IJCMP | Examining the Factors Associated with Customer Satisfaction using Smartphones

Indexing and Abstracting

Examining the Factors Associated with Customer Satisfaction using Smartphones

Noora Shrestha

International Journal of Chemistry, Mathematics And Physics(IJCMP), Vol-4,Issue-4, July - August 2020, Pages 65-70 , 10.22161/ijcmp.4.4.1

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Abstract:

This paper aims at identifying the key factors that influence customers’ satisfaction to use smartphones. A survey questionnaire was designed to capture the opinions of the customers about a number of characteristics of their smartphone. Correlation and regression analysis were carried to study the association and influence of the factors with the customer satisfaction. The result shows that the predictors found to be the most important to improve customer satisfaction are product price, product attractiveness, perceived quality, and brand experience. It is observed that the brand experience is more rational cause for the customer satisfaction than other predictors. In addition, the study shows that female smartphone users are more likely to have positive attitude towards their phone compared to the male users. Further research can be conducted by expanding the scope of the study with additional predictors, more sample size, and moderating variables.

Keywords:

Customer satisfaction, Regression analysis, Smartphones, Perceived quality, Brand experience.

References:

[1] E. Grigoroudis and Y. Siskos, Customer satisfaction evaluation: Methods for measuring and implementing service quality, Springer: New York, 2010.
[2] R. S. Kenett, and S. Salini, Modern analysis of customer surveys: with applications using R, John Willey & Sons: U.K., 2012.
[3] L. Barkhuus, and V.E. Polichar, Empowerment through seamfulness: Smartphones in everyday life, Personal and Ubiquitous Computing, Vol.15: 2011, pp. 629-639, https://doi.org/10.1007/s00779-010-0342-4.
[4] J. J. Brakus, B. H. Schmitt, and L. Zarantonello, Brands experience: what is it? How is it measures? Does it affect loyalty? Journal of Marketing, Vol. 73(3), 2009, pp. 52-68.
[5] A. Mosquera, E. Juaneda-Ayensa, C. Olarte-Pascual, and J. Pelegrin-Borondo, Key factors for in-store smartphone use in an omnichannel experience: millennials vs. non-millennials, Complexity, 2018, https://doi.org/10.1155/2018/1057356.
[6] L.K. Keller, Strategic brands management-building, measuring, and managing brands equity (3rd edition), Pearson Prentice Hall: United States, 2008.
[7] P. Kotler, and K.L. Keller, Marketing management (12th edition), Pearson Prentice Hall: New Jersey, 2006.
[8] S. Paul, Researching customer satisfaction and loyalty: how to find out what people really think, Market Research in Practice: London, 2005.
[9] H. Nigel, S. Bill, and R. Greg, Customer satisfaction measurement for ISO 9000:2000, Butterworth-Heinemann: Great Britain, 2003.
[10] G.D. Garson, Validity, and reliability, Statistical Associates Publishing: USA, 2013.
[11] B.E. Hayes, Measuring customer satisfaction and loyalty: survey design, use, and statistical analysis methods (3rd edition), ASQ Quality Press Milwaukee: USA, 2008.
[12] W.B. Russell, Handbook of qualitative research methods in marketing, Edward Elgar: United Kingdom, 2006.
[13] R. Arboretti, A. Bathke, S. Bonnini, P. Bordignon, E. Carrozzo, L. Corain, and L. Salmaso, Parametric and non-parametric statistics for sample surveys and customer satisfaction data, Springer: Switzerland, 2018.
[14] H. Nigel, J. Brierley, and R. MacDougall, How to measure customer satisfaction, Gower: England, 2003.
[15] T. Phyllis, R. Donohue, and B. Cooper, Management research methods, Cambridge University Press: New York, 2007.
[16] G. Lancaster, Research methods in management: A concise introduction to research in management and business consultancy, Elsevier Butterworth-Heinemann: USA, 2005.
[17]N. Shrestha, Detecting multicollinearity in regression analysis, American Journal of Applied Mathematics and Statistics, Vol. 8(2), 2020, pp. 39-42, doi: 10.12691/ajams-8-2-1.
[18] E.J. Pedhajur, Multiple regression in behavioral research: explanation and prediction (3rd edition), Thomson Learning: Wadsworth, USA, 1997.