Overview

Personalization has become a routine application of consumer data in the digital economy, but on what grounds is it mutually beneficial to consumers and corporations alike?

My thesis explores consumer values as a novel resource to personalization in coffee product, service and experience interaction. A priority has been to understand ‘personalized’ objects and interactions as distinct from the ‘universal’ or ‘individualized’; broadly making a more holistic use of both ‘context’ and ‘personal data’ [1]. While literature points to a strong and established precedent for successful matching of product portfolios to consumer archetypes - based largely on physical product qualities and achieved by means of large scale, inferential statistical modelling [2][3] - this has grown to additionally encompass preference matching based on comparatively abstracted qualities [4]. In both cases however, even if alignment of product to archetype is proven effective, a contention of my thesis is that this does not in itself constitute a real sense of personalization. This is not to say that analysis itself - visualizations of values-orientated interaction - should be overlooked as an artefact for personalization. With many tools existing for the organization of multivariate sources of consumer interaction into intelligible data artefacts [5]; a creative element of my work is the design, deployment, and analysis of such; for stakeholder appropriation and critical reflection.

Personalization solutions in human computer interaction (HCI) have been shown to elicit both fine-grain preference qualities (literal and abstract); as well as grounded accounts of their emergence within socio-technical interactions [6][7][8] To the extent that consumer technologies harnessing both data-driven and situated knowledge can better demonstrate personalization in coffee consumption, I am particularly interested in why and on what conditions this could be mutually beneficial  to consumers and corporations. I am especially interested in implications of distinguishing ‘individual’ from ‘archetype’, and how this may be negotiated as a necessary/inevitable feature of speculated technology(s), and hope to contribute particularly to areas of ‘data work’ (retrospective user accounts) and the growing field of ‘value co-creation’ [9] [10]. Regarding theoretical work in structuralist/post-structuralist sociology of digital consumption, the analogous study of ‘landscapes’ of consumer interaction has inspired use of the term ‘valuescape’, as others have adopted, to convey the multitude of drivers to consumer preference, choice and situated interaction [11] [12].

Methodology & Progress

A key deliverable is development of a ‘novel survey tool’, informed by a combination of statistical inference and approaches more commonly seen in qualitative ethnomethodology. Consequently, I am adopting a mixed-methods approach, developing a ‘survey component’ within a framework for practical interaction. To date I have contributed to a contemporary body of work on ‘practical values’ - themes emergent from provocations of routine interaction to expose contingency [13]. I have conducted research testing novel tools for the understanding of certainty/uncertainty as a qualifier of consumer preference response [14]. Currently, I am focusing on SME and corporate standpoints, further saturating my understanding of ‘practical values’ of digital and social consumer interaction.

References

[1] S. Sackmann, J. Strüker, and R. Accorsi, “Personalization in privacy-aware highly dynamic systems,” Communications of the. ACM, vol. 49, no. 9, pp. 32–38, 2006.

[2] S. Wajrock, N. Antille, A. Rytz, N. Pineau, and C. Hager, “Partitioning methods outperform hierarchical methods for clustering consumers in preference mapping,” Food Quality and Preference., vol. 19, no. 7, pp. 662–669, 2008.

[3] M. Perrot et al., “Use of multi-market preference mapping to design efficient product portfolio,” Food Quality and Preference., vol. 64, pp. 238–244, Mar. 2018.

[4] M. Masson, J. Delarue, S. Bouillot, J.-M. Sieffermann, and D. Blumenthal, “Beyond sensory characteristics, how can we identify subjective dimensions? A comparison of six qualitative methods relative to a case study on coffee cups,” Food Quality and Preference., vol. 47, pp. 156–165, Jan. 2016.

[5] G. Ares, P. Varela, G. Rado, and A. Giménez, “Identifying ideal products using three different consumer profiling methodologies. Comparison with external preference mapping,” Food Quality and Preference., vol. 22, no. 6, pp. 581–591, Sep. 2011.

[6] C. Mavropoulos and Ping-Tsai Chung, “A rule-based Expert System: Speakeasy - Smart Drink Dispenser,” in IEEE Long Island Systems, Applications and Technology (LISAT) Conference 2014, 2014, pp. 1–6.

[7] P. L. Larissa, T. E. Ella, R. Gianni, and S. C. Chris, “Bitbarista: Exploring perceptions of data transactions in the Internet of Things,” in Conference on Human Factors in Computing Systems - Proceedings, 2017.

[8] A. Boucher and W. Gaver, “Designing and Making the Datacatchers: Batch Producing Location-Aware Mobile Devices,” Proc. Elev. Int. Conf. Tangible, Embed. Embodied Interact., pp. 243–251, 2017.

[9] J. E. Fischer, A. Crabtree, J. A. Colley, T. Rodden, and E. Costanza, “Data Work: How Energy Advisors and Clients Make IoT Data Accountable,” Comput. Support. Coop. Work CSCW An Int. J., vol. 26, no. 4–6, pp. 597–626, 2017.

[10] F. Guzmán, A. K. Paswan, and E. Kennedy, “Consumer Brand Value Co-creation Typology,” J. Creat. Value, vol. 5, no. 1, p. 239496431880471, 2019.

[11] M. Venkatraman and T. Nelson, “From servicescape to consumptionscape: A photo-elicitation study of starbucks in the New China,” J. Int. Bus. Stud., vol. 39, no. 6, pp. 1010–1026, 2008.

[12] S. Nöjd, J. W. Trischler, T. Otterbring, P. K. Andersson, and E. Wästlund, “Bridging the valuescape with digital technology: A mixed methods study on customers’ value creation process in the physical retail space,” J. Retail. Consum. Serv., vol. 56, 2020.

[13] O.Miles, M. Flintham, N. Wikoff, “Interactions with CoffeeWizard”, 2021 [forthcoming]

[14]Z. Ellerby, O. Miles, J. McCulloch, and C. Wagner, “Insights from interval-valued ratings of consumer products - A DECSYS appraisal,” in IEEE International Conference on Fuzzy Systems, 2020.