Social media big data: a diary study of ten pharmaceutical firms
- Authors: Baker, Nadia Samantha
- Date: 2020
- Subjects: Big data , Internet in medicine , Social media in medicine , Internet marketing -- Evaluation , Pharmacy management -- South Africa
- Language: English
- Type: text , Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10962/140737 , vital:37914
- Description: Purpose: The goal of the research was to demonstrate how firms can use social media big data, to make strategic business decisions, through the lens of Resource Based Theory (RBT) and Dynamic Capability Theory (DCT), that could lead to a sustained competitive advantage. In and of its own, big data, does not constitute a competitive advantage. It may hold value for the firm, but lacks rarity, inimitability, and is not substitutable (Braganza, et al. 2017; Mata, Fuerst and Barney, 1995; Delmonte, 2003). It is in the analysis of this data, through RBT and DCT, that will turn the information into useful business intelligence (Amit and Schoemaker, 1993; Barney, 1991; 1995; Marr, 2015; Gupta and George, 2016; Kurtmollaiev, et al., 2018). Most importantly, firms must constantly reconfigure their resources in line with the dynamic business environment to ensure superior performance (Teece, Pisano and Shuen, 1997; Helfat, et al., 2007; Teece, 2014; 2018). Method: In this study, a qualitative approach was used to examine the RBT (Value, Rarity, Inimitability and Non-Substitutable - VRIN Framework) and DCT, to describe and understand the relevant theories and to build upon the quantitative results. While a quantitative approach was used to analyse the social media sentiment as depicted by Social Mention metrics. A novel technique, Chernoff Faces, was used to analyse and visualize the data (de Vos, Strydom, Fouche and Delport, 2011). Results and Findings: The research results show that, while the 10 firms in the study all have a presence on social media, it is on selective platforms. The content that is posted, is on very specific topics (Narayan, 2017; Cornejo, 2018). The Chernoff Faces indicate that the firms’ Social Mention metrics, over the 30 day period, was at low values. Since strength of social mention is depicted by the face line, the thin, long, generally sad looking faces implies that more than 70 percent of the firms’ social media strength over the study period, was weak. Conclusion: The literature indicates that the true value of big data and big data analytics can only be realised if firms make sound business decisions and act upon it swiftly.
- Full Text:
- Date Issued: 2020
- Authors: Baker, Nadia Samantha
- Date: 2020
- Subjects: Big data , Internet in medicine , Social media in medicine , Internet marketing -- Evaluation , Pharmacy management -- South Africa
- Language: English
- Type: text , Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10962/140737 , vital:37914
- Description: Purpose: The goal of the research was to demonstrate how firms can use social media big data, to make strategic business decisions, through the lens of Resource Based Theory (RBT) and Dynamic Capability Theory (DCT), that could lead to a sustained competitive advantage. In and of its own, big data, does not constitute a competitive advantage. It may hold value for the firm, but lacks rarity, inimitability, and is not substitutable (Braganza, et al. 2017; Mata, Fuerst and Barney, 1995; Delmonte, 2003). It is in the analysis of this data, through RBT and DCT, that will turn the information into useful business intelligence (Amit and Schoemaker, 1993; Barney, 1991; 1995; Marr, 2015; Gupta and George, 2016; Kurtmollaiev, et al., 2018). Most importantly, firms must constantly reconfigure their resources in line with the dynamic business environment to ensure superior performance (Teece, Pisano and Shuen, 1997; Helfat, et al., 2007; Teece, 2014; 2018). Method: In this study, a qualitative approach was used to examine the RBT (Value, Rarity, Inimitability and Non-Substitutable - VRIN Framework) and DCT, to describe and understand the relevant theories and to build upon the quantitative results. While a quantitative approach was used to analyse the social media sentiment as depicted by Social Mention metrics. A novel technique, Chernoff Faces, was used to analyse and visualize the data (de Vos, Strydom, Fouche and Delport, 2011). Results and Findings: The research results show that, while the 10 firms in the study all have a presence on social media, it is on selective platforms. The content that is posted, is on very specific topics (Narayan, 2017; Cornejo, 2018). The Chernoff Faces indicate that the firms’ Social Mention metrics, over the 30 day period, was at low values. Since strength of social mention is depicted by the face line, the thin, long, generally sad looking faces implies that more than 70 percent of the firms’ social media strength over the study period, was weak. Conclusion: The literature indicates that the true value of big data and big data analytics can only be realised if firms make sound business decisions and act upon it swiftly.
