Selling to Millennials with online reviews

Millennials have heavily influenced social media's evolution into an important source of product information. They are increasingly basing their product evaluations on information gathered from online reviews. Thus, companies targeting Millennials may wish to pay heed to online reviews. Which products are affected by Millennials’ online reviews? Do the reviews tend to be positive or negative? Where do Millennial customers look for information amongst the plethora of online venues? As presented herein, a survey of 227 Millennials reveals that this generation is undeniably posting reviews online and being influenced by these reviews. Respondents show a definite preference for two online venues, Facebook and company websites, when voicing their opinions. Reviews are broken down by positive and negative comments, and how product categories fare for each type. Contrary to popular thinking, the respondents were more prone to post positive reviews than negative reviews. Males voice their opinions online significantly more often than females, and specific gender differences are observed by product category. Recommendations are provided for selling to Millennials by leveraging online reviews.     

Creating and detecting fake reviews of online products

Customers increasingly rely on reviews for product information. However, the usefulness of online reviews is impeded by fake reviews that give an untruthful picture of product quality. Therefore, detection of fake reviews is needed. Unfortunately, so far, automatic detection has only had partial success in this challenging task. In this research, we address the creation and detection of fake reviews. First, we experiment with two language models, ULMFiT and GPT-2, to generate fake product reviews based on an Amazon e-commerce dataset. Using the better model, GPT-2, we create a dataset for a classification task of fake review detection. We show that a machine classifier can accomplish this goal near-perfectly, whereas human raters exhibit significantly lower accuracy and agreement than the tested algorithms. The model was also effective on detected human generated fake reviews. The results imply that, while fake review detection is challenging for humans, “machines can fight machines” in the task of detecting fake reviews. Our findings have implications for consumer protection, defense of firms from unfair competition, and responsibility of review platforms.     

How online reviews with different influencing factors affect the diffusion of new products

Online reviews are becoming increasingly important for the diffusion of new products. Based on the content of online reviews on JD.com , the study utilizes the Latent Allocation model to identify the critical factors of interest to consumers and categorized them into two categories, namely, product quality and supporting service factors. Thereafter, we use the system generalized method of moments to analyse the dynamic model of the panel data and further study the relationship between online reviews and new product diffusion under the two types of factors. We further investigate how the interaction between the critical metrics of online reviews affects the diffusion of new products. The results indicate that as various factors are considered, online reviews have a significantly different impact on the diffusion of new products. Moreover, compared with supporting service factors, online reviews of product quality factors have a more significant impact. In addition, the interactions between online review metrics demonstrate complex characteristics, especially the volume and positive emotional tendency, which does not promote new product diffusion, as expected.     

Relative persuasiveness of repurchase intentions versus recommendations in online reviews

This paper examines the effects of loyalty expressions (i.e., repurchase intentions vs. recommendations) on review persuasiveness. Specifically, we propose that repurchase intentions have a stronger positive effect on review persuasiveness compared to recommendations because of reviewer credibility. We test the above proposition using both an empirical dataset and multiple experimental studies. In addition, we examine frequency of purchase as a boundary condition for our proposition. Accordingly, we find that for frequent purchases, repurchase intentions (vs. recommendations) increases credibility, which, in turn, augments review persuasiveness. For infrequent purchases, however, we observe that recommendations (vs. repurchase intentions) enhance review persuasiveness, which occurs because of increased credibility. This research offers contributions to theory in the areas of online reviews, loyalty, source credibility, and cue-diagnosticity, as well as to practice regarding how firms should seek to elicit loyalty expressions (i.e., repurchase intentions vs. recommendations) when soliciting reviews.     

