of mobile users and the dynamic impact of the external environment (Weichbroth, Kowal & Kalinowski 2022).

The integration of non-relational databases ensures that these systems remain effective tools for data analysis in the big data era. Organisations throughout the world are battling to benefit from MBI initiatives, resulting in financial loss, waste of time and resources, as well as unrealised benefits (Alali, Nofal & Alharafsheh 2019; Al-Okaily et al. 2021). Thus, it is important that organisations looking to adopt MBI solutions be guided by a readiness model that will enable these organisations to explore all factors necessary for the successful adoption of MBI and to understand MBI users’ behaviours, satisfying their real needs.

The readiness factors will help to ensure that all important factors are considered in a timely manner, which will, in turn, save organisational resources such as time and costs in realising anticipated benefits.

Through the use of handheld devices, MBI gives a mobile workforce access to real-time data and analytics reports at any time and from any location (Michael et al. 2024). Mobile Business Intelligence improves the quality and timeliness of data by embracing a broad range of technologies and applications that acquire, store, evaluate and then make integrated data available for use in making decisions (Bharadiya 2023a; Edirisinghe 2022; Tavera et al. 2021). The effectiveness and promptness of MBI can determine not just the distinction between loss and profit but also whether the organisation survives or fails in the best and worst times.

Mobile Business Intelligence improves the ability to access, analyse and act on data from anywhere, leading to better business outcomes and more flexible and informed decision-making processes (Jaradat et al. 2024). Mobile Business Intelligence lowers expenses, increases operational effectiveness and improves customer happiness, giving a competitive edge, offering real-time information and improving network performance and service delivery (Roberts 2022). Ensuring that the organisation has real-time insights at its fingertips.

South African organisations have limited Internet access, particularly in remote areas where there is no infrastructure, and there is a scarcity of critical services such as electricity, water and Internet (Musonda et al. 2024). The exorbitant cost of data and mobile devices in South Africa is also a hurdle, as organisations must invest in data and mobile devices so as to make MBI solutions accessible to their employees. Other challenges include political and regulatory aspects, inadequate knowledge and skills, inadequate infrastructure, primitive user interface, MBI implementation failures, lack of readiness and more (Al-Okaily et al. 2021).

Furthermore, insufficient skills and knowledge, complexities of mobile devices, lack of in-house expertise, high cost and data irregularities continue to discourage many organisations from adopting MBI solutions (Matoza et al. 2021). Adopting a cloud computing paradigm that delivers MBI solutions as a service over the Internet will dramatically remove the obstacles of high costs and lack of in-house expertise and assist in speeding the adoption of mobile solutions (Muriithi & Kotzé 2013).

It is imperative for organisations seeking to invest in MBI solutions to be knowledgeable and adept at factors affecting MBI readiness. This will assist in ensuring that organisations make informed decisions towards MBI investments. Knowing and understanding factors affecting MBI readiness will potentially save organisations a considerable amount of time and resources, as well as ensure the return on investments. Although the established factors are based on the South African telecommunications sector, other organisations in other industries can adapt factors relevant to their specific environment.

There is a need for increased privacy and security of organisational data on mobile devices and applications.

With these portable devices (i.e. laptops, smartphones, Personal Digital Assistant [PDA] etc.), data transmitted can be intercepted with ease, and working in public places increases the risk of ubiquitous access and accidental dissemination of information to unauthorised parties.

Ensuring that data usage respects user privacy and security requirements is critical for retaining trust (Hutt, Das & Baker 2023). As data in high-security contexts becomes more open and accessible, it is crucial to prioritise improving privacy so as to preserve the integrity, confidentiality and availability of data on MBI solutions (Zaerens 2023).

Organisational personnel may be a significant security vulnerability, whether or not they are aware of it; therefore, it is imperative that all personnel receive security awareness, policy and practice training to safeguard organisations against security threats and vulnerabilities and to bridge the digital divide (Aljedaani et al. 2020). Implementing MBI becomes more secure and seamless when employees comprehend and adhere to mobile-security requirements and cybersecurity measures employed by the organisation.

Cybersecurity is a multi-faceted domain that refers to professional practices, tested processes and technologies designed to protect networks, systems, devices, information, programmes and data from unauthorised access, damage or attack (Ahmad et al. 2024; Peslak & Hunsinger 2019). Mobile devices such as tablets and smartphones are the most highly used smart devices across the globe and are considerably pervasive in society.

