State-Dependence Effects in Surveys

In recent years academic research has focused on understanding and modeling the survey response process. This paper examines an understudied systematic response tendency in surveys: the extent to which observed responses are subject to state dependence, i.e., response carryover from one item to another independent of specific item content. We develop a statistical model that simultaneously accounts for state dependence, item content, and scale usage heterogeneity. The paper explores how state dependence varies by response category, item characteristics, item sequence, respondent characteristics, and whether it becomes stronger as the survey progresses. Two empirical applications provide evidence of substantial and significant state dependence. We find that the degree of state dependence depends on item characteristics and item sequence, and it varies across individuals and countries. The article demonstrates that ignoring state dependence may affect reliability and predictive validity, and it provides recommendations for survey researchers

Complicating Decisions: The Work Ethic Heuristic and the Construction of Effortful Decisions

The notion that effort and hard work yield desired outcomes is ingrained in many cultures and affects our thinking and behavior. However, could valuing effort complicate our lives? In the present article, the authors demonstrate that individuals with a stronger tendency to link effort with positive outcomes end up complicating what should be easy decisions. People distort their preferences and the information they search and recall in a manner that intensifies the choice conflict and decisional effort they experience before finalizing their choice. Six experiments identify the effort-outcome link as the underlying mechanism for such conflict-increasing behavior. Individuals with a stronger tendency to link effort with positive outcomes (e.g., individuals who subscribe to a Protestant Work Ethic) are shown to complicate decisions by: (a) distorting evaluations of alternatives (Study 1); (b) distorting information recalled about the alternatives (Studies 2a and 2b); and (3) distorting interpretations of information about the alternatives (Study 3). Further, individuals conduct a superfluous search for information and spend more time than needed on what should have been an easy decision (Studies 4a and 4b)

Frontiers: Polarized America: From Political Polarization to Preference Polarization

In light of the widely discussed political divide and increasing societal polarization, we investigate in this paper whether the polarization of political ideology extends to consumers’ preferences, intentions, and purchases. Using three different data sets—the publicly available social media data of over three million brand followerships of Twitter users, a YouGov brand-preference survey data set, and Nielsen scanner panel data—we assess the evolution of brand-preference polarization. We find that the apparent polarization in political ideologies after the election of Donald Trump in 2016 stretches further to the daily lives of consumers. We observe increased polarization in preferences, behavioral intentions, and actual purchase decisions for consumer brands. Consistent with compensatory consumption theory, we find that the increase in polarization following the election of Donald Trump was stronger for liberals relative to conservatives, and that this asymmetric polarization is driven by consumers’ demand for “Democratic brands” rather than the supply of such brands. From a brand perspective, there is evidence that brands that took a political stance observed a shift in their customer base in terms of their customers’ political affiliation. We provide publicly available (http://www.social-listening.org) access to the unique Twitter-based brand political affiliation scores

The Shape of Marketing Research in 2021

From an organizational strategy perspective, attributing changes to sweeping environmental triggers or long-term strategic planning means taking either an ecological or strategic viewpoint. The ecological-versus-strategic debate centers on the issue of environmental determinism versus strategic choice. Marketing research, as an industry, is faced with having to adapt to environmental changes (mostly technology-driven) with autonomous processes that vary from one company to another. Marketing research paves the way to customer relationship building, through which the marketing function introduces the customer to the firm. There is an increasing body of both academic and trade literature that addresses the strategic role of marketing and how marketing contributes to a firm\u27s performance. Marketing researchers will have to adapt beyond adjusting their skills and highlighting the newly gained powers to senior leaders

Personalization and Targeting:how to experiment, learn & optimize

Personalization has become the heartbeat of modern marketing. The rapid expansion of individual-level data, the proliferation of personalized communication channels, and advancements in experimentation have fundamentally reshaped how firms tailor their marketing strategies. Furthermore, causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic overview of these developments. We formalize personalization as a causal inference problem embedded in the test and learn framework. We review key challenges and solutions that arise when personalization is approached through causal inference, including data limitations, treatment effect heterogeneity, policy evaluation, and ethical considerations. Finally, we identify emerging research trends stemming from new methodologies such as generic and double machine learning, direct policy learning, foundation models, and generative AI

Adaptive Self-Explication of Multiattribute Preferences

The authors propose a Web-based adaptive self-explicated approach for multiattribute preference measurement (conjoint analysis) with a large number (ten or more) of attributes. The proposed approach overcomes some of the limitations of previous self-explicated approaches. The authors develop a computer-based self-explicated approach that breaks down the attribute importance question into a ranking of attributes followed by a sequence of constant-sum paired comparison questions. In the proposed approach, the questions are chosen adaptively for each respondent to maximize the information elicited from each paired comparison question. Unlike the traditional self-explicated approach, the proposed approach provides standard errors for attribute importance. In two studies involving digital cameras and laptop computers described on 12 and 14 attributes, respectively, the authors find that the ability to correctly predict validation choices of the proposed adaptive approach is substantially and significantly greater than that of adaptive conjoint analysis, the fast polyhedral method, and the traditional self-explicated approach. In addition, the adaptive self-explicated approach yields a significantly higher predictive validity than a nonadaptive fractional factorial constant-sum paired comparison design. </jats:p

Using Social Network Activity Data to Identify and Target Job Seekers

An important challenge for many firms is to identify the life transitions of its customers, such as job searching, expecting a child, or purchasing a home. Inferring such transitions, which are generally unobserved to the firm, can offer the firms opportunities to be more relevant to their customers. In this paper, we demonstrate how a social network platform can leverage its longitudinal user data to identify which of its users are likely to be job seekers. Identifying job seekers is at the heart of the business model of professional social network platforms. Our proposed approach builds on the hidden Markov model (HMM) framework to recover the latent state of job search from noisy signals obtained from social network activity data. Specifically, we use the latent states of the HMM to fuse cross-sectional survey responses to a job-seeking status question with longitudinal user activity data, resulting in a partially HMM. Thus, in some time periods, and for some users, we observe a direct measure of the true job-seeking status. We demonstrate that the proposed model can predict not only which users are likely to be job seeking at any point in time but also what activities on the platform are associated with job search and how long the users have been job seeking. Furthermore, we find that targeting job seekers based on our proposed approach can lead to a 29% increase in profits of a targeting campaign relative to the approach that was used by the social network platform. This paper was accepted by Juanjuan Zhang, marketing. </jats:p