Abstract/Description
Purpose: The objective of this research is to use social listening in exploring the factors which affect the consumers’ perceptions, attitude, and emotions which lead to purchase intentions.
Introduction and Literature Review: Over 3.8 billion people use social media that is around 45% of the world’s population(Statista, 2022). The ever evolving technologies and the adaption of these technologies is changing how we access information, how we communicate, and how we respond to different stimuli (Ospina, 2019). How we communicate, listen, and interpret using a domain of social media is termed as social listening (Stewart & Arnold, 2018). Specifically, social listening refers to “an active process of attending to, observing, interpreting, and responding to a variety of stimuli through mediated, electronic, and social channels” (Stewart & Arnold, 2018, p. 86). Consumers produce and are encircled by more textual communication than ever before (Humphreys & Wang, 2018). Listening to this textual communication in the form of product reviews and discussions can be the potential sources of consumer attitudes and behaviour (Humphreys & Wang, 2018). Thus, I propose investigating the social listening through the automated textual analysis can help us to understand the factors affecting the consumers’ perception, attitudes, and emotions which can further help us to predict the consumers’ purchase intentions.
Research Problem: Consumers generate a large quantity of brand related data on social media platforms. A body of research has used this user generated data to investigate the valuable insights about product and brand perceptions (Dzyabura & Peres, 2021). For instance reviews (Lee & Bradlow, 2011), social tags (Nam et al., 2017), blogs (Gelper et al., 2018), and visual social media content (Jalali & Papatla, 2016; Liu et al., 2020) have been used to explore the consumer insights. Though aforementioned stream of research has explored the user generated content to understand the consumer insights but there is not a single research that explores the factors affecting consumers’ attitudes, perception, and emotions which lead to their intentions to purchase.
Methodology: As mentioned the objective of this research includes the understanding of the factors which affect the consumers’ perception, attitudes, and emotion and predicting the purchase intention, therefore, automated text analysis can be used to understand and predict aforementioned constructs (Berger et al., 2020) . Automated text analysis (ATA), a machine learning technique, is a useful tool to investigate the patterns from the textual data that a human cannot detect (Humphreys & Wang, 2018). Moreover, ATA provides ecological validity in investigating psychological and social constructs from the consumer generated content (Humphreys & Wang, 2018). Thus, this is an effective and objective technique to make a sense out of user generated content. Python and R languages can be used in ATA technique.
The data source for this research will be the social media platforms such as Facebook, Instagram, and Twitter. After that, this research can further explore financial products such as stocks and bonds. The data sources for such research will be different than consumer products, i.e., newspaper archives and financial magazines archives.
Contribution: This research will benefit to academic researchers and practitioners to focus on the explored factors in order to design an effective marketing programs which may lead to actual to positive product perceptions, emotions, and attitudes which will ultimately lead to their purchase intentions.
Keywords
Social Listening, Consumer Perception, Consumer Attitudes, Consumer Emotions, Purchase Intentions
Track
Contemporary Issues in Marketing
Session Number/Theme
Session 3D: Digital Marketing
Session Chair
Dr. Talha Salam, Institute of Business Administration, Karachi
Session Discussant
Dr. Amber Gul, Sumayyah Khursheed, Dr. Farah Naz
Start Date/Time
24-6-2022 2:20 PM
End Date/Time
24-6-2022 2:40 PM
Location
Boardroom 3, Marriott Hotel, Karachi
Recommended Citation
Khoso, U. (2022). The use of social listening in understanding the consumer perception, attitudes, emotions, and predicting the purchase intentions. 3rd IBA SBS International Conference 2024. Retrieved from https://ir.iba.edu.pk/sbsic/2022/program/54
The use of social listening in understanding the consumer perception, attitudes, emotions, and predicting the purchase intentions
Boardroom 3, Marriott Hotel, Karachi
Purpose: The objective of this research is to use social listening in exploring the factors which affect the consumers’ perceptions, attitude, and emotions which lead to purchase intentions.
Introduction and Literature Review: Over 3.8 billion people use social media that is around 45% of the world’s population(Statista, 2022). The ever evolving technologies and the adaption of these technologies is changing how we access information, how we communicate, and how we respond to different stimuli (Ospina, 2019). How we communicate, listen, and interpret using a domain of social media is termed as social listening (Stewart & Arnold, 2018). Specifically, social listening refers to “an active process of attending to, observing, interpreting, and responding to a variety of stimuli through mediated, electronic, and social channels” (Stewart & Arnold, 2018, p. 86). Consumers produce and are encircled by more textual communication than ever before (Humphreys & Wang, 2018). Listening to this textual communication in the form of product reviews and discussions can be the potential sources of consumer attitudes and behaviour (Humphreys & Wang, 2018). Thus, I propose investigating the social listening through the automated textual analysis can help us to understand the factors affecting the consumers’ perception, attitudes, and emotions which can further help us to predict the consumers’ purchase intentions.
Research Problem: Consumers generate a large quantity of brand related data on social media platforms. A body of research has used this user generated data to investigate the valuable insights about product and brand perceptions (Dzyabura & Peres, 2021). For instance reviews (Lee & Bradlow, 2011), social tags (Nam et al., 2017), blogs (Gelper et al., 2018), and visual social media content (Jalali & Papatla, 2016; Liu et al., 2020) have been used to explore the consumer insights. Though aforementioned stream of research has explored the user generated content to understand the consumer insights but there is not a single research that explores the factors affecting consumers’ attitudes, perception, and emotions which lead to their intentions to purchase.
Methodology: As mentioned the objective of this research includes the understanding of the factors which affect the consumers’ perception, attitudes, and emotion and predicting the purchase intention, therefore, automated text analysis can be used to understand and predict aforementioned constructs (Berger et al., 2020) . Automated text analysis (ATA), a machine learning technique, is a useful tool to investigate the patterns from the textual data that a human cannot detect (Humphreys & Wang, 2018). Moreover, ATA provides ecological validity in investigating psychological and social constructs from the consumer generated content (Humphreys & Wang, 2018). Thus, this is an effective and objective technique to make a sense out of user generated content. Python and R languages can be used in ATA technique.
The data source for this research will be the social media platforms such as Facebook, Instagram, and Twitter. After that, this research can further explore financial products such as stocks and bonds. The data sources for such research will be different than consumer products, i.e., newspaper archives and financial magazines archives.
Contribution: This research will benefit to academic researchers and practitioners to focus on the explored factors in order to design an effective marketing programs which may lead to actual to positive product perceptions, emotions, and attitudes which will ultimately lead to their purchase intentions.
