A social media analytic framework for improving operations and service management: A study of the retail pharmacy industry

https://doi.org/10.1016/j.techfore.2020.120504Get rights and content

Highlights

  • Develops a framework for improving operations and service management.

  • Identifies eight conceptual basis of subjects in the retail pharmacy industry.

  • Issues with marketing, customer service and product are the key areas for improvement.

  • Boots received an overall better sentiment performance than lloyds and superdrug.

  • Generates insights to determine the relationships amongst the important concepts.

Abstract

The revolution in the digital economy is forcing the retail pharmacy industry to develop new business models to achieve operational excellence. A large amount of user-generated content on social media can be captured and analysed to help organisations gain insights into market requirements and enhance business intelligence. Accordingly, this study proposes an analytic framework for retail pharmacy organisations to: a) use social media and highlight the most-discussed topics by consumers, b) to identify the key areas for improvement based on the most negative comments received, and c) to determine the connections amongst the important concepts and enhance customer loyalty by adding values to consumers. We conduct an in-depth analysis on the Twitter platforms of the three largest retail pharmacy organisations in the UK: Boots, Lloyds and Superdrug. The findings show that issues with marketing, customer service and product are the key improvement areas for the retail pharmacies. Particularly, Boots received an overall better sentiment performance than Lloyds and Superdrug. We also determine the relationships amongst the important concepts discussed by consumers. The analysis generates insights into the use of social media for supporting pharmacy organisations in developing their social media strategies as well as improving their operations and service quality.

Introduction

Social media is revolutionising the way people consume, communicate, collaborate and create (Aral and Walker, 2012; Chae, 2015; Laurell and Sandström, 2017; Cui et al., 2018; Wang et al., 2020). It represents one of the most transformative impacts of information communication technologies on business, both within and outside organisations (Aral et al., 2013; Itani et al., 2017). In particular, social media has changed the ways firms associate with their competitors and marketplace (He et al., 2016), making a new world of opportunities and challenges for all aspects of the firm (Itani et al., 2017; Meel and Vishwakarma, 2019), from marketing and operations to innovation management and company finance (Kalampokis et al., 2013; Schumaker et al., 2016; Bollen et al., 2011; Bashir et al., 2017; Zhan et al., 2020). In addition, it has changed production activities amongst many retailers and manufactures (Singh et al., 2017). Online reviews generated from different social media platforms can be captured for retail networks to develop a model with unique marketing strategies and service operations. This will change manufacturers’ and retailers’ operation and production planning by offering them more purchasing options to select from (Ramanathan et al., 2017). Besides, since consumer behaviour has always been unpredictable (Aral and Walker, 2012; Itani et al., 2017), social media can help overcome this by allowing greater interaction between marketing and operations activities within the retail operations network (Chen et al., 2015; Wang et al., 2020).

In order to extract value from social media, organisations need to be able to quickly understand the user-generated data and transform those data into relevant information (Davenport, 2013; Chae, 2015). By gaining a better understanding of their customers and competitors, organisations can innovate more rapidly and effectively (Wang et al., 2016; Chan et al., 2017a). For example, operations managers can integrate the information generated from social media analytics to make fact-based decisions or to gain a better understanding of their services and products (Macnamara and Zerfass, 2012). Also, organisations are increasingly expected to harvest social media data from different platforms to gain a comprehensive understanding of both their customers and their competitors, and thus to achieve competitive advantages (Aral, 2013).

However, retail operations and service management have been relatively slow to address the potential value and application of social media for research and practice (Aral et al., 2013; Chae, 2015; Schoenherr and Speier-Pero, 2015). While the use of big data and analytical models has been increasingly studied in the context of the operations (Trkman et al., 2012; Zhang et al., 2011; Zikopoulos and Eaton, 2011; Tan et al., 2015), the focus has generally remained on traditional data sources (e.g., survey and point-of-sale transactional data) and analytical models (e.g., optimisation algorithms), and their application to operations planning and management practices (Zikopoulos and Eaton, 2011; Trkman et al., 2012; Tan et al., 2015). Some studies have pointed out that harvesting social media data can be very time-consuming and challenging (Macnamara and Zerfass, 2012; Bello-Orgaz et al., 2016). Furthermore, the global increase in the quantity of social media data requires organisations to adopt automatic analytic techniques to analyse not just their own social media platforms but also the information generated from their competitors (Chen et al., 2015; He et al., 2016). Therefore, organisations need to develop the right skills and infrastructures to harvest values from social media and support their business operations in the production of goods or services (Davenport, 2013).

