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

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

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

Theoretical model

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

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)

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 (Nunnally, 1978). As shown in Table 2, Cronbach alpha value for five constructs ranges from to 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.”

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

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

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;

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