Listen, Learn, Leverage : How Social Listening Enhance Organizations’ Marketing Strategies

Listen, Learn, Leverage:

How Social Listening Enhance Organizations’ Marketing Strategies.

BACHELOR THESIS WITHIN: Business Administration NUMBER OF CREDITS: 15 ECTS

PROGRAMME OF STUDY: Marketing Management AUTHORS: Linn Grönqvist & Sandra Hillergren JÖNKÖPING May 2020

Acknowledgements

The authors would like to express their sincere gratitude and appreciation to all people who contributed with guidance and participation in the development of this thesis. The valuable feedback, suggestion, and support often led to new insights that contributed to the progression in our work. This research would not have been possible without your

tenacious support throughout the entire process.

The authors would also like to thank our tutor Nadia Arshad and Associate Professor of Business Administration at Jönköping International Business School Anders Melander

whose guidance and advice was helpful in the progression of this thesis.

Finally, we would like to express appreciation to all the participants in this research for providing us with valuable knowledge which enabled us

Bachelor Thesis in Business Administration

Title: Listen, Learn, Leverage: How Social Listening Enhance Organizations’

Marketing Strategies

Authors: Linn Grönqvist & Sandra Hillergren Tutor: Nadia Arshad

Date: 2020-05-18

Key terms: Social listening, Marketing strategy, Dissemination, Social Listening Process, Intentions of Social Listening, Impacts of Social Listening

Abstract

Background

Social media has increased tremendously in users during the last years, which consequently has transformed the way people create, collaborate and communicate information. Given the wide usage, the opportunity for businesses to listen and analyze consumers' opinions online has increased. The technique of analyzing user-generated content from various social media channels is a tactic where companies can utilize their capacity and improve future business. Social listening is an active process where companies can attend, observe, interpret and respond to a variety of stimuli created by consumers on social media platforms.

Purpose

The purpose of this research is to understand how organizations manage social listening and how the activities in the process can be implemented to leverage impacts for organizations’ marketing strategies. The purpose is refined by addressing the research questions:

- How do organizations manage the social listening process?

- How can the activities in the social listening process be implemented to enhance the leverage of impacts on the organization’s marketing strategy? Method

To carry out the research purpose, a qualitative research through a descriptive multiple case study design was performed. Empirical data was collected through eight semi-structured interviews with professionals within the field. The primary data was reviewed in relation to previously conducted research by thematic analysis to answer the research questions.

Conclusion

The research finds that organizations’ intention of integrating social listening is to deepen the understanding of the market to incorporate the results in their marketing strategy. Empirical findings explain the social listening process by emphasizing on the importance of establishing intentions to facilitate the choice of process, separating qualitative and quantitative data, and to carefully chosen dissemination strategy, to leverage the impacts on marketing strategies as a result of social listening.

