1. The Influence of Values on Technology Adoption: using a Mixed-Method Design Ashwin Mehta University of Leeds, 15 th June 2016

2. Agenda Introduce my study Mixed Method Design Method comparison: Sampling Reliability & validity Conclude

3. Values influence on technology adoption Study: how do values influence the adoption of e-learning in the training of medical fieldworkers in West Africa (The Gambia) and East Africa (Kenya) and a UK control group. Theory: UTAUT (Davis 1989, Venkatesh 2012), Values (Schwartz 2012) Importance/Impact: Addresses a literature gap of values in acceptance of e-learning in Africa; Explores the validity of this theory in resource constrained environments; Considers uniformity of variables in different contexts Measures the values structure at a professional level in East Africa and West Africa

4. Theoretical model

5. Method Partial Least Squares Modelling of Survey/System Data Advantages: Robust in non-normal distributions Suitable for smaller sample sizes Suitable for strongly collinear data Suitable for different variable types Dominant method in technology adoption research Theme Analysis of Semi-structured Interviews Partially pre-planned questions Advantages Allows for replicable quantitative structure Allows for spontaneous exploration of issues Gives contextually detailed data

6. Sampling adequacy Surveys & System Data Whole population sampling A Priori calculation of sampling adequacy: 10x maximum construct relationships (>70) (Hair et al 2014) GPower 3.1 based on power and effect size (ca. 130 for a small effect) (Faul et. al. 2007) Interviews Participants selected by age, gender & e-learning participation A priori/posteriori determination of sampling adequacy is difficult (Guest et al. 2006, Mason 2010)

7. Reliability Examples Mtebe & Raisamo (2014) – UTAUT; HE students; m-learning; Tanzania; survey “The value of Cronbach’s Alpha should be positive and even greater than 0.700 (Nunnally, 1978). As shown in Table 2, Cronbach alpha value for five constructs ranges from 0.763 to 0.884. All these values are above 0.700” Park (2009) – TAM; HE Students; e-learning; South Korea; lecture transcripts “The transcripts were analyzed by two independent raters (Cohen`s κ:.94). Cohen`s Kappa coefficient was used to measure the inter-rater agreement. The value of.94 shows a very good agreement.”

8. Reliability & Resilience Survey Data: validate the data Construct Validity (Hair et. al. 2014) Internal Consistency (e.g. Cronbach’s Alpha, Composite Reliability) Convergent Validity (e.g. Average Variance Extracted) Discriminant Validity (Fornell & Larcker 1981) Low Resilience Interview Data: validate the process Inter-rater reliability: Coefficients (Hayes & Krippendorff 2007) (e.g % agreement, Cohen’s κ, Krippendorff’s α) Unitisation (Campbell et. al. 2007) Number of “raters” Training of “raters” High Resilience

9. Conclusions The influence of values on technology adoption is investigated Interviews are used to determine the consistency of meaning of the model variables in different contexts Interview data: sampling adequacy tests are rare; data resilience is higher; reliability is process oriented Survey and system data: sampling adequacy tests are explicit; data resilience is lower; reliability is data oriented

10. Thanks to: Research Assistance: Teams at MRC Unit, The Gambia; MRC Uganda; KEMRI Wellcome Trust Programme in Kenya; British Geological Survey (NERC UK) Funding and Supervision: MRC Head Office, UK; University of Leeds; Technical Support: E-learning Studios; MRC Head Office, UK;

11. References Campbell, J.L. et al. 2013. Coding In-depth Semistructured Interviews: Problems of Unitization and Intercoder Reliability and Agreement. Sociological Methods & Research [online]. 42(3),pp.294–320. Available from: http://smr.sagepub.com/content/42/3/294.abstract?etoc. Davis, F.D. 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly. (September),pp.319–340. Faul, F. et al. 2007. G*Power3: A Flexible Statistical Power Analysis Program for the Social, Behavioral, and Biomedical Sciences. Behavioral Research Methods [online]. 39(2),pp.175–191. Available from: http://www.psycho.uni-duesseldorf.de/abteilungen/aap/gpower3/. Fornell, C. and Larcker, D. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research [online]. 18(3),pp.39–50. Available from: http://www.jstor.org/stable/3151312\nhttp://www.jstor.org/stable/10.2307/3151312. Guest, G. et al. 2006. How Many Interviews Are Enough ? An Experiment with Data Saturation and Variability. Family Health International. 18(1),pp.59–82. Hair, J.F. et al. 2012. The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications. Long Range Planning [online]. 45(5-6),pp.320–340. Available from: http://dx.doi.org/10.1016/j.lrp.2012.09.008. Hayes, A.F. and Krippendorff, K. 2007. Answering the Call for a Standard Reliability Measure for Coding Data. Communication Methods and Measures. 1(1),pp.77–89. Holden, R.J. 2012. Social and personal normative influences on healthcare professionals to use information technology: towards a more robust social ergonomics. Theoretical Issues in Ergonomics Science. 13(5),pp.546– 569. Mason, M. 2010. Sample Size and Saturation in PhD Studies Using Qualitative Interviews. Forum: QUALITATIVE SOCIAL RESEARCH. 11(3). Mtebe, J.S. and Raisamo, R. 2014. Investigating students’ behavioural intention to adopt and use mobile learning in higher education in East Africa. International Journal of Education and Development using Information and Communication Technology. 10(3),pp.4–20. Park, S.Y. 2009. An analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning. Journal of Educational Technology & Society. 12(3),pp.150–162. Saldaña, J. 2009. The coding manual for qualitative researchers New. SAGE Publications, Inc. Schwartz, S.H. et al. 2012. Refining the theory of basic individual values. Journal of Personality and Social Psychology. 103(4),pp.663–688. Udo, G.J. et al. 2012. Exploring the role of espoused values on e-service adoption: A comparative analysis of the US and Nigerian users. Computers in Human Behavior [online]. 28(5),pp.1768–1781. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0747563212001215 [Accessed November 21, 2014]. Venkatesh, V. et al. 2012. Consumer Acceptance and Use of Information technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly. 36(1),pp.157–178.