Factors affecting Mobile Business Intelligence readiness in the South African telecom sector

The way organizations deliver their business intelligence (BI) has changed substantially with the introduction of mobile technologies. Mobile BI, which incorporates mobile technologies and BI, is now used to enhance the decision making process in the organization. This study investigated information quality in mobile BI and some factors influencing the intention to use mobile BI using the DeLone and Mclean model. Through a quantitative research approach, it was found that information quality had an impact on the intention to use mobile BI. Moreover, the type of mobile BI application examined in this research, had no significant influence on either information quality or intention to use. It was also found that mobile BI provided accurate, complete and relevant information with respect to the BI applications examined in this research.

The Role of Implementing Mobile Business Intelligence (MBI) in Decision Making Process: An Empirical Study at Jawwal Company

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Huge amount of data produced in business can be processed to get useful information using B usiness intelligence (BI). Business intelligence comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current and predictive views of business operations. Mobile business intelligence (MBI) is the ability to access BI-related data such as Key Performance Indicators (KPIs), business metrics, and dashboards on mobile devices. The MBI provides users with necessary information and content, regardless of their location and time, in order to gain business insights through information analysis using applications optimized for mobile devices. Although in recent years, the business intelligence and analytics has gained an increasing amount of interest among researchers and practitioners, there is a little academic literature available on MBI. This survey is intended to study the adoption of Business Intelligence (BI) with special emphasis on mobile business intelligence in various fields during past 10 years and highlights the challenges faced by users in its implementation. The study also emphasized on relationship between MBI and big data analytics, application of MBI, and difficulties in using the MBI tools. Keywords : Mobile business intelligence, business intelligence, big data, analytic

Mobile business intelligence used for business intelligence mobile service applications increasingly. According to Gartner (2011), global smartphone sales had arrived at 630 million in 2012, and are supposed to reach 1,105 million items in 2015. As a result, business intelligence users not only rely on desktop computers, while they as well want mobile access to joint and used data. Nevertheless, few studies have been consummate on mobile business intelligence services and the user acceptance rate of mobile BI is still moderately low. For these reasons, the current article centred on the significant of the factors and levels of mobile business intelligence user acceptance that affect the mobile business intelligence user Acceptance. The conceptual model planned and data collected between mobile business intelligence users and quantitative method used. The collected data, analysed by SPSS software. The result of data analysis exposed that how factors such as organization climate, information quality, system quality, society effect and individual effect were influenced user acceptance in mobile business intelligence applications.

Mobile data is factually the spine of modern business communications today. Capitulating the current trend of wireless connectivity. Mobile business intelligence (MBI) is termed as a technology-enabled process of extracting meaningful insights from data and delivering them to end-users via mobile devices. Mobile BI users can conduct data analysis in real time using smartphones, tablets, and wearables to make quick data-driven decisions. MBI has become a primary strategy for the growth of many small, medium and large corporations in India. However, mushrooming growth of startups in India lags in implementation and exploitation of MBI solutions. The Mobile Business Intelligence Market is expected to register a CAGR of 22.43% over the forecast period 2021 to 2026. The adoption of mobile BI by large enterprises in the Banking, Financial services and Insurance sector (BFSI sector) can be attributed to numerous factors, such as the growing acceptance of data analytics, an increase in mobile data generation, proliferation of mobile devices and apps, and the improved efficiency of BI tools. To counter competition, startups must be able to monitor and effectively use all their resources, in particular information resources, to assist them in making vital strategic business decisions. Unisex salons have mushroomed in India with specific focus on cities and towns owing to open culture adopted by current generation. According to a recent report, the Indian salon industry was valued at approximately INR 90,000 crore in 2020 and is expected to reach INR 1.75 trillion by 2026, growing at a CAGR of 15% from 2021 to 2026. This paper highlights the challenges faced by Unisex Salon -startup in towns and villages such as lack of strategic planning, technical expertise and limited budget when implementing a Mobil BI solution within its operations in Miraj city. This paper highlights how these challenges can be overcome through applying various BI tools and strategies and their probable paybacks.