TABLE OF CONTENTS

1

INTRODUCTION ...1

2

FRAME OF REFERENCE...5

2.1 Defining the Concept of Social Listening ... 5

2.2 Proposed Processes of Social Listening in Existing Literature ... 6

2.3 Key Techniques in the Social Listening Process ... 8

2.4 Challenges in the Social Listening Process... 9

2.5 Social Listening’s Impact on Marketing Strategies ... 11

2.5.1 Social Listening’s Impact on Product Management ... 11

2.5.2 Social Listening’s Impact on Brand Management ... 12

2.5.3 Social Listening’s Impact on Marketing Planning ... 12

2.5.4 Social Listening’s Impact on Customer Satisfaction ... 13

3

METHODOLOGY ... 15

3.1 Research Philosophy ... 15

3.2 Research Approach ... 15

3.3 Research Classification ... 16

3.4 Research Method... 17

3.5 Methods of Primary Data ... 17

3.5.1 Research Strategy ... 17

3.5.1.1 Case Study Selection ... 18

3.5.2 Sampling Method ... 18

3.5.3 Case Study Participants ... 20

3.5.4 Interviews ... 20

3.5.4.1 Pilot Interview ... 21

3.5.4.2 Procedures of Interviews ... 21

3.5.5 Analysis of Empirical Data... 22

3.6 Method of Secondary Data ... 23

3.7 Quality and Trustworthiness of Data ... 24

3.7.1 Credibility ... 24

3.7.2 Transferability ... 25

3.7.3 Dependability ... 25

3.7.4 Confirmability ... 26

4

EMPIRICAL FINDINGS ... 27

4.1 Theme Statement for Primary Data ... 27

4.2 Presentation of Empirical Findings ... 28

4.2.1 Intention for Implementing Social Listening ... 28

4.2.2 Capabilities of Social Listening ... 29

4.2.3 Chosen Process of Social Listening ... 30

4.2.3.1 P1’s Implemented Social Listening Process ... 30

4.2.3.2 P2’s Implemented Social Listening Process ... 32

4.2.3.4 P4’s Implemented Social Listening Process ... 34

4.2.3.5 P5’s Implemented Social Listening Process ... 35

4.2.3.6 P6’s Implemented Social Listening Process ... 36

4.2.3.7 P7’s Implemented Social Listening Process ... 37

4.2.3.8 P8’s Implemented Social Listening Process ... 38

4.2.3.9 Cross-case Comparison of Implemented Social Listening Process ... 39

4.2.4 Implementation of the Social Listening Process... 40

4.2.5 Social Listenings’ Impact on Future Business ... 41

4.2.5.1 Customer Satisfaction ... 41

4.2.5.2 Brand Management ... 42

4.2.5.3 Product Development ... 42

4.2.5.4 Marketing Planning ... 42

4.2.6 Challenges When Conducting Social Listening ... 43

4.2.7 Concise Overview of Quotes... 44

5

ANALYSIS ... 46

5.1 Intentions to Conduct Social Listening ... 46

5.1.1 Increased Knowledge of Target Market ... 46

5.1.2 Product Development... 47

5.1.3 Honest Opinions on Social Media ... 47

5.1.4 Intentions Affecting Choice of Process ... 48

5.2 The Social Listening Process ... 49

5.2.1 Perspective on Processes ... 49

5.2.1.1 Preparations for Social Listening ... 49

5.2.1.2 Activities of Data Collection and Analysis ... 49

5.2.1.3 Evaluation of the Social Listening Activities ... 51

5.2.2 Identified Challenges of Social Listening ... 51

5.2.2.1 Data Volume, Variety, Veracity, and Velocity ... 51

5.2.2.2 Vulnerability in Execution of Process ... 52

5.3 Leveraging the Impact of Social Listening ... 53

5.3.1 Enhanced Understanding of Market ... 53

5.3.2 Dissemination of Insights... 53

6

CONCLUSION ... 55

7

DISCUSSION ... 57

REFERENCE LIST ... 60

FIGURES

Figure 1 P1’s Implemented Social Listening Process ...31

Figure 2 P2’s Implemented Social Listening Process ...32

Figure 3 P3’s Implemented Social Listening Process ...33

Figure 4 P4’s Implemented Social Listening Process ...34

Figure 5 P5’s Implemented Social Listening Process ...35

Figure 6 P6’s Implemented Social Listening Process ...36

Figure 7 P7’s Implemented Social Listening Process ...37

Figure 8 P8’s Implemented Social Listening Process ...38

TABLES

Table 2 Participants and Interview Details ...20

Table 3 Criteria of Literature Search ...23

Table 4 Theme Statements Description...27

Table 5 Cross-case Comparison ...40

Table 6 Concise Overview of Quotes ...45

APPENDIX

Appendix B: Participation Agreement ... 66

Appendix C: Participants ... 67

Appendix D: Interview Guide ... 69

Appendix E: Theme Chart for Literature Review ... 72

Appendix F: Original Interview Quotes ... 74

Definition List

Activity - A set of tasks performed within a process

Click-through Rate (CTR) - The number of clicks that an ad receives divided by the

number of times the ad is shown.

Dissemination - Transmission of information.

Engagement Rate – A metric that measures the level of engagement that a piece of

created content is receiving from an audience. It shows how much people interact with the content.

Key Performance Indicators (KPI) or Metrics – A Key Performance Indicator or

metrics is a measurable value that demonstrates how effectively a organization is achieving key business objectives.

Marketing Strategy – An organization's strategy that combines all of its marketing goals

into one comprehensive plan.

Persona – A marketing technique used to embody target markets by illustrating the ideal

customer.

Return on Investment (ROI) - A measurement used to evaluate the gain or loss in

comparison to the initial investment.

Sentiment – The interpretation and classification of emotions (positive, negative and

neutral) within text data using text analysis techniques.

Social Listening – An active process where organizations can attend, observe, interpret

Unstructured Data – Information that either does not have a pre-defined data model or

is not organized in a pre-defined manner.

User-generated Content - Content on online platforms, such as images, videos, text, and

1

Introduction

In this chapter, the topic of social listening is presented. It aims at describing the relevance and motivation for conducting the research by presenting the background and definition of the problem, as well as the research purpose and questions. Lastly, the research delimitations are presented.

1.1

Background

Social media has increased tremendously in users during the last years, which consequently has transformed the way people create, collaborate, and communicate information (Stieglitz, Dang-Xuan, Bruns, & Neuberger, 2014). Facebook continues to be the leading social media platform, however, 52 percent of all adults who are online claim to use at least two or more social media platforms (Stewart & Arnold, 2018). The wide usage and a high number of different social media platforms create a landscape with information shared and spread in various forms (Dong & Yang, 2020). These different social media platforms can be categorized as social networking sites, blogs, content-sharing sites, and opinions content-sharing sites (Lee, 2018). Facebook, LinkedIn, Snapchat, and Google+ are categorized as social networking sites where people create online communities to network, collaborate, and communicate. Contrary, blogs are often managed by an individual who expresses thoughts in online journals. Blog hosting sites, like Twitter, are platforms for individuals to share their opinions. Instagram, Pinterest, and YouTube are content-sharing sites where people share multimedia files, often used in marketing purposes. Lastly, opinion sharing sites similar to TripAdvisor or Yelp, allow people to share reviews about products and services.

Given the wide usage of different social media platforms for all ages across the globe (Stewart & Arnold, 2018), the opportunity for organizations to listen and analyze consumers' opinions online has increased. The tactics of listening to online consumer conversations is a new technique that enables organizations to maximize opportunities when monitoring decision-making in order to improve customer experiences and strengthening of the brand (Barnes & Jacobsen, 2013).

Social listening is an active process where organizations can attend, observe, interpret, and respond to a variety of stimuli created by consumers on social media platforms (Stewart & Arnold, 2018). The tactic is also an opportunity for organizations to improve customer relationships and derive value from consumer’s ideas for future decision making (Lee, 2018). The importance for organizations to implement social listening is due to its unique perspective achieved from social media, which is limited in traditional marketing research (Lee, 2018). In other words, social listening creates an opportunity for organizations to obtain a comprehensive perspective by analyzing user-generated content from various social channels online, where they can utilize their capacity and improve future business (Barnes & Jacobsen, 2013).

1.2

Problem

The development of technology and science during the last years has increased the value of social media for organizations (Zhang & Vos, 2014). However, the ever-changing landscape of social media and the increasing use of different channels influence how people interact (Stewart & Arnold, 2018). This creates a continuous need for organizations to be active on online platforms that enhance interactive dialogues. Further, the amount of different social media channels creates a variety of user-generated content which results in multiple forms of data, such as texts, images, audios, and videos (Dong & Yang, 2020). This is referred to as unstructured data, which has high volume, veracity, variety, and velocity (Dong & Yang, 2020) that consequently creates challenges for organizations when collecting and analyzing content retrieved from online sources. Current literature has proposed different processes and techniques of social listening which can be implemented in order to efficiently organize and effectively integrate the steps of social listening (Fan & Gordon, 2014; Holsapple, Hsiao, & Pakath, 2014; Lee, 2018; Mayeh, 2015; Stieglitz, Mirbabaie, Ross, & Neuberger, 2018). However, the research field has yet to define a generic framework for the social listening process, as well as providing sufficient details for the activities organizations employ (Fan & Gordon, 2014; Holsapple et al., 2018).

By listening to consumers on online platforms organizations can utilize their capacity and improve future business (Barnes & Jacobsen, 2013). Research conducted by Kurniawati, Shanks, and Bekmamedova (2013) explores the business impact of social listening and

portraits a framework of capabilities and benefits for the tactic. The stated benefits gained from social listening include increased understanding of consumer behavior, satisfaction, needs, and desires (Kurniawati et al., 2013). However, Kurniawati et al. (2013) state that further research requires detailed case studies to refine the framework of impacts gained from social listening. A rationale for this is that the intention of conducting social listening is different for every organization, and how it affects from which platform data is obtained, the amount of information retrieved, and application of the insights, is still unexplored. This results in a lack of knowledge of the activities organizations engage in to transform information acquired through social media to create business value (Mayeh, 2015).

Current literature in the research field is dominated by proposed processes and potential impacts gained from social listening. Although social listening has been recognized to be important for organizations in order to generate and improve financial, perceptual and behavioral aspects, such as customer experiences, brand strengthening and decision-making (Barnes & Jacobsen, 2013; Lee, 2018; Stewart & Arnold, 2018; Stieglitz et al., 2014), current literature lacks empirical findings regarding the impact on organizations marketing strategy as a result of social listening. Further, no existing research has demonstrated how the intention of social listening has affected the chosen process, such as activities, challenges, and selected channels, which further influences how organizations leverages the impacts gained from social listening.

