WHAT DRIVES MOBILE SHOPPINGBEHAVIOUR BEYOND INTENTION:AN INVESTIGATION INTO THE CONTEXTS OFPURCHASING ON MOBILE DEVICES
BACKGROUND                 Market size             By 2017, Mobile commerce will quadruple with retail                 & p...
PROBLEM IDENTIFICATION                      Research             Empirical related research on consumer behaviour toward m...
OBJECTIVES1. Explore the theoretical drivers of technology usage.2. Explore the contexts & scenariosin which consumers use...
THEORETICAL FRAMEWORK                    CONTEXTUAL COMMERCE                      TECHNOLOGY USAGE MODELS                 ...
THEORHETICAL MODEL                                                     = scenarios / contexts /                           ...
REFERENCESCARPENTER. N. (2011). M-commerce spend to hit £19.3bn by 2021. Direct Marketing Association. [Online]Available f...
REFERENCESLAMARRE. A, GALARNEAU. S, BOECK. H. (2012).Mobile Marketing and Consumer Behaviour Current ResearchTrend. Intern...
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An investigation into the conditions of purchasing on mobile devices

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The aim of this research is to explore and identify the situational and contextual factors where a purchase is made on a Mobile device. The aim is to extend limited research into when, where, how and why consumers use their Mobile devices to make purchases.
The outcome will be a verified taxonomy of mobile purchasing contexts.

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An investigation into the conditions of purchasing on mobile devices

  1. 1. WHAT DRIVES MOBILE SHOPPINGBEHAVIOUR BEYOND INTENTION:AN INVESTIGATION INTO THE CONTEXTS OFPURCHASING ON MOBILE DEVICES
  2. 2. BACKGROUND Market size By 2017, Mobile commerce will quadruple with retail & potential recognised as the fastest-growing category1. By 2021 worth £19.3 billion in the UK by 20212. Consumer behaviour 12% of consumers in the UK have made a purchase on their Mobile device “1 or 2 times a week’1. Clothing purchases represent 13% of all purchases and the single biggest retail category3. Usage behaviour 60% of customers have purchased through both and app and a mobile website3. 89% of usage is on the go, 54% in social occasions4.1. Euromonitor 2012a 2. Carpenter 2011 3. Mintel 2012 4. Econsultancy 2012.
  3. 3. PROBLEM IDENTIFICATION Research Empirical related research on consumer behaviour toward m- gaps commerce service implementations is still limited1 The focus of Mobile Shopping research is on the categories of: adoption/acceptance, intention, marketing and payments 2 Academics recommend the research of a “taxonomy of contexts” to develop Mobile knowledge3,4 Limited research exploring situational and contextual factors, which enable/drive a purchase action via a Mobile device4,5The aim of this research is to explore and identify the situational and contextual factorswhere a purchase is made on a Mobile device. The aim is to extend limited research intowhen, where, how and why consumers use their Mobile devices to make purchases.The outcome will be a verified taxonomy of mobile purchasing contexts.1. Tang &Kuo 2010 / 2. Lammare et al 2012 / 3. Heijden et al 2005 / 4. Maity 2010 / 5. Xu et al 2008
  4. 4. OBJECTIVES1. Explore the theoretical drivers of technology usage.2. Explore the contexts & scenariosin which consumers use Mobile devices to make retail purchases.3. Develop a verified taxonomy of contexts & scenarios in which consumers purchases Mobile shopping occurs.4. Evaluate why certain contexts are more predisposed to retail purchases on Mobile devices.
  5. 5. THEORETICAL FRAMEWORK CONTEXTUAL COMMERCE TECHNOLOGY USAGE MODELS • Personal contexts • There are multiple theoretical models rooted in a range of • Environmental contexts sciences1. • Social contexts • Theory of Reasoned action1 • Decision making process • Innovation Diffusion Model2 MOBILE DEVICE USAGE & COMMERCE • Xu et al (2008) • Heijden et al (2005) • Kim et al (2002)1. Venkatesh 2003 / 2. Shih &Venkatesh 2004
  6. 6. THEORHETICAL MODEL = scenarios / contexts / moderators Technology usage models Contexts of Mobile purchase behavior Contextual commerce QUALITATIVE & QUANTITATIVE QUALITATIVE & QUANTITATIVE High quantity of research Low quantity of research1. (based on ) Jones et al 2003
  7. 7. REFERENCESCARPENTER. N. (2011). M-commerce spend to hit £19.3bn by 2021. Direct Marketing Association. [Online]Available from: http://www.dma.org.uk/news/mcommerce-spend-hit-%C2%A3193bn-2021 [Last accessed01/01/13]ECONSULTANCY (2012). Internet Statistics Compendium UK. 612-749. [Online] Available from:http://econsultancy.com/uk/reports/uk-internet-statistics-compendium. [Last accessed 22/11/12]EUROMONITOR (2012b). Consumer Buying Behaviour in the Recession: Global Online Survey. Global MarketInformation Database. [Online] Available from: http://0-www.portal.euromonitor.com.lispac.lsbu.ac.uk/Portal/Pages/Search/SearchResultsList.aspx [Last accessed01/01/13]HEIJDEN. H, OGERTSCHNIG. M, GAAST. L. (2005).Effects of context relevance and perceived risk on useracceptance of Mobile information services. Proceedings of the Thirteenth European Conference on InformationSystems, Regensburg, Germany. [Online] Available from: http://sdaw.info/asp/aspecis/20050024.pdf [Lastaccessed 31/10/12]JONES. M, REYNOLDS. K, WEUN. S, BEATTY. S. (2003).The product-specific nature of impulse buyingtendency. Journal of Business Research. 56, 505-511.
  8. 8. REFERENCESLAMARRE. A, GALARNEAU. S, BOECK. H. (2012).Mobile Marketing and Consumer Behaviour Current ResearchTrend. International Journal of Latest Trends in Computing. 3 (1), 1-10.MAITY. M. (2010). Critical Factors of Consumer Decision-Making on M-Commerce: A Qualitative Study in theUnited States. International Journal Of Mobile Marketing. 5 (2). 87-101.MINTEL (2012). Smartphone Purchasing Habits – UK. Mintel Ltd. [Online] Available from:https://www.mintel.com . [Last accessed 01/01/13]TANG. M & KUO. C . (2010). Toward an Integrative Model for Consumer Behaviour regarding Mobile CommerceAdoption. Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2010 InternationalConference on. 142-149. [Online] Available from:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5615682&isnumber=5615501VENKATESH. V, MORRIS. G, DAVIS. G, DAVIS. F. (2003).User Acceptance of Information Technology: Toward aUnified View. MIS Quarterly. 273 (3), 425-478.XU. Z, ZHANG. C, LING. H. (2008). A Contextual Acceptance Model of Mobile Commerce Based onTAM. Computing in the Global Information Technology, 2008. ICCGI 08. The Third International Multi-Conference on. 75-79. [Online] Available from: http://0-ieeexplore.ieee.org.lispac.lsbu.ac.uk/stamp/stamp.jsp?tp=&arnumber=4591348&isnumber=4591328

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