The present study concerns the retail pharmacy industry, one of the largest and most important markets, with a direct impact on people's health (Jambulingam et al., 2005; Mousazadeh et al., 2015). In this rapidly growing market, retail pharmacies are transforming their operations in order to become more customer-centric through market research, surveys and the development of customer advisors to gain direct feedback for further improvements (Kukreja et al., 2011; Bharadwaj et al., 2012; Chen et al., 2015). In particular, with the advent of information communication technologies, there is a huge amount of customer-generated data available on different social media platforms, which can be captured and analysed to identify customer opinions (Dahan and Hauser, 2002; Davenport, 2013). Compared to the survey-based data, the social media data reflect the “real signal” from the customer, and this is not measured by intension. It reflects the “perception” of a service or a product provided by the firms. Therefore, effective management of social media data can offer insights into customer purchasing behaviour and attitudes concerning one or more specific issues (Bharadwaj et al., 2012).

The main aim of this study is to transform the social media data, which is unstructured text data into supportive information for improving operations and service management. This is challenging for managers since most of the social media data is provided by laypeople (Tse et al., 2018). The managers and the academics need to mine the useful information from the massive unstructured data and transform them into some quantified measurements which can help to improve the operations in the retail pharmacy industry (Xu et al., 2016). To the best of our knowledge, much of the recent literature has been too general for application by a retailer, as studies have focused on trends in the use of big data (LaValle et al., 2011; Chen et al., 2015; Brynjolfsson and McElheran, 2016) and analytical models (Tan et al., 2015; Trkman et al., 2012; Chan et al., 2015), whereas this study is more specific and therefore more directly applicable. While it is imperative to understand the value of social media analytics in the context of the retail pharmacy industry, as echoed by industry reports and academics (Aral et al., 2013; Schoenherr and Speier-Pero, 2015; Cerhoef et al., 2015), it is clear that the application of social media in the retail pharmacy industry is at a primitive stage. This leads to the following research question:

  • How can retail pharmacy managers harvest social media data to improve their operations and service management?

To address the question, we propose a framework for analysing social media information, explore the current use of different analytics, and develop insights into the potential role of social media in: a) highlighting the most-discussed topics by consumers, b) identifying the key areas for improvement based on the most negative customer comments received, and c) determining the connections amongst the important concepts and enhance customer loyalty by adding values to consumers. The three largest retail pharmacy organisations in the UK are selected in the analysis. This study is important in several ways. First of all, this study contributes to social media literature with the development of an analytic framework for retail organisations to use social media data in their operations and service improvements. Moreover, this study summarises the relevant studies and determines eight constructs from the existing literature as the key subjects for the retail pharmacy industry. Furthermore, the findings offer managers insights into the use of social media for supporting pharmacy organisations in developing their social media strategies as well as improving their operations and service quality.

The rest of this paper is organised as follow. Section 2 reviews the literature on social media and analytics methods; it provides an understanding of the current trends, techniques and technologies that can be used to analyse social media data. Section 3 proposes an overall conceptual framework for applying different analytical approaches to analyse social media data from competing organisations. Section 4 explains the methodology of the present case study of the three largest retail pharmacy organisations in the UK, consisting of data collection, data coding and data analysis, and Section 5 presents the findings. Finally, Section 6 discusses the managerial and resaerch implications, points out the limitations of the current study and makes recommendations for future research.

Section snippets

Literature review

According to Kaplan et al., p.61), the term social media refers to “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content”. Compared with traditional methods of acquiring information on and from customers (e.g., filling in questionnaires and conducting interviews), social media provides a broader range of conduits through which operations managers can glean valuable

A framework for social media analytics

The rapid increase in the amount of social media data has become a significant issue for most organisations, as they do not have the systematic approaches and analytical skills to process such vast quantities of user-generated data (Aral et al., 2013; Singh et al., 2017). Therefore, what managers need is a framework that takes social media data as inputs to improve operations management and make more informed strategic decisions. Accordingly, this study proposes such a framework. It integrates