Social listening has been recognized as an essential process for organizations in order to generate a unique perspective of intelligence due to the highly valued informative landscape on social media (Lee, 2018). It is, thus today’s wide usage of social media and its potential value for organizations, timely to gain a deeper knowledge of how social listening is managed by organizations and how the chosen processes impacts can be leveraged in the organizations’ marketing strategies. Due to the shortage of research on the impact of the social listening process for organizations, the authors will explore the gaps of how social listening insight can impact organizations marketing strategies by collecting, analyzing, and interpreting user-generated content on social media platforms.

1.3

Purpose

Given today’s wide usage of social media and its potential value for organizations, it is timely to gain a deeper knowledge of how social listening is managed by organizations and how the chosen processes impacts can be leveraged in the organizations’ marketing strategies. Due to the lack of existing research on this matter, the purpose of this research is to understand how organizations manage social listening and how the activities in the process can be implemented to leverage impacts for organizations’ marketing strategies. The purpose is refined by addressing the research questions:

- How do organizations manage the social listening process?

- How can the activities in the social listening process be implemented to enhance the leverage of impacts on the organization’s marketing strategy?

1.4

Delimitation

This research was delimited to the marketing field since the participants’ main tasks assignment are connected to marketing activities. Thus, the presented impacts of social listening focus on marketing strategies, although existing literature demonstrates that impacts may be realized in several other operations in organizations as well. Furthermore, social listening can be examined in different extents, ranging from technical details of how to search for specific topics or names by using commands, to simple statements of how engagement can be tracked. Nevertheless, the authors limited the research to discuss technicalities identified and presented in the frame of reference. Thus, the authors emphasize that this research may also be addressed in a much more extensive manner.

2

Frame of Reference

This chapter presents the existing body of literature on the topic of social listening. The main themes are concerned with defining the concept of social listening, presenting the different perspectives of the general process of social listening, as well as the impacts the practicing organizations may experience. The selection process of literature is presented in section 3.6.

2.1

Defining the Concept of Social Listening

The concept of social listening is defined in several different ways in the research field. The term is used interchangeably with synonyms including, but not excluded to, social media analytics, social media monitoring, social media intelligence, and electronic word of mouth. Several authors provide definitions to cover the essential elements of the concept, presented in Table 1.

Table 1 Descriptions of the Concept in the Research Field

Social Media Analytics (Kurniawati, Shanks & Bekmamedova, 2014, p. 2)

“Social media analytics (SMA) involves the use of analytics-based capabilities to analyze and interpret vast amounts of semi-structured and unstructured data from online sources.”

Social Media Analytics

(Holsapple, Hsiao & Pakath, 2014, p. 4)

“Business SMA refers to all activities related to gathering relevant social media data, analyzing the gathered data, and disseminating findings as appropriate to support business activities such as intelligence gathering, insight generation, sense making, problem recognition/opportunity detection, problem solution/opportunity exploitation, and/or decision making undertaken in response to sensed business needs.”

Social Media Analytics (Sinha, Subramanian, Bhattacharya, & Chaudhary, 2012, p. 68)

“Social Media Analytics tools are used to monitor exchange of information on social networking sites.” Social Media Monitoring (Mayeh, 2015, p. 9)

“An organizational capability that involves scanning social media to identify and analyze information about an organization’s external environment in order to utilize the acquired external intelligence in supporting business decision-making.”

Social Media Monitoring

(Zhang & Vos, 2014, p. 3)

“The results of social media monitoring provide a ‘snapshot’ of a discussion at a particular moment or show developments in a discussion over time.”

Electronic Word-of-Mouth (Barnes & Jacobsen, 2013, p. 147)

“…marketers are becoming more aware of the possibility that online discussions (electronic word-of-mouth [eWOM]) can, and do, impact sales, reputations, and brands.” Social Media Intelligence (Lee, 2018, p. 207)

“Social media intelligence enables managers to prescribe what should be done with the results of social media analytics.”

Social Listening

(Stewart & Arnold, 2018, p. 86)

“… an active process of attending to, observing, interpreting, and responding to a variety of stimuli through mediated, electronic, and social channels.”

Social Listening

(Moe & Schweidel, 2017, p. 700)

“According to a survey of users of social listening platforms, among the purposes for which brands engage in social media listening are monitoring brand health, measuring the success of campaigns, and gaining a better understanding of customers.”

Description Source

Term

Accordingly, several definitions exist when describing the tactic of scanning and analyzing consumer opinions on social media. To be clear and consistent, the authors refer to the concept of social listening throughout the paper, in order to avoid confusion. The term social listening is chosen due to its comprehensive definition when referring to the steps of the process, such as attending, observing, interpreting, and responding through social channels (Stewart & Arnold, 2018) as well as gaining insight to leverage impacts (Moe & Schweidel, 2017). Social media analytics and social media monitoring highlight the analyzing step, however, it excludes the importance of interaction. Further, electronic word-of-mouth provides a limited description that focuses on the possibilities of the process. Similarly, social media intelligence is limited in the description as it focuses on the insights gained from the process. However, the different synonyms of the term social listening: social media monitoring, social media intelligence, social media analytics, and electronic word-of-mouth, is not excluded considering existing research, due to its interchangeable usage in the existing literature.

2.2

Proposed Processes of Social Listening in Existing Literature

Fan and Gordon (2014), Stieglitz et al. (2018), Mayeh (2015), Lee (2018), and Holsapple, Hsiao, and Pakath, (2018) were among the most cited suggestions on ways to approach the process. Each is different from one another, with a diverse emphasis on the most crucial aspect of the process. Detailed figures for each proposed process are provided in appendix A.

Several of the proposed processes in the literature initiate with the discovery of relevant data sources or establishments of goals for the analysis. Holsapple et al. (2018) advocate for the importance of the development of both overarching and specific goals in order to decide the context of the organization’s social listening. In similarity, development of KPI:s is crucial to facilitate evaluation the objectives and performance of the business, and works as a guide for which activities to engage in the social listening process in order to generate intelligence to be utilized in business decisions (Lee, 2018; Mayeh, 2015). Stieglitz et al. (2018) argue that if an organization addresses the discovery of relevant data sources, and the collection and preparation of the data successfully, the social listening will be more likely to succeed.

