Pre- and post-launch emotions in new product development: Insights from twitter analytics of three products

Social Listening for Product Design Requirement Analysis and Segmentation: A Graph Analysis Approach with User Comments Mining

This study investigates customers' product design requirements through online comments from social media, and quickly translates these needs into product design specifications. First, the exponential discriminative snowball sampling method was proposed to generate a product-related subnetwork. Second, natural language processing (NLP) was utilized to mine user-generated comments, and a Graph SAmple and aggreGatE method was employed to embed the user's node neighborhood information in the network to jointly define a user's persona. Clustering was used for market and product model segmentation. Finally, a deep learning bidirectional long short-term memory with conditional random fields framework was introduced for opinion mining. A comment frequency-invert group frequency indicator was proposed to quantify all user groups' positive and negative opinions for various specifications of different product functions. A case study of smartphone design analysis is presented with data from a large Chinese online community called Baidu Tieba. Eleven layers of social relationships were snowball sampled, with 14,018 users and 30,803 comments. The proposed method produced a more reasonable user group clustering result than the conventional method. With our approach, user groups' dominating likes and dislikes for specifications could be immediately identified, and the similar and different preferences of product features by different user groups were instantly revealed. Managerial and engineering insights were also discussed.

Developing an evidence-based TISM: an application for the success of COVID-19 Vaccination Drive

The study illustrates an application of evidence data for performing Total Interpretive Structural Modeling (TISM). TISM is widely used to analyze the critical success factors or inhibitors and their interlinkages. This study uses learning from evidence data, specifically social media analytics, to identify the relationship between the elements. Thus, it leads to the advancement of the TISM-P methodology with evidence-based TISM (TISM-E). This study uses Twitter as a source of evidence data. Further, 2,60,297 tweets were used to illustrate the process of TISM-E. The paper demonstrates the application of TISM-E for the success of the COVID-19 vaccination drive. The pandemic effects are long-term; therefore, the hierarchical model developed shows a sustainable approach for vaccinating maximum population. Further, the framework developed will ensure the distribution efficacy of vaccines. In addition, it will aid practitioners in developing future vaccination policies. The enhanced model provides evidence for polarity (positive/negative) of relationships and can help to build propositions for theory development. The study contributes to healthcare, modeling research, and graph-theoretic literature.

PRDECT-ID: Indonesian product reviews dataset for emotions classification tasks

Recognizing emotions is vital in communication. Emotions convey additional meanings to the communication process. Nowadays, people can communicate their emotions on many platforms; one is the product review. Product reviews in the online platform are an important element that affects customers' buying decisions. Hence, it is essential to recognize emotions from the product reviews. Emotions recognition from the product reviews can be done automatically using a machine or deep learning algorithm. Dataset can be considered as the fuel to model the recognizer. However, only a limited dataset exists in recognizing emotions from the product reviews, particularly in a local language. This research contributes to the dataset collection of 5400 product reviews in Indonesian. It was carefully curated from various (29) product categories, annotated with five emotions, and verified by an expert in clinical psychology. The dataset supports an innovative process to build automatic emotion classification on product reviews.

The Impact of Mood, Familiarity, Acceptability, Sensory Characteristics and Attitude on Consumers' Emotional Responses to Chocolates

Studies on emotions linked to sensory characteristics to understand consumers’ choice behaviour have grown in number rapidly. Internal consumer behaviour variables, namely mood, familiarity, acceptability, and attitude (MFAA), have been found to influence emotional response. The aim of this paper was to determine the impact of MFAA on consumers’ emotional responses towards chocolate as well as the effect of the sensory characteristics of chocolate on consumers’ emotional responses. Upon ethical approval, three chocolates were selected by a trained sensory panel based on 14 sensory attributes regarded relevant. Screened respondents (n = 149) completed an online survey based on the tasting of the chocolates by means of a home-use test (HUT). The questionnaire captured consumers’ mood (Quick mood scale), familiarity (QFFQ), acceptability (FACT), the sensory characteristics of the chocolate samples and emotional response (EsSense25 Profile), and lastly attitude (ACQ). Descriptive and inferential statistics were examined to answer the hypotheses of the study. The findings indicate that emotions are related to the bitter sensory attributes of chocolate and that this emotional response is influenced by MFAA variables, supporting the known fact that consumer behaviour is complex and multi-dimensional. Internal consumer behaviour variables play an important role in the emotions experienced during the consumption of chocolate. Investigating the relative importance of consumer behaviour components in sensory studies could allow for the design of food products such as chocolates based on a more “holistic” view of the consumer.