Notably, hackers or cybercriminals gradually develop malware (malicious software) to compromise mobile devices, consequently making mobile devices the main targets for malware attacks (Ahmad et al. 2024; Alazab et al. 2020).

Malware compromises information stored on mobile devices by spreading onto these devices. Cybercriminals can undermine businesses by installing malicious apps on mobile devices that pose as trustworthy apps (Ferdous et al. 2025). Mobile devices can further be used to launch Distributed Denial of Service attacks against corporate networks (Ahmad et al. 2024; Siris 2020). This can cause enormous financial losses and negatively affect the integrity, confidentiality and availability of mobile devices (Alazab et al. 2020; Gao & Zhuang 2020).

The telecommunication sector is overwhelmingly becoming more susceptible to cyberattacks with the rise of the Internet of Things extending to interconnected devices such as smart buildings, driverless cars, digitalised home appliances, smart devices, drones, etc. (Ahmad et al. 2024). Traditional protection mechanisms are ineffective and incapable. Thus, there is a need to employ modern security measures that will empower organisations to identify, protect, detect and timeously respond to and recover from security breaches (Kamoun et al. 2020).

Recent studies identified some key dimensions that are likely to influence cybersecurity, such as digital literacy, security awareness and support availability (Vrhovec et al. 2024). It is, therefore, fundamental that organisations seeking to invest in MBI technology are guided on critical factors for such investments in order to ascertain their level of readiness. Furthermore, this will ensure that the correct measures are employed suitable to the organisational needs, thus saving time and resources.

The study employed a descriptive design, distinguished by research questions beginning with or including ‘What’, ‘Who’, ‘When’, ‘How’ or ‘Where’ (Saunders, Lewis & Thornhill 2019). According to Leedy and Ormrod (2016), descriptive research investigates a situation rather than changing or modifying it. The study extensively reviewed literature relating to MBI. The questionnaire’s measuring items were then designed using the information that had been gathered. Following data collection, Statistical Package for Social Sciences (SPSS) version 20 (IBM Corporation, Armonk, NY, United States) was used to code and transcribe the questionnaire for analysis. Quantitative analysis and validation were carried out.

Furthermore, the quantitative research design was adopted in this study, which provides an in-depth understanding of the present state of MBI readiness in the South African telecommunication sector. The design provided deeper knowledge related to the research. This led to critical factors that are significantly affecting MBI readiness within South Africa (Bentouhami, Casas & Weyler 2021).

The research was conducted with a focus on the South African telecommunications sector, which is dominated by Vodacom, MTN, Telkom, Cell C and newer entrants, including Rain and several Mobile Virtual Network Operators. The participants were chosen using a probability random sampling method; the study also ensured that the survey was as inclusive as possible, including employees from various business units such as information and communications technology, communications, marketing, strategic enablement, operations, human resources management, governance risk and compliance, finance, supply chain management, audit and so on. The total number of 128 questionnaire responses was received and tested and was found to be valid and reliable.

Employees who were deemed to understand BI and MBI participated in the study with a background in the telecommunications industry. Executives, managers, specialists, analysts, architects, developers, testers, support and BI users were among the workers.

Demographics, knowledge and experience with computers or mobile devices, the Internet and BI, general experience, occupation and BI involvement, as well as their impressions of BI concepts and effects on preparation, were all collected as shown in Table 1. Furthermore, to confirm the relevance and correctness of the obtained data, participants were asked to indicate whether or not they were familiar with MBI.

There was a total number of 128 participants. The male participants contributed 70.3%, while female participants contributed 29.7%. The respondents were grouped into different factors such as gender, education level, work experience, business unit, occupation and their involvement in BI.

The quantitative approach was employed to effectively achieve the study objectives. This approach allowed the researchers to gather large-scale data and to use statistical techniques to analyse trends, correlations, and the influence of variables (Saunders et al. 2019; Watson, Kim & Liu 2023). The quantitative research provides objectivity, control and accurate measurement.

This strategy was the best option for providing unambiguous, data-driven insights regarding MBI readiness across a broad sector in which objectivity, generalisability and scalability are essential (Dos Santos et al. 2021).

