Online Social Marketing Website Factors in Behavioural Change

Online Social Marketing: Website Factors in Behavioural Change Brian Cugelman, MA A thesis is submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy January 2010 Parts of this thesis have been previously published while one portion is currently under peer review Save for any express acknowledgements, references, and/or bibliographies cited in the work, I confirm that the intellectual content of the work is the result of my own efforts and of no other person The right of Brian Cugelman to be identified as author of this work is asserted in accordance with ss.77 and 78 of the Copyright, Designs, and Patents Act 1988 At this date, copyright is owned by the author Signature Date Abstract A few scholars have argued that the Internet is a valuable channel for social marketing, and that practitioners need to rethink how they engage with target audiences online However, there is little evidence that online social marketing interventions can significantly influence behaviours, while there are few evidence-based guidelines to aid online intervention design This thesis assesses the efficacy of online interventions suitable for social marketing applications, presents a model to integrate behavioural change research, and examines psychological principles that may aid the design of online behavioural change interventions The primary research project used meta-analytical techniques to assess the impact of interventions targeting voluntary behaviours, and examined psychological design and adherence correlations The study found that many online interventions demonstrated the capacity to help people achieve voluntary lifestyle changes Compared to waitlist control conditions, the interventions demonstrated advantages, while compared to print materials they offered similar impacts, but with the advantages of lower costs and broader reach A secondary research project surveyed users across an international public mobilization campaign and used structural equation modelling to assess the relationships between website credibility, active trust, and behavioural impacts This study found that website credibility and active trust were factors in behavioural influence, while active trust mediated the effects of website credibility on behaviour The two research projects demonstrated that online interventions can influence an individual’s offline behaviours Effective interventions were primarily goal-orientated: they informed people about the consequences of their behaviour, encouraged them to set goals, offered skills-building support, and tracked their progress People who received more exposure to interventions generally achieved greater behavioural outcomes Many of these interventions could be incorporated into social marketing campaigns, and offer individually tailored support capable of scaling to massive public audiences Communication theory was used to harmonize influence taxonomies and techniques; this proved to be an effective way to organize a diversity of persuasion, therapy, and behavioural change research Additionally, website credibility and users’ active trust could offer a way to mitigate the negative impacts of online risks and competition I Publications from this Thesis • Cugelman, B., Thelwall, M., & Dawes, P (2009, under peer review) The Psychology of Online Behavioural Influence Interventions: a Meta-Analysis • Cugelman, B., Thelwall, M., & Dawes, P (2009) Communication-Based Influence Components Model Persuasive 2009 Claremont, ACM • Cugelman, B., Thelwall, M., & Dawes, P (2009) The Dimensions of Web Site Credibility and Their Relation to Active Trust and Behavioural Impact Communications of the Association for Information Systems, 24, 455-472 • Cugelman, B., Thelwall, M., & Dawes, P (2008) Website Credibility, Active Trust and Behavioural Intent In Oinas-Kukkonen, H (Ed.) Persuasive 2008, LNCS 5033 Berlin, Heidelberg, Springer-Verlag • Cugelman, B (2008) GCAP Websites Report Johannesburg, South Africa, CIVICUS and Statistical Cybermetrics Research Group • Cugelman, B., Thelwall, M., & Dawes, P (2007) Can Brotherhood be Sold Like Soap…Online? An Online Social Marketing and Advocacy Pilot Study Synopsis Persuasive Technology Stanford University, Springer II Table of Content Introduction Social Marketing Background 2.1 Describing Social Marketing 2.2 Behavioural Influence 11 2.3 Efficacy and Effectiveness 12 2.4 Roots of Social Marketing Problems 14 2.5 Campaign Process (Research and Planning) 17 2.6 Behavioural Change Principles 19 2.7 History 24 2.8 Online Social Marketing 29 2.9 Summary 31 Online Intervention Efficacy 35 3.1 Motives to Develop Online Interventions 36 3.2 Interventions and Individualization 38 3.3 Macro and Micro Behaviours 41 3.4 Online Intervention Efficacy Studies 43 3.4.1 Literature-based Research 43 3.4.2 Real-World Research 46 3.4.3 Real-World Case Studies 48 3.5 Trends across Research Types 51 3.6 Summary 56 Online Intervention Design 59 4.1 Designing Real-world Interventions 60 4.2 Persuasive Online Design 61 4.3 Influence Systems 72 4.4 Technology as a Social Actor 77 4.5 Communication Model Applications 79 4.6 Summary 82 Research Questions and Projects .85 5.1 Research Questions 85 5.2 Research Projects 87 Online Influence Factors (Exploratory Studies and 2) 91 6.1 Research Project Overview 92 6.2 Wiebe’s (1951) Criteria (Pilot Study 1) 98 6.2.1 Research Model Development 