Twitter sentiment analysis of app based online food delivery companies

Purpose

There is a strong need for companies to monitor customer-generated content of social media, not only about themselves but also about competitors, to deal with competition and to assess competitive environment of the business. The purpose of this paper is to help companies with social media competitive analysis and transformation of social media data into knowledge creation for decision-makers, specifically for app-based food delivery companies.

Design/methodology/approach

Three online app-based food delivery companies, i.e. Swiggy, Zomato and UberEats, were considered in this study. Twitter was used as the data collection platform where customer’s tweets related to all three companies are fetched using R-Studio and Lexicon-based sentiment analysis method is applied on the tweets fetched for the companies. A descriptive analytical method is used to compute the score of different sentiments. A negative and positive sentiment word list is created to match the word present on the tweets and based on the matching positive, negative and neutral sentiments score are decided. The sentiment analysis is a best method to analyze consumer’s text sentiment. Lexicon-based sentiment classification is always preferable than machine learning or other model because it gives flexibility to make your own sentiment dictionary to classify emotions. To perform tweets sentiment analysis, lexicon-based classification method and text mining were performed on R-Studio platform.

Findings

Results suggest that Zomato (26% positive sentiments) has received more positive sentiments as compared to the other two companies (25% positive sentiments for Swiggy and 24% positive sentiments for UberEats). Negative sentiments for the Zomato was also low (12% negative sentiments) compared to Swiggy and UberEats (13% negative sentiments for both). Further, based on negative sentiments concerning all the three food delivery companies, tweets were analyzed and recommendations for business provided.

Research limitations/implications

The results of this study reveal the value of social media competitive analysis and show the power of text mining and sentiment analysis in extracting business value and competitive advantage. Suggestions, business and research implications are also provided to help companies in developing a social media competitive analysis strategy.

Originality/value

Twitter analysis of food-based companies has been performed.

Decoding the global trend of “vaccine tourism” through public sentiments and emotions: does it get a nod on Twitter?

Purpose

This paper aims to fill the major research gap prevalent in the tourism literature on the new form of tourism branching out from the COVID-19. While there are newspaper reports mentioning about the government’s reaction to vaccine tourism, there is no such study or report that tries to understand what the global masses feel about it; thus, a preliminary investigation of the social sentiment and emotion accruing around vaccine tourism on Twitter is carried out.

Design/methodology/approach

This exploratory study serves as a preliminary investigation of the social sentiment and emotion accruing around vaccine tourism on Twitter and tries to categorise them into eight basic emotions from Plutchik (1994) “wheel of emotions” as joy, disgust, fear, anger, anticipation, sadness, trust and surprise. The results are presented through data visualisation technique for analysis. The study makes use of R programming languages and the extensive packages offered on RStudio.

Findings

A total of 12,258 emotions were captured. It is evident that Vaccine Tourism has got maximum of positive sentiments (28.14%) which is almost double of the negative sentiment (14.05%). It is visible that the highest sentiment is “trust” (12.74%) and is followed by “fear” (8.97%). The least visible sentiment is “surprise” (4.32%). Polarity has been found for maximum tweets as positive (55.52%) which yet again surpasses negative polarity (33.7%), and neutral polarity is the least (10.67%).

Research limitations/implications

It can be said that people bear a positive emotion regarding vaccine tourism such as “trust” and “joy” which also denotes a positive sentiment score for testing polarity. But there are still concerns of high prices of the packages, fear-prevalent people to step out, and the uncertainty of right precautionary measures being taken still puts vaccine tourism under the radar of doubt with a fourth population having negative and neutral sentiments each. This is indicative with “fear” being the second highest emotion to the users. There are mixed emotions for vaccine tourism, but positive dominates the results.

Practical implications

The study attempts to see the global reaction on social media on vaccine tourism trend for giving food for thought to marketers. It can be said that Asians can be the target group.

