A Combination of Self-Reported Data and Social-Related Neural Measures Forecasts Viral Marketing Success on Social Media

Consumers often share product-related content (e.g., advertising), and highly shared advertising has a huge impact on consumer behavior. Despite its apparent effectiveness, prediction of whether such advertising will be highly shared is a poorly understood area of marketing. Advances in brain imaging techniques may allow researchers to forecast aggregate consumer behavior beyond subjective reports. Using neuroimaging techniques, previous research has established models showing that expectations of self-related outcomes (potential for self-enhancement) and the social impact of sharing (potential for social approval) contribute to the likelihood of users sharing non-commercial static content (i.e., text-based health news). However, whether this finding can be applied to forecasting the virality of dynamic commercial stimuli, which is more relevant to interactive marketing (i.e., video ads), remains unknown. Combining brain imaging techniques, cross-validation methods, and real-world data regarding sharing on social media, the present study investigated whether brain data can be used to forecast the viral marketing success of video ads. We used neuroimaging (functional magnetic resonance imaging: fMRI) to measure neural activity during three sets of theory-driven neural measures implicated in value, self, and social (mentalizing) processes while 40 participants viewed video ads that brands had posted on Facebook. Contrary to previous findings regarding value-related virality in non-commercial static content, our results indicate that social-related neural activity contributes significantly to forecasting the virality of dynamic marketing-related content. The model that included both social-related neural measures and subjective intentions to share forecasted viral marketing success better than the model that included only social-related neural measures. The model that included only subjective intention to share did not forecast viral marketing success. Overall, these findings provide a novel connection between neurophysiological measures and real-world dynamic commercial content. Contrary to previous neuroforecasting findings, social-related but not value-related neural measures can significantly improve our ability to predict market-level sharing of video ads.     

Consumer innovativeness and its correlates: A propositional inventory for future research

This article summarizes the results of a systematic review of the literature on consumer innovativeness and its correlates and provides a propositional inventory for future research. The authors identified seventy-nine relevant empirical articles from international journals through a search of multiple databases using specific search terms, a manual search of marketing and consumer behavior journals and a cross-reference search. The results show that innovativeness consists of different levels of conceptualization and operational processes. Based on these different conceptualizations, the authors offer propositions for further empirical exploration on consumer innovativeness.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

Recent Advances in Structural Econometric Modeling: Dynamics, Product Positioning and Entry

In the empirical analysis of consumer markets, recent literature has begun to explore the dynamics in both consumer decisions as well as in firms' marketing policies. Other research has begun to explore the strategic aspects of product line design in a competitive environment. In both cases, structural models have given us new insights into consumer and firm behavior. For example, incorporating consumer and firm dynamics may help explain patterns in our data that are not well-captured by static models. Similarly, the strategic aspects of firm entry and product-positioning may be intrinsically linked to firm conduct and the intensity of competition in a market. Structural analysis of these consumer and firm decisions raise a number of substantial computational challenges. We discuss the computational challenges as well as specific empirical applications. The discussions are based on the session “Structural Models of Strategic Choice” from the 2004 Choice Symposium.     

Effects of voice assistant recommendations on consumer behavior

The present study compares the influence of text-based recommendations; traditionally known as online consumer reviews, and the influence of voice-based recommendations provided by voice-driven virtual assistants on consumer behaviors. Based on media richness theory, the research model investigates how voice versus text modality influences consumers' perceptions of credibility and usefulness, as well as their behavioral intentions and actual behaviors. In addition, the study analyses if these relationships vary based on the type of product and compares the influence of masculine and feminine voices. Two studies were conducted using between-subjects experimental designs, partial least squares-structural equation modeling, and logistic regression. The core finding is that voice-based recommendations are more effective than online consumer reviews in altering consumer behaviors. In addition, the first study showed that the influence of recommendations on behavioral intentions is mediated by consumers' perceptions of their credibility and usefulness. The second study confirmed, in a realistic setting, that voice-based recommendations affect consumer choices to a greater extent. Recommendations for search products and provided by males are also found to be more effective. These results contribute to the voice assistant and e-WOM literature by highlighting the effectiveness of voice-based recommendations in predicting consumer behaviors, confirming that credibility and usefulness are key factors that determine the influence of recommendations, and showing that recommendations are more effective when they focus on search products.     

Mobile Marketing in the Retailing Environment: Current Insights and Future Research Avenues

Mobile marketing, which involves two- or multi-way communication and promotion of an offer between a firm and its customers using the mobile, a term that refers to the mobile medium, device, channel, or technology, is growing in importance in the retailing environment. It has the potential to change the paradigm of retailing from one based on consumers entering the retailing environment to retailers entering the consumer's environment through anytime, anywhere mobile devices. We propose a conceptual framework that comprises three key entities: the consumer, the mobile, and the retailer. The framework addresses key related issues such as mobile consumer activities, mobile consumer segments, mobile adoption enablers and inhibitors, key mobile properties, key retailer mobile marketing activities and competition. We also address successful retailer mobile marketing strategies, identify the customer-related and organizational challenges on this topic, and outline future research scenarios and avenues related to these issues.     

