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AN OFFER THEY CANNOT REFUSE: A Behavioural Approach to Stimulating Consumer Demand for Innovations in the Telecommunications Sector

By DAYANA YERMEKBAYEVA

Abstract

Mobile advertising (m-advertising) is one of the most exciting new research areas in the marketing field. The personal, always-on and always-at-hand nature of a mobile phone, its interactive features, combined with its near universal ubiquity give the mobile device unrivalled potential as an advertising platform. In addition, mobile phone operators are uniquely positioned to further enhance its potential- their real-time access to customers’ demographic, geographic and historical data enables them not only to help retailers establish a strong electronic presence but also to allow them to customise advertising content to target specific people in specific situations. With the growing awareness of these advantages, retailers are increasingly looking to integrate m-advertising into their marketing communications. However, turning a mobile phone into an effective advertising medium poses a formidable challenge as prior consumer permission is a legal prerequisite for m-advertising practices. It is apparent that to fully embrace the potential of m-advertising, retailers need to identify the precise factors that influence consumer opt-in choice.\ud \ud This thesis is unique in investigating factors influencing consumer opt-in choice with the ultimate purpose of developing an effective solution to reliably stimulate opt-ins. To this end, it adopts a radical behaviourist perspective, applying a Behavioural Perspective Model (BPM) in order to explore the influence of both contextual and consumer-related factors, account for their interactive effects and, most importantly, focus on the actual opt-in choice rather than the pre-behavioural variables of “willingness” and “intention” commonly used in previous m-advertising studies. Additionally, accounting for the fact that m-advertising is a relatively new service, this thesis integrates consumer innovativeness variable into the BPM and explores its respective influence on the opt-in choice. \ud \ud The thesis builds upon three consecutive empirical projects, each having its own objective: Project One conducts a preliminary exploratory investigation of the opt-in phenomenon; Project Two measures the factors identified systematically; and Project Three experimentally tests the instrument developed. Overall, the results of this investigation suggest that consumer opt-in choice is largely contingency-shaped and is affected by numerous contextual variables. In particular, among the BPM components, consumers’ past experience with m-advertising and/or m-advertisers, utilitarian benefits associated with m-advertising and its content characteristics are the three most important opt-in choice determinants. Of particular significance is the consumer situation, which has been proven to greatly affect opt-in likelihood. The importance of the newly incorporated innovativeness factor is two-fold. First, it functions as one of the strongest direct predictors of the opt-in choice. Second, it serves in a moderating capacity, further amplifying the positive effects of other choice antecedents in the BPM. On this basis, it is concluded that the opt-in choice is amenable to the behaviourist explanation and that in new service contexts the innovativeness factor further contributes to the BPM’s predictive capacity

Topics: Electronic advertising, Mobile advertising, Innovation adoption, New service adoption, Consumer choice, Consumer opt-in, Behaviourism, Behavioural perspective, the BPM
Year: 2011
OAI identifier: oai:etheses.dur.ac.uk:3239
Provided by: Durham e-Theses