Originality/value

To the best of the authors’ knowledge, there is no study that addresses the new trend of “Vaccine Tourism” or attempts to understand the emotions and sentiments of people globally.

Advancing on weighted PLS-SEM in examining the trust-based recommendation system in pioneering product promotion effectiveness

The advancement in digital technologies has led to an explosive information phenomenon, particularly in Internet shopping. This paper attempts to examine the trust element in the current pervasive use of the recommendation system for product promotion effectiveness. Owing to the nature of high-volume online consumers and the nonexistence of the online consumer sampling frame, sampling weight adjustment approach was utilised for ensuring sample representativeness. Additionally, the responses collected were further analysed according to gender for a holistic understanding of the trust element. A cross-sectional quantitative research approach was adopted. Specifically, snowball sampling method was used to collect responses from online consumers. The findings revealed that benevolence, integrity, and competence trust are found to be positively associated with product promotion effectiveness. Competence trust recorded a large effect size followed by benevolence and integrity trust. Both male and female consumers shown different degrees of trust level. The findings provide practical implications for online merchants. They were suggested to focus on enhancing online consumers' trust level and capitalize on competence trust for effective product promotion. They should also recognize the gender differences in the trust level for product promotion effectiveness when they are promoting gender-based products and services.

Digitally driven new product development: an involved contemporary innovation case

Purpose

The purpose of this paper is to study the impact of real-time data emerging from implementation of the Internet of Things (IoT) and netnography on the efficiency of the new product development (NPD).

Design/methodology/approach

Customer-oriented organizations are the ones that survive in the market with a flow of new products to the market. Expectations like reduced timelines with quality focus provoke innovations. Customer inputs become the soul for a successful product wherein it becomes important to keep a constant stream of information flow back from the market. Literature review states that real-time data gathering with the implementation of IoT ensures the same. Along with real-time data, researchers have envisaged the need to identify the customer persona before incorporating customer opinion and sentiments vide netnography.

Findings

The organization can leverage the collaboration of IoT origin real-time data and sentiment analysis to effectively manage the NPD. Real-time customer data coupled with customer opinions and sentiments prove to be a game changer in the NPD process.

Originality/value

The originalities of this study are impact of IoT origin real-time data coupled with sentiment analysis on the NPD process. While impact of IoT origin data is reported in isolation similar to sentiment analysis, influence of collaboration of real-time data with sentiment analysis on NPD process is reported in this study.

How Dramatic Events Can Affect Emotionality in Social Posting: The Impact of COVID-19 on Reddit

The COVID-19 outbreak impacted almost all the aspects of ordinary life. In this context, social networks quickly started playing the role of a sounding board for the content produced by people. Studying how dramatic events affect the way people interact with each other and react to poorly known situations is recognized as a relevant research task. Since automatically identifying country-based COVID-19 social posts on generalized social networks, like Twitter and Facebook, is a difficult task, in this work we concentrate on Reddit megathreads, which provide a unique opportunity to study focused reactions of people by both topic and country. We analyze specific reactions and we compare them with a “normal” period, not affected by the pandemic; in particular, we consider structural variations in social posting behavior, emotional reactions under the Plutchik model of basic emotions, and emotional reactions under unconventional emotions, such as skepticism, particularly relevant in the COVID-19 context.

What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the "Digital Service Usage Satisfaction Model"

Mobile payment services have become increasingly important in daily lives in India due to multiple planned and unplanned events. The objective of this study is to identify the determinants of usage satisfaction of mobile payments which could enhance service adoption. The "Digital Service Usage Satisfaction Model" has been proposed and validated by combining technology adoption and service science literature. First the data was extracted from Twitter based on hashtags and keywords. Then using sentiment mining and topic modelling the large volumes of text were analysed. Then network science was also used for identifying clusters among associated topics. Then, using content analysis methodology, a theoretical model was developed based on literature. Finally using multiple regression analysis, we validated the proposed model. The study establishes that cost, usefulness, trust, social influence, credibility, information privacy and responsiveness factors are more important to increase the usage satisfaction of mobile payments services. Also methodologically, this is an endeavour to validate a new approach which uses social media data for developing a inferential theoretical model.