ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 3, March 2016
An Analysis on Factor That Impact in Choosing Internet Service Providers U.Vageeswari M.Phil(CS) Scholar Department of CSA SCSVMV University Enathur,Kanchipuram
T.Nirmalraj Assistant Professor Department of CSA SCSVMV University, Enathur,Kanchipuram
Abstract: Internet account providers are companies that accommodate you with Internet admission so that your computer can become affiliated to the Internet. If you wish cream the web, use e-mail, or babble online, again you charge an Internet provider through which your computer can acquaint with the blow of the world. There are abounding altered types of Internet providers alms assorted means of Internet access. Admission fees can ambit from beneath than ten dollars for accepted buzz admission to over 100 dollars for reliable accelerated admission for businesses. ISP Automation Projects capital abstraction is to apparatus a online complaint arrangement for barter for adopting complaints on the issues accompanying to ISP provider and accommodate best chump affliction account for users appliance this application. There are abounding Internet aegis providers in a country that will accommodate internet casework for users on altered packages. Basically ISP works on three connections, Dial Up appliance blast service, Broad bandage and wireless connections. KeyWords: Internet, Broadband I.
INTRODUCTION
Using these interface users can accession complaint with data of encountered botheration and abide it to ISP account provider through online. Internet account Provider Company will advance DIAL UP and WEB hosting departments. User’s appliance internet beneath Dial Up admission will be maintained by Dial Up administration and Web hosting administration will attend afterwards web pages concept. ISP will yield affliction of WEB hosting casework which should attending afterwards errors acquired by appliance this service. By appliance this appliance users can anon acquaintance with the WEB Hosting administration to break the problem. Internet account providers are companies that accommodate you with Internet admission so that your computer can become affiliated to the Internet. If you wish cream the web, use e-mail, or babble online, again you charge an Internet provider through which your computer can acquaint with the blow of the world. There are abounding altered types of Internet providers alms assorted means of Internet access. Admission fees can ambit from beneath than
ten dollars for accepted buzz admission to over 100 dollars for reliable accelerated admission for businesses.ISP Automation Projects capital abstraction is to apparatus a online complaint arrangement for barter for adopting complaints on the issues accompanying to ISP provider and accommodate best chump affliction account for users appliance this application. There are abounding Internet aegis providers in a country that will accommodate internet casework for users on altered packages. Basically ISP works on three connections, Dial Up appliance blast service, Broad bandage and wireless connections II. LITERATURE REVIEW 2.1E-Satisfaction and E-Loyalty: A Contingency Framework The authors [Anderson] [5] investigate the impact of satisfaction on loyalty in the context of electronic commerce. Findings of this research indicate that although e-satisfaction [6] has an impact on e-loyalty [8], this relationship is moderated by (a) consumers’ individual level factors and (b) firms’ business level factors. Among consumer level factors, convenience motivation and purchase size were found to
4
ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 3, March 2016
accentuate the impact of e-satisfaction on e-loyalty, whereas inertia suppresses the impact of esatisfaction on e-loyalty [8]. With respect to business level factors, both trust and perceived value, as developed by the company, significantly accentuate the impact of e-satisfaction on e-loyalty [8] 2.2 Compatibility Effects in Evaluations of Satisfaction and Loyalty The goal of this research is to help understand the difference between satisfaction and loyalty [11] based on the nature of the judgment tasks involved. By drawing on the notion of the prediction–decision inconsistency, we posit satisfaction as a consumption/experience utility and loyalty as a decision utility to explain the missing link between satisfaction and loyalty [8]. An important assumption that may be driving the prediction–decision inconsistency [12], but has not been addressed, is the different criteria that consumers use in arriving at the two different types of utilities. The authors [12][ Cadotte] argue that this difference affects the compatibility, and resulting influence, of quality versus price information on satisfaction and loyalty evaluations. An empirical study of 183 firms using data from the American Customer Satisfaction Index is reported which supports the proposed compatibility effects. Implications for marketing theory and practice are discussed. 