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In July 2024, the UK witnessed a notable decline in clothing imports, which fell by 4.5% to APS1.56 billion compared to the same month last year. This decrease reflects a [...]

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The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platformcould help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior. [ABSTRACT FROM AUTHOR]

Byline: A. Samad, S. Kumari, Md J. Hossain, A. M. M. Nurul Alam, So-Hee Kim, Chan-Jin Kim, Eun-Yeong Lee, Young-Hwa Hwang and Seon-Tea Joo Keywords: Plant-based meat, sustainability, food shortage, [...]

Although a multimodal data analysis, comprising physiological and questionnaire survey data, provides better insights into addressing management science concerns, such as challenging the predictions of consumer choice behavior, studies in this field are scarce because of two obstacles: limited sample size and information privacy. This study addresses these challenges by synthesizing multimodal data using deep generative models. We obtained multimodal data by conducting an electroencephalography (EEG) experiment and a questionnaire survey on the prediction of skilled nurses. Subsequently, we validated the effectiveness of the synthesized data compared with real data regarding the similarities between these data and the predictive performance. We confirmed that the synthesized big data were almost equal to the real data using the trained models through sufficient epochs. Conclusively, we demonstrated that synthesizing data using deep generative models might overcome two significant concerns regarding multimodal data utilization, including physiological data. Our approach can contribute to the prevailing combined big data from different modalities, such as physiological and questionnaire survey data, when solving management issues. [ABSTRACT FROM AUTHOR]

This research illustrates how marketing perks can be leveraged to spur WOM. Specifically, this research introduces a previously overlooked yet practically relevant dimension on which perks differ: "contractuality," defined as the extent to which consumers perceive a perk to be conditional on specific behaviors and contingencies dictated by a company. Importantly, consumers can perceive the exact same perk as more versus less contractual depending on the way it is conferred, structured, or framed. This research demonstrates that low-contractuality perks can be more effective than high-contractuality perks at fostering WOM in the absence of explicit incentives to do so. In particular, low-contractuality perks are more likely than high-contractuality perks to convey a relational signal that motivates consumers to help the company by sharing WOM. Seven experiments, two of which were conducted in the field, support this hypothesis and illustrate conditions under which the effect attenuates or reverses. On the whole, this work offers insights into how managers might design interventions that foster WOM, and it reveals potential trade-offs of commonly used high-contractuality perks. [ABSTRACT FROM AUTHOR]

This study explores and explains how duality enables disruptive innovation to encroach on the market and redefine its boundaries under constraints of consumer preferences, purchasing power, technological performance, and complementary technologies. The findings indicate (1) disruptive innovation introduces a new value dimension into the market and enhances the heterogeneity of consumers' demand, which creates prerequisites for its market encroachment while avoiding competing directly with incumbent enterprises; (2) when considering purchasing power constraints, the disadvantage of disruptive innovation in the preexisting value dimension becomes a price advantage of encroaching on the low-end market; (3) under the constraint of complementary technologies, disruptive innovation can open up new markets that incumbent enterprises have not yet touched by virtue of its advantages in the new value dimension; (4) disruptive innovation does not rely on technological performance to encroach on the market, indicating technological performance is not a necessity for identifying disruptive innovations. [ABSTRACT FROM AUTHOR]

Recommender systems position themselves as powerful tools in the support of relevance and personalization, presenting remarkable potential in the area of marketing. The cold-start customer problematic presents a challenge within this topic, leading to the need of distinguishing user features and preferences based on a restricted set of transactional information. This paper proposes a hybrid recommender system that aims to leverage transactional and portfolio information as indicating characteristics of customer behaviour. Four independent systems are combined through a parallelised weighted hybrid design. The first individual system utilises the price, target age, and brand of each product to develop a content-based recommender system, identifying item similarities. Secondly, a keyword-based content system uses product titles and descriptions to identify related groups of items. The third system utilises transactional data, defining similarity between products based on purchasing patterns, categorised as a collaborative model. The fourth system distinguishes itself from the previous approaches by leveraging association rules, using transactional information to establish antecedent and precedence relationships between items through a market basket analysis. Two datasets were analysed: product portfolio and transactional datasets. The product portfolio had 17,118 unique products and the included 4,408,825 instances from 2 June 2021 until 2 June 2022. Although the collaborative system demonstrated the best evaluation metrics when comparing all systems individually, the hybridisation of the four systems surpassed each of the individual systems in performance, with a 8.9% hit rate, 6.6% portfolio coverage, and with closer targeting of customer preferences and smaller bias. [ABSTRACT FROM AUTHOR]

Neuromarketing is a scientific discipline that unlocks the mystery of the human mind, answering why consumers prefer a particular product. It is a step ahead of other research methods because it can analyze consumer desires and provide more precise results. In this sense, cognitive neuroscience studies consumer behavior with the aim of gaining a better understanding of the brain mechanisms underlying certain functions such as reasoning, decision-making, emotion, and memory, providing relevant insights into areas such as advertising response and brand loyalty. Indeed, neuroscience is a field that seeks to understand the structure and function of the human brain, how it encodes and represents the environment and how it controls the body and provides theoretical frameworks to assess correlations between the brain and behavioral states, to understand the causal links between stimulus and response. Thus, neuroscience has carved out a place in marketing to study, with much greater precision, the neural mechanisms of individuals exposed to marketing actions and stimuli. [ABSTRACT FROM AUTHOR]

Customers increasingly expect companies to understand their wants and needs and to market to those desires. Unfortunately, such levels of personalisation can be difficult to accomplish for traditional brick-and-mortar retailers, particularly when the fixed cost of personalised marketing is significant. This paper considers methods for providing customised promotions to customers in the form of weekly flyers. We consider how multiple versions of a weekly flyer can be used by a retailer, which products should be included in each flyer version, and how customer preferences, markup, and inventory considerations impact these decisions. Specifically, transaction histories are used to estimate customer preferences for various product offerings using market basket analysis. These probabilities are then incorporated into an optimisation model for grouping customers into market segments and presenting option sets within each segment that maximise expected marginal profits across the flyers. A heuristic is proposed for solving larger problems and evaluated against lower and upper bounds on the optimal profit. Computational results indicate that leveraging customer transaction data to optimise the product selection and assignment of four unique flyers can increase profit by 7.7% over the optimal single-flyer solution. [ABSTRACT FROM AUTHOR]