Osamah M. Al-Qershi, Junbum Kwon, Shuning Zhao and Zhaokun Li
For the case of many content features, This paper aims to investigate which content features in video and text ads more contribute to accurately predicting the success of…
Abstract
Purpose
For the case of many content features, This paper aims to investigate which content features in video and text ads more contribute to accurately predicting the success of crowdfunding by comparing prediction models.
Design/methodology/approach
With 1,368 features extracted from 15,195 Kickstarter campaigns in the USA, the authors compare base models such as logistic regression (LR) with tree-based homogeneous ensembles such as eXtreme gradient boosting (XGBoost) and heterogeneous ensembles such as XGBoost + LR.
Findings
XGBoost shows higher prediction accuracy than LR (82% vs 69%), in contrast to the findings of a previous relevant study. Regarding important content features, humans (e.g. founders) are more important than visual objects (e.g. products). In both spoken and written language, words related to experience (e.g. eat) or perception (e.g. hear) are more important than cognitive (e.g. causation) words. In addition, a focus on the future is more important than a present or past time orientation. Speech aids (see and compare) to complement visual content are also effective and positive tone matters in speech.
Research limitations/implications
This research makes theoretical contributions by finding more important visuals (human) and language features (experience, perception and future time). Also, in a multimodal context, complementary cues (e.g. speech aids) across different modalities help. Furthermore, the noncontent parts of speech such as positive “tone” or pace of speech are important.
Practical implications
Founders are encouraged to assess and revise the content of their video or text ads as well as their basic campaign features (e.g. goal, duration and reward) before they launch their campaigns. Next, overly complex ensembles may suffer from overfitting problems. In practice, model validation using unseen data is recommended.
Originality/value
Rather than reducing the number of content feature dimensions (Kaminski and Hopp, 2020), by enabling advanced prediction models to accommodate many contents features, prediction accuracy rises substantially.
Details
Keywords
Eathar Abdul-Ghani, Jungkeun Kim, Junbum Kwon, Kenneth F. Hyde and Yuanyuan (Gina) Cui
Given the socialisation of men and women to their gender roles and expression of emotion, this study aims to investigate whether there are gender differences in the use of emotive…
Abstract
Purpose
Given the socialisation of men and women to their gender roles and expression of emotion, this study aims to investigate whether there are gender differences in the use of emotive language in electronic word-of-mouth (eWOM), specifically in online reviews. The authors propose that female reviewers will use strong emotive terms, such as love, more frequently in online reviews than do male reviewers. The authors further propose that the gender of the reviewer influences audience responses to the reviewer’s use of emotive terms in online reviews.
Design/methodology/approach
The authors conducted secondary data analysis of restaurant reviews (Study 1) to provide evidence on whether the gender of the reviewer affects the frequency of use of emotive terms in an online review. In addition, three separate experiments (Studies 2–4) were conducted to test the theoretical arguments.
Findings
The results of the secondary data analysis indicated that female online reviewers used the term “love” much more frequently in their reviews than male reviewers, whereas there was no usage difference for the term “like”. The experimental studies further showed that an emotive review by a male reviewer containing the word “love” resulted in a higher evaluation of the restaurant being reviewed than a non-emotive review containing the word “like”. This difference was stronger when the overall rating was less salient and for consumers who believe (vs do not believe) that men and women use emotional language differently.
Research limitations/implications
First, the paper extends our understanding of gender differences in emotional expression within the domain of eWOM and online reviews as well as our understanding of consumer responses to these gender differences. Second, the authors identify a boundary condition for these gender effects, namely, the overall rating score. Third, the authors find that consumer beliefs regarding gender stereotypes in emotional expression provide an explanation for these effects.
Practical implications
The results of the research indicate that the electronic algorithms operating on review sites might be modified in terms of their criteria for selecting the reviews to display to consumers, as consumer decision-makers may find greater utility in reviews written by male reviewers that contain strongly positive emotive terms.
Originality/value
The research extends the knowledge on gender differences in emotional expression in online reviews by demonstrating the actual usage patterns and differing responses to the emotional expressions of each gender.
Details
Keywords
Ka Wing Chan, Felix Septianto, Junbum Kwon and Revathi Sridhar Kamal
This paper aims to theorize and investigate the use of effective color features in artificial intelligence (AI) influencers, an emerging marketing trend in the social media…
Abstract
Purpose
This paper aims to theorize and investigate the use of effective color features in artificial intelligence (AI) influencers, an emerging marketing trend in the social media context.
Design/methodology/approach
By analyzing 6,132 pictures posted by ten AI influencers on Instagram, this paper examines the effect of warm colors in AI influencers’ social media posts on consumer responses, and how other color features may moderate the effect of warm color. In addition, two experimental studies reveal the underlying process driving the effect of warm color.
Findings
Warmer color generated more favorable consumer responses, with brightness significantly moderating the relationship between warm color and favorable consumer responses. Moreover, the results of the experiments establish that perceived warmth and emotional trust mediate the causal effect of warm colors on consumer responses.
Research limitations/implications
There is still little understanding about consumer perceptions of AI influencers and their acceptance of AI influencers’ product recommendations. As such, this research offers theoretical understanding of the color features influencing the effectiveness of recommendations by AI influencers.
Practical implications
Brands have started deploying AI influencers as their brand ambassadors to make product recommendations, representing a new wave of advertising on social media. The findings will thus benefit marketers in developing effective product recommendations using AI influencers.
Originality/value
The present research provides a novel understanding of how visual features, such as color can influence the effectiveness of AI influencers.