Abstract
Mobile Business Intelligence (MBI) is among the latest trends in business technology, which allows users to access business intelligence information from mobile devices and platforms via specific applications. The continued development of mobile technologies and applications can potentially provide users with opportunities to analyse business information directly on their mobile devices, which can decrease business costs by cutting down on formal IT analytics. The adoption of MBI can have a number of benefits to the organisations, including enhanced decision-making and improved productivity. However, there are certain factors that can affect the implementation of MBI technologies and the user’ acceptance, which creates a need to study MBI adoption in context. This study aims to outline the key internal and external drivers of MBI adoption in Saudi Arabia through a quantitative investigation among the leading Saudi Arabian Organisations.
Business intelligence (BI) is a relatively new concept in business research and technology, which has evolved considerably over the last two decades (Lim, Chen, & Chen, 2012). Business intelligence involves the collection and analysis of information that is useful to the decision-making process, such as competitive environment, market data, behavioural patterns of consumers, and more. As explained by Howson (2013), “Business intelligence cuts across all functions and all industries” (p. 4), meaning that it is widely applicable and effective, if used correctly. According to Bucur (2012), BI systems allow businesses to be more effective in identifying market opportunities and deficient market segments, determining successful products and product lines, analysing sales and distribution patterns, and decreasing costs by assessing the profitability of operations. Moreover, BI technology can also become a useful source of information for decision-making, thus helping businesses to be more reactive to internal and external changes (Lim et al., 2012).
However, as the businesses grew more and more proficient in the use of BI technologies, the demand for BI practitioners to provide mobile technologies has risen (O’Donnell, Spisma, & Watt, 2012). Mobile Business Intelligence (MBI) systems allow businesses to access BI data and features from mobile devices, such as tablets and smartphones. Using MBI can further enhance the speed of decision-making, as it allows users to quickly access intelligence data from anywhere. The use of MBI can provide many important benefits to businesses, such as accelerated decision-making and increased operational productivity (Brodzinski, Crable, Ariyachandra, & Frolick, 2013). Moreover, mobile technologies can offer certain features that are not available on other types of devices, such as tracking near real-time data and issuing alerts to the user (Chan, Tan, Lau, & Yeoh, 2013). These features make MBI technologies appealing to a wide range of businesses. In 2012, nearly 33% of U.S.-based organisations planned to adopt MBI in the future (Chan et al., 2013). It is anticipated that this figure will grow all over the world along with the development of mobile devices usage. According to the Ministry of Information and Communication Technology, the total number of mobile subscriptions has reached 44.5 million individual subscribers by the end of the first quartile of 2017. The majority of these subscriptions (over 80% in total) are prepaid, while the rest constitutes for freeware (“ICT Indicators,” 2017).
However, despite the benefits that MBI use may offer to organisations, there are many considerations that need to be taken into account prior to the adoption of this technology. For instance, in certain countries, low user acceptance of mobile technology is perceived to be a widespread limitation (Bargshady, Pourmahdi, Khodakarami, Khodadadi, & Alipanah, 2014). Security considerations are also crucial to the implementation of MBI systems (Chan et al., 2013). This is due to the fact that the security of high-volume data transmission via 3G or 4G networks is a persistent issue, as well as because mobile devices can be subject to loss or theft (Chan et al., 2013). Overall, the adoption of MBI can pose a number of challenges, and thus has to be evaluated within a larger context of social, economic, political, technological, and cultural factors that might have an influence on the implementation process.