The data for this study were collected using the quantitative technique; namely, a survey was conducted using closed-ended questionnaires based on a five-point ordinal scale. The selected data-collection method (i.e. closed-ended questionnaires) empowered the researcher to accomplish the goals of the study, which is considered the best method for the study, particularly when examining specialised phenomena such as MBI readiness in the telecom industry. This method ensured that the study is more objective, is cost-efficient, saves time and is easy to complete; the data are easy to analyse; the method speeds up the process of collecting data and coding it using statistical packages (Babbie 2020).

The use of this method assisted in reducing respondent bias by limiting the potential for being too subjective or giving long responses, which may be influenced by personal interpretation (Creswell 2021). This method provides a form of standardisation that facilitates ensuring that all respondents answer the same set of questions in the same way, thus improving the reliability of results (Fink 2017). The collected data were coded and transcribed using the SPSS version 20, and Cronbach’s alpha was used for reliability testing (Ghanem 2023).

Furthermore, the study employed quantitative analysis because it makes it possible to extrapolate findings from a sample to a larger population, providing findings that are accurate, quantifiable and amenable to statistical analysis, making it easier to compare groupings over time and eliminating bias (Taherdoost 2021). A large number of participants could be readily reached using quantitative data-collection methods, such as online surveys, which make them appropriate for extensive evaluations. The efficiency and representativeness of research are enhanced by extensive data collection, especially in MBI readiness-assessment contexts (Pallant 2020).

According to Johnson and Christensen (2014), one of the main advantages of quantitative research is its reproducibility, which enhances its legitimacy and decision-making value. Thus, this method is particularly useful for this study in that it allows organisations to objectively test readiness, compare results across units and forecast the influence of readiness factors on MBI readiness. The scalability and statistical rigour of quantitative research render it indispensable for evidence-based decision-making.

Data screening was performed to uncover potential mistakes, miscoding, missing data and normalcy, as well as to guarantee that statistical analysis processes were precise and estimations had a solid foundation. A normality assessment was performed to identify how to handle nonnormality. To eliminate biased results, the data were checked for univariate and multivariate outliers. Furthermore, all items were checked for accuracy. Microsoft Excel 2013 (Microsoft Corporation, Redmond, WA, United States) and SPSS version 20 were further used to check the missing data in the questionnaires, and the incomplete questionnaires were discarded.

Descriptive statistics were used to summarise data in a manner that explains the correlation between variables in a population. This was a necessary first step towards computing statistics, which is undertaken during research, and it is always recommended to carry out prior to making inferential statistical comparisons (Kaur, Stoltzfus & Yellapu 2018). The variables were descriptively analysed, and the minimum, maximum, mean, standard deviation and skewness were determined. The questionnaire used for the variables was based on a five-point Likert scale.

Reliability testing strengthens and ensures the replicability and validity of study findings, enhances credibility and relevancy and ensures that the construct represents the concept being studied, enabling meaningful and trustworthy conclusions. It ensures that constructs assess what they should measure throughout time or across diverse settings (DeVellis 2016). Before testing for the validity and reliability of constructs, each item was evaluated for its reliability and validity. The validity and reliability of the collected data were then examined to ensure that it was dependable and could be utilised to derive reasonable conclusions.

Cronbach’s alpha was used to assess questionnaire reliability. The surveys have an aggregate α-coefficient of 0.960, exceeding the minimum value of 0.7. As a result, the surveys were proven to be reliable and usable. Furthermore, the reliability and validity of each construct were assessed. Ninety-five per cent (95%) of constructs exceeded the minimum α-coefficient of 0.7. Five per cent (5%) of constructions with α-coefficient values below 0.7 were abandoned.

Ethical clearance to conduct this study was obtained from the Faculty of Research Ethics, Tshwane University of Technology (No. FCRE/ICT/2017/05/002[2]).

Mobile Business Intelligence solutions have the potential to improve decision-making, but adoption challenges remain; these include, among others, high costs, limited skills, perceived complexity and technology distrust. Furthermore, the MBI adoption process is hampered by a reluctance to change, inadequate resources and a lack of support from relevant stakeholders, including the organisational leadership, as well as data security and privacy concerns.