The selected relevant data sources are used to track and obtain information (Fan & Gordon, 2014; Holsapple et al., 2018). Data are commonly acquired through tracking likes, comments, and shares related to relevant topics, hashtags, or keywords from a range of data sources, such as social media platforms, forums, blogs, news, and videos (Fan & Gordon, 2014; Holsapple et al., 2018; Lee, 2018; Mayeh, 2015; Stieglitz et al., 2018). As data from these sources tend to be unstructured, pre-processing activities such as clustering, removing noise and spam from the data spam set, or adding filters, are required to make the data approachable (Fan & Gordon, 2014; Lee, 2018; Stieglitz et al., 2018). In contrast to the other proposed frameworks, Lee (2018) propose that organizations should cluster qualitative and quantitative data separately, in order to emphasize both quantitative data metrics, such as the number of followers, frequency of posts, and engagement rate on each post, or qualitative data metrics, such as sentiment orientation and subjectivity.

The analysis is the key to assort the data in order to generate actual insights to be utilized in business decisions (Mayeh, 2015; Fan & Gordon, 2014). Both Holsapple et al. (2018) and Stieglitz et al. (2018) notes that the approach of the analysis is highly dependent on the sources used to acquire the data, and thus need to be carefully considered. Analysis activities involved in all of the proposed processes are analysis of platform-based statistics, sentiment analysis, social network analysis, statistical analysis, content analysis, and trend analysis, among others (Fan & Gordon, 2014; Holsapple et al., 2018, Lee, 2018; Mayeh, 2015; Stieglitz et al., 2018).

Mayeh (2015) stresses that the dissemination of insights generated from the analysis has not received sufficient attention in existing literature, and argues that the way the organizations disseminate the insights is of great importance. Mayeh (2015) separates informal and formal dissemination, where the latter is suggested to be based on two criteria; verifiability and spontaneity, referring to the ability of the messenger to substantiate the information and preparation of the presentation of the information, respectively. If the information is disseminated frequently and formally, it is more likely to positively affect the utilization of the information (Mayeh, 2015). Another process that is different from the others, is Lee (2018) who suggests combining intelligence obtained through social media with traditional intelligence activities, in order to build intelligence

that enables managers to prescribe how the information and knowledge acquired through social listening should be leveraged in the organization.

Utilization is one of the most important elements of social listening, as it ensures that all benefits accumulated in previous steps could be useless if it is not utilized to generate business value (Mayeh, 2015). Fan and Gordon (2014) supports this notion, by emphasizing that understanding of the data is the core of the entire process, as its results will have a significant impact on the value captured by the organization from its social listening practices. Holsapple et al. (2018) favor to measure this thorough evaluation of the accomplishment of KPI:s or metrics decided in the initial activities in the social listening process. This entails evaluating, summarizing, and presenting the findings, implementations, and insights, in a comprehensive way (Fan & Gordon, 2014; Holsapple et al., 2018).

Although the proposed social listening processes, in theory, are seen as a series of activities conducted in a pre-set sequence, both Lee (2018) and Holsapple et al. (2018) emphasizes the iterative nature of the social listening process. Lee (2018) notes that the activities executed in the process evolve as new online platforms are used. In similarity, Holsapple et al. (2018) emphasize the notion that each organization’s social listening process is unique. Although most of the activities implemented by one organization are general and replicable for others, the proposed activities may vary for each analysis as the process is highly context-dependent, especially when implementing changes and evaluating the results of social listening (Holsapple et al., 2018).

2.3

Key Techniques in the Social Listening Process

Social listening encompasses a variety of activities, tools, and techniques to collect, analyze, and interpret data. Mayeh (2015) categorizes the methods in quantitative and qualitative activities, where quantitative activities concern numerical metrics, and qualitative activities concern text data. The simplest quantitative activity to employ in social listening are basic techniques of platform-based statistics, such as the reach of posts, number of followers, time spent on a website, click-through rate (CTR), number of likes, or shares. Lee (2018) emphasizes the use of such metrics as one of the most important techniques to successfully manage social listening, as it allows organizations

to visualize changes in several areas of the business over time. Stieglitz et al. (2018), Lee (2018), and Mayeh (2015) suggests organizations to employ metrics or KPI:s, in order to support decision-making processes. These metrics are required to assess the effectiveness of activities, and to ensure that social media activities create business value (Mayeh, 2015). Besides, it can subsequently be used in statistical analysis, such as factor analysis and regression models (Lee, 2018).

Sentiment analysis, also referred to as opinion mining, is a technique of identifying and extracting information from a vast amount of user-generated content through text analysis (Dong & Yang, 2020; Fan & Gordon 2014; Lee, 2018; Zhang & Vos, 2014). The technique is both qualitative and quantitative in the sense that the analysis needs to be done qualitatively, however, the results are usually presented in quantitative measures. It is commonly executed by computational technology tools through machine learning, where the tool learns to evaluate the content based on the knowledge it obtains from having evaluated a large number of posts, or a lexical-based method, where the tool identifies prescored words or phrases to evaluate the content (Lee, 2018).

Similar to the sentiment analysis is clustering, also referred to as topic modeling, which is an activity aimed to identify dominant themes in the data (Fan & Gordon, 2014; Zhang & Vos, 2014). Clustering is executed through machine learning and lexical-based methods, similar to sentiment analysis. Additionally, clustering is also used for trend analysis to gain deeper knowledge or forecast changes in issues or topics over time (Fan & Gordon, 2014; Kurniawati et al., 2013; Mayeh, 2015; Stieglitz et al., 2018). These issues are described as shifts in consumer demand, the effectiveness of marketing campaigns, the growth of customers and sales, or hypothetical risks (Dong & Yang, 2020; Fan & Gordon, 2014).

2.4

Challenges in the Social Listening Process

Data collected by listening to consumers online tend to have high volume, variety, velocity, and veracity (Dong & Yang, 2020). As a consequence, social listening practices contains several challenges that the organizations who implement it need to be aware of. The main challenges of the process concern the number of data points and data sets

(volume), the different forms of data (variety), the dynamic nature of the data (velocity), and the uncertainty of the data (veracity) (Mayeh, 2015; Stieglitz et al., 2018).

Firstly, one of the most prominent challenges in social listening is the vast volume of information to be acquired through social media, however, it is also the most prominent benefit (Fan & Gordon, 2014; Holsapple et al., 2018). Social media generates enormous amounts of data, and a lot of organizations do not have the integrated tools needed to effectively capture and analyze the information (Moe & Schweidel, 2017; Sinha, Subramanian, Bhattacharya, & Chaudhary, 2012). The user-generated content that is collected contains irrelevant data points as well, resulting in voluminous data points to filter before analysis (Holsapple et al., 2018). Additional factors contributing to this challenge is the vast amount of sources to collect data from (Kurniawati et al., 2013; Lee, 2018; Stieglitz et al., 2018), as well as the fact that social media data tend to be context-dependent, which create the need to extract additional data in order to understand the context, rather than only collecting relevant data (Mayeh, 2015).