98 6.2.2 Methods 99 6.2.3 Findings 99 III 6.2.4 Conclusions and Research Implications 101 6.3 Qualitative Investigation (Study 2) 102 6.3.1 Research Model and Interview Schedule 102 6.3.2 Informant Selection 103 6.3.3 Analysis and Findings 107 6.3.4 Methodological Lessons Learned 113 6.3.5 Conclusions and Research Implications 114 6.4 Conclusions and Research Implications 115 Website Credibility and Trust (Study 3) 117 7.1 Research Model Development 118 7.2 Questionnaire Development 124 7.3 Analysis 126 7.4 Findings (Hypothesis Testing) 129 7.4.1 Theoretical Implications 131 7.4.2 Practitioner Implications 132 7.5 Conclusions and Research Implications 134 Communication-based Influence Components Model (CBICM) 135 8.1 Theoretical Foundations 136 8.2 Communication Models 137 8.3 Influence Components 141 8.4 A New Model: the CBICM 145 8.5 An Overview of the CBICM 148 8.6 Conclusions and Research Implications 153 Intervention Psychology Meta-Analysis (Study 4) 155 9.1 Methods 156 9.1.1 Searching 156 9.1.2 Selection 157 9.2 Validity Assessment 158 9.3 Data Abstraction 160 9.4 Quantitative Data Synthesis 161 9.4.1 Overall Effect Size Estimates 163 9.4.2 Psychological Design 164 9.4.3 Dose Correlations 164 9.5 Results 165 9.5.1 Study Characteristics 165 9.5.2 Overall Effect Size Estimates 167 9.6 Psychological Design 171 9.6.1 Psychology Descriptives (Absolute Coding) 171 9.6.2 Psychology Analysis (Relative Coding) 174 9.7 Dose (Adherence and Attrition) 176 IV 9.8 Findings 178 9.8.1 Overall Heterogeneity Assessment 180 9.8.2 Comparisons 181 9.8.3 Theoretical Implications 183 9.8.4 Practitioner Implications 186 9.9 Conclusions 188 10 Discussions 189 10.1.Research Questions and Findings 189 10.2.Theoretical Implications 192 10.3.Social Marketing Implications 196 10.3.1 Social Exchange Theory and the 4Ps 197 10.3.2 Applications to Social Marketing 198 10.3.3 Elaboration Likelihood Model 200 10.3.4 Mass-Interpersonal Campaigns 202 10.3.5 Practitioner Implications 204 10.4.Contributions 207 10.5.Limitations 208 10.6.Future Research 209 10.7.Summary 210 11 Conclusions 213 12 References 217 13 Appendices 227 13.1.Social Marketing Bibliographic Analysis Methodology (1971-2008) 227 13.2.Website Credibility and Trust Study 229 13.2.1 Campaign Online Network 229 13.2.2 Website Audit Code Sheet 230 13.2.3 Research Partnership 231 13.2.4 Qualitative Study Materials 232 13.2.5 Engagement Tools and Needs Assessment 235 13.2.6 Website Credibility and Trust Questionnaire 236 13.3.Intervention Psychology Meta-Analysis 252 13.3.1 Meta-Analysis Code Sheet 252 13.3.2 Meta-Analysis Code Book 256 V Table of Figures Figure 1-1: Thesis Structure Figure 2-1: Social Marketing Efficacy by Time (data from Stead et al., 2007) 13 Figure 2-2: Social Marketing Journal Publications (1971-2008) 25 Figure 3-1: Individualization and the Mass/Interpersonal Gap (adapted from Kreuter et al., 2000) 39 Figure 4-1: Generalized Trust and Internet Use (ESS 2004 Data) 63 Figure 6-1: Online Network (January 2008) 95 Figure 7-1: Two- and Three-Dimensional Models 119 Figure 7-2: Visualization of Deutsch’s (1962) Trust Model 122 Figure 7-3: SEM Regression Weights and Covariance Estimates for Both Models 129 Figure 8-1: Four Types of Communication Models 137 Figure 8-2: Three-stage Model of Behavioural Change 141 Figure 8-3: Conceptualization of Influence Components Model 144 Figure 8-4: Communication-based Influence Components Model (CBICM) 148 Figure 9-1: Selection Process Flow Chart 158 Figure 9-2: Funnel Plot of Intervention 159 Figure 9-3: Overall Effect Size Forrest Plot 168 Figure 9-4: Effect Size by Control Group 169 Figure 9-5: Long-term Effect Size Groupings 169 Figure 9-6: Effect Size by Intervention Duration 170 Figure 9-7: Sum of Influence Components by Effect Size (d) 175 Figure 9-8: Forrest Plots of Correlation Effect Size Estimates 176 Figures 9-9: Study and Intervention Adherence by Effect Size 177 Figure 9-10: Adherence Variables and Correlation Effect Sizes 178 Figure 10-1: Unified Research Framework and the 4Ps 197 Figure 10-2: Vision of Mass-Interpersonal Social Marketing Campaigns 203 Figure 13-1: Social Marketing Journal Publications (1971-2008) 227 VI Acknowledgments This thesis has been shaped by many people, to whom I am indebted Some influenced my intellectual approach, providing support, insight, or inspiration; a few helped me transition from the workforce to academia; and many cheered me on I cannot thank my supervisors enough My primary supervisor, Prof Mike Thelwall provided top class guidance, support, and inspiration I could not have asked for better; though if I did, I could not have found it Prof Phil Dawes was a solid guide and pushed me to achieve a high standard I only worked with Dr Jenny Fry on one investigation, and her guidance made a huge difference Finally, I’d like to thank Dr Mike Haynes who helped to create the environment I needed, in order to join the university I am also indebted to people from a various groups who shaped my research, and offered critical feedback and inspiration These groups include the Georgetown social marketing discussion group; the annual Persuasive conference series; the UK’s National Social Marketing Centre, and most importantly, the Statistical Cybermetrics Research Group Special thanks goes out to an unknown person who complained about his online social marketing frustrations, which inspired my meta-analysis study Others who influence the