Objectives, Aims, and Research Questions
There are many factors that could have affected the adoption of MBI by Saudi Arabian businesses, within the context of individual organisations and companies. The overall implementation and use of BI systems are among the factors that influence user acceptance of MBI services (Brockmann, Stieglitz, Kmieciak, & Diedrich, 2012). Moreover, companies that use BI systems extensively and on a regular basis will find the adoption of MBI crucial, whereas those that do not rely much on BI might deem it ineffective and unnecessary. Due to the benefits it provides, MBI is more effective in business sectors with high levels of competition, as well as in rapidly changing business environments. However, in sectors where the competition is low and the environment is more stable, MBI systems are less needed. The use of MBI can also vary by the size of companies: although MBI is just as useful in SMEs as in large businesses (Adeyelure, Kalema, & Bwalya, 2016), the latter tend to be more innovative and are thus more likely to embrace mobile technologies. Therefore, determining the business landscape and the prevalence of small, medium, and large companies will help to predict the interest in MBI. The advantages that MBI provide can be general, such as improved performance and decision-making, or specific to the business environment of the country, which is why it is necessary to obtain both secondary and primary information. Furthermore, it is important to understand that the adoption of MBI is a substantial process that involves a lot of considerations and consequences. The considerations of adopting MBI in Saudi Arabia include information security, user acceptance, and technical requirements (Chan et al., 2013). The overall organisations’ readiness for MBI implementation should also be taken into account. Readiness is among the key factors influencing the success of BI systems implementation (Anjariny, Zeki, & Hussin, 2012), which is why a good level of readiness may become a driver for MBI adoption. Verkooij and Sprut (2013) state that, upon the implementation of MBI, device management becomes one of the leading concerns. For example, incorrect use of mobile devices that support MBI may result in data loss and leaks, which can be damaging to the business (Verkooij & Sprut, 2013). These potential consequences could be considered inhibitors that have slowed down the adoption of MBI in the Saudi Arabian region.
The primary aim of this research is to study and outline the key drivers of MBI adoption in the context of Saudi Arabia. Doing so would improve the perspective on MBI services, their usefulness, implementation methods, and the reasons behind their implementation for individual companies, big or small, as well as for entire industries as a whole. This understanding will allow for more accurate prognoses on MBI development in the region as well as for the projection of the results of the research on other countries in the Arabian Peninsula, as business practices and national specifics bare many similarities. Other than the direct aim of studying the responses of many different organisations and companies as well as identifying the internal and external factors that promoted or inhibited the adoption of MBI within the particular region, the study also seeks to provide statistical data and a theoretical basis for further research on the subject. It could potentially be used as a framework for similar research to be conducted in the future, in a different setting.
The list of research questions that will be answered in the proposed study is thus as follows:
- What were the main external drivers that led the companies in Saudi Arabia to extensively use MBI?
- What were the main internal drivers that led the companies in Saudi Arabia to extensively use MBI?
- What are the main external inhibitors that prevented or delayed certain Saudi Arabian companies from implementing MBI in their daily management processes and routines?
- What are the main internal inhibitors that prevented or delayed certain Saudi Arabian companies from implementing MBI in their daily management processes and routines?
Literature Review
Despite the increasing attention towards the use of mobile technologies for increasing productivity of business operations, the number of studies available on the topic of MBI and its adoption is very limited. The recent literature on MBI can be roughly divided into three categories: benefits of MBI, implications and challenges for adoption, and user acceptance.
Benefits of MBI
Perhaps, the majority of articles available on the topic of MBI aim to study and review the possible benefits of its adoption. For instance, Chen, Chiang, and Storey (2012) outline the key characteristics of MBI as presented in the BI&A 3.0. They show that most of the benefits of mobile BI apply to the analysis process. For instance, MBI features allow producing location-aware analysis (Chen et al., 2012). This is useful to businesses that are present in different areas of the country, as this feature allows to generate location-specific information. For example, location-aware analysis can be used to identify business opportunities in other geographical areas or predict threats in the current location.
A study conducted by Golapakrishnan Nair in 2015, titled “Information mobility and business transforms” addresses the topic of the dynamic growth of information technology in business and organisational management around the world. It covers the use of innovative information mobility technologies in various parts of the world, including the Saudi Arabia in order to improve productivity, Human Resource management, marketing efforts, logistics, strategic decision-making, customer relationships and interactions, as well as sales management overall. Information mobility technology is expected to transform enterprise patterns and its operations through various modes of liberal coupling of users and a variety of business processes. The enterprise mobility needs to deal with terms like BYOD, CYOD, MDM, M-Security and M-Analytics to ensure a successful implementation leading to expected transformation (Golapakrishnan, 2015).