2.3The Antecedents and Consequences of Customer Satisfaction for Firms This research investigates the antecedents and consequences of customer satisfaction [13]. We develop a model to link explicitly the antecedents and consequences of satisfaction in a utility-oriented framework. We estimate and test the model against alternative hypotheses from the satisfaction literature. In the process, a unique database is analyzed: a nationally representative survey of 22,300 customers of a variety of major products and services in Sweden in 1989–1990. Several well-known experimental findings of satisfaction research are tested in a field setting of national scope. For example, we find that satisfaction is best specified as a function of perceived quality and “disconfirmation”—the extent to which perceived quality fails to match pre purchase expectations. Surprisingly, expectations do not directly affect satisfaction, as is often suggested in the satisfaction literature. In addition, we find quality which falls short of expectations has a greater impact on satisfaction and repurchase intentions than
quality which exceeds expectations. Moreover, we find that disconfirmation is more likely to occur when quality is easy to evaluate. Finally, in terms of systematic variation across firms, we find the elasticity of repurchase intentions with respect to satisfaction to be lower for firms that provide high satisfaction. This implies a long-run reputation effect insulating firms which consistently provide high satisfaction. 2.4The impact of corporate image and reputation on service quality, customer satisfaction and customer loyalty: testing the mediating role. Case analysis in an international service company The purpose of this paper [14] is to explore the relationship among corporate image and reputation, service quality, customer satisfaction and customer loyalty through a case analysis on one of the biggest Egyptian company. A structured questionnaire was developed. The hypotheses were simultaneously tested on a sample of 650 customers out of 800 distributed, giving a response rate of 81.25 per cent. Several analytical techniques were used to assess the relationships among the variables under investigation such as Pearson correlation, chi-square, and multiple linear regressions. Hierarchical regression was used to assess the mediating role. The findings of this study [14] have shown significant relationships among the variables under investigation. It is imperative to explore how an international company can effectively and efficiently work in the Egyptian culture gaining their customers satisfaction and loyalty. The research was limited to one of the biggest international company that is working in Egypt. Also the use of cross-sectional design restricts inferences being drawn regarding casualty. Despite the significant academic interest in service quality, customer satisfaction and customer loyalty, this study contributes in adding to the body of the Egyptian culture knowledge. Also, to the best of the authors’ [Bryman] knowledge there is no study published that explores the influence of corporate reputation and image and its relationship to how customers perceive the offered service, whether they are satisfied or not and most importantly whether they will retain dealing with the organization or not. III. METHODOLOGY In this work data mining technique based on analysis finding factor an analysis on factor that impact in choosing internet service providers using CfsSubsetEval attributes evaluator and Greedy
5
ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 3, March 2016
Stepwise search method in ranking algorithm. The data analysis helps us to provide a better understanding of large set of data 3.1 Questionnaire Design The researcher was very careful to develop questions clearly. So that respondent has no doubt to answer the question. Researcher use different point scale for each questions. 3.2 Tools WEKA (Waikato Environment for Knowledge Analysis) is a popular software written in java developed at the university of Waikato. WEKA contains collection of visualization tools and algorithms for data analysis. WEKA supports several standard data mining tasks more specifically data preprocessing, classification, clustering, association, attribute selection and visualization. WEKA consists of Explorer, Knowledge flow, Experimenter and Simple command line interface. WEKA’s main user interface is Explorer. SPSS is most widely used statistical software. The graphical user interface has two views which can be toggled by clicking on the tab in the bottom of the left in SPSS window. The data view shows the spread sheet view of the cases (rows) and variable view shows variables(columns). The data cells only contain numbers or text. The variable view displays the metadata dictionary each row represents a variable and shows variable name, variable label, value label, measurement type and variety of other characteristics. SPSS 16.0 version is used in this research.
