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This paper aims to investigate textual characteristics of customer reviews that motivate companies to respond (sentiment negativity and sentiment deviation) and how aspects of these company responses (response intensity, length and tailoring) affect subsequent customer review quality (comprehensiveness and readability) over time.

Leveraging a large data set from a leading app website (Shopify), the authors combine text mining, natural language processing (NLP) and big data analysis to examine the antecedents and outcomes of online company responses to reviews.

This study finds that companies are more likely to respond to reviews with more negative sentiment and higher sentiment deviation scores. Furthermore, while longer company responses improve review comprehensiveness over time, they do not have a significant influence on review readability; meanwhile, more tailored company responses improve readability but not comprehensiveness over time. In addition, the intensity (volume) of company responses does not affect subsequent review quality in either comprehensiveness or readability.

This paper expands on the understanding of online company responses within the digital marketplace – specifically, apps – and provides a new and broader perspective on the motivations and effects of online company responses to customer reviews. The study also extends beyond the short-term focus of prior works and adds to literature on long-term effects of online company responses to subsequent reviews. The findings provide valuable insights for companies (especially those with apps) to enhance their online communication strategies and customer engagement.

The purpose of this paper is to demonstrate how consumers choose among three different options offered by a firm in a monopolistic setting, namely, to buy a standard product with a non-customizable design, to ask the firm to customize a product using the consumer’s ideal design or to do the entire design task by themselves. The authors also investigate how social preference intensity and the possibility of reselling a product influence a consumer’s decision.

The authors develop an analytical (game theoretical) consumer choice framework and incorporate a psychological factor into the model. The authors also empirically validate the analytical findings using simulations.

The authors find that as social preference intensity increases, the number of co-producers can either decrease or increase. The authors offer a closed-form solution and interval graphs showing that when the setup price is large (small), the proportion of the market that chooses to do-it-yourself (DIY) is large (small) and an increase in social preference intensity leads to a decrease (increase) in co-production.

This is the first paper to incorporate a social factor into an economic model in a consumer behavior setting. It is also the first paper to explain how customers’ preferences among possible options, such as DIY (without the firm’s help), co-production (with the firm’s help) and a standard product might change while considering other people’s preferences, as well as given associated costs.

While prior research has investigated factors that predict consumers’ information search behaviors as they relate to automobiles, such studies were conducted prior to the COVID-19 pandemic. Given that the pandemic has necessitated lockdowns, social distancing, business closures and other disruptions to normal shopping activities, consumer information search behaviors have also been substantially altered as the psychological distance between consumers and marketers has increased. Thus, this study aims to examine these changes and identify patterns of search behavior for a major durable product: automobiles.

Using survey data from before and during the pandemic, the study implements Finite Mixture Modeling to unveil latent segments of U.S. consumers’ search behaviors and choices for Japanese automobiles. This analytic method enables capturing consumer unobserved heterogeneity through mixing probabilities guided by individual characteristics. These segments are determined based on consumers’ information search for online and offline marketer-controlled and nonmarketer-controlled sources.

The study identifies that two segments of consumers emerge both prior to the pandemic and during the pandemic. These empirically validated findings indicate that the pandemic has led to shifts in consumers’ information search behaviors for Japanese automobiles by relying more on nonmarketer-controlled sources of information.

This work is among the first comprehensive empirical analyses of consumer search for a major durable product by comparing pre- and during pandemic patterns. Using analytics and econometrics, the first-hand analysis findings offer meaningful implications for marketers and product managers in the automotive industry.