Business intelligence (BI) is the process of deriving relevant information from data to facilitate informed decision-making and reveal undiscovered resources for data management. With solid understanding of market trends and data-driven decision-making, business intelligence (BI) enables organizations to stay ahead in today's competitive world. This changes ways in which businesses operate by simplifying learning,refining procedures, automating operations, and reducing expenses with integrate AI. This chapter discusses Future Directions and Trends in AI-powered business Intelligence and addresses the disadvantages of traditional BI reporting and offers new possibilities for extracting value from data. Many of the top developing trends in business intelligence are mentioned in this chapter such as Mobile business intelligence (mobile BI), Advanced data visualization, Cloud-based BI - BI as a service, Data storytelling, Augmented analytics, Self-service analytics, Natural language processing (NLP), and Ethical data governance

Mobile Business Intelligence (m-BI) enhances the capabilities of organizations to take decisions ‘on-the-move’. M-BI emerged as an extension of BI and many companies have or are considering implementing it. Being an innovation, m-BI reflects an organizing vision, created by a broad community, which is prominent during the comprehension process of this innovation. Little is known how the organizing vision affects the decision to adopt a certain innovation, and moreover these studies are completely lacking within the m-BI area. A qualitative approach has been embraced and interviews with different organizations that have already decided to adopt m-BI have been conducted. This study revealed that industrial research reports, vendors marketing campaigns and success stories produced by the collaboration among vendors, analysts, journalist and early adopters have a strong influence on the early phases of the organizational decision-making process to adopt m-BI. Increasing internal and external legitimacy, reducing the risk to be left behind, gaining competitive advantage, following the footsteps of high-performing companies, increasing their prestige and reputation, are the main reasons for why organizations may start the decision-making process to adopt m-BI; all this under the umbrella of the organizing vision.

<p>Managerial decision-making has always involved the use of numerous distinct information resources. Modern managerial decision-making processes require a wealth of information that is enhanced and transformed into knowledge in order to take effective action. Mobility in business is increasingly exercising influence on core business processes of organisations. Recent advances in wireless technologies coupled with the rapid growth of mobile devices in business have led to a new era in business computing. Mobile Business Intelligence (Mobile BI) is a system that has been conceived to assist, accelerate and to enhance the managerial decision-making processes. Drawing from an array of previous studies that attempted to measure the value of Business Intelligence (BI) and other IT systems in organisations, this study develops a new kind of measure which is based on an understanding of the distinct properties of Mobile BI systems in an organisational-oriented context.</p>

Enterprise business intelligence has solved many problems of massive data analysis for business decision-making. With the growing popularity of 3G mobile and its communication devices, it is impossible for businesses or other organizations to implement mobile business intelligence. As current hardware and software facilities for most traditional business intelligence systems cannot be simply applied to mobile appliances, this paper first proposes a lightweight framework for mobile business intelligence under 3G communication standards, then describe in detail its physical and logic architectures both in the server side and mobile terminals, finally provides its implementation solutions by using Pentaho and Android platform tools.

While prior research has examined the role of smart technologies (e.g., IoT and AI) in sustainability, the combined influence of IoT, AI, and organizational capabilities on hotel sustainable performance, particularly through the mediating roles of data-driven decision-making and innovation capability, remains underexplored. This study investigates how the integration of smart technologies, specifically the Internet of Things (IoT) and artificial intelligence (AI), as well as dynamic managerial capabilities focusing on data-driven decision-making (DDM) and innovation capability (IC), enhances hotel sustainable performance (HSP) within the context of Saudi Arabia’s hospitality sector. Grounded in the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), the research develops and tests a conceptual model that explores both the mediating roles of DDM and IC in the link between IoT and HSP and the moderating role of AI application in the relationships between IoT and DDM, IC, and HSP. Using data collected from 312 managers of four- and five-star hotels across Saudi Arabia, the study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the hypothesized relationships. The results reveal that IoT has a significant positive effect on HSP, DDM, and IC. Further, the IoT-HSP relationship is partially mediated by both DDM and IC. Furthermore, AI significantly strengthens the relationships between IoT and DDM, IoT and IC, and IoT and HSP, highlighting AI’s crucial role as an enabler of digital transformation and sustainability. The findings extend the RBV and DCT by demonstrating how technological resources, when combined with dynamic managerial capabilities, lead to superior sustainability outcomes. Practically, the study emphasizes that hotels must pair digital adoption with employee training, innovation culture, and AI-powered analytics to enhance HSP.

