Veena Chattaraman

Preferences for Aesthetic Attributes in Clothing as a Function of Body Image, Body Cathexis and Body Size:

This study examines the relationship between physical and psychosocial attributes of the body, and aesthetic attribute preferences in clothing. Building upon a clothing comfort model, the purpose is to determine whether womens aesthetic response to apparel is related to their body size, body cathexis and body image and if so, to provide insight into underlying patterns of similarity in their response. An Internet survey was administered to a random sample of 199 female undergraduate students. The results indicated that body image and body cathexis had a negative linear relationship with aesthetic preference in styling, implying that lower body image and body cathexis correlate with preference for greater body coverage through clothing and vice versa. Body size showed a positive linear association with styling preferences, implying that increase in body size correlates with preference for greater body coverage in clothing and vice versa. Theoretical, methodological and practical implications are discussed.

Data‐driven services marketing in a connected world

Purpose – The purpose of this paper is to provide insights into the benefits of data-driven services marketing and provide a conceptual framework for how to link traditional and new sources of customer data and their metrics. Linking data and metrics to strategic and tactical business insights and integrating a variety of metrics into a forward-looking dashboard to measure marketing ROI and guide future marketing spend is explored. Design/methodology/approach – A detailed synthesis of the literature is conducted and contemporary sources of marketing data are categorized into traditional, digital and neurophysiological. The benefits and drawbacks of each data type are described and advantages of integrating different sources of data are proposed. Findings – The findings point to the importance and untapped potential of data in its ability to inform tactical and strategic marketing decisions. Future challenges, including top management support, ethical considerations and developing data and analytic capabilities, are discussed. Practical implications – The results demonstrate the need for executive service marketing dashboards that include key metrics that are service-relevant, complementary and forward-looking, with proven linkages to business outcomes. Originality/value – This paper provides a synthesis of data-driven services marketing and the value of traditional and contemporary metrics. Since the true potential of data-driven service management in a connected world is still largely unexplored, this paper also delineates fruitful avenues for future research

Age, Body Size, Body Image, and Fit Preferences of Male Consumers

This study examines the influence of specific physical factors (body size), demographic factors (age), and psychosocial factors (body satisfaction, social physique anxiety, and drive for muscularity) on apparel-specific (jeans, khakis, dress shirts, and polo shirts) fit preferences of male consumers. Data were collected through an online survey administered to 141 men in the age group of 19-66 years. Results revealed that increase in body size significantly predicted preferences for apparel (jeans, dress, and polo shirts) with looser fits, and jeans with higher waistlines. Increase in age also predicted preferences for dress and polo shirts with looser fits and jeans with higher waists. With respect to the body-image-related factors, increase in body dissatisfaction predicted increased preferences for dress shirts with looser fits and khakis with higher waistlines. Contrary to expectations, increase in mens drive for muscularity predicted preferences for jeans with lower waistlines. This study offers important implications and creates actionable market information on fit strategy for male consumer segments.

Virtual shopping agents

Purpose – The purpose of this study is to investigate whether the visual presence of a virtual agent on a retail Web site reveals positive outcomes for older users with respect to enhancing perceived interactivity, social support, trust and patronage intentions and alleviating user anxiety. Design/methodology/approach – A between-subjects laboratory experiment was conducted with 50 older users, which included an interaction experience of 30 minutes followed by a paper-based questionnaire. The visual presence of the agent was manipulated in a mock retail Web site through the presence or absence of a virtual agent image, while maintaining the same agent functionality. Findings – The contrasts of senior users’ shopping experiences between two agent-mediated Web sites (with or without agent image) support the direct “persona” effects of a virtual agent’s visual presence on enhancing perceived interactivity, social support, trust and patronage intentions in the retail Web site, while alleviating user anxiety. ...

Predicting Purchase Decisions Based on Spatio-Temporal Functional MRI Features Using Machine Learning

Machine learning algorithms allow us to directly predict brain states based on functional magnetic resonance imaging (fMRI) data. In this study, we demonstrate the application of this framework to neuromarketing by predicting purchase decisions from spatio-temporal fMRI data. A sample of 24 subjects were shown product images and asked to make decisions of whether to buy them or not while undergoing fMRI scanning. Eight brain regions which were significantly activated during decision-making were identified using a general linear model. Time series were extracted from these regions and input into a recursive cluster elimination based support vector machine (RCE-SVM) for predicting purchase decisions. This method iteratively eliminates features which are unimportant until only the most discriminative features giving maximum accuracy are obtained. We were able to predict purchase decisions with 71% accuracy, which is higher than previously reported. In addition, we found that the most discriminative features were in signals from medial and superior frontal cortices. Therefore, this approach provides a reliable framework for using fMRI data to predict purchase-related decision-making as well as infer its neural correlates.

Virtual Sales Associates for Mature Consumers Technical and Social Support in e-Retail Service Interactions

This study examines whether social presence through a virtual sales associate (VSA) affects mature consumers’ perceptions of technical and social supports from an apparel retail website and whether these perceptions influence consumers’ attitudes and patronage intentions toward the website. Sixty mature consumers participated in a laboratory experiment and completed a shopping task on a mock apparel website with or without a VSA. Results revealed that participants in the VSA (vs. no-VSA) condition perceived significantly greater social support; however, no significant difference existed in perceived technical support between the two conditions. This study also revealed that mature consumers’ perceived social support and ease of use of the retail website positively influenced their attitude toward the retail website, which in turn led to their website patronage intentions. Theoretical and managerial implications are discussed with respect to the potential of VSAs in enhancing the e-tail service quality for mature consumers.

Developing and Validating a Naturalistic Decision Model for Intelligent Language-Based Decision Aids

People make mundane and critical consumption decisions every day using choice processes that are inherently constructive in nature, where preferences emerge ‘on the spot’ or ‘on the go’ using multiple strategies based on the task at hand (Bettman, Luce, & Payne, 1998; Sproule & Archer, 2000). This implies that applying a single, invariant algorithm will not solve decision problems that humans face (Tversky, Sattath, & Slovic, 1988). Instead, consumers need adaptive, multi-strategy decision aids since they shift between multiple strategies in a single decision as they acquire increasing information during the decision-making process (Bettman et al., 1998). This paper puts forth a cognitive computing approach to develop and validate a naturalistic decision model for designing language-based, mobile decision-aids (MoDA©) based on adaptive and intelligent information retrieval and multi-decision strategy use. The approach integrates established psychological theories, Elaboration Likelihood Model (ELM) and Construal Level Theory (CLT), to develop the scientific base for predicting decision-making under contingencies. ELM delineates whether human information processing is effortful or heuristic based on a person’s ability and motivation to engage in an object-relevant elaboration (Petty & Cacioppo, 1981). CLT determines whether the cognitive construal of the decision object is abstract or concrete based on psychological distance (Liberman, Trope, & Wakslak, 2007). Integrating the derivatives of these theories, the Human-Elaboration-Object-Construal (H-E-O-C) Contingency Decision Model’s central thesis is that the decision-making strategy employed by a decision-maker can be predicted by using natural language cues to infer the extent of human elaboration (low-high) on the decision and the type of knowledge (abstract-concrete) possessed on the decision object. Specifically, an extensive (vs. limited) decision strategy is likely to be employed when human elaboration revealed through natural language cues is high (vs. low). Further, an attribute-based (vs. alternative-based) strategy may be employed when the cognitive representation of the decision object is abstract (vs. concrete). Based on this theorizing, the H-E-O-C Contingency Decision Model can predict the use of four common decision strategies that systematically differ based on the amount (extensive vs. limited) and pattern (attribute- vs. alternative-based) of processing: Lexicographic or LEX (limited, attribute-based processing), Satisficing or SAT (limited, alternative-based processing), Elimination-by-Aspects or EBA (extensive, attribute-based processing), and Weighted Adding or WADD (extensive, alternative-based processing) (Bettman et al., 1998). To validate the H-E-O-C Contingency Decision Model, we conducted observational studies that simulated in-store purchase decision-making with real consumers. A total of 48 shopping sessions (n = 48) were held in a simulation home improvement retail store, and decision-making dialog between consumers and a customer service agent (trained research assistant) was recorded using wearable voice recorders. To ensure that there were fairly equal numbers of consumers who were either motivated or not to elaborate on their decisions, we created two shopping conditions – low risk (replacement AC filter purchase) and high risk (AC filter purchase to address allergy and asthma). The recorded decision dialogs were first transcribed verbatim, resulting 48 units of analysis, which were then analyzed using the grounded theory approach through open and axial coding processes (Corbin & Strauss, 1990). The open coding first identified the construal level, which was followed by axial coding to infer the decision strategy (LEX, EBA, SAT, or WADD) employed by the consumer at the initial and final stages of decision-making. This process was conducted by two coders with adequate inter-coder reliability. Two different coders coded the transcripts for the elaboration level (low vs. high) of the consumer based on specific definitions, with adequate inter-coder reliability. The H-E-O-C Contingency Decision Model proposes that high elaboration consumers will employ either WADD or EBA, whereas low elaboration consumers will employ either SAT or LEX. This proposition was supported in over 80% of the decision transcripts, offering an important validation of the framework. The main contribution of the H-E-O-C Contingency Decision Model is that it is derived from universal psychological constructs and predicts decision-making strategies that apply to many types of products and services related to healthcare, education, and finance that are characterized by attributes and alternatives. This ensures its broad applicability across a wide variety of disciplines and use cases.

Modeling Conversational Flows for In-Store Mobile Decision Aids

Based on the Human-Elaboration-Object-Construal (HEOC) Contingency Model, we propose design principles for modeling conversational flows between consumers and an in-store mobile decision aid (MoDA) with artificial intelligence, functioning as a virtual sales associate. Through an on-going assessment of the quantity, type, and specificity of the decision preferences from the user’s spoken input, MoDA is modeled to identify the user’s levels of decision elaboration and construal, which leads to its recognition of the user’s use of and shifts across four decision strategies commonly applied in consumer decision-making contexts. Upon identification of the user’s decision-making strategy, MoDA is modeled to (1) identify strategy-relevant assistive tasks, (2) generate or access strategy- and task-relevant intelligence, and (3) utter strategy-, task-, and intelligence-relevant speech to naturally support the user’s decision making strategy. The proposed design principles further map the types and examples of the agent tasks, intelligence, and speech required across the four consumer decision making strategies.

Creating an Affective Design Typology for Basketball Shoes Using Kansei Engineering Methods

Research has shown that for athletic shoes, visual attributes such as color and style can be more important than ergonomic or technical attributes in consumer purchase decisions. Previous studies have also shown that psychological feelings and emotions are in fact tied to products based on individual design characteristics that create a ‘gestalt’ feel for the product. Kansei engineering is one method commonly used in product development to gain a better understanding of emotions and their linkages with specific design characteristics, which can then be used to design products that communicate the desired ‘feel’. The current study posits that the design characteristics of shoes and the emotions that they elicit can be statistically grouped together, creating Kansei/affective design types that have applications for product development, marketing, and mass customization. An exploratory study using male millennial athletes revealed four affective design types for basketball shoes, which are associated with differing design characteristics.

Inferring a User’s Propensity for Elaborative Thinking Based on Natural Language

Natural language-based aids (e.g., intelligent cognitive assistants) that assist humans with various tasks and decisions, often need to recognize the user’s propensity (low-high) to elaborate on the task or decision, to ensure that the information provided matches the user’s thinking level. We conducted two qualitative studies of natural language usage in customers’ written product reviews (Study 1) and conversational transcripts of customer-store associate interactions (Study 2) to generate (Study 1) and validate (Study 2) four rules that can be employed to infer a user’s propensity for elaborative thinking. These include: consideration of multiple (2+) attributes/alternatives; detailed description (word count) about a single attribute/alternative; demonstration of specific knowledge (use of specific terms) about an attribute/alternative; and consideration of pros and cons about an attribute/alternative. Implications for natural language-based, intelligent cognitive assistants emerge as a result of this work.