Another challenge when conducting social listening is that data from social media is diverse (Zhang & Vos, 2014). The data tend to be unstructured, meaning that it is a high variety in the type of data points extracted from different platforms, such as pictures from Instagram and text from blogs, which complicates the analysis (Dong & Yang, 2020). More specifically, language use and interpretation are mentioned as a difficulty, considering the fast-changing language, intentional or unintentional misspellings, irony, synonyms or rumor spreading, which makes the retrieved data high in variety (Fan & Gordon, 2014; Kurniawati et al., 2013; Lee, 2018; Mayeh, 2015). Moreover, topics or language use are different for each platform, resulting in those dynamics exhibited on one platform, which may not be true for another (Moe & Schweidel, 2017). Fan and Gordon (2014) note the growing challenge of analyzing several languages from all around the world and highlight the fact that tools or systems do not yet have the knowledge to understand sarcasm or irony, resulting in a risk for misinterpreting messages if they are not manually interpreted by a human.

Data veracity concerns the validity and quality of data retrieved from social media. Social media platforms only reach a subset of consumers, which makes the data suffer from an unrepresentative population (Lee, 2018; Moe & Schweidel, 2017). Moreover, organizations need to develop methods for evaluating if the gathered data is valid, or else organizations risk making decisions driven on false statements, rather than facts (Lee, 2018). For instance, the continuous distribution of false rumors, fake news or spam, or content created from false users, is common content in social media platforms (Lee, 2018; Stieglitz et al., 2018). Lastly, concerning velocity, Holsapple et al. (2014) discuss the crucial factor of time as a large issue when extracting data, by emphasizing that the data is only accurate as of the moment of collection. The dynamic nature of social media results in that no data point is absolute, as one data point can have different values at different points in time, depending on when it is retrieved. If a data point is collected shortly after publication, it may show a low value of quantitative measures, but as it culminates the very same data point can have a different value, and hence should be weighted differently, later on (Holsapple et al., 2014).

2.5

Social Listening’s Impact on Marketing Strategies

2.5.1 Social Listening’s Impact on Product Management

Several authors emphasize the impact social listening has on the organization's product management. Holsapple et al. (2014) argue that there is documented evidence that social listening is efficient in revealing unexplored business opportunities by identifying new product opportunities. Social listening is used to obtain consumers' opinions about innovations or to understand what features to include or exclude, in both new and current products (Dong & Yang, 2020; Kurniawati et al., 2013; Lee, 2018; Mayeh, 2015). Moreover, social listening is also valuable in product development regarding risk assessing; there is an impending risk of new technology arising during the development, making the idea obsolete, or reliance on the wrong components, and suggests social listening to reduce this risk through trend analysis (Fan & Gordon, 2014). In other words, social listening can be used for crowdsourcing of ideas (Moe & Schweidel, 2017). In addition to social listening’s use in product development, it is also beneficial for understanding current users’ needs. Organizations may obtain important information regarding both collective and individual opinions about the products and services that are currently offered by analyzing consumers’ discussions on social media (Zhang & Vos,

2014). By developing a deeper understanding of the user’s background, tastes, and buying behavior through techniques such as sentiment analysis, topic modeling, and trend analysis, the organizations can leverage more effective customer segmentation (Fan & Gordon, 2014).

2.5.2 Social Listening’s Impact on Brand Management

Barnes and Jacobsen (2013) highlight the importance for organizations to implement social listening as intelligence for decision making, where the impact of strengthening the brand is an important component. When organizations seek to promote their brand and strengthen their position in the market, it is important to monitor the outcome of online activities (Zhang & Vos, 2014). Online influential individuals, also known as “influencers”, creates and uploads user-generated content where the information is interrelated with the brand (Zhang & Vos, 2014). User-generated content creates word-of-mouth on online platforms implemented by consumers through, for instance, retweets, reblogs, and social tagging, which can be used to share both positive and negative thoughts about a brand (Dong & Yang, 2020). Since opinions regarding brands are increasing online, marketers aim to be aware of the content shared to improve brand management (Zhang & Vos, 2014).

Kurniawati et al. (2013) state that 30 percent of organizations have benefitted from better brand awareness and reputation management from cases where social listening has been implemented successfully. This has been done by monitoring brand and product reputation while trying to maintain a reputation in the market. Furthermore, social listening can also benefit from an HR perspective where processes can be used for employee branding and employer branding, where the social media data is analyzed to understand the hiring life cycle from recruitment to transition (Sinha et al., 2012).

2.5.3 Social Listening’s Impact on Marketing Planning

Social listening enables organizations to modify the development and planning of marketing strategies since user-generated online content usually focuses on the experience with a product or service. Listening to customers online, analyzing, and interpreting the opinions regarding their experiences, provide useful insights when developing future marketing plans (Dong & Yang, 2020; Holsapple et al., 2018; Stewart & Arnold, 2018; Sinha et al., 2012). In addition, by collecting data on consumers through

profiles, social listening can potentially be of great value when forming a marketing plan (Dong & Yang, 2020; Mayeh, 2015).

However, Moe and Schweidel (2017) argue that traditional marketing research tools are well established and create a grounded and reliable standard when developing future marketing plans. By comparing social listening efforts and results with other marketing research tools, organizations can evaluate the added success rate gained from the social listening process. By comparing different marketing strategies, organizations can consider whether social listening efficiently obtains consumer insights (Dong & Yang, 2020), and creates an increased return of investment (ROI) (Moe & Schweidel, 2017). Thus, Lee (2018) suggests that managers should consider a time gap when measuring the ROI of social listening. Due to the premature stage of social listening practices, investing in the tactics might not reveal results immediately, and therefore, the ROI of the process is not always accurate (Lee, 2018).

2.5.4 Social Listening’s Impact on Customer Satisfaction

One main impact for organizations when implementing social listening is the understanding of the customer. There has been a gain of customer insights in 45 percent of cases where social listening has been successfully implemented (Kurniawati et al., 2013). In these cases, the need to obtain an in-depth understanding of the customers' preferences, behaviors, and values have been in focus. Similarly, social listening can be leveraged to increase the performance of particular operations within an organization, by identifying issues and misunderstandings expressed in social media (Dong & Yang, 2020; Mayeh, 2015). By identifying these issues, organizations may obtain insight into how services are perceived by customers and thus, provide better service (Dong & Yang, 2020). Consequently, the organization can address these issues through social listening, which would not have been detected otherwise.