Methodology

In this study, we demonstrate our data analytical process by collecting social media in the retail pharmacy industry. The retail pharmacy industry in the UK is highly competitive (Jambulingam et al., 2005; Chen and Fu, 2015). Apart from providing over-the-counter and prescription medications, many retail pharmacies offer other goods and services, including opticians, household items, cosmetics and stationery. Successful retail pharmacy chains adopt social media via different online platforms

Findings

We collected quantitative data from each retail pharmacy organisation's individual Twitter platform. The proposed analytic framework was applied to understand the key issues related to the three retail pharmacy organisations, based on their customer reviews on Twitter. It aims to help organisations to identify a wide array of strategic information such as the reasons behind customer comments, key areas for improvement in products or services, key topics/themes in the tweets, and trends in

Discussion and conclusion

Social media enables organisations to involve customers in a relatively inexpensive way and at great levels of efficiency (Aral et al., 2013; Wang et al., 2020). Although many organisations have already applied social media platforms such as Facebook, Twitter, Instagram to enhance customer loyalty and satisfaction, an effective application of social media analytics for improving operations and service management is not always easy (Kaplan and Haenlein, 2010; Zhan et al., 2020). Accordingly,

Author statement

AuthorContribution
Yuanzhu ZhanConceptualisation; Supervision; Methodology; Framework Development; Resources; Validation
Runyue HanWriting (Original Manuscript Draft); Investigation; Framework Development; Formal Analysis
Mike TseSoftware; Framework Development; Writing (Review & Editing); Validation
Mohd Helmi AliFramework Development; Writing (Review & Editing);
Jiayao HuFramework Development; Writing (Review & Editing);

Acknowledgement

The authors would like to thank Stella Tham for her help in the data collection. This research was supported by grants from the ULMS Pump-Priming Grant, and BA/Leverhulme Small Research Grant (SRG20\200985).

Yuanzhu Zhan is a lecturer in Operations Management at the University of Liverpool. His-research focused on investigating how organisations can improve their competitiveness by attaining the accelerated product innovation process (measured by enhanced market performance, improved production effectiveness and product innovativeness) in a big data environment. Yuanzhu has a large amount of industrial experience in both the UK and China. His-research has been published in various journals

References (89)

  • R. Hanna et al.

    We're all connected: the power of the social media ecosystem

    Bus Horiz

    (2011)
  • W. He et al.

    Social media competitive analysis and text mining: a case study in the pizza industry

    Int J Inf Manage

    (2013)
  • N.F. Ibrahim et al.

    A text analytics approach for online retailing service improvement: evidence from Twitter

    Decis Support Syst

    (2019)
  • O.S. Itani et al.

    Social media use in B2b sales and its impact on competitive intelligence collection and adaptive selling: examining the role of learning orientation as an enabler

    Industrial Marketing Management

    (2017)
  • T. Jambulingam et al.

    Entrepreneurial orientation as a basis for classification within a service industry: the case of retail pharmacy industry

    J operations management

    (2005)
  • A.M. Kaplan et al.

    Users of the world, unite! The challenges and opportunities of Social Media

    Bus Horiz

    (2010)
  • P. Kukreja et al.

    Use of social media by pharmacy preceptors

    Am J Pharm Educ

    (2011)
  • G. Lansley et al.

    The geography of Twitter topics in London

    Comput Environ Urban Syst

    (2016)
  • C. Laurell et al.

    The sharing economy in social media: analysing tensions between market and non-market logics

    Technol Forecast Soc Change

    (2017)
  • G. Martinho et al.

    Factors affecting consumer’ choices concerning sustainable packaging during product purchase and recycling

    Resources, Conservation and Recycling

    (2015)
  • M. Mousazadeh et al.

    A robust possibilistic programming approach for pharmaceutical supply chain network design

    Comput Chem Eng

    (2015)
  • R.P. Schumaker et al.

    Predicting wins and spread in the Premier League using a sentiment analysis of twitter

    Decis Support Syst

    (2016)
  • A. Singh et al.

    Social media data analytics to improve supply chain management in food industries

    Transportation Research Part E: Logistics and Transportation Review

    (2018)
  • K.H. Tan et al.

    Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph

    International Journal of Production Economics

    (2015)
  • G. Wang et al.

    Big data analytics in logistics and supply chain management: certain investigations for research and applications

    Int J Production Economics

    (2016)
  • J. Xu et al.

    Influence of social media on operational efficiency of national scenic spots in china based on three-stage DEA model

    Int J Inf Manage

    (2016)
  • S. Ahmad et al.

