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The Behavioral Economics of Decision Making: Explaining Consumer Choice in Terms of Neural Events.

1. Introduction

Neuroscientific methods have an advantage over the established ones of marketing analyses to grasp and delve into the conduct of consumers. Such improved insight inspires managers to update and improve their products and mechanisms, thus raising the consumer satisfaction that brings about enhanced consumer loyalty. The inclination to a certain brand brought about via neuroresearch has a significant level of association with the buying conduct of the consumers. Thoroughness in the management operations inferred from neuroscientific approaches may enable the marketer to comprehend the neural level mechanisms in decision making and choices of the brain (Bratu, 2018; Meila, 2018; Sion, 2018), thus being decisive in producing commodities considerably supporting the demands of the consumers, cutting down the product deficiencies and refusal expenses, and providing the resources for firms to innovate and work for communities in a remarkable way (Agarwal and Xavier, 2015).

2. Literature Review

Neural methods, especially from functional magnetic resonance imaging (fMRI) scans, may pursue subtleties of an individual's feedback to diverse characteristics of a choice, or decision target, while the preference process advances, even before an informed decision is made. Beyond choices, neural data may provide insight into the strength of consumer-oriented interactions in shaping behavior. By assessing a well-defined series of signals from individuals, neural methods supply new-fashioned facets of insights into their choices and behavior, and the capacity to possibly surpass established behavioral measures. (Karmarkar and Plassmann, 2017) Brain imaging enables the assessment of neural activity throughout concrete marketing-significant conducts (Hoffman and Friedman, 2018; Popescu Ljungholm, 2017; Sponte (Pi[section]talu), 2018) and in the phases directly anticipating and following them. In such phases, relevant data processes are occurring that are essential to better comprehend consumer conduct. Neuroimaging tools may assist in substantiating, enhancing, or advancing current marketing approaches by supplying insights into the intrinsic process. Neuroimaging can establish disconnections between psychological mechanisms. fMRI and associated tools may differentiate whether two distinct types of choices employ comparable or distinct neural processes and consequently whether they may entail analogous or dissimilar psychological processes. Integrating neural measures into decision-making patterns may enhance expectations of marketing-significant conduct. (Plassmann et al., 2015)

3. Methodology

Using data from LivePerson, MarketingCharts, McKinsey, and Salesforce Research, I performed analyses and made estimates regarding top reasons for abandonment during online purchase, consumer goods pre-purchase research, and top three factors that influence whether a product is considered at each stage of the consumer decision journey, and clarified that neuroscientific methods enable direct inspection of implicit/unconscious processes.

4. Results and Discussion

A lot of behavioral measures of implicit processes are to a certain extent surrogates for the mechanisms of interest and may be unsuccessful in providing multilayered or accurate insights. Neuroscientific methods enable more direct inspection of implicit/unconscious processes by facilitating the detection of neural mechanisms triggering consumers' reactions (Lazaroiu et al., 2018; Popescu Ljungholm, 2018; Taylor and Kliestikova, 2018) with appropriately rough spatial and temporal decisions. An appealing prospect in consumer neuroscience integrates machine learning approaches with fMRI information (neural decoding). The decoding proposal singles out, from the entire brain, series of areas or separate voxels that convert abstract unquantifiable features or psychological notions while rigorously preventing overfitting. fMRI represents the most high-priced method in marginal expenditure and has prolonged temporal resolution. fMRI can assess neural activity related to particular mental processes without questioning consumers what they are sorting out or about the mental systems involved. Human single-neuron recording encompasses high spatial and temporal resolution, enabling substantial evaluations of neural processes: ultra-thin electrodes are transiently embedded to record firing rates in certain groups of neurons (Camerer and Yoon, 2015). (Figures 1-3)

5. Conclusions

Where organized market analyses take advantage of emotion and proclivity, with the utilization of cutting-edge and enhanced technologies, neuromarketing strives to circumvent the consumer's deliberate sense-making ability, aiming a broader degree of persuasion bottomed on instinctive feedback. Making a run at sidestepping consumers' meaning-making strengths, neuromarketing focuses on accurate alluring types of advertising. (Nemorin, 2017) Neuroscience provides cutting-edge ways to assess incongruity in consumer behavior by evaluating dissimilarities in separate sensitivity throughout areas or structural discrepancies in the brain. As limitations in this review, more hypotheses should be tested to prove that exposing distinct disparities at the neural level can bring about notions for how marketers may identify sections of consumers in markets. The possible uses of neuroscience investigations to marketing are outstanding in endeavors to exploit the surmising capacity afforded by assimilating neural information in patterns of marketing-significant behavior (Camerer and Yoon, 2015). At a mechanistic level, a conceivable source of state-dependent flexibility is the context-sensitive adjustment of brain undertaking by neuromodulators whose levels are modulated in reaction to particular states and experiences in the setting. Neuromodulators both indicate the present environment and influence neuronal activity to robustly fit it. (Karmarkar and Plassmann, 2017) Consumer neuroscience research should assist in advancing more operational interventions and enhanced furtherance of consumer decision making and in detecting weaknesses that may apprise of guidelines for consumer protection. (Smidts et al., 2014)

doi:10.22381/EMFM14120192

Acknowledgements

This paper was supported by Grant GE-1367612 from the American Association for Economic Research. The author wishes to thank the three anonymous reviewers for their constructive comments.