An impacted factor in choosing Internet service providers research is chosen to analyses the broadband service usage among the metropolitan, non metropolitan, working and not working people. 4.1 Experiments using WEKA for Ranking Algorithm Data consists of list of items with some partial order. This order is induced by giving a numerical or ordinal score or binary judgement. The ranking algorithm is used to find the influencing factor of the large set of data.
Figure 4.1.1 Screen shot for ranking based on working In this attribute evaluator method chosen is CfsSubsetEval attribute evaluator and search method chosen is Stepwise. Attribute selection mode is use full training set. Here output percentage merits of best subset found is 0.963 and selected attribute is not working, metro, non metro and class.
IV. ANALYSIS The data mining based on reason for usage of broadband among metropolitan and non metropolitan. A study has been conducted to measure the effectiveness of data mining based predicting factors that affect the internet service provider. The main influencing factors are first and foremost thing is metropolitan and non metropolitan. The main objectives of this work are: 1 To identify the usage of broadband among metropolitan city and non-metropolitan city. 2 To provide the security important while choosing the broadband service provider.
6
ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 3, March 2016
chosen is Stepwise. Attribute selection mode is use full training set. Here output percentage merits of best subset found is 0.887 and selected attribute is working and not working. 4.2 Ranking Here CfsSubsetEval attribute evaluator Ranking Filter is used as Attribute Evaluator and Greedy Stepwise is used as search method in WEKA.
Figure 4.1.2 Screen shot for ranking based on not working In this attribute evaluator method chosen is CfsSubsetEval attribute evaluator and search method chosen is Stepwise. Attribute selection mode is use full training set. Here output percentage merits of best subset found is 0.963 and selected attribute is working. Figure 4.2.1 Screen shot for ranking based on metropolitan
Figure 4.1.3 Screen Shot for ranking based on class In this attribute evaluator method chosen is CfsSubsetEval attribute evaluator and search method
Figure 4.2.2 Screen Shot for ranking based on non-metropolitan
7
ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 3, March 2016
no of respondent
percentage
non working metro Politian
120
37.5
60
18.75
working
140
43.75
stage
CONCLUSION
total 320 100 In this attribute evaluator method chosen is CfsSubsetEval attribute evaluator and search method chosen is Stepwise. Attribute selection mode is use full training set. Here output percentage merits of best subset found is 0.445 and selected attribute is working and class. 4.3 Frequency Table The frequency table helps to understand the respondents based on working, metropolitan, non metropolitan and non working Table 4.3.1 No of respondents based on working, metropolitan, non metropolitan and non working
stage
no of respondent
Thus from the above data’s we can conclude stating that approximately 80% of the metro people mainly rely on broadband speed and download capacity and 45 % of the non-metro people rely on Broadband cost, Landline connection and Mode of payment. The metro people prefer online payment as their best mode of payment and nonmetro people prefer Outlet payment as their best mode of stages No of Percentage payment respondent . Approxi metro 70 21.875 mately non 120 37.5 96% of metro the working 50 15.625 working not 80 25 people working mainly rely on total 320 100 broadba nd speed and download capacity and also 96% of the stages
No of respondent
Percentage
working
140
43.75
percentage
metro
180
56.25
total
320
100
non working metro Politian
180
56.25
140
43.75
total
320
100
Table 4.3.2 No of respondents based on working, metropolitan
Table 4.3.3No of respondents nonworking, metropolitan
Table 4.3.4 No of respondents based on working, metropolitan, not working
based
on
non working people preferred for broadband cost, Landline connection and Mode of payment is Outlet payment is their best mode of payment. The working people prefer online payment as their best mode of payment. REFERENCES [1]. Anderson, E.W., Fornell, C. and Lehmann, D.R. (1994) ‘Customer Satisfaction, Market Share, and Profitability: Findings from Sweden’, Journal of Marketing, 58(3), pp. 53 66. [2]. Anderson, R.E. and Srinivasan, S.E. (2003) ‘E-Satisfaction and E-Loyalty: A Contingency Framework’, Psychology & Marketing, 20(2), pp. 123 - 38.