    New product development: impact of project characteristics and development practices on performance

    J Product Innovation Management

    (2013)
  • S. Aral et al.

    Identifying influential and susceptible members of social networks

    Science

    (2012)
  • S. Aral et al.

    Introduction to the special issue—Social media and business transformation: a framework for research

    Information Systems Research

    (2013)
  • S. Balasubramanian et al.

    Customer satisfaction in virtual environments: a study of online investing

    Manage Sci

    (2003)
  • The Most Innovative Companies 2015

    (2015)
  • S. Bennett

    Twitter Was The Fastest-Growing Social Network in 2012, Says Study [Online]

    (2013)
  • N. Bharadwaj et al.

    Explicating hearing the voice of the customer as a manifestation of customer focus and assessing its consequences

    J product innovation management

    (2012)
  • J. Bhattacharjya et al.

    An exploration of logistics-related customer service provision on Twitter: the case of e-retailers

    Int J Physical Distribution & Logistics Management

    (2016)
  • D.M. Blei et al.

    Latent dirichlet allocation

    Journal of Machine Learning Research

    (2003)
  • David M. Blei

    Probabilistic topic models

    Commun ACM

    (2012)
  • E. Brynjolfsson et al.

    Digitisation and innovation the rapid adoption of data-driven decision-making

    Am Econ Rev

    (2016)
  • C. Campbell et al.

    Understanding consumer conversations around ads in a web 2.0 world

    J Advert

    (2011)
  • Y. Cao et al.

    Internet pricing, price satisfaction, and customer satisfaction

    International Journal of Electronic Commerce

    (2003)
  • Unlocking the Power of Data and Analytics: Transforming Insight Into Income, Capgemini

    (2012)
  • H.K. Chan et al.

    A mixed-method approach to extracting the value of social media data

    Production and Operations Management

    (2015)
  • H.K. Chan et al.

    The role of social media data in operations and production management

    Int J Production Research

    (2017)
  • D.Q. Chen et al.

    How the use of big data analytics affects value creation in supply chain management

    J Management Information Systems

    (2015)
  • Y. Chen et al.

    The behavioral consequences of service quality: an empirical study in the Chinese retail pharmacy industry

    Health Mark Q

    (2015)
  • Cited by (0)

    Yuanzhu Zhan is a lecturer in Operations Management at the University of Liverpool. His-research focused on investigating how organisations can improve their competitiveness by attaining the accelerated product innovation process (measured by enhanced market performance, improved production effectiveness and product innovativeness) in a big data environment. Yuanzhu has a large amount of industrial experience in both the UK and China. His-research has been published in various journals including the European Journal of Operational Research, the International Journal of Operations and Production Management, the International Journal of Production Research, the International Journal of Production Economics and R&D Management.

    Runyue Han is a PhD candidate at the University of Liverpool. Her research includes social media analytics, new product development and operational efficiency and innovativeness in high-technology companies. Runyue has presented her research findings at several esteemed international conferences and workshops. Prior to that, she has two years of experience in a leading high-technology company in London.

    Mike Tse is a reader in Operations Management at Cardiff University. His-research crosses over different disciplines, including empirical research in risk and resilience and supply chain management, social media analytics in company crisis, decision support in supply chain management and development of OM educational simulation platform. He has published more than 40 academic articles including high quality journals such as British Journal of Management, International Journal of Operations and Production Management, IEEE Transactions on Engineering Management, International Journal of Production Economics, International Journal of Production Research, Supply Chain Management: an International Journal, Journal of Business Research amongst others.

    Mohd Helmi Ali is a Senior Lecturer at the Faculty of Economics and Management, The National University of Malaysia. He holds a PhD in Business and Management from the University of Nottingham. He have experiences in multiple industries such as food, oil and gas, maritime, transportation, and construction. Despite of his recent involvement in academics, he has worked with many research grants, in particular on food integrity and halal-hub. His-research interest focus on food integrity, halal food supply chain, sustainable development, operations management and innovation.

    Jiayao Hu is a lecturer in Operations and Supply Chain Management at Newcastle University. His-research is multi-disciplinary, integrating supply chain management, operations research, and customer management. As a professionally trained researcher, Jiayao is adept at both quantitative and qualitative research methods. Most of his research work concentrates on big data analysis, sustainable supply chains, circular economy, low carbon vehicle routing problem, green consumer behaviour, and quality management.

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