Data-driven decision-making (DDDM) has become a foundational strategic capability for modern business organizations, enabling evidence-based choices that enhance performance, competitiveness, and innovation. This review synthesizes contemporary academic and industry research on the mechanisms, enablers, and outcomes associated with DDDM, drawing from 25 peer-reviewed sources. The study examines the technological underpinnings of DDDM—including big data analytics, artificial intelligence, machine learning, cloud platforms, and business intelligence systems—and analyzes their integration into organizational processes. It evaluates cultural and structural factors such as data literacy, leadership support, analytical capability, and data governance. A comparative table contrasts traditional decision-making with DDDM frameworks, highlighting differences in speed, accuracy, scalability, and risk management. The analysis reveals that organizations adopting DDDM achieve improvements in operational efficiency, customer insights, innovation rates, and strategic agility. The paper concludes by addressing future challenges—including data ethics, algorithmic transparency, talent shortages, and security risks—while proposing pathways for maximizing the value of data-driven strategies.

SAFETY CULTUREcircumstances and influenced by changing attitudes and the experiences of individuals and organisations.A working organisational culture is a structure of shared and common values that affects almost every part of an organisation's interactions to maintain a culture as an essential strategy for success.It is more than just following rules and regulations.The development of an organisational culture is not

The paper examines the adoption of a data‐driven decision‐making (DDDM) process in organizations from purposefully selected European Union (EU) countries. It determines what organizational changes are required to adopt this process in organizations. This study uses a mixed‐method approach to identify organizational changes required for DDDM adoption. The responses from quantitative research in 10 EU countries (1091 respondents) and qualitative research with 20 C‐level managers are analyzed. The study offers the following organizational changes needed to implement DDDM in organizations: culture and mindset changes, digitalization, process improvements, new competencies, re‐organization, and legal requirements. This research contributes to a better understanding of the usage and adoption of DDDM globally and suggests specific organizational changes required to adopt this process.

The introduction and enforcement of the COVID-19 guidelines by the government of Kenya adversely affected the church due to its social nature of existence and operation. The leadership of the church is expected to play a key role in effecting the implementation of government guidelines to curb the spread of the COVID-19 pandemic. However, this is beside the core spiritual mandate of the church. The implementation of these guidelines is a significant addition of responsibilities to the church leadership which has adversely affected the operations of the church in delivering services to the members. The study sought to establish the influence of strategic leadership factors on implementation of COVID-19 government guidelines in evangelical churches in Nairobi registered by Evangelical Alliance of Kenya. This research adopted a descriptive research design and the target population was 500 evangelical churches. The study results showed that strategic leadership factors have a significant influence on the implementation of COVID-19 government guidelines. However, the study established that there is an insignificant influence of strategic planning on the implementation of COVID-19 government guidelines. Organizational culture, human resource development, and organizational communication had a significant influence on the implementation of COVID-19 government guidelines. The study concluded that strategic leadership factors were very important for the achievement of proper implementation of COVID-19 government guidelines. The study recommended that church leaders should involve the congregants in the strategic planning process. The church should also gather data on the pandemic and other external factors affecting members to inform the strategic planning process. Further, the church should inculcate a culture of recognizing and rewarding best performance to motivate the staff. The church should carry out a periodical needs assessment to make strategic decisions on staff development. In addition, partnerships with the government and other stakeholders in the health sector should be sought to develop a training program that will meet the needs identified from the needs assessment. Further, the church leaders should review and improve the turn-around –time of communicating with the congregants on all matters concerning the implementation of the guidelines.

Employee performance is a crucial factor in achieving organizational goals, and it is influenced by various internal factors, including the work environment and organizational culture. This research was conducted to examine whether the work environment and organizational culture have a positive and significant effect, either partially or simultaneously, on employee performance. The study was motivated by the observation that a supportive work environment and strong organizational values are often linked to higher productivity and employee engagement. This research used a quantitative approach with a survey method. The population consisted of all production department employees at PT. Setya Kuliner Mandiri Jakarta, totaling fifty individuals. The sampling technique used was a saturated sample, where the entire population was involved as the sample. Data were collected through a closed-ended questionnaire using a Likert scale. Data analysis was carried out using multiple linear regression with the help of SPSS version 22. The data testing stages included descriptive analysis, validity and reliability tests, classical assumption tests, and multiple regression analysis (both partial and simultaneous tests). The results showed that both the work environment and organizational culture had a positive and significant influence on employee performance. These findings imply that management should continuously enhance the physical, psychological, and social aspects of the work environment, and reinforce organizational values to foster better performance among employees.