8
ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 3, March 2016
[3]. Andreassen, T.W. (1994) ‘Satisfaction, Loyalty and Reputation as Indicators of Customer Orientation in the Public Sector’, International Journal of Public Sector Management, 7(2), pp. 16 - 34.
[12]. Cadotte, E.R., Woodruff, R.B. and Jenkins, R.L. (1987) ‘Expectations and Norms in Models of Consumer Satisfaction’, Journal of Marketing Research, 23(3), pp. 305 – 314.
[4]. Andreassen, W. and Lindestad, B. (1998) ‘Customer Loyalty and Complex Service: The Impact of Corporate Image on Quality, Customer Satisfaction and Loyalty for Customers with Varying Degrees of Service Expertise’, International Journal of Service Industry Management, 9(1), pp. 7 - 23.
[13]. Caruana, A. (2003) ‘The Impact of switching cost on customer loyalty: A study among corporate customers of mobile telephony’, Journal of Targeting Measurement and Analysis for Marketing, 12(3), pp. 256-268
[5]. Anderson, E.W. and Sullivan, M.W. (1993) ‘The Antecedents and Consequences of Customer Satisfaction for Firms’, Marketing Science, 12, (2), pp. 125 - 143. [6]. Auh, S. and Johnson, M. D. (2005) ‘Compatibility effects in evaluations of satisfaction and loyalty’, Journal of Economic Psychology, 26, pp. 35-57. [7]. Aydin, S. and Ozer, G. (2005) ‘The Analysis of Antecedents of Customer Loyalty in the Turkish Mobile Telecommunication Market’, European Journal of Marketing, 39(7), pp. 910 – 25 [8]. Boulding, W., Kalra, A., Staelin, R. and Zeithaml, V.A. (1993) ‘A Dynamic Process Model of Service Quality: From Expectation’, Journal of Marketing Research, 30(1), pp. 7 27.
[14]. Chow, S. and Holden, R. (1997) ‘Toward an Understanding of Loyalty: The Moderating Role of Trust’, Journal of Managerial Issues, 9(3), pp. 275 - 98. [15]. Cronin, J. J. and Taylor, S. A. (1992) ‘Measuring Service Quality - a Reexamination and Extension’, Journal of Marketing, 56(3), pp. 55-68. [16]. Deutsch, M. (1958) ‘Trust and Suspicion’, Journal of Conflict Resolution, 2(4), pp. 265 - 79. [17]. Dick, A.S. and Basu, K. (1994) ‘Customer Loyalty: Toward an Integrated Conceptual Framework’, Academy of Marketing Science, 22(2), pp. 99 – 113. [18]. Edvardsson, B., Johnson, M.D., Gustafsson, A. and Strandvik, T. (2000) ‘The Effects of Satisfaction and Loyalty on Profits and Growth: Products Versus Services’, Total Quality Management, 11(7), pp. S917 - S27.
[9]. Bruhn, M. and Grund, M.A. (2000) ‘Theory, Development and Implementation of National Customer Satisfaction Indices: the Swiss Index of Customer Satisfaction (SWICS)’, Total Quality Management, 11(7), pp. S1017 - S28.
[19]. Eshghi, A., Haughton, D. and Topi, H. (2007) ‘Determinants of Customer Loyalty in the Wireless Telecommunications Industry’, Telecommunications Policy, 31, pp. 93 - 106.
[10]. Bryman, A. and Bell, E. (2007) Business Research Methods, Oxford: Oxford University Press.
[20]. [20] Fe, I., and Ikova, C. (2004) ‘An index method for measuring of customer satisfaction’, The TQM Magazine, 16 (1), pp. 57 - 66.
[11]. Burnham, T.A., Frels, J.K. and Mahajan, V. (2003) ‘Consumer Switching Costs: A Typology, Antecedents, and Consequences’, Journal of the Academy of Marketing Science, 31(2), pp. 109 - 126.
9