In the same cases of successful social listening, 65 percent of the organizations received better customer engagement by improving communication through their customers' preferred communication channels (Kurniawati et al., 2013). Holsapple et al. (2014) support this by suggesting that social listening can enable the identification of the target customers' values and preferred channels for communications. It is necessary to monitor

customers' preferred social media platforms in order to maximize customer communication, and consequently, customer satisfaction. Other facilitators for organizations who implement social listening is the development of communities for customers in order to build support, enable job applications, and distribute information (Sinha et al., 2012). However, the constantly changing landscape of social media requires the organization to keep up to date with the rapid emergence of new media, in order to not miss out on customers' desired communication channel (Lee, 2018).

3

Methodology

This chapter first presents the research methodology and then specifies the method applied by the authors. The first part elaborates on research philosophy, approach, design, and strategy, while the second presents methods of data collection, sampling method, and data analysis method. Lastly, motivation for research quality is provided.

3.1

Research Philosophy

Research philosophy refers to the system and assumptions implemented when conducting scientific research for the development of new knowledge. The two most common philosophies which can be implemented when conducting a business and management scientific research are positivism and interpretivism (Saunders, Lewis & Thornhill, 2016). Positivism aims to interpret collected data as scientific and un-influenced by humans, which was not manageable in this research due to interactive interviews. The second philosophy, interpretivism, aims to deepen the understanding of social worlds and contexts (Collis & Hussey, 2014). Thus, interpretivism was implemented in this research as the authors intended to understand the process and impact of social listening, which was exposed as a gap in the existing literature. As interpretive research aims to investigate different perspectives, the data collection derived from eight different professionals who managed social listening in different organizations, to broaden the knowledge of the collected data (Saunders et al., 2016).

3.2

Research Approach

The research approach guides the methods to be used in data collection and analysis, and the suitable approach for each research depends on the emphasis of the research and nature of the research question (Saunders et al., 2016). The two main approaches are inductive research, which develops theory from observations of empirical reality, and deductive research, which refers to research where a theoretical framework is developed and subsequently tested by empirical observation (Collis & Hussey, 2014). This research was influenced by both approaches.

The deductive influence was because the research initiated from the existing body of knowledge within the field of social listening, and the knowledge obtained from the literature review constituted the theoretical framework that guided the following interviews. However, the emphasis of this research was the data collected through the case studies, which was used as the primary data source to conclude upon. Thus, the authors argue that the dominant approach was inductive. This was leveraged by the qualitative nature of the data collected through interviews, as well as no hypotheses were developed and tested, according to the inductive approach (Collis and Hussey ,2014). Saunders et al. (2016) acknowledges that this combination is true for a lot of research as it is common to combine the different approaches in the same piece of research, and considers it to be advantageous for the outcome of the research.

3.3

Research Classification

To classify the research, three main categories: exploratory, descriptive, and explanatory, can be used (Saunders et al., 2016). The authors argue that this research was mainly descriptive research. Descriptive research is conducted to describe a phenomenon as they exist, to identify or obtain information on the characteristics or aspects of a specific issue, and answer research questions including the words ‘what’, ‘how’, ‘which’, ‘where’ and ‘when’ (Collis & Hussey, 2014; Saunders et al., 2016). When the authors critically evaluated the existing research within the field of social listening, gaps were discovered regarding how organizations analyze and interpret consumer opinions on social media platforms and how insights can be leveraged in the marketing strategy. Since this research aimed to address these gaps by describing how organizations manage the social listening process and how impacts can be leveraged in the marketing strategy, the authors found the design to be descriptive research, which was suitable when examining certain phenomena characteristics, by describing the current practices of the organizations (Collis & Hussey, 2014).

However, the authors emphasize that the research was influenced by the exploratory classification as well. Exploratory research is conducted to understand an issue in which little or no existing research has been conducted, thus the aim is to identify patterns and ideas regarding the subject (Collis & Hussey, 2014). The research was influenced by the exploratory classification since it addresses gaps where the existing body of literature

lacks in knowledge, as well as the data identifies patterns and ideas, regarding social listening. However, the authors aimed to get a deeper understanding than merely getting familiar with the subject as the exploratory approach suggests (Saunders et al., 2016), thus the descriptive classification was dominant due to the aim of understanding, to describe current practices.

3.4

Research Method

Two different methods, qualitative or quantitative, can be adopted when designing a research. Quantitative research often considers numerical measurements, statistics, and graphical techniques, compared to qualitative research where the aim is to analyze and interpret relationships between participants and the theoretical contribution (Saunders et al., 2016). The authors of this research implemented a qualitative method from a business perspective in order to get a deeper understanding of the connection between an organization’s social listening process and how impacts could be leveraged in the marketing strategy. The data collection was a mono method qualitative research where a single data collection technique of semi-structured interviews is used (Saunders et al., 2016).

3.5

Methods of Primary Data

3.5.1 Research Strategy

Different research strategies can be implemented to collect qualitative data aimed to answer the research questions and purpose, including grounded theory, narrative inquiry, action research, case studies, and ethnography (Saunders et al., 2016). For the authors to be able to thoroughly investigate the topic of social listening, case studies were implemented to understand the relationship between the topic and the chosen cases. The cases were investigated through interviews to get a deeper understanding of social listening. Saunders et al. (2016) describe how the cases studied can be of different levels, such as organization, group, event, or person. To gain in-depth knowledge of the topic, the authors chose to research organizations through interviews with individuals who manage social listening in their organizations. Since the interviews provided a snapshot of the participants’ social listening practices at a particular time, the research adopted a cross-sectional time horizon (Saunders et al., 2016).

The strategy chosen in this research is classified as descriptive case studies, where the descriptive research aims to identify and obtain information on the characteristics of social listening to deepen the understanding of the topic (Collis & Hussey, 2014). A multiple case study was implemented as the possibility of interviewing multiple professionals from different industries increased the amount of knowledge gained, which enabled the authors to obtain in-depth knowledge of the topic, to consequently answer the research questions.

3.5.1.1 Case Study Selection

The cases selected to be investigated in this research were based on an opportunity case study since the possibility to examine the phenomenon of social listening arose due to the authors' access to participants (Collis & Hussey, 2014). In addition, the case study was also of a comparative nature, as the organizations where the participants were employed at was purposely selected from different industries and then compared, in order to gain a deep and nuanced understanding of the topic under study (Mills, Durepos & Wiebe, 2010). The cases in the research were chosen to represent a wide range of industries. The only common factor of significance of the participants was the practice of social listening. Thus, this research investigated multiple cases classified as typical, since the cases were not chosen based on exceptional instances, in order to establish common practices (Mills et al., 2010). This allowed the authors to demonstrate similarities and differences in how organizations manage social listening across industries. This resulted in more conclusive answers to the research purpose, compared to single case studies, since it demonstrates the phenomenon from a more varied range of circumstances and perspectives (Mills et al., 2010).