How managerial responses to online reviews affect customer satisfaction: An empirical study based on additional reviews

This study investigates the internality of managerial responses (MRs) to online reviews on the responded customers' (i.e., customers who have posted an initial review and then being responded by the company through an MR) satisfaction. By utilizing the data of additional reviews (ARs), an innovative social media module for customers to express follow-up opinions that complement their initial reviews, we examine the impact of MR treatment on the customers' AR valence. We leverage the insight that the observability of MR at the time of the responded customer posts an AR is a crucial condition to the impact of the MR, and thus regard the observability of MR as an exogenous treatment for model identification. Fixed effects models with extensive control variables are proposed to estimate the MR treatment effects. The results show that MRs have significant positive impacts on customers' satisfaction in ARs. Further explorations show that the positive impacts are mainly due to the positive effects of MRs on nonpositive initial reviews (MR-Ns), suggesting that MR-Ns are an effective management tool for customer complaints. Moreover, this study identifies MR delay as a boundary condition for the internality of MRs because MR delay negatively moderates the positive impacts of MRs. Therefore, companies should promptly respond to customers' negative opinions in their reviews. This study is among the very first to clearly identify the internality of MRs on the responded customers’ satisfaction. We show that the existing results on the externality of MRs are not directly applicable to the internality of MRs, highlighting the novelty of this study. The obtained new insights provide practical guidelines for companies to adjust their intervention strategies on e-commerce platforms.     

An integrated model for predicting consumer's intention to write online reviews

For marketers, consumers’ increasing use of online reviews in arriving at purchase decisions is a double edged sword. On one hand, it presents opportunity for manufacturers and sellers to be highlighted for their good work, but at the same time, there remains a possibility of negative review impacting goodwill and reputation. Hence, it is important for managers to identify factors that impact consumers’ intentions to write online reviews. This research delves into this aspect by trying to identify the factors that impact reviews writing intentions of consumers for restaurants. The study extends the theory of planned behaviour with additions of variables such as ego involvement, taking vengeance and helping a restaurant. For this, 252 valid responses were obtained, and structural equation modeling was used to test the proposed model. Results indicate that perceived behavioural control, subjective norms, ego involvement and taking vengeance are significant predictors of intention to write online reviews for the restaurants.     

Do consumers who conduct online research also post online reviews? A model of the relationship between online research and review posting behavior

The purpose of the research is to investigate whether consumers who conduct online product research also more likely to post online product reviews. An information theory-based classification algorithm is used to estimate the likelihood of a consumer posting an online review conditional on having conducted online product research. Data from a nationally representative probability sample of American internet users are used to estimate the model. The results indicate that the characteristics of consumers who have a greater propensity to conduct online product research but a lower tendency to post online product reviews differ substantially from those who are more likely to post online product reviews but less likely to conduct online product research. The research is important because the degree to which consumers who conduct online product research are similar to those who post online product reviews can be used to track the effectiveness of online word-of-mouth marketing campaigns.     

How and why (imagined) online reviews impact frontline retail encounters

This research examines how frontline retail employees respond to customers whom they think might write an online review about their experience. Across six experiments (one field and five online) we show that when employees identify potential online review authors, often by what the customer says or does, it catalyzes them to deliver better service. This ensues because they experience a rise in determination to do well, motivated by the prospect of being associated with a positive review, which they believe will impress the retailer. Thus, they go ‘above and beyond’. However, determination is tempered by two boundary conditions. When employees (i) do not consider that being associated with an online review is beneficial (i.e., not goal relevant) or (ii) feel poorly equipped to serve the customer (i.e., low in self-efficacy), then a better service delivery will not occur. We also show that retailers can enhance customer service through internal championing of the importance of online reviews, so long as this is framed as promotional rather than punitive.     

Emotional contagion triggered by online consumer reviews: Evidence from a neuroscience study

People tend to align with the emotional state of the person that is talking to them (the observed). Similarly, while processing information consumers can also experience this emotional contagion. Emotional contagion can activate in those who process information (the observer) similar responses in the autonomic nervous system and the neural responses as in those who create such information (the observed), triggering a certain level of arousal. Neuroscience enables researchers to study emotional contagion by monitoring the activation of neural structures and physiological responses. This study draws on the theory of arousal to investigate how different combinations of online consumer review (OCR) valence can trigger different emotions and customer experiences in the observer (the one who reads the OCRs). This study conducts a consumer neuroscience experiment to monitor emotional arousal. The physiological analyses (through skin conductance response) confirm that the emotional arousal of the observer aligns with that of the observed. The neural analyses (through electroencephalography) show the valence of the arousal, which indicates that negative OCRs activate arousal and pleasure in the observer, while positive OCRs are associated with arousal deactivation and displeasure.     