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Citations

  1. (2003). 9-11). Factors Affecting Consumer Adoption Decisions and Intents in Mobile Commerce: Empirical Insights. Paper presented at the
  2. (1960). A Coefficient of Agreement for Nominal Scale. doi
  3. (1998). A Communication-Based Marketing Model for Managing Relationships. doi
  4. (1995). A Comparison of Leading Theories for the Prediction of Goal-Directed Behaviours. doi
  5. (2001). A Comparison of Mail, Fax and Web-based Survey Methods.
  6. (1969). A New Product Growth Model for Consumer Durables. doi
  7. (1979). A Paradigm for Developing Better Measures of Marketing Constructs. doi
  8. (2000). Adoption of Internet Shopping: The Role of Consumer Innovativeness. doi
  9. (1993). Alternative ways of assessing model fit. In doi
  10. (1992). An Experimental Approach to Making Retail Store Environmetal Decisions.
  11. (1974). An Exploratory Assessment of Situational Effects in Buyer Behavior. doi
  12. (2003). Applied multiple regression/correlation analysis for the behavioral sciences doi
  13. (1981). Attitudes, Intentions, and Behavior: A Test of Some Key Hypotheses. doi
  14. (1970). Back-Translation for Cross-Cultural Research. doi
  15. (2002). Beyond the Intention–Behaviour Mythology An Integrated Model of Recycling. doi
  16. (2011). Census of the Republic of Kazakhstan, doi
  17. (1983). Characteristics of Adopters and NonAdopters of Home Computers. doi
  18. (2008). Children‟s Influences on In-store Purchases. doi
  19. (1982). Choice and Foraging. doi
  20. (2006). Communication Dominate Brands: Business and Marketing Challanges for the 21st Century London:
  21. (1976). Comparative Research Methodology: Cross-Cultural Studies. doi
  22. (2002). concerning the processing of personal data and the protection of privacy in the electronic communications sector (Directive on privacy and electronic communications). doi
  23. (2005). Conspicuous Consumption and Sophisticated Thinking. doi
  24. (1984). Construct Validity and Error Components in Survey Measures: A Structural Modeling Approach. doi
  25. (2010). Consumer Adoption of Technological Innovations. doi
  26. (1992). Consumer Choice and Segmentation in the Restaurant Industry. The Service industries doi
  27. (2000). Consumer Choice Between Hedonic and Utilitarian Goods. doi
  28. (2010). Consumer Innovativeness and its Correlates: A Propositional Inventory for Future Research. doi
  29. (1999). Consumer Innovativeness. In doi
  30. (2007). Consumer Perceptions and Attitudes towards SMS Advertising: Recent Evidence from New Zealand. doi
  31. (1973). Cross-cultural research methods. doi
  32. (2005). Designing surveys: a guide to decisions and procedures. Thousand Oaks:
  33. (2009). Discovering statistics using SPSS (and sex and drugs and rock 'n' doi
  34. (2005). Driving Consumer Acceptance of Mobile Marketing: A Theoretical Framework and Empirical Study.
  35. (1990). Ease of Message Processing as a Moderator of Repetition Effects in Advertising. doi
  36. (1968). Ecological Psychology: Concepts and Methods for Studying the Environment of Human Behavior. doi
  37. (2001). Embarrassment in Consumer Purchase: The Roles of Social Presence and Purchase Familiarity. doi
  38. (2003). Empirical Testing of a Model of Online Store Atmospherics and Shopper Responses. doi
  39. (2002). Exploring the Implications of M-Commerce for Markets and Marketing. doi
  40. (1998). Extending the Theory of Planned Behavior: A Review and Avenues for Further Research. doi
  41. (2004). Families and Innovative Consumer Behavior: A Triadic Analysis of Sibling and Parental Influence. doi
  42. (1991). Focus Groups: A Qualitative Opportunity for Researchers. doi
  43. (2008). Gearing Up for Mobile Advertising: A Cross-Cultural Examination of Key Factors That Drive Mobile Messages Home to Consumers. doi
  44. (2008). Global Takeoff of New Products: Culture, Wealth, or Vanishing Differences? doi
  45. (1996). Good and Bad Shopping Vibes: Spending and Patronage Satisfaction. doi
  46. (2009). How Does Assortment Affect Grocery Store Choice? doi
  47. (1969). How Information is Used to Adopt an Innovation.
  48. (1998). Identifying Early Adopters of New IT Products: A Case of Windows 95. doi
  49. (1993). Identifying Global and CulturalSpecific Dimensions of Humor in Advertising: A Multinational Analysis. doi
  50. (2005). Identifying the Initial Target Consumer for Innovations: an Integrative Approach. doi
  51. (2007). Implementing the Legal Criteria of Meaningful Consent in the Concept of Mobile Advertising. doi
  52. (1998). Impulse Buying: Modeling its Precursors. doi
  53. (1994). Innovator Buying Behavior over Time: The Innovator Buying Cycle and the Cumulative Effects of Innovations. doi
  54. (1976). Interactions of Consumption Situations and Brand Attitudes. doi
  55. (1993). Investment Decisions and the Theory of Planned Behaviour. doi
  56. (2009). Key Trends in Converged Communications: Vendor Strategies, Market Development and Emerging Opportunities: Business Insights.
  57. (1979). Learning Theory. doi
  58. (1994). Losing Control: How and Why People Fail at Self-Regulation.
  59. (2008). Managing Consumer Uncertainty in the Adoption of New Products: Temporal Distance and Mental Simulation. doi
  60. (2007). MCORE: a Context-Sensitive Recommendation System for the Mobile Web. Expert Systems, doi
