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Customer Engagement in Evolving Technological Environments: The Case of the DIY Smart-home Converter

  • School: Nottingham Business School

Overview

New technology can be used at any stage of the marketing process, including during the segmentation, targeting or positioning sub-processes, to support or transform any of the marketing mix elements, thereby affecting consumer engagement with brands (Hollebeek et al., 2014). For example, product customization options (e.g. personalized Starbucks beverages) can alter the product offering, thereby enabling firms to more responsively cater to customers’ unique needs, wants or preferences (Keeling et al., 2019). Technology can also be used for promotional purposes (e.g. social media-based micro-targeting or virtual reality(VR)-based gamification to engage consumers; see Carlson et al., 2019). It can facilitate distribution (e.g. via additive manufacturing or 3 D printing) or influence consumers’ willingness to adopt or pay for focal offerings (e.g. via mobile or contactless payments; Kuppelwieser et al., 2014), thereby exerting potential effects on any of the marketing mix elements.

Inherent in the notion of evolving technology is its continuous innovative nature, whether radical or incremental. As such, firms need to prepare for and invest in their own adaptive capability within fast-changing business environments. To do so, knowledge or skill-based resources form an important foundation for customer engagement (CE; Vargo and Lusch, 2016; Hollebeek, 2017), which predominantly reside in human capital, including (internal) personnel or (external) customers who are prepared to exhibit high brand-related engagement (e.g. peer-to-peer user support in Apple Support Communities). Here, the latter group takes on the role of co-producers through their brand- and category-related activities (Xie et al., 2008). Based on these observations, the following key managerial question emerges: How can companies motivate customers to invest their scarce resources in interacting with their (vs competitors’) brands or competing activities? To foster insight into this question, companies require a deeper understanding of customers’ available resources and those they are willing to invest into particular brand- and category-related interactions and activities. This understanding can, in turn, be converted into marketing mix customization tools to optimally cater for specific customer needs, wants, or preferences (e.g. BMW’s Luxury Car Customizer; Hollebeek et al., 2019). Our specific interest is in the smart home, and how consumers work to customise ‘regular’ homes (i.e. the norm, with no in-built smart capability) into something more interactive (Gram-Hanssen and Darby, 2018).  Although smart homes are now being built at an increasing rate these will remain the exception for many years (Harvey, et al., currently under review) and ‘smart’ will mostly emerge where consumers themselves look to customise their homes through individual creativity and access to recipe-based facilities now increasingly available on the web (e.g. www.IFTTT.com).

Research Questions

a) To what extent are regular customers motivated to ‘smarten’ their own homes?
b) What are the smart utilities or innovations that appeal most to do-it-yourself (DIY) smart home converters?
c) What resources do DIY smart home converters use/call upon to help them create their own smart environment?
d) How can organisations best exploit the opportunity to providing DIY smart home converters with the resources they really need and value most?

Method and Concepts

A mixed methods approach is recommended to investigate how companies can motivate customers to invest their scarce resources into customising/developing their ‘regular’ homes into smart/interactive environments.  Both qualitative and quantitative methods will be necessary to capture the complex insights necessary to address the questions above. A range of theories may be deployed for the investigation and one of the successful candidates’ tasks will be to find the best conceptual focus for the work. Potentially useful theoretical lenses include service dominant logic (Vargo and Lusch, 2004); customer engagement theory (Brodie et al., 2011) and actor network theory (Latour, 2005)

References

Carlson, J., Gudergan, S., Gelhard, C. and Rahman, M. (2019), “Customer engagement in social media platforms: configurations, equifinality and sharing”, European Journal of Marketing, Vol. 53, pp. 1733-1758.

Gram-Hanssen, K., and Darby, S. S. J. (2018). “Home is where the smart is”? Evaluating smart home research and approaches against the concept of home. Energy Research and Social Science, Vol. 37, 94-101.

Hollebeek, L.D. (2017), “Developing business customer engagement through social media engagement- platforms: an integrative S-D logic/RBV-informed model”, Industrial Marketing Management, Vol. 81, pp. 89-98.

Hollebeek, L.D. and Macky, K. (2019), “Digital content marketing’s role in fostering consumer engagement, trust, and value: framework, fundamental propositions, and implications”, Journal of Interactive Marketing, Vol. 45, pp. 27-41.

Hollebeek, L.D., Srivastava, R.K. and Chen, T. (2019), “S-D logic-informed customer engagement: integrative framework, revised fundamental propositions, and application to CRM”, Journal of the Academy of Marketing Science, Vol. 47, pp. 161-185.

Hollebeek, L., Glynn, M. and Brodie, R. (2014), “Consumer brand engagement in social media: conceptualization, scale development and validation”, Journal of Interactive Marketing, Vol. 28, pp. 149-165.

Huang, M.-H. and Rust, R.T. (2018), “Artificial intelligence in service”, Journal of Service Research, Vol. 21, pp. 155-172.

Keeling, D., de Ruyter, K., Mousavi, S. and Laing, A. (2019), “Technology push without a patient pull: examining digital unengagement (DU) with online health services”, European Journal of Marketing, Vol. 53, pp. 1701-1732.

Kuppelwieser, V.G., Sarstedt, M. and Tuzovic, S. (2014), “The role of context and motivation variables in mobile commerce usage – a further perspective on Chong (2013)”, Technological Forecasting and Social Change, Vol. 88, pp. 156-161.

Latour, Bruno. (2005). Re-assembling the Social: An Introduction to Actor-Network Theory. Oxford: Oxford University Press.
Robertson, N., et al. (2019), “Consumer engagement on Twitter: perceptions matter”, European Journal of Marketing. Vol. 53, pp.2018-2023.

Rust, R. and Oliver, R.L. (2000), “Should we delight the customer?”, Journal of the Academy of Marketing Science, Vol. 26, pp. 86-94.

Vargo, S. and Lusch, R.F. (2016), “Institutions and axioms: an extension and update of service-dominant logic”, Journal of the Academy of Marketing Science, Vol. 44, pp. 5-23.

Weiger, W., et al. (2019), “Who’s pulling the strings? The role of motivations in shaping user engagement through marketer-generated content”, European Journal of Marketing. Vol. 53, pp. 1808-1832.

Xie, C., Bagozzi, R.P. and Troye, S. (2008), “Trying to prosume: toward a theory of consumers as co-creators of value”, Journal of the Academy of Marketing Science, Vol. 36, pp. 109-122.

Supervisor

Doctor Mojtaba Poorrezaei

Entry qualifications

For more information on entry qualifications please visit our how to apply page.

How to apply

For more information about our PhD programme, and how to apply, please visit our research degrees in business page.

Please email the university's Doctoral School for an application pack.

For informal enquiries about this project, please email Sheilagh Resnick.

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Mojtaba Poorrezaei