The informational value of multi-attribute online consumer reviews: A text mining approach

In the digital age, customers use online reviews to minimize the risks associated with purchasing a product. Major online retailers help customers choose the right product by exposing reviews that received many “helpful” votes at the top of the review section. Given that reviews that have received the maximum helpfulness votes are considered more important in purchase decisions, understanding determinants of helpfulness votes offers clear benefits to online retailers and review platforms. This study focuses on the effect of review informativeness, which is measured by the number of attributes discussed in a review, and its interplay of review valence on customers' perception of review helpfulness. We applied a word-level bigram analysis to derive product attributes from review text and examined the influence of the number of attributes on the review's helpfulness votes. More importantly, we also suggested the moderating role of review valence. Estimation results of the Zero-inflated Poisson models on 21,125 reviews across 14 wireless earbuds indicated that as more attributes are discussed in a review, the more the review can earn helpfulness votes from customers. Furthermore, the positive association between the number of attributes and helpfulness was enhanced among negative reviews. This study contributes to customers' information processing literature and offers guidelines to online retailers in designing a better decision support system.     

Consumer responses to functional,aesthetic and symbolic product design in online reviews

This research addresses calls to consider consumer responses to product design along three dimensions: functional, aesthetic, and symbolic design. Based on an analysis of online consumer reviews, this research sheds light on the role of all three design dimensions and reveals how each uniquely contributes to consumers' behavioral responses directly and indirectly though emotional arousal—a key mediator. Furthermore, an important distinction—rooted in consumer information processing models—is made between low-involvement and high-involvement products. The results indicate that the effectiveness of each of the three design dimensions differs depending on the level of product involvement. Taken together, the research findings highlight the importance of taking all three design dimensions into account, including the symbolic design dimension, which has received relatively little attention to date.     

Guilt appeals in cause-related marketing

Despite the growth of cause-related marketing (CRM), little is known about how consumers process cause-focused messages that contain emotional appeals. The present research seeks to further the understanding of guilt appeals in CRM by clarifying the moderating roles of product type and donation magnitude, and exploring the situations when a guilt appeal backfires. Although experimental results indicate that a guilt appeal is more effective than a non-guilt appeal, a guilt appeal backfires when the perceived hedonic value of a product is high. A high donation magnitude also eliminates CRM effectiveness of the guilt appeal. There is an interaction between guilt appeal and donation magnitude when promoting hedonic products with CRM. The findings underscore the importance for marketers of learning more about how guilt appeals work, and in turn describe how practitioners can avoid negative consumer reactions to their guilt appeals.     

The impact of text valence,star rating and rated usefulness in online reviews

The influence of text valence, star rating and rated usefulness of online reviews on review readers’ impression of the review and their positive word-of-mouth intention is tested in an experimental study (n = 431). In addition, we investigate the moderating role of review readers’ product category involvement and susceptibility to interpersonal influence on the effect of the three review components. The influence of review text valence on evaluative responses is stronger for more highly involved people and for people who are more susceptible to interpersonal influence. The influence of rated review usefulness on review impression is marginally stronger for people who are more susceptible to interpersonal influence. Star ratings do not influence evaluative responses, and their effect is not moderated by either involvement or susceptibility.     