Another benefit of MBI compared to computer-based BI is the possibility to conduct person-centred analysis (Chen et al., 2012). Mobile devices are private and are normally used by a single person. They are also highly personalised, which allows MBI practitioners to produce applications providing person-centred analysis. This means that the information available through MBI applications is different for each employee. This allows for higher information security, as the managers can set access boundaries. Moreover, this increases the efficiency of MBI, as each employee will only receive analytical information that is relevant to his or her work specifically. Most importantly, however, user-centred mobile intelligence systems can provide users with intuitive insight without requiring extensive IT analytics reports (Jeong, Kim, Hwang, Lee, & Jung, 2012), which can expedite the decision-making and help to cut the costs of IT services. The possibility of context-relevant analysis is another benefit of mobile BI, according to Chen et al. (2012). Using business intelligence within a particular context allows users to get a deeper insight into the information, thus making the data more useful to the business. Finally, mobile devices offer extensive opportunities for visualisation, which enhances the presentation of data, making it more accessible to the viewer (Tona & Carlsson, 2013).
Another source that provides extensive information on the benefits of MBI is Stodder’s (2012) Best Practice report on MBI and analytics. Stodder (2012) provides a useful insight into the topic by presenting data from a primary qualitative research. The study involved 406 respondents who are employed in organisations that were planning to implement BI and analytics on mobile devices. The majority of the participants (65%) believed that adoption of MBI would lead to improved sales, service, and support (Stodder, 2012). 60% agreed that the use of MBI would bring more efficiency and coordination into business processes and operations, whereas 50% thought that MBI would result in “Faster deployment of BI and analytics applications and services” (Stodder, 2012, p. 12). When asked about information access and management benefits of MBI, most of the respondents agreed that the technology would give faster and easier access to information, providing right-time data that are relevant to the user’s role (Stodder, 2012). Overall, it is clear that the perceived benefits of MBI stream directly from the possibilities that are offered by mobile intelligence services.
Implications and Challenges for Adoption
Chan et al. (2013) point to a lack of systematic studies regarding the successful implementation of MBI, despite the fact that the adoption of such technology has many implications and considerations. For instance, as discussed above, data security might be impaired if sensitive information is stored on mobile devices and transmitted through the use of mobile networks (Chan et al., 2013). Moreover, there are also certain technical considerations that have to be taken into account before adopting MBI technologies. For instance, the mobile interface does not allow for the same range of features that are available to desktop users (Chan et al., 2013). Screen limitations, for instance, can lead to disruption of graphs and other visual data, which is inconvenient to users. Moreover, successful business intelligence requires transmitting and receiving large volumes of data; one of the key considerations is the storage of such data on mobile phones or in the cloud, as both options offer a limited volume of storage (Chen et al., 2013). High-volume cloud data storage, indeed, can result in additional costs for the business. While storing information locally may be a viable option (Verkooij & Sprut, 2013), smartphones that have higher storage capacity are more expensive. Finally, another significant concern is the use of a mobile network. While MBI allows users to have instant and fast access to information via 3G or 4G (Tona & Carlsson, 2013), unstable connection or poor coverage may limit the possibilities of this technology.
User Acceptance
User acceptance of mobile business intelligence systems has received a lot of attention in recent scholarly research on the topic of MBI. Brockmann et al. (2012) explain the concept of user acceptance of MBI through the Technology Acceptance Model for Mobile Services (TAMMS). According to TAMMS, factors such as perceived value, perceived ease of use, and trust influence the users’ intention to use the new technology, which in turn affects its adoption (Brockmann et al., 2012). This model is also useful in studying the key drivers for the adoption of MBI in organisations, as it describes the factors that influence implementation decisions. For instance, the availability of MBI supported by reliable service providers can have a positive impact on the management’s decisions to adopt the technology (Brockmann et al., 2012). On the other hand, Bargshady et al. (2014) propose a different model to determine user acceptance. According to the research, factors influencing adoption decisions include information quality, system quality, individual effect, social effect, and organisation climate (Bargshady et al., 2014). This model is important as it examines the social and organisational factors that are at play in adoption and use decisions. For instance, researchers suggest that manager and coworker support of the technology will increase user acceptance, whereas the lack of essential skills and the need for training will have an adverse effect on it.
Research Design and Methodology
The proposed research will be a quantitative study that will take form of a survey. The study will be separated into several stages: literature review, questionnaire formulation, research sampling, data collection, and data analysis. All sections are relevant to the research as using a mix of secondary and primary data allows for increasing the scope of research and gaining a valuable insight into the topic. The literature review will focus on recent scholarly articles from peer-reviewed journals that offer information relevant to the research questions proposed. In order to gather primary information, however, semi-structured interviews will be conducted with employees and management of Saudi companies operating in different business sectors.
