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

Abstract

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.