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Adoption of Technology Facilitated Services

Presentation on "Adoption of Technology Facilitated Services" delivered by Indranil Roy Chowdhury at Asia Competitiveness Forum 2012 in Thought Leadership Track

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Adoption of Technology Facilitated Services

  1. 1. ADOPTION OF TECHNOLOGY FACILITATED SERVICES Presentation By Indranil Roy Chowdhury (Ph.D)
  2. 2. INTRODUCTION SERVICE ENCOUNTER & SELF SERVICE TECHNOLOGY• SERVICE ENCOUNTER - Dyadic Interaction between a Customer and the Employee of a firm during Service Delivery• SELF-SERVICE TECHNOLOGIES - • “Technological interfaces that enable customers to produce a service independent of direct service employee involvement” (Meuter et.al, 2000) • ATMs, online ticket booking, Phone banking, Mobile Commerce etc.
  3. 3. FORMS OF TECH INFUSION &LIMITATIONSTFS: Technology – CSP – Customer coexist
  4. 4. THE SERVICE CONTINUUM Human - Technology Human Technology
  5. 5. TECHNOLOGY FACILITATED SERVICES FEW EXAMPLES• Business Correspondent Banking • Bank partnerships with non-banks, typically retail commercial outlets such as pharmacies, post offices, construction goods stores etc. to provide distribution outlets for financial services• Tele-medical Treatment • Arrangement of providing clinical services by medical specialty centers to distant patients with the help of technologically connected local health centers• E-Governance Service Tele-centers • Public place where people can access digital technologies that enable them to access governance services that include(a) procuring information (e.g., about health, education, agriculture, etc.), (b) transactions between citizens and government, and (c) procuring entitlements (including certificates, licenses, etc.)
  6. 6. ATTITUDE INTENTION MODEL Curram, Meuter and Surprenant (2003)
  7. 7. FINAL CONCEPTUAL MODEL SUMMARYPerceived Ease of UsePerceived Usefulness Attitude towards TechPerceived RiskTrust towards technology Intention to use TFSFacilitating Conditions Network Externalities Attitude towards CSPTrust towards CSP
  8. 8. RESEARCH METHOD (Survey)Sampling Technique – Non-Probabilistic Convenience - Population that is exposedto BC bankingSample Size Targeted - Ad-hoc rules of thumb - subjects to number of variablesratio at 10:1 (Tanaka, 1987). Since, higher the sample size better the model fit, anattempt was made to procure approximately 250 sample unitsSample Size Obtained – 222 – Success Rate – 75% acceptable responses ( 297contacted ) – List of consumers obtained from a BC banking service provider
  9. 9. M- COMMERCE VALUE CHAIN
  10. 10. BUSINESS CORRESPONDENT BANKING
  11. 11. ANALYSIS & RESULTS Respondent Profile - Summary
  12. 12. ANALYSIS & RESULTS (contd) Fit Index Values Recommended Range χ2 (DOF) 564.519 (365) Normed χ2 1.547 (p < 0.001) ≤3 GFI 0.87 ≥0.90 CFI 0.96 ≥0.90 RMSEA 0.050 0.50 -1.0 Measurement Model Fit Indices Fit Index Values Recommended Range χ2 (DOF) 741.338 (413) Normed χ2 1.782 (p < 0.001) ≤3 GFI 0.84 ≥0.90 CFI 0.93 ≥0.90 RMSEA 0.059 0.50 -1.0 Structural Model Fit Indices
  13. 13. ANALYSIS & RESULTS (contd) √ 0.323 X 0.197 √ 0.241 0.162X (p>0.1) √0.351 √ √-0.731 0.614 √ 0.165 √ 0.404 √0.370 √ 0.507
  14. 14. ANALYSIS AND RESULTS HYPOTHESES TESTING Att 0.406 Education > 10 0.380 Attitude – Intention strength 0.336 Age>25 Age < 25 0.225 Education < 10 0.241 0.280 0.437 0.539 Acsp 0.027 0.035 0.257 0.314 NENE – Intention
  15. 15. CONTRIBUTIONS• Attempts have been made to study a new type of Technology based service unlike earlier studies on SST• Studied the antecedents of Attitude towards CSP – haven’t been studied earlier• Importance of Network Externalities in a TFS – not many earlier studies on technology have focused on this concept• Impact of demographic variables – Age & Education - in a TFS
  16. 16. THANK YOU !! Q?

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