# Analysis And Mathematical Modeling Of Consumer Behavior In Mobile Telecommunications Industry

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6 For strategy formulation of a mobile telecom company, it was deemed that only the current users and those who plan to purchase connections in the next 6 months are relevant. 5 HYPOTHESIS TESTING AND THE RESULTS 5.1 Hypothesis Testing 338

7 It was noted during the hypothesis testing that the effect each identified independent variable has on the preference for each mobile operator is different. In effect there were only a few universally applicable determinants affecting the consumer s preference of the service provider. The author deduced that this is as a result of the diverse consumer segments that are attracted to the services of a particular company. Therefore the results indicate that whilst some criteria are important to a certain segment of consumers, they are ignored by (and thus does not affect the behavior of) other consumer segments. Hypothesis testing for one mobile service provider (Dialog Axiata)is shown in detail (See Table 03). Table 04 shows the summary of the correlation testing for all operators. The Pearson Correlation Coefficient is denoted with a ** where the level of significance is 0.01 and with a * where it s From the results of the survey, it emerged that two factors (i.e. Level of influence of Previous Experience and Influence Level of Retailer Recommendations) do not have any significant influence over the determination of the consumer s preference for a particular mobile operator. It is interesting to note the varying behavior of the consumer segments of each mobile operator with respect to each of the variables tested. For example consumers who have a higher preference to technology are seen to have a higher preference to Airtel, Dialog or Mobitel, while the correlation is negative in concern with Etisalat and Hutch. 5.2 Regression Analysis The regression analysis evaluates how much of the variance of the dependent variable (i.e. the level of preference of the mobile operator) can be attributed to the variables identified. As it s evident from Table 05, a significant portion of the variance of the consumer s preference of the mobile operator has been explained by the variables identified by the author. The remaining unexplained variance can be attributed to variables yet to be identified and the effects of random events. It s to be noted that the mathematical model proposed assumes rational thinking consumers and the availability of all required information to the consumers. When the accuracy of the above two assumption is reduced so will the % variance explained by the mathematical representation. 5.3 Proposed Mathematical Model Using the correlation data obtained the author was able to quantify the proposed conceptual model. Based on the data on correlation (Table 4) it s clear that different mathematical models can be developed for each of the mobile operators. One point of note is that correlation does not mean causality. Therefore the interpretation of the results should be done with that in mind. Figure 04 represents the behavior of the consumers of Dialog presented as a graphical form of Equation (1) discussed previously. The arrows denote a statistically significant relationship between the dependent variable (i.e. Level of preference of mobile operator) and the independent variables. The numbers alongside the arrows depict whether the relationship is positive or negative. 6 CONCLUSION AND RECOMMENDATIONS Through this research study the author was able to identify and quantify the effect of the various factors that affect the behavior of consumers in the mobile telecom industry. Interpretation of the raw results of the statistical analysis could be difficult for anyone without an understanding of statistical techniques; figure 04 provides a simpler representation. 6.1 Support for Strategy Formulation As noted before, key to success in any business strategy is to understand the needs and the preferences of your existing customers as well as potential new customers. The mathematical presentation can assist a firm to understand the different consumer segments that view the firm s service offering as attractive and those that do not. Thereby considering the whole population, the mobile network operator will be able to identify the reasons why certain consumer segments are not attracted to try its services. This would be the first step in developing a sustainable competitive strategy. Further the model can work as an indicative guideline to gauge the response of consumers to a change in the company s service offering, price, technology or brand. For example Figure 04 indicates that consumers who have a high level of preference of technology have an inclination towards using the services of Dialog Axiata thereby resulting in the strong positive correlation. Therefore in order to keep the tech savvy segment of the population satisfied the company will need to invest in technological developments or risk losing the interest of that consumer segment. In addition, a company not seen as attractive by the segment of consumers seeking the latest technology innovation will be able to estimate the potential benefit of investing in technology (i.e. in terms of increased revenue from new customers) vs. the required capital investment. Further the consumer segment surveyed showed negative price sensitivity. This indicates that a statistically significant portion of consumers who prefer Dialog are not price sensitive. Similarly it s evident that there s a strong correlation of Service Quality and the level of preference for the mobile operator (factor of ). Feature availability also shows a positive correlation with the level of 339

8 preference shown to the mobile operator. In addition it is clear that a substantial segment of consumers preferring Dialog perceive comparatively high levels of cost of switching. This can be explained partially by the fact that Dialog holds the largest market share in the industry. Therefore the floor prices set by the Government (with higher call and SMS charges for communications made to other operators) work for the benefit of the operators with higher subscriber shares by acting as a deterrent to customer migration to a different network.the other significant factor is the attachment or the importance placed by the consumer on the mobile number itself. As inferred during the literature review, the perceived shortcomings of a change of the mobile number (i.e. by moving to a different service provider) too act to increase the perceived cost of switching. Therefore it can further be deduced that a future introduction to Sri Lanka a technology such as Mobile Number Portability could be detrimental to Dialog as consumers are likely to perceive a reduction in the cost of switching (due to not having to change one s phone number although the mobile operator changed). Therefore, in a similar manner the developed mathematical representation of the consumer behavior can be interpreted to evaluate the effect of company strategy as well as macro-economic changes. In order to facilitate the changing economic conditions and the evolving consumer needs, the author proposes that the model be updated on a periodic basis (e.g. bi-annually). It can also reveal customer segments that are becoming dissatisfied with the company s services and thereby having an increased risk of migration to another network. Therefore the author believes that the discussed mathematical representation helps mobile telecom companies to rationalize their investment decision making process by providing a measurement of the resulting change to the brand preference. 6.3 Limitations of the Study The author identified the following limitations in the presented study. Due to economical reasons the data collection was limited to the Colombo District of the country. Therefore the data may not be representative of the whole country or the mobile telecommunications sector in general. Further gathering of data will be required in order to develop the model to cover the whole country. Change in industry factors during the data collection phase is also likely to affect the outcome of the research. For example both Etisalat and Hutch launched 3G services following the data collection process of this research. This is likely to have an impact on how the brands are perceived by the consumers. However the results as of the data collection period still holds and any change in industry or economic factors can be overcome by a fresh gather of data and a reevaluation of the model. The author received only 19 samples of subscribers using Hutch (the operator with the smallest market share) out of the valid 391 responses received. Thus the reliability of the findings for that operator could be limited without additional data. 7 ABBREVIATIONS 2G: Second Generation Mobile Services 3G: Third Generation Mobile Services GSM: Global System for Mobile Communications MNO: Mobile Network Operator 8 ACKNOWLEDGEMENTS The author wishes to thank his colleagues who assisted in the research, especially during the data collection phase. 9 REFERENCES [1] Telecommunications Regulatory Commission of Sri Lanka l, Visited 1st May 2015 [2] Financial Times Sri Lanka Online Edition, Dialog dials more investments, Visited on 1st May [3] The Island Online Edition, Mobitel signs BOI agreement for US\$ 60mn expansion project, 340

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