  61. (1990). Measuring the Hedonic and Utilitarian Sources of Consumer Attitudes. doi
  62. (2008). Misery Is Not Miserly: Sad and Self-Focused Individuals Spend More. doi
  63. (2004). Mobile Marketing: The Role of Permission and Acceptance. doi
  64. (1991). Multiple regression: Testing and interpreting interactions. doi
  65. (2007). On the Consumption of Negative Feelings. doi
  66. (2003). Optimal Foraging Online: Increasing Sensitivity to Delay. doi
  67. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, doi
  68. (2002). Permission-Based Mobile Advertising. doi
  69. (1988). Possessions and the Extended Self. doi
  70. (1998). Predicting Behavior from Actions in the Past: Repeated Decision Making or a Matter or Habit? doi
  71. (1995). Predicting Behavior from Intention-to-Buy Measures: The Parametric Case. doi
  72. (2007). Privacy Issues in Mobile Advertising. doi
  73. (1999). Profiling Potential Adopters and Non-adopters of an Interactive Electronic Shopping Medium. doi
  74. (1969). Radical Behaviorism in Reconciliation with Phenomenology. doi
  75. (1984). Research Design Effects on the Reliability of Rating Scales: A Meta-Analysis. doi
  76. (1994). Research Design: Qualitative and Quantitative Approaches. doi
  77. (2004). Research method: A tool for life.
  78. (2002). Research Methods in Applied Behavior Analysis. Thousand Oaks:
  79. (1983). Research to Accelerate Diffusion of a New Invention.
  80. (2002). Residual Effects of Past on Later Behavior: Habituation and Reasoned Action Perspectives. Personality and Social Psychology Review, doi
  81. (2010). Rethinking the TAM model: Time to Consider Fun. doi
  82. (1977). Sampling techniques (3rd ed.).
  83. (2006). Scents and Sensibility: When Do (In)Congruent Ambient Scents Influence Product Evaluations? doi
  84. (1996). Should the Behavioral Sciences Become More Pragmatic? The Case for Functional Contextualism doi
  85. (1975). Situational Variables and Consumer Behavior. doi
  86. (2007). SMS Advertising, Permission and the Consumer: A Study. doi
  87. (1997). Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation. doi
  88. (1979). Sociological Paradigms and Organisational Analysis. doi
  89. (1979). Some Clarifications on the Meaning of a Behavior Analysis of Child Development.
  90. (1994). Store Atmosphere and Purchasing Behavior.
  91. (1982). Store Atmosphere: An Environmental Psychology Approach.
  92. (1995). Studies in the New Consumer Behaviour. In
  93. (2005). Tailoring New Websites to Appeal to Those Most Likely to Shop Online. doi
  94. (1984). The Effect of Humor on Advertising Comprehension.
  95. (2002). The Entertaining Way to M-Commerce: Japan's Approach to the Mobile Internet- A Model for Europe? Electronic Markets, doi
  96. (2006). The Experimental Method in Psychology.
  97. (2007). The good research guide: for small-scale social research projects. Berkshire: doi
  98. (2005). The Influence of Attitudes on Behavior. In doi
  99. (1993). The Influence of Background Music on Shopping Behavior: Classical Versus Top-Forty Music in a Wine Store.
  100. (1994). The Influence of In-Store Lighting on Consumers‟ Examination of Merchandise in a Wine Store. doi
  101. (2002). The Influence of Multiple Store Environment Cues on Perceived Merchandise Value and Patronage Intentions. doi
  102. (1990). The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach. doi
  103. (1975). The Innovative Communicator in the Diffusion Process. doi
  104. (1987). The Market Maven: A Diffuser of Marketplace Information. doi
  105. (1975). The Objective Situation as a Determinant of Consumer Behavior.
  106. (1973). The Psychologies of Structure, Function and Development. doi
  107. (1991). The Reliability of Survey Attitude Measurement : The Influence of Question and Respondent Attributes. doi
  108. (2009). The Role of Consumer Innovativeness and Perceived Risk in Online Banking Usage. doi
  109. (2003). The Sweet Smell of Success: Olfaction in Retailing. doi
  110. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, doi
  111. (1982). The Use of Extrinsic Cues to Facilitate Product Adoption. doi
  112. (1986). The Wording and Translation of Research Instruments.
  113. (2007). Toward an Explanatory Model of Innovative Behavior. doi
  114. (2000). Translating questionnaires and other research instruments: Problems and solutions (Vol. 07-131). Thousand Oaks,
  115. (1990). Trying to Consume. doi
  116. (1994). Understanding Behaviorism: Science, Behaviour, and Culture.
  117. (2003). Use it or Lose it: Purchase Acceleration Effects of Time-Limited Promotions. doi
  118. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. doi
  119. (2007). Visual Influence on In-Store Buying Decisions: An Eye-Track Experiment on the Visual Influence of Packaging Design. doi
  120. (1999). When can Affective Conditioning and Mere Exposure Directly Influence Brand Choice? doi
  121. (2007). Where Consumers Diverge from Others: Identity Signaling and Product Domains. doi
  122. (2010). Will It Spread or Not? The Effects of Social Influences and Network Topology on Innovation Diffusion. doi
  123. (2002). Wireless Digital Advertising: Nature and Implications.
  124. (1969). Word-of-mouth communication by the innovator, doi
  125. (1994). Work and/or fun? Measuring hedonic and utilitarian shopping value, doi

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