meta-analysis include Filip Drozd who provided important advice on intervention psychology; Prof Per Hasle, who inspired me to read Aristotle and Cicero, which aided the study’s theory; and both Dr William Smith and Nancy Lee who offered sound advice For the website credibility study, Henri Valot was the key person who made the study possible, along with my early research partner, Kanti Kumar Dr Fogg advised me on studying online credibility, and Dr Graham Massey offered critical statistical advice during the final analysis Many people cheered me on My father has always supported my ambitions, and if my mother had lived, she would have been proud My bubby, Frances Cugelman, is always an inspiration, and has advised me to what makes me happy—thus the PhD Maxine Henry provided much support during my studies A number of friends and family have offered supported and indirect contributions In alphabetical order, grouped by family units, thank you: Alan Liang; Arlene and Bernie Cugelman; Bette, Jesse, Jordan, and Stan Klimitz; Carrie Assheuer; Darko Gavrilovic; Eva Otero; Evy Cugelman and Eion McMahon; Jayne Cravens; Dr Jonathan Levitt; Rena Cugelman; Robert Manaryd; Susanna Shankman; all my friends from UNFCC who saw me off, in tweed; and finally, VII Eugene Codrington who ensured my mind was focused through my studies I'm sure my late grandfather Jack Cugelman would have been a keen supporter, and I owe much to Diane and Sam and Sniderman I'm sure my grandfather Sam's long stories, lectures, and lessons helped stoke my inquisitive disposition My transition from the workforce to academia was very difficult, and would have been impossible without backing from many people Dr Mike Bement provided critical advice and major support Dr Justin Davis-Smith backed my numerous university applications, while in the workforce he was a role model Barry Kavanagh and Caroline Stiebler backed my postgraduate applications while Kevin Grose accommodated and supported my work/study Masters degree—without this stepping-stone, my PhD would have been impossible Finally, when I was waffling over quitting a nice job with the United Nations to become a poor, but intellectually satisfied student (a scary prospect), Dr Eva Friedlander offered an encouraging nudge when I needed it Finally, I have to step back many years to thank Janet and Jacky who were the initial inspirations for this thesis In 1997, Janet McKay loaned me her copy of Doug McKenzie-Mohr’s book on community-based social marketing and taught me the practice at LEAF Around this time, my stepbrother Mike King suggested I set up a website on GeoCities, and thanks to Mike, I taught myself how to build websites Then one year later, in 1998, Jacky Kennedy hired me as a social marketing coordinator for Canada’s first Walk a Child to School Day During this job, I used the Internet to surpass all the campaign objectives I knew there was something to online social marketing and had decided to serious research on it some day VIII Downs and Black’s (1998) Research Instrument for Randomized and NonRandomized Studies Reporting (11) Is the hypothesis/aim/objective of the study clearly described? (Yes=1, No=0) Are the main outcomes to be measured clearly described in the Introduction or Methods section? (Yes=1, No=0) If the main outcomes are first mentioned in the Results section, the question should be answered no Are the characteristics of the patients included in the study clearly described? (Yes=1, No=0) In cohort studies and trials, inclusion and/or exclusion criteria should be given In case-control studies, a case-definition and the source for controls should be given Are the interventions of interest clearly described? (Yes=1, No=0) Treatments and placebo (where relevant) that are to be compared should be clearly described Are the distributions of principal confounders in each group of subjects to be compared clearly described? (Yes=2, Partially=1, No=0) A list of principal confounders is provided Are the main findings of the study clearly described? (Yes=1, No=0) Simple outcome data (including denominators and numerators) should be reported for all major findings so that the reader can check the major analyses and conclusions (This question does not cover statistical tests which are considered below) Does the study provide estimates of the random variability in the data for the main outcomes? (Yes=1, No=0) In non normally distributed data the inter-quartile range of results should be reported In normally distributed data the standard error, standard deviation or confi- dence intervals should be reported If the distribution of the data is not described, it must be assumed that the estimates used were appropriate and the question should be answered yes Have all important adverse events that may be a consequence of the intervention been reported? (Yes=1, No=0) This should be answered yes if the study demonstrates that there was a comprehensive attempt to measure adverse events (A list of possible adverse events is provided) Have the characteristics of patients lost to follow-up been described? (Yes=1, No=0) This should be answered yes where there were no losses to follow-up or where losses to follow-up were so small that findings would be unaffected by their inclusion This should be answered no where a study does not report the number of patients lost to follow-up 10 Have actual probability values been reported( e.g 0.035 rather than