3.5.2 Sampling Method

Sample selection can be approached in several ways, with the main categories being probability and non-probability sampling methods. In probability sampling, each member of the population has an equal chance of being selected for the research, while non-probability sampling includes an element of subjective judgment (Saunders et al., 2016). In accordance with Collis and Hussey (2014) recommendation regarding the link between the interpretive research philosophy and choice of sampling technique, this research employed a non-probability sampling method.

Two criteria were developed in order to obtain sample cases which enabled the authors to answer the research questions, resulting in a purposive sampling technique (Saunders et al., 2016). The first criterion was that the organization the participant was employed at had to practice social listening, and the second was that the interviewee had to be responsible for the organization’s social listening practices. If none or only one of these criteria would have been satisfied by any case in the sample, it would be deemed irrelevant for the research. Furthermore, to ensure access to potential participants who met the criteria, a convenience sampling technique by approaching the authors’ appropriate LinkedIn connections was used to choose cases to sample. Convenience sampling is favorable in sample selection due to easy access, although it carries a risk of bias and less credibility (Saunders et al., 2016). However, in combination with a purposive sampling technique, Saunders et al. (2016) argue that it could be a facilitator of obtaining a relevant sample.

In contrast to probability sampling, there are no general recommendations regarding sample size for non-probability sampling techniques (Saunders et al., 2016). The single recommendation by Saunders et al. (2016) is to continue collecting empirical data until a stage of data saturation is reached, or in other words; when additional data provides little, if any, new information. Initially, the researcher approached 14 LinkedIn connections that were deemed suitable for the research, by explaining the topic and purpose of the research. The responses were of a mixed character, with some declining due to lack of time. Nevertheless, six accepted the request and thus, took part in the initial sample. However, after the sixth interview was held, the authors did not consider data saturation to be reached, and thus approached five more LinkedIn connections. Three of them accepted the invitation. However, one of them could not do the interview in the preferred time frame. In consensus, it was decided to, if necessary, do the interview later on. However, after conducting two more interviews, the authors deemed that data saturation was reached and ultimately, eight participants took part in the sample.

3.5.3 Case Study Participants

The participants in the research were required to have specific skills and knowledge of social listening. Thus, each participant was responsible for social listening at their practicing organization. The organizations operated in different industries, due to the diminishing risk of discovering industry narrated results. Table 2 presents details of the participants and the conducted interviews. Before participating in the research, each interviewee signed a participation agreement, which is presented in appendix B. The contribution from the interviewees was voluntary and the participants did not receive any compensation.

Table 2 Participants and Interview Details

Each organization and interviewee in this research is anonymous, due to the potential trade secrets the social listening process and its activities may carry. To ensure the privacy of the participants, a brief description of each organization is presented in appendix C.

3.5.4 Interviews

The primary data in this research was collected through eight interviews with professionals who managed social listening, which is an original source collected specifically for this research (Collis & Hussey, 2014). Since social listening is a complex phenomenon, especially when taking the individualistic nature of the process

implemented by each organization into consideration, Saunders et al. (2016) argue that interviews are advantageous for gathering valid and reliable data that is highly relevant to the research purpose. Aligning with the interpretive philosophy and qualitative method of this research, the authors used semi-structured interviews to collect qualitative primary data. According to Saunders et al. (2016) recommendations, the authors prepared an interview guideline consisting of a list of key questions to be covered during the interviews, but adopted a free flow of the conversation and thus altering the order of the questions or topics discusses. The interview guideline is presented in appendix D.

3.5.4.1 Pilot Interview

To ensure that the questions were valid, understandable, relevant, and possible to address, the authors conducted a pilot interview. In order to get a comprehensive picture of the topic, the pilot interview was held with a business development consultant specialized in social listening, to get as extensive knowledge as possible early in the process. The pilot interview primarily contributed to an understanding of how the questions were posed, in order to not ask leading questions. As a consequence, questions that were deemed leading were removed. Thus, since the pilot interview ended up being similar to the other interviews, and generated rich and relevant data to the research, the authors assessed it suitable to be included in the empirical findings of the research.

3.5.4.2 Procedures of Interviews

All of the interviews were performed in Swedish, as this was the native language for all participants, as well as the authors. Three of the interviews were conducted face-to-face, and five were conducted through video calls. Each interview was recorded through the phone application ‘Voice Memo’ to facilitate transcription of the answers, which was subsequently sent to the interviewee for validation to ensure credibility. The findings were then translated into English by the authors. Semi-structured interviews were beneficial for both the interviewee and the authors, since it offered the opportunity for explanation or elaboration of thoughts through further discussion, in contrast to structured interviews (Saunders et al. 2016). However, this can also serve as a disadvantage since it carried the risk of collecting irrelevant information (Saunders et al., 2016). In order to minimize this risk, the authors put great effort to design the key topics to be covered during the interviews and made sure to allocate sufficient time and discussion about each topic. Furthermore, the authors frequently used probes to ask the interviewee for a more

detailed answer, which is argued to be an efficient strategy to maximize the collection of relevant information in a semi-structured interview, as well as to some extent steer the conversation (Collis & Hussey, 2014).

3.5.5 Analysis of Empirical Data

The analysis of the primary data followed a six-step thematic approach presented by Braun and Clarke (2006). A thematic approach is defined as “a method for identifying, analyzing, and reporting patterns (themes) within data” (Braun & Clarke, 2006, p. 79). The authors considered the structure and logic of the chosen approach suitable for deriving rich descriptions and knowledge of the phenomenon of social listening, in order to answer the research question. To ensure credibility, the chosen approach was combined with investigator triangulation, which is a process where several authors investigate the same problem individually, before discussing perceptions or insights together (Collis & Hussey, 2014).

According to the six-steps thematic approach presented by Braun and Clarke (2006), the authors began with (1) Familiarizing with the data, by individually read the interview transcription to identify direct quotes, keywords, or views. The next step (2) Coding consisted of first comparing patterns and insights gathered in the first step, to proceed to identify groups of data by label them with codes. Similar to the first step, the authors conducted the coding individually, before comparing and agreeing on codes. The comparison was made systematically, to ensure that no code remained unnoticed. The resulting codes, as well as indicators for each code, is presented in Appendix F. Step (3) Searching for themes was conducted by clustering codes and patterns into overarching themes. Each code was considered in this discussion, to ensure that the authors included all possible themes. Proceeding with the identified themes, the next step was (4) Reviewing themes. In contrast to the previous steps, this was performed in conjunction to facilitate discussion and consensus on the most relevant themes. The next step (5) Defining themes, where the authors identified the essence of the concluding themes, was also conducted in conjunction, with the same purpose as the previous step. The final step (6) Producing the report, concerns the analysis in this research, and involved analyzing the themes together with the findings in the literature. According to Braun and Clarke (2006), this final step is when the goal of this approach is accomplished, as embedding

the data into an analytic narrative contributes better to the research than merely summarizing the empirical data.