Mining the text of online consumer reviews to analyze brand image and brand positioning

The growth of the Internet has led to massive availability of online consumer reviews. So far, papers studying online reviews have mainly analysed how non-textual features, such as ratings and volume, influence different types of consumer behavior, such as information adoption decisions or product choices. However, little attention has been paid to examining the textual aspects of online reviews in order to study brand image and brand positioning. The text analysis of online reviews inevitably raises the concept of “text mining”; that is, the process of extracting useful and meaningful information from unstructured text. This research proposes an unified, structured and easy-to-implement procedure for the text analysis of online reviews with the ultimate goal of studying brand image and brand positioning. The text mining analysis is based on a lexicon-based approach, the Linguistic Inquiry and Word Count (Pennebaker et al., 2007), which provides the researcher with insights into emotional and psychological brand associations.     

Analysing customers' reviews and ratings for online food deliveries: A text mining approach

The purpose of this study was to explore the relationship between online reviews and ratings through text mining and empirical techniques. An Indian food delivery portal ( Zomato.com ) was used, where 50 restaurants on Presence Across Nation (PAN) basis were selected through stratified random sampling. A total of 2530 reviews were collected, scrutinized, and analysed. Using the NVivo software for qualitative analysis, seven themes were identified from collected reviews, out of which, the ‘delivery’ theme was explored further for identifying sub-themes. Linear regression modelling was used to identify the variables affecting delivery ratings and sentiment analysis was also performed on the identified sub-themes. Regression results revealed that hygiene and pricing (delivery subthemes) demonstrated lower delivery ratings. These variables can be established as indicators for restaurants and related online food delivery services to build their business model around them. Similarly, negative sentiments were observed in pricing and hygiene sub-themes. Restaurants and online food services can enhance hygiene levels of their food delivery process in order to receive higher delivery ratings. Similarly, pricing of food items can be modified such that customers are not deterred from ordering the items—food and ordering service do not become cost-prohibitive. This study devised a standardized methodology for analysing vast amounts of online user-generated content (UGC). Findings from this study can be extrapolated to other sectors and service industries such as, tourism, cleaning, transportation, hospitals and engineering especially during the pandemic.     

Environmental threat appeals in green advertising

This research examines guilt appeals in green advertising by clarifying moderating roles of issue proximity and environmental consciousness. Advantageous effects of guilt appeals are produced in two contexts: promoting a highly proximal issue to consumers with weak environmental consciousness or promoting a less proximal issue to those with strong environmental consciousness. Guilt appeals are no more effective than non-guilt appeals when a low-proximity issue is presented to individuals with weak environmental consciousness. Guilt appeals backfire when promoting a high-proximity issue to highly conscious individuals. The implications of these findings are discussed, as are the limitations and directions for future research.     

The more the better? Exploring the effects of reviewer social networks on online reviews

ABSTRACT

Conventional wisdom suggests that firms leverage key influencers (e.g. individuals with high centrality) in online communities to stimulate buzz. Using a large panel dataset including 1,569,264 online Yelp reviews and the ego-network of 366,715 individual reviewers over a nine-year period, this study examines the effects of number of ties and network density on the volume and valence of online reviews. In contrast with the general belief that key influencers always generate positive buzz, the findings show that they can adversely affect future review valence. Specifically, reviewers with many connections on Yelp can reduce the positivity of reviews of the same business in the next period. This finding has implications for marketing practice in online community management and social media intervention.     

Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews

In recent years, there has been proliferation of grocery mobile apps as grocery shopping on mobile has found increasing acceptance among customers accelerated by multiple factors. Maintaining high level of customer satisfaction is important for grocery mobile apps in the highly competitive app market. Online reviews have been a rich source of information to analyze customer satisfaction with a product or service. This paper explores the determinants of customer satisfaction for grocery mobile apps using online reviews. Latent Dirichlet Analysis (LDA), which is a text mining technique, is used to analyze online customer reviews of 27,337 customers to identify determinants of customer satisfaction. The determinants identified were further analyzed using a series of analysis to understand the importance of each determinant. Dominance analysis examined the relative importance of the determinants of customer satisfaction based on the overall rating. Correspondence analysis identified determinants which cause satisfaction separately from the determinants which cause dissatisfaction. The results from this study will provide insights to business managers of grocery mobile apps for decision-making on customer satisfaction management.