Magic odd numbers: The effect of numerical parity on variety-seeking

Numbers are widely employed to convey product and other marketing related information and thereby influence consumer judgment and behavior. Drawing on the literature of numerical cognition, associative learning, goal activation, and variety-seeking, the current research examines how numerical parity affects consumers' variety-seeking behavior. Across one Implicit Association Test (IAT) and four experiments, we demonstrate that consumers associate odd (vs. even) numbers with changeability (vs. stability) in their minds (Study 1). We further find that exposure to product and other marketing related information that is presented with odd (vs. even) numbers increases consumers' tendency to seek variety in subsequent purchase decisions, and that this effect consistently appears in the voluntary selection of a set of options (Study 2a), the preference for diversified products (Study 2b and 3), and the choice of diversified product bundles (Study 2c). We further demonstrate that desire for change underlines this effect, such that exposure to odd numbers (vs. even numbers) activates consumers' desire for change, which in turn elevates their subsequent variety-seeking behavior (Study 3). The findings of the current research not only contribute to the literature, but also provide marketers with effective ways to facilitate consumers' variety-seeking behavior, such as by strategically using odd numbers in their marketing efforts.     

Identifying the critical factors of customer behavior: An integration perspective of marketing strategy and components of attitudes

The purpose of this empirical study is to operationalize the relationship of green marketing's influence on consumer attitudes via the mediating role of marketing mix towards green products to validate the proposed research model in the Taiwanese context of explaining consumers' willingness to be environmentally friendly. The model is based on structural equation modeling (SEM) from data collected from 977 online consumers. The findings revealed that green consumption intention was significantly and indirectly driven by attitude to green products. Additionally, the effect of perceived quality on marketing mix and consumer willingness in environmental concern is both significant and positive. However, when a restaurant has high consumer social responsibility (CnSR) for marketing mix, the consumer attitudes of cognitive, affective, and behavioral model (C-A-B model) is less effective. These findings have contributed to the revival of the theory of planned behavior (TPB) and offer a comprehensive understanding of consumer attitude, consumer social responsibility, marketing mix and perceived quality impact that a restaurant has on the ability to raise consumer willingness to purchase green products or food. We provides valuable suggestions to marketers to design from the perspective of green marketing policies and strategies in order to accommodate Taiwan's indigenous green restaurants.     

ChatGPT and consumers: Benefits,Pitfalls and Future Research Agenda

The need of the hour is to encourage research on topics with newness and novelty. In this context, this article discusses multidimensional benefits and potential pitfalls of using artificial intelligence-based Chat Generative Pre-trained Transformer (ChatGPT), and provides numerous ideas for future research in consumer studies and marketing in the context of ChatGPT. ChatGPT provides algorithm-generated conversational responses to text-based prompts. Since its launch in the late 2022, ChatGPT has generated significant debate surrounding its hallmarks, benefits and potential pitfalls. On the one hand, ChatGPT can offer enhanced consumer engagement, improved customer service, personalization and shopping, social interaction and communication practice, cost-effectiveness, insights into consumer behaviour and improved marketing campaigns. On the other hand, potential pitfalls include concerns about consumer well-being, bias, misinformation, lack of context, privacy concerns, ethical considerations and security. The article concludes by outlining a potential future research agenda in the area of ChatGPT and consumer studies. Overall, this article provides valuable insights into the benefits and challenges associated with ChatGPT, shedding light on its potential applications and the need for further research.     

Universal dimensions of individuals' perception: Revisiting the operationalization of warmth and competence with a mixed-method approach

A dynamic and growing body of marketing literature draws from the Stereotype Content Model's (SCM) dimensions, warmth and competence, to describe individuals' perception of various market-related stimuli (e.g., salespeople, employees, products) and predict their influence on subsequent behavior. However, the operationalization of these fundamental dimensions in empirical marketing research has been inconsistent and problematic, hindering comparability of findings across studies and limiting the overall usefulness of warmth–competence in relevant research. Using a mixed-method approach, the present paper scrutinizes the warmth–competence dimensions in order to validate and optimize their measurement. Through seven studies using different methodologies and metrics, we identify a set of trait items that (a) capture perceptions of warmth and competence consistently across a variety of impression formation settings, and (b) display strong psychometric properties. Our findings offer a parsimonious approach that promotes the consistent operationalization of warmth and competence across marketing studies and enables the integration of empirical findings, both of which are crucial requirements for meta-analytic investigations, replication studies, and integrative reviews.     

Do product reviews really reduce search costs?