Questionnaires can be used to collect primary data from the respondents. They will be distributed to managers of major Saudi Arabian organisations, who have been working in the field during the time of implementation of the MBI or have knowledge about what factors influenced their decisions to undertake the adoption of these techniques. The questionnaire will be constructed in a way to directly outline all the major internal and external factors that either promoted or inhibited the adoption of MBI in Saudi Arabia. After the questionnaires have been filled, the data will be analysed using standard statistical methods like the Z-test, in order to outline the incidence and prevalence of certain factors over others. Other information to be taken into consideration in this research is the industry that the companies and organisations are operating in, their size at the moment of adoption, and perceived effectiveness of MBI performance. The information will be processed with the use of computerized software, in order to exclude the possibility of human error during calculations, as well as to speed up the process. In order to protect the privacy of the respondents and the companies that chose to participate in the research, all names and critical data will be subjected to anonymity. The data will be presented in the form of a written report, supported by graphs and tables where relevant for effective presentation. Overall, I believe that choosing such design and methodology will help me to conduct a thorough research of the topic and to answer to all of the proposed research questions.
References
Adeyelure, T. S., Kalema, B. M., & Bwalya, K. J. (2016). Development of Mobile Business Intelligence framework for small and medium enterprises in developing countries: Case study of South Africa and Nigeria. In 4th International Symposium on Computational and Business Intelligence (ISCBI) (pp. 11-14). IEEE.
Anjariny, A. H., Zeki, A. M., & Hussin, H. (2012). Assessing organizations’ readiness toward business intelligence systems: A proposed hypothesized model. In 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT) (pp. 213-218). Web.
Bargshady, G., Pourmahdi, K., Khodakarami, P., Khodadadi, T., & Alipanah, F. (2014). The effective factors on user acceptance in mobile business intelligence. Jurnal Teknologi, 72(4), 49-54.
Brockmann, T., Stieglitz, S., Kmieciak, J., & Diederich, S. (2012). User acceptance of mobile business intelligence services. In 15th International Conference on Network-Based Information Systems (NBiS) (pp. 861-866). Web.
Brodzinski, J., Crable, E., Ariyachandra, T., & Frolick, M. (2013). Mobile business intelligence. International Journal of Business Intelligence Research (IJBIR), 4(2), 54-66.
Bucur, C. (2012). Implications and directions of development of web business intelligence Systems for business community. Economic Insights-Trends & Challenges, 64(3), 96-108.
Chan, L. K., Tan, H. K., Lau, P. Y., & Yeoh, W. (2013). State-of-the-art review and critical success factors for mobile business intelligence. Communications of the IBIMA, 2013(1), 1-10.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
Gopalakrishnan, N. (2015). Information mobility and business transforms. International Research Journal of Electronics & Computer Engineering, 1(1), 19-23.
Howson, C. (2013). Successful business intelligence: Unlock the value of BI & big data. New York City, NY: McGraw-Hill Education Group.
ICT Indicators in K.S.A by end Q1-2017. (2017). Web.
Jeong, D. H., Kim, J., Hwang, M., Lee, S., & Jung, H. (2012). User-centered mobile technology intelligence system. In Proceedings on Advanced Computer Science and Technology (pp. 194-199). Web.
Lim, E. P., Chen, H., & Chen, G. (2013). Business intelligence and analytics: Research directions. ACM Transactions on Management Information Systems (TMIS), 3(4), 17-27.
O’Donnell, P., Sipsma, S., & Watt, C. (2012). The critical issues facing business intelligence practitioners. Journal of Decision Systems, 21(3), 203-216.
Tona, O., & Carlsson, S. A. (2013). The organizing vision of Mobile Business Intelligence. In Proceedings of the 21st European Conference on Information Systems (pp. 114-126). Web.
Stodder, D. (2012). Mobile Business Intelligence and analytics. TDWI Best Practices Report. Web.
Verkooij, K., & Spruit, M. (2013). Mobile business intelligence: Key considerations for implementations projects. Journal of Computer Information Systems, 54(1), 23-33.