A Best Practise Business Intelligence Framework for the Telecommunications Industry: An Empirical Study

A Best Practise Business Intelligence Framework for the Telecommunications Industry: An Empirical Study

Sam Aubrey Fabian February
Copyright: © 2018 |Pages: 16
DOI: 10.4018/IJSDS.2018040103
OnDemand:
(Individual Articles)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

This article endeavors to assess and evaluate the current level of efficiency by which the business intelligence (BI) department is able to deliver decision-making support and to propose a suitable business intelligence framework. The framework would be recommended for the local telecommunications industry towards the enhancement of decision making thereby improving productivity and overall decision-making efficiency. Every company needs a clear set of goals to achieve the maximum benefits from its BI solution. Articulating organisational BI goals are essential. An organisation must however do more than just state its goals to achieve its BI objectives; it needs a working framework that provides a blue print for success. The success of a business intelligence program depends on the approach or methodology used to implement the business intelligence strategy and the related components.
Article Preview
Top

1. Introduction

According to McLeod (2014, pp. 3-4), South Africa’s telecommunications industry has never been in such a heightened state of flux as it is today. The regulator, the Independent Communications Authority of South Africa (ICASA), is responsible for regulating operators such as MTN and Vodacom SA. Large operators control most of the local market, and resent decisions taken by ICASA in favour of small operators. A greater degree of competition among operators could lower retail prices. The degree to which advanced business intelligence models are used for service delivery and decision-making could determine viability and long-term survival in the local telecommunications industry. There is a shortage of studies conducted in this regard. This study assessed the degree to which advanced business intelligence models are used for business decision making. The study recommends a framework that is usable for constructing a business intelligence model that is suitable for the South African telecommunications industry.

The overall objective of the study was to assess and evaluate the degree to which business intelligence frameworks is used for making strategic and operational decisions in the private telecommunications industry of South Africa. The study is conducted in Vodacom SA. The study had the following specific research objectives:

  • To identify factors that affect utilisation of business intelligence frameworks in Vodacom SA;

  • To show the potential benefits of utilising a business intelligence framework for enhancing overall productivity and efficiency; and

  • To construct a business intelligence framework that is suitable for enhancing efficiency and productivity in Vodacom SA.

Top

2. Research Methods

The research used a combination of quantitative and qualitative methods of data collection and analysis. The design of the research was descriptive and cross-sectional. As part of the quantitative aspect of research, data was collected from known users of the Business Intelligence platforms within Vodacom SA. A 26-item questionnaire was used using a 5-point ordinal scale. As part of the qualitative aspect of the study, data was collected from a purposive sample of 12 high-ranking decision makers working for Vodacom SA. Twelve individual in-depth interviews were conducted as part of the qualitative aspect of research. Qualitative data was collected by using a tape recorder. The interviews were transcribed for coding and tallying. All indicators of performance and viability to be used in the research were pre-fined, standardised, and validated. Therefore, objective comparisons were made by gathering data on key indicators of performance and viability.

Research requires a detailed outline of how data will be collected. According to Wahyuni, (2012:72) data collection methods must be relevant to the research and within the context of a particular paradigm; various techniques could be used to gather primary data. These include personal interviews, self-administered surveys, postal surveys, telephonic surveys, and observations (Roberts-Lombard, 2006, p. 31; Cant et al., 2005, p. 89). According to Watkins (2008, p. 67), a questionnaire is a list of carefully structured questions, chosen after considerable testing with a view to elicit reliable responses from a chosen sample. The questionnaire has the advantage of observing data beyond the observer’s physical reach. On the other hand, according to Quinlan (2011, pp. 220 - 230), one-on-one interviews establish rapport with the interviewee; the researcher has the opportunity to explain the research in detail to the interviewee, and lastly issues and the questions can be discussed with the interviewee.

The data for this research was collected by means of two methods, 1. An online questionnaire consisting of 26 questions using a 5-point ordinal scale, and two. One-on-one in-depth semi structured interviews consisting of a list of open-ended questions. Twelve senior management employees from Vodacom were interviewed during the research process. For the online questionnaire, 182 possible respondents were asked to participate. Hundred and twenty-one responses were received. For each of the 26 questions, five possible answers are provided using a 5-point ordinal scale. A 5-point ordinal scale is most appropriate for the research as it enables respondents not to be biased in cases where they lack a reliable opinion, firm stand, a well-informed opinion, conviction, adequate knowledge, or experience. This technique is commonly used as a means of minimising measurement-related bias.

Complete Article List

Search this Journal:
Reset
Volume 15: 1 Issue (2024): Forthcoming, Available for Pre-Order
Volume 14: 1 Issue (2023)
Volume 13: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 12: 3 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing