Measuring Mobile Internet Communication End User Cost and System Latency as Data Limitations

This paper examines the use of automated measurement as a tool to support the development on mobile internet communication. The focus is on analysis on end user inputs; data connection cost, time spent on moving data, and end user effort in the use of mobile internet communication service. The paper compares results of two case studies in UK and in Finland to the 2010 created 50 €s roaming limit for Telecommunications in EU, and supports the year 2011 Proposal for the Digital Agenda of EU by analyzing the data cost per megabyte in actual use. The results in UK show that mobile service users were concerned about the cost of mobile service usage. This seems to limit the actual data amounts moved. Pilot user in Finland in 2010 exceeded 12 times the data limit valid in UK a year earlier. The results suggest that mobile system latency is a continuous development initiative; in the second pilot in Finland, end user could move 40 mega bytes in 20 minutes with a fixed price of 9.8 € a month; 25 cents per megabyte, if no additional data was moved. By understanding the end user limitations linked to actual use cases, the potential value or end user may become more transparent.

[1]  Judith Mariscal,et al.  Mobile communications in Mexico: policy and popular dimensions , 2008 .

[2]  Timo Smura,et al.  The role of VoIP: Future evolution paths of voice communications , 2004 .

[3]  John R. Hauser,et al.  How Consumers Allocate Their Time When Searching for Information , 1993 .

[4]  Pl Law,et al.  Mobile networks : migrant workers in southern China , 2008 .

[5]  Jen-Her Wu,et al.  What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model , 2005, Inf. Manag..

[6]  James E. Katz,et al.  Handbook of Mobile Communication Studies , 2008 .

[7]  T. J. Gerpott,et al.  Communication behaviors and perceptions of mobile internet adopters , 2010 .

[8]  Yuanyuan Sun,et al.  Exploring Revenue Distribution of Mobile Value-Added Service Chain based on Game Theory , 2011 .

[9]  Antero Kivi Measuring mobile service usage: methods and measurement points , 2009, Int. J. Mob. Commun..

[10]  E. Hippel The dominant role of users in the scientific instrument innovation process , 1993 .

[11]  Peter Jones,et al.  Service productivity: Towards understanding the relationship between operational and customer productivity , 2004 .

[12]  Kee-Young Kwahk,et al.  Examining the determinants of Mobile Internet service continuance: a customer relationship development perspective , 2010, Int. J. Mob. Commun..

[13]  Hannu Verkasalo,et al.  Analysis of mobile internet usage among early‐adopters , 2009 .

[14]  Brian Whitworth,et al.  The Social Requirements of Technical Systems , 2009 .

[15]  James E. Katz Mainstreamed Mobiles in Daily Life: Perspectives and Prospects , 2008 .

[16]  Zhaohua Deng,et al.  An empirical analysis of factors influencing users' adoption and use of mobile services in China , 2010, Int. J. Mob. Commun..

[17]  H. Chesbrough Open Business Models: How to Thrive in the New Innovation Landscape , 2006 .

[18]  Bernd H. Schmitt Customer Experience Management: A Revolutionary Approach to Connecting with Your Customers , 2003 .

[19]  Lorenzo Zirulia,et al.  Me and you and everyone we know: An empirical analysis of local network effects in mobile communications , 2009 .

[20]  Bernd H. Schmitt,et al.  Customer Experience Management , 2009 .

[21]  Carliss Y. Baldwin,et al.  How User Innovations Become Commercial Products: A Theoretical Investigation and Case Study , 2006 .

[22]  Ephraim R. McLean,et al.  The DeLone and McLean Model of Information Systems Success: A Ten-Year Update , 2003, J. Manag. Inf. Syst..

[23]  Janice Redish,et al.  User and task analysis for interface design , 1998 .

[24]  Lara Srivastava The Mobile Makes Its Mark , 2008 .

[25]  Ivar Jacobson,et al.  The Unified Modeling Language User Guide , 1998, J. Database Manag..