Tae Hyun Baek, Seeun Kim, Sukki Yoon, Yung Kyun Choi, Dongwon Choi and Hyejin Bang
The authors aim to examine how emojis interact with assertiveness in social media posts to encourage social media engagement and cooperation in environmental campaigns.
Abstract
Purpose
The authors aim to examine how emojis interact with assertiveness in social media posts to encourage social media engagement and cooperation in environmental campaigns.
Design/methodology/approach
Two experiments were used to test three hypotheses.
Findings
Study 1 shows that when assertive Twitter messages include the smiley-face emoji, study participants indicate stronger social media engagement and behavioral intentions to recycle used jeans. In Study 2, participants indicate stronger social media engagement and behavioral intentions to sign a petition for reducing plastic pollution when (non) assertive Facebook messages (do not) include emojis.
Originality/value
The current research advances our understanding about how emojis interact with assertive and nonassertive message tonality in environmental social media campaigns. This research also provides new insights showing that positive emotion is the psychological mechanism underlying matching effects of emoji and message assertiveness.
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Jongdae Kim, Youseok Lee and Inseong Song
The purpose of this paper is to develop a predictive model for box office performance based on the textual information in movie scripts in the green-lighting process of movie…
Abstract
Purpose
The purpose of this paper is to develop a predictive model for box office performance based on the textual information in movie scripts in the green-lighting process of movie production.
Design/methodology/approach
The authors use Latent Dirichlet Allocation to determine the hidden textual structure in movie scripts by extracting topic probabilities as predictors for classification. The extracted topic probabilities are used as inputs for the predictive model for the box office performance. For the predictive model, the authors utilize a variety of classification algorithms such as logistic classification, decision trees, random forests, k-nearest neighbor algorithms, support vector machines and artificial neural networks, and compare their relative performances in predicting movies' market performance.
Findings
This approach for extracting textual information from movie scripts produces a valuable typology for movies. Moreover, our modeling approach has significant power to predict movie scripts' profitability. It provides a superior prediction performance compared to previous benchmarks, such as that of Eliashberg et al. (2007).
Research limitations/implications
This work contributes to literature on predicting the box office performance in the green-lighting process and literature regarding suggesting models for the idea screening stage in the new product development process. Besides, this is one of the few studies that use movie script data to predict movies' financial performance by proposing an approach to integrate text mining models and machine learning algorithms with movie experts' intuition.
Practical implications
First, the authors’ approach can significantly reduce the financial risk associated with movie production decisions before the pre-production stage. Second, this paper proposes an approach that is applicable at a very early stage of new product development, such as the idea screening stage. The authors also introduce an online-based movie scenario database system that can help movie studios make more systematic and profitable decisions in the green-lighting process. Third, this approach can help movie studios estimate movie scripts' financial value.
Originality/value
This study is one of the few studies to forecast market performance in the green-lighting process.
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Cheng Xu, Jooyoung Park and Jacob C. Lee
This research investigates the novel questions of whether and how specific forms of shopping channels (online vs offline) influence consumers' decision-making. Moreover, this…
Abstract
Purpose
This research investigates the novel questions of whether and how specific forms of shopping channels (online vs offline) influence consumers' decision-making. Moreover, this research investigates marketing firms' proper marketing strategies across different shipping channels.
Design/methodology/approach
The authors conducted three studies using a large sample (N = 703) recruited from a diverse pool (students and adults) that examined multiple products (camera and car) across different shopping channels (online vs offline). Study 1a (n = 251) and Study 1b (n = 252) examined the effect of an online versus offline channel on consumers' decision-making using a one-factor (shopping channel: online vs offline) between-subjects design. Meanwhile, Study 2 (n = 200) investigated the effective strategies that firms should employ across different shopping channels using a 2 (shopping channel: online vs offline) × 2 (mental simulation: outcome vs process) between-subjects design. Participants in the online condition evaluated the product on a computer screen, whereas participants in the offline condition evaluated the real product assuming a real-world retail store setting.
Findings
The three studies supported the predictions that shopping channels (online vs offline) affect consumers' psychological distance and, in turn, affect their decision process. Specifically, results reveal that the online (offline) channel increases (decreases) psychological distance and leads consumers to pay more attention to a product's desirability (feasibility) aspects.
Originality/value
Given that many firms sell the same products through multiple channels, the findings of this research offer insightful theoretical and practical implications.
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Sang M. Lee, Taewan Kim and Seung Hoon Jang
The purpose of this paper is to investigate the relationship between corporate venture capital (CVC) investment and the level of knowledge transferred from start-ups to corporate…
Abstract
Purpose
The purpose of this paper is to investigate the relationship between corporate venture capital (CVC) investment and the level of knowledge transferred from start-ups to corporate investors. It also delineates the conditions under which CVC investment facilitates the knowledge transfer.
Design/methodology/approach
A longitudinal design is used to examine annual snapshots of CVC investment and patent citing activities for the period from 1995 to 2005. This paper uses a negative binomial Poisson regression model to test proposed research hypotheses.
Findings
The authors found that that there is an inverted U-shaped relationship between the number of CVC investments and the level of knowledge transferred from the start-up. The results of this study also found that knowledge diversity of the investing firm moderates the inverted U-shaped relationship.
Originality/value
This research contributes to the search literature by conceptualizing CVC investment as a distant search process for sourcing external knowledge from start-ups. By arguing theoretically and demonstrating empirically the effects of tie strength of CVC structure and technological knowledge diversity on organizational knowledge transfer, this current study extends the previous understanding and applicability of social relations and technological diversity to understand CVC activity.
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Yun Kyung Oh and Jisu Yi
The evaluation of perceived attribute performance reflected in online consumer reviews (OCRs) is critical in gaining timely marketing insights. This study proposed a text mining…
Abstract
Purpose
The evaluation of perceived attribute performance reflected in online consumer reviews (OCRs) is critical in gaining timely marketing insights. This study proposed a text mining approach to measure consumer sentiments at the feature level and their asymmetric impacts on overall product ratings.
Design/methodology/approach
This study employed 49,130 OCRs generated for 14 wireless earbud products on Amazon.com. Word combinations of the major quality dimensions and related sentiment words were identified using bigram natural language processing (NLP) analysis. This study combined sentiment dictionaries and feature-related bigrams and measured feature level sentiment scores in a review. Furthermore, the authors examined the effect of feature level sentiment on product ratings.
Findings
The results indicate that customer sentiment for product features measured from text reviews significantly and asymmetrically affects the overall rating. Building upon the three-factor theory of customer satisfaction, the key quality dimensions of wireless earbuds are categorized into basic, excitement and performance factors.
Originality/value
This study provides a novel approach to assess customer feature level evaluation of a product and its impact on customer satisfaction based on big data analytics. By applying the suggested methodology, marketing managers can gain in-depth insights into consumer needs and reflect this knowledge in their future product or service improvement.