3.6

Method of Secondary Data

The literature that constitutes the frame of reference which served as the secondary data in this research was selected through a systematic process of the existing body of knowledge within the research field of social listening. The authors collected data from existing literature in order to deepen the knowledge of social listening which consequently favored the purpose of the research by instituting the interviews. The collected secondary data was later used to facilitate the analysis of the empirical findings collected through the interviews. The literature was selected through a systematic process in three phases: (1) Search, (2) Selection, and (3) Analysis. Firstly, the (1) Search initiated with a general search to achieve an entirety comprehension of the subject. A brief screening of results was conducted before determining criteria to lead the search in the desired direction. Thus, the (2) Selection phase consisted of excluding irrelevant results based on several criteria, which are presented in Table 3.

Table 3 Criteria of Literature Search

Criteria Rationale

Keywords

”social listening”, ”social media analytics”, ”social media monitoring”, ”eWoM”, ”Social media intelligence”, ”impact*”, ”result*”, ”process*”, ”activity*”, ”marketing”, ”marketing strategy”

To obtain the most relevant literature in the research field. Databases

and Search Engines

Primo, Google Scholar and Science Direct. To obtain scholarly literature. The three databases were used due to the convenience of specific and narrow searches.

Literature Classification

Academic peer-reviewed articles, journals

and books. To increase credibility, quality and trustworthiness.

Number of social media platforms used

>1 Eliminate data related to one single platform. Eliminate language barriers and ensure confirmability. English

Language

Literature Search Criteria of Secondary Data Collection

Exclude political related articles to ensure that the insights derived from the literature apply to the case study in this research.

Private entities Focus of

Sector

Contemporary nature of social listening. 2010-2020

Published

To ensure credibility. When applicable, the ABS or CORE ranking

should be above 1 or C. ABS or

CORE ranked

The authors identified key papers within the first search which was frequently referenced in literature within the research field. Several articles were excluded based on weak quality or questionable trustworthiness, thus the last criterion was implemented later than the others to ensure high quality of the literature review. The ABS or CORE ranking criterion facilitated validation of the quality of both journal articles, or papers presented at conferences, through an external party. This was crucial in the literature search, since key research within social listening was repeatedly presented at conferences, thus the exclusion of conference papers carried a risk to ignore valuable information due to the lack of a strategy of validating the research. Hence, two conference papers with the CORE ranking A were included in order to fulfill the purpose of this research. Moreover, the authors decided to include Mayeh's work from 2015, although dissertations are not peer-reviewed. The authors considered it key research that contributed to the detailed and accurate knowledge needed to fulfill the purpose of this research. Ultimately, the chosen articles were thoroughly examined in the (3) Analysis, which identified recurrent themes and patterns within the literature. The results of the analysis are presented in the frame of reference, and the selected literature is presented by a theme chart in Appendix E.

3.7

Quality and Trustworthiness of Data

Executing trustworthy interpretive research is a challenge considering the analysis of the collected data. The authors required a systematic and rigorous approach for the analysis and needed to be familiar with the data to draw a trustworthy conclusion (Collis & Hussey, 2014). Lincoln and Guba (1985) suggest four different criteria for authors to take into account in order to evaluate interpretive research in a trustworthy matter. To assess and assure the quality of the data analysis, the following aspects should be considered: credibility, transferability, dependability, and confirmability (Collis & Hussey, 2014; Lincoln & Guba, 1985).

3.7.1 Credibility

Credibility refers to the correctness of identification and description of the analysis in the investigated research (Collis & Hussey, 2014). High credibility consequently requires immersed authors with a deep understanding of the research. Investigator triangulation was implemented to assure the credibility of the research. Investigator triangulation is a process where several authors investigate the same problem, which brings different

perceptions and consequently decrease biases (Anney, 2014; Collis & Hussey, 2014). According to Cope (2013), the credibility of the research can be enhanced if the findings are verified with the participants. Therefore, the transcript from the interviews was sent to the participants for approval. The confirmed support from all participants in the research validated an objective and credible process for the analysis. Further, peer-examination, which refers to scholarly guidance (Anney, 2014; Lincoln and Guba, 1985) has continuously been implemented throughout the research by both peers and an assigned tutor. The received feedback and guidance has improved the quality and credibility of the research.

3.7.2 Transferability

In the case of qualitative research, the analysis aims to create value for the reader who subsequently should be able to transfer the results to practice or future research (Cope, 2013). The research increases the transferability, which is concerned with the generalization of the findings, by being clear throughout the report (Anney, 2014). Firstly, the authors provide a detailed description of the methodology, sampling method, and analysis to increase transferability. The process of the purposive sampling method and in-depth data analysis allow future researchers to replicate the research. This is of interest to similar situations where the findings should be applicable in order to be ranked as highly transferable (Collis & Hussey, 2014). Secondly, the result of the research is transferable for practice for marketing managers, who will be able to get a deeper understanding of the process and impact of social listening which can facilitate the development of future business. However, even if the multiple case study intends to create a generalization of social listening, more research needs to be conducted to create a fully transferable and general strategy for the concept of social listening (Cope, 2013).

3.7.3 Dependability

Dependability is a term used to evaluate how consistent and repeatable the research is. Research is qualified for being dependable if the method of the research is systematic, rigorous, and well documented (Collis & Hussey, 2014). By clearly describing the previously mentioned parts, another researcher would arrive at a similar analysis and conclusion if the same process and description were followed (Cope, 2013). All audio records and transcripts from the interviews were saved and sent to participants who reviewed and confirmed the collected data. Furthermore, the investigator triangulation

allowed the authors to separately analyze the interviews and compare the different results. The different authors aligned at similar results which increased the dependability and enhanced the quality of data (Anney, 2014).

3.7.4 Confirmability

Research with high confirmability contains evident and described processes where the findings are clearly derived and analyzed from the data (Collis & Hussey, 2014). This indicates that the data collected from the interviews were derived from the participant and not the authors. To establish confirmability, the authors have included rich quotes in both English and the original language from participants to clearly demonstrate that the data is not influenced by authors’ biases (Cope, 2013). Confirmability is also strengthened through investigator triangulation where the biases were reduced as both authors reached the same conclusion while analyzing the same data separately (Anney, 2014).