- Full Text:
- Date Issued: 2020
Establishing opportunities for using big data analysis at the Herald
- Authors: Joshua, Nadeem
- Date: 2018
- Subjects: Big data , Business intelligence -- Data processing Data mining
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10948/30529 , vital:30957
- Description: A few years ago, merely mentioning the term ‘big data’ within industry circles, would more than likely have received a quirky and confused look; however, the term big data has gained huge popularity in recent years among IT professionals and academics. The big data phenomenon has exploded in popularity worldwide, and continues to grow exponentially with each passing day. It has been good news for many industries, as industries are going ablaze with the huge volume, variety and velocity of data. As technology advances it is lifting and removing so many boundaries, and answering questions that are not currently being asked. Therefore, it is that big data is taking the world by storm, and it is safe to say that big data has gone mainstream with countless benefits being developed within industries. The opportunity for employing big data strategies are many, according to McKinsey and Company, and the growth in big data will spark a new wave of ‘innovation, competition and productivity’ within businesses (McKinsey & Company, 2011). Taking advantage of these opportunities will be challenging for companies, creating the need for new skills, tools and ways of thinking. Implementing big data would help in creating new innovative business models, as executives are challenged to make their organisations resilient and agile in today’s challenging business environment. This research paper aimed to unpack the understanding of big data, the challenges, and the value to an organisation and provide a guideline or framework to implement a big data strategy. Furthermore, this research examines the opportunities and the potential value that organisations would obtain from implementing big data, as well as the challenges that could hinder implementation. Due to the rapid growth and size of data, decision-makers need to be able to gain valuable insights from such varied and rapidly changing data that will help organisations make far better, intelligent and data-driven decisions which may help in improving operations.
- Full Text:
- Date Issued: 2018
- Authors: Joshua, Nadeem
- Date: 2018
- Subjects: Big data , Business intelligence -- Data processing Data mining
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10948/30529 , vital:30957
- Description: A few years ago, merely mentioning the term ‘big data’ within industry circles, would more than likely have received a quirky and confused look; however, the term big data has gained huge popularity in recent years among IT professionals and academics. The big data phenomenon has exploded in popularity worldwide, and continues to grow exponentially with each passing day. It has been good news for many industries, as industries are going ablaze with the huge volume, variety and velocity of data. As technology advances it is lifting and removing so many boundaries, and answering questions that are not currently being asked. Therefore, it is that big data is taking the world by storm, and it is safe to say that big data has gone mainstream with countless benefits being developed within industries. The opportunity for employing big data strategies are many, according to McKinsey and Company, and the growth in big data will spark a new wave of ‘innovation, competition and productivity’ within businesses (McKinsey & Company, 2011). Taking advantage of these opportunities will be challenging for companies, creating the need for new skills, tools and ways of thinking. Implementing big data would help in creating new innovative business models, as executives are challenged to make their organisations resilient and agile in today’s challenging business environment. This research paper aimed to unpack the understanding of big data, the challenges, and the value to an organisation and provide a guideline or framework to implement a big data strategy. Furthermore, this research examines the opportunities and the potential value that organisations would obtain from implementing big data, as well as the challenges that could hinder implementation. Due to the rapid growth and size of data, decision-makers need to be able to gain valuable insights from such varied and rapidly changing data that will help organisations make far better, intelligent and data-driven decisions which may help in improving operations.
- Full Text:
- Date Issued: 2018
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