The literature on online product reviews is based on the fundamental premise that reviews impact search costs and also affect consumers’ confidence in their purchase decisions. However, this proposition has not been proven in the literature. To this end, we conducted an experiment using an eye-tracking machine to measure the impact of online editorial and customer reviews on consumer’s information search costs and on decision confidence. Search costs in this study are defined in terms of time costs and cognitive effort costs. We find that when present, both editorial reviews and customer reviews separately reduce both search time and cognitive effort considerably, but not when present together. We also find that the presence of both types of reviews increases decision confidence considerably, but do not lower search costs. These results suggest that ecommerce firms can benefit from the presence of either or both editorial and customer reviews through either lower search costs, or higher decision confidence. We conclude with several managerial recommendations for ecommerce firms.     

Using consumer perceptions to operationalize the service construct: Exploratory research in theory extension and validation

Despite the ascendancy of services marketing as a topic of interest, an absence of empirical validation measures and well-grounded research techniques in the area is evident. This article explores the need for marketing to develop an approach by which services marketing empirical research can be advanced. A methodology is proposed that operationalizes the service construct by identifying consumer perceptions of goods and services, and which provides a practical basis for conducting empirical research. Empirical evidence is offered that suggests strong reliability and face validity in support of this approach. © 1995 John Wiley & Sons, Inc.     

Decision neuroscience and consumer decision making

This article proposes that neuroscience can shape future theory and models in consumer decision making and suggests ways that neuroscience methods can be used in decision-making research. The article argues that neuroscience facilitates better theory development and empirical testing by considering the physiological context and the role of constructs such as hunger, stress, and social influence on consumer choice and preferences. Neuroscience can also provide new explanations for different sources of heterogeneity within and across populations, suggest novel hypotheses with respect to choices and underlying mechanisms that accord with an understanding of biology, and allow for the use of neural data to make better predictions about consumer behavior. The article suggests that despite some challenges associated with incorporating neuroscience into research on consumer decision processes, the use of neuroscience paradigms will produce a deeper understanding of decision making that can lead to the development of more effective decision aids and interventions.     

Beyond explicit measures in marketing research: Methods,theoretical models,and applications

Whereas traditional self-report procedures and methods mainly tap into consumers explicit attitudes, the implicit cognition paradigm provide researchers and marketing practitioners with a unique tool to uncover consumer implicit attitudes, choices, decisions, and action tendencies. By means of the sheer extent and validated models and empirical studies in cognitive psychology, the application of implicit measures and techniques in marketing and consumer studies is in its infancy; thus, this special issue focuses on theories, tools and approaches for examining consumers unconscious attitude and sustainable consumption patterns. The objective of this special issue is to illustrate theoretical and methodological application of implicit consumer cognition paradigm in marketing and consumer studies. This special issue of Journal of Retailing and Consumer Services highlights the implicit cognition approach as a greater alternative in measuring consumer attitude and tendencies where scientist and marketers are impotent to capture.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

THE APPLICATION OF EMPIRICAL STRATEGIC MANAGEMENT RESEARCH TO SUPPLY CHAIN MANAGEMENT

Researchers in supply chain management (SCM) have traditionally relied most heavily on the core disciplines of logistics, marketing, and operations; most are less familiar with empirical literature in strategic management. However, with the evolution of logistics into the more interdisciplinary SCM along with increasing attention within strategic management to supply chain issues, recent empirical research in strategy has much to offer. This study reviews and categorizes more than one hundred articles relevant to SCM drawn primarily from top strategic management journals.     

The Benefits and Effective Application of Audio in Direct Marketing on the Internet

ABSTRACT

This paper primarily discusses the benefits of using audio on the Internet. Based on qualitative interviews with Internet experts and reviews of extant literature on consumer behavior and marketing, it develops a set of recommendations for effective application of audio on the Internet. While these recommendations constitute a set of viable strategies for direct marketing, they also form a set of testable hypotheses for academic researchers. Marketers are now beginning to consider audio as a tool to add value to a Website and to promote brands. Essentially, they offer a basis for future academic experiments aimed at better understanding the impact of audio on the Internet.     

Comparing consumer-produced product reviews across multiple websites with sentiment classification

ABSTRACT

Online consumer reviews have been extensively studied. However, existing literature analyzing online consumer review data mostly relies on a single data source, resulting in potentially biased analytics conclusions. Many websites encourage consumers to post reviews of their purchased products, so that new consumers can evaluate these reviews for the same product across different websites to help them make purchasing decisions. Confusions often arise in this process, because there often exist substantial discrepancies in customer reviews across different retailers on the same product. Clarifying such confusions can help consumers reduce concerns to make up their mind for their purchases, therefore benefiting both consumers and retailers. Through text analytics and sentiment analysis, we comparatively examine the underlying patterns of online consumer reviews of three large retailers including Sears, Home Depot, and Best Buy for a same product. Afterward, we combine online consumer reviews from these large retailers and conduct an overall text analytics and sentiment analysis. The overall results are further compared with the results from individual retailers. The findings show that the sentiment of the online consumer reviews could vary substantially so relying on a single data source to make purchase decision is not a wise idea. Based on the results, we further devise a framework to comparatively examine and integrate multiple data sources for social media analytics of online consumer reviews. This study offers important managerial implications and identifies several new research directions for social media analytics.