There is a need for more economical and user-friendly solutions and a culture that values technology, teamwork, continuous learning, innovation and data-driven approaches to improve the adoption and use of MBI.

South African organisations continue to struggle with the integration of MBI technologies, which is worsened by a lack of qualified personnel. According to Dube and Ruxwana (2022), the difficulty of comprehending the unique requirements and challenges encountered by South African organisations when implementing MBI solutions is further compounded by the lack of local research. This is mainly because mobility brings about various challenges as well as opportunities, which the telecommunications sector needs to embrace.

The challenges presented by mobility include among others: Security and privacy of proprietary information; limited processing, graphics, display, etc.; lack of standardisation in mobile offerings from mobile platform vendors; lack of adoption of globally available mobility solutions; fragmented mobile platforms and diversity issues, e.g., different types of mobile browsers, inputs methods, operating systems, inter alia; adherence to policies and standards and lack of MBI readiness factors that will enable organisations to make informed decisions in adopting MBI solutions.

Fourteen critical factors that affect MBI readiness were identified, which will aid in improving the adoption of MBI within organisations. South African organisations are lagging behind in terms of the adoption of MBI compared with other countries. This is exacerbated by the fact that South Africa is plagued by high levels of poverty, an unequal society, a high cost of mobile data, unequal access to technology and a persistent digital divide, among other factors. Internet connectivity remains a dream in some poor and disadvantaged communities despite MBI’s heavy reliance on Internet access.

The adoption of MBI within the South African telecommunications sector will most likely increase significantly in the future. The anticipated increase will be attributed to, among others, unprecedented culture change, government programmes, increased MBI models and the prevalence of rare diseases such as the outbreak of COVID-19, which forced most businesses and workforce locally and across the globe to operate remotely.

The policy landscape assists organisations in navigating compliance and security challenges while implementing mobile technologies. South Africa continues to focus on digital literacy and technical capability improvements. A large part of MBI adoption is dependent on employee readiness, which encompasses training employees to effectively utilise MBI solutions. It is anticipated that South Africa will be particularly deliberate about upskilling and reskilling programmes and continue to employ various measures to fight the high levels of poverty, positioning MBI as a critical tool for improving data-driven decision-making.

The use of mobile technology can be a beacon of hope, enabling billions of people across the world to connect. Since the reign of COVID-19, most organisations in South Africa and across the globe have adapted so as to conduct their businesses remotely. It is vital for organisations to focus on designing and integrating mobile solutions that are specifically tailored to local demands. According to Botha and Smit (2020), MBI solutions must be customised to address the unique issues and challenges that each organisation faces, including cultural diversity, varied languages and industry-specific requirements.

To increase the adoption of MBI, organisations must continue to focus on various initiatives, such as improving mobile infrastructure, building capacity through training, fostering government and policy support, encouraging industry-specific adoption and addressing security concerns. Together, these initiatives create a more conducive atmosphere for the adoption of MBI, enabling organisations to use real-time data to make decisions and boost operational effectiveness.

The study focused on South Africa, with data gathered especially from South African organisations in the telecommunications sector. As a result, it does not accurately reflect all countries around the world. Although some readiness factors may be comparable across countries and industries, generalisation of the results should be approached with care. Future research should consider a broader range of industries to gain a more comprehensive knowledge and understanding of the problem and its implications across sectors.

A cross-sectional survey was used in the study; data were gathered at a specific time. The results of this study may not be as useful in predicting future trends; results could be attributed to a variety of circumstances, including the increasing use of the Internet, rapid technological advancements, natural or man-made disasters, rare disease outbreaks (e.g., COVID-19), cultural shifts and the increasing automation of business operations.

The study makes a significant theoretical, methodological and practical contribution by providing organisations with a practical guide for assessing their MBI readiness, reflecting opportunities to look into critical readiness factors that will help them realise anticipated benefits and make informed decisions while saving money, time and resources. The identification of the key factors influencing MBI readiness through various theories, literature and surveys makes a substantial theoretical, methodological and practical contribution and has paved the way for future research. Organisations could use the knowledge provided by these critical readiness factors and their impact on practice to design better strategies and tactics for MBI adoption. These organisations could further experience a high success rate on MBI investments and better support while exploring MBI initiatives.