Author Contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

REFERENCES

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Camerer, C., and C. Yoon (2015). "Introduction to the Journal of Marketing Research. Special Issue on Neuroscience and Marketing," Journal of Marketing Resesearch 52(4): 423-426.

Hoffman, S. F., and H. H. Friedman (2018). "Machine Learning and M eaningful Careers: Increasing the Number of Women in STEM," Journal of Research in Gender Studies 8(2): 11-27.

Karmarkar, U. R., and H. Plassmann (2019). "Consumer Neuroscience. Past, Present, and Future," Organizational Research Methods 22(1): 174-195.

Lazaroiu, G., M. Kovacova, J. Kliestikova, P. Kubala, K. Valaskova, and V. V. Dengov (2018). "Data Governance and Automated Individual Decision-Making in the Digital Privacy General Data Protection Regulation," Administratie si Management Public 31: 132-142.

Meila, A. D. (2018). "Regulating the Sharing Economy at the Local Level: How the Technology of Online Labor Platforms Can Shape the Dynamics of Urban Environments," Geopolitics, History, and International Relations 10(1): 181-187.

Nemorin, S. (2017). "Neuromarketing and the 'Poor in World' Consumer: How the Animalization of Thinking Underpins Contemporary Market Research Discourses," Consumption Markets & Culture 20(1): 59-80.

Plassmann, H., V. Venkatraman, S. Huettel, and C. Yoon (2015). "Consumer Neuroscience: Applications, Challenges, and Possible Solutions," Journal of Marketing Research 52(4): 427-435.

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Smidts, A., Ming Hsu, Alan G. Sanfey, Maarten A. S. Boksem, Richard B. Ebstein, Scott A. Huettel, et al. (2014). "Advancing Consumer Neuroscience," Marketing Letters 25(3): 257-267.

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Taylor, L., and J. Kliestikova (2018). "The Operational Coherence of the Workforceas-a-service Pattern: Digitally Mediated Service Work, the Contingency of Ondemand Employment, and the Low-income Labor Market," Psychosociological Issues in Human Resource Management 6(1): 178-183.

CATALINA-OANA MIRICA (DUMITRESCU)

catalina.dumitrescu26@yahoo.com

The Bucharest University of Economic Studies, Romania

Received 1 March 2018 * Received in revised form 25 April 2018

Accepted 28 April 2018 * Available online 1 June 2018

Caption: Figure 1 Consumer goods pre-purchase research
Figure 2 Top reasons for abandonment during online purchase

Unexpected delivery costs                  66%

Lack of information about product          53%

Do not trust website/security concerns     47%

Website difficult to navigate/can't find   43%
what I'm looking for

Complicated registration/login process     34%

Want to ask a question--can't find         33%
the answer

Checkout problems                          31%

Decided to do more research                26%
before purchasing

Difficulty in getting any help/            25%
customer service on the website

Reconsidered a purchase and                22%
decided I didn't need the product

Sources: LivePerson; MarketingCharts;
my survey among 2,500 individuals conducted August 2017.

Note: Table made from bar graph.

Figure 3 Top three factors that influence whether a product is
considered at each stage of the consumer decision journey (%)

                          In mature markets

Stage 1               Advertising              27
Initial conside-      t Previous usage         23
ration set            Word of mouth            15

Stage 2               Internet information     26
Active evaluation     Shopping                 17
                      Word of mouth            16

Stage 3               Internet information     61
Moment of             Shopping                 17
purchase              Word of mouth             7

                        In developing markets

Stage 1               Word of mouth         15
Initial conside-      Advertising           17
ration set            Previous usage        16

Stage 2               Word of mouth         25
Active evaluation     Advertising           23
                      Previous usage        11

Stage 3               61 Word of mouth      42
Moment of             Advertising           36
purchase              Previous usage         6

Sources: McKinsey; my survey among 2,800 individuals
conducted October 2017.

Note: Figures do not sum to 100%, because percentages
for several other factors are not shown.

Note: Table made from bar graph.
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Author:MIRICA, CATALINA-OANA "DUMITRESCU"
Publication:Economics, Management, and Financial Markets
Article Type:Report
Date:Mar 1, 2019
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