Communicating big data in the healthcare industry

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Communicating big data in the healthcare industry

Master’s thesis

Author: María Castaño Martínez & Elizabeth Johnson Supervisor: Selcen Öztürkcan

Examiner: Richard Afriyie Owusu Term: VT20

Subject: International Business Strategy Level: Master

Course code: 5FE40E

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Abstract

In recent years nearly every aspect of how we function as a society has transformed from analogue to digital. This has spurred extraordinary change and acted as a catalyst for technology innovation, as well as big data generation. Big data is characterized by its constantly growing volume, wide variety, high velocity, and powerful veracity. With the emergence of COVID- 19, the global pandemic has demonstrated the profound impact, and often dangerous consequences, when communicating health information derived from data. Healthcare companies have access to enormous data assets, yet communicating information from their data sources is complex as they also operate in one of the most highly regulated business environments where data privacy and legal requirements vary significantly from one country to another.

The purpose of this study is to understand how global healthcare companies communicate information derived from data to their internal and external audiences. The research proposes a model for how marketing communications, public relations, and internal communications practitioners can address the challenges of utilizing data in communications in order to advance organizational priorities and achieve business goals. The conceptual framework is based on a closed-loop communication flow and includes an encoding process specialized for incorporating big data into communications.

The results of the findings reveal tactical communication strategies, as well as organizational and managerial practices that can position practitioners best for communicating big data. The study concludes by proposing recommendations for future research, particularly from interdisciplinary scholars, to address the research gaps.

Key words

Big data, Information and knowledge creation, Corporate communication, Multinational corporations, International business, Healthcare.

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Acknowledgments

Writing a Master’s thesis (during a global pandemic no less!) proved to be an extraordinary learning experience. We were so lucky to have a strong network of family, friends, colleagues, classmates, and professors from all over the world who supported us throughout the process. We would like to express our thanks and sincere appreciation.

First, we would like to thank our thesis advisor, Dr. Selcen Öztürkcan, who was a bright beacon of light and guidance throughout the entire thesis writing process. She recognized the value in our interdisciplinary topic from the very beginning and helped us navigate the murky complexity of a topic that touches so many dimensions of business, health care, technology, and corporate communications. Selcen provided insightful critiques, challenged us to consider multiple perspectives, and ultimately brought out the best in us as students. Notably, she also made sure we prioritized our health and wellbeing.

We also appreciated that during a very scary time in world history Selcen offered the ultimate comfort: inviting her cat to join our Zoom advisor meeting sessions.

We would also like to thank Dr. Amy Leval, who inspired this study. Elizabeth worked with her in the fall of 2019 and watched Amy’s team transform highly complex scientific data into communications that made a real world impact.

Though it is still plausible that this is simply magic innate within Amy, we are pleased to share the findings of this thesis which suggest it is possible to replicate and cultivate her ingenuity within other professionals and teams in the healthcare industry. Many months before COVID-19 emerged and it became inescapably clear that there are severe implications for poorly handled big data communications, Amy helped us identify this pertinent and timely topic. She is not only a brilliant epidemiologist, but also a champion for women in their academic pursuits and careers. We are lucky to know her and grateful for the wisdom she shared with us.

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Additionally, we are so appreciative for all our research participants. Thank you for taking time out of your work days that are already busy during

“business-as-usual” but especially busy during an economic meltdown and infectious disease outbreak. We are glad to have the opportunity to dignify the important work you do in the healthcare industry by documenting the incredible talent and skill it takes to effectively communicate information derived from data. Your reflections, experiences, and contributions were invaluable. It was extremely rewarding to write this thesis about the work you do to meet medical needs, cure disease, and improve quality of life for people around the world.

Sincerely,

María Castaño Martínez Elizabeth Ripley Johnson

Helsinki, Finland Stockholm, Sweden

22 May 2020

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Appendices

Appendix A: Example of disinformation

Appendix B: Graph of Moore’s Law

Appendix C: Interview guide

Appendix D: Research participants

Appendix E: Conceptual framework

Appendix F: Research participants’ consent form

Appendix G: Research participants’ stakeholders

Appendix H: Amended conceptual framework

Figure and table index

Figure 1: Data-information-knowledge-wisdom pyramid

Figure 2: Sender-message-channel-receiver model of communication Figure 3: Phases of strategic big data usage in corporate communication Figure 4: Conceptual framework

Figure 5: Amended conceptual framework

Table 1: Systematization of corporate communication fields of activity Table 2: Journal scan search keywords

Table 3: Research participant healthcare industry sectors Table 4: Operationalization of interviews

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1 Introduction

Chapter 1 aims to provide an overview of the research topic: corporate communications and big data within the context of the healthcare industry. The following section includes a recent case that demonstrates the relevance of the topic in today’s international business, public health, and digital communications landscape.

The chapter also provides background on the historical development of big data in multinational corporations (MNCs), offers an overview of global healthcare markets, illustrates how corporate communications functions differently in the healthcare industry compared to other sectors of MNCs, and defines key concepts relevant to the study. Additionally, the problem discussion explains why big data in the healthcare industry is unique, how researchers have examined this topic in the past, and demonstrates the lack of existing literature regarding how communicators can harness big data to enhance communications that reach broad audiences, both internal to the organization and externally to public stakeholders, and ultimately drive corporate strategies forward to meet business goals and objectives. The introduction concludes with the research questions, purpose, and delimitations.

1.1 Background

In January 2020, global media began reporting a mysterious virus was affecting Wuhan, a city in central China. People were falling ill with pneumonia-like symptoms, which scientists were calling a coronavirus (Barbaro, 2020). 17 years ago, Severe Acute Respiratory Syndrome (SARS) broke out in China and resulted in a global health crisis that infected more than 8,000 people and killed more than 800 (Barbaro, 2020). Public health analysts attribute part of the deadly spread of the virus was due to the Chinese government both withholding information and perpetuating inaccurate communication. Because of this lack of credible data, journalists were not able to provide the public with factual and up-to-date news regarding how severe the virus was, as well as whether people were getting the care they needed, taking appropriate

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precautions, and if the government was treating the situation as urgent (Barbaro, 2020).

This same phenomenon is seen in the case of the coronavirus today; but now the public is increasingly turning to social media for information. This has sent technology conglomerates, including Facebook, Google, and Twitter, scrambling to prevent a surge of “half-truths and outright falsehoods” about the outbreak (Romm, 2020). This carries immense risks, particularly in the fields of health and medicine, where the posts, photos, and videos people share can shape how people think and their decisions to seek and obtain much needed care (Romm, 2020). Public health authorities along with these technology multinational companies have long struggled to curtail dangerous health disinformation, deliberate misleading information, and misinformation, false information that is spread regardless of whether there is an intent to mislead, which includes posts, photos, and videos that scare people away from much needed medical care. For example, the Chinese state-run media perpetuated disinformation when they tweeted photos purporting to show a brand-new hospital in Wuhan, but the images were actually stock photos from a company that sells modular containers (see Appendix A). Likewise, the Facebook group, “Coronavirus Warning Watch” is an example of a repository of misinformation where thousands of Facebook users trade theories about the disease’s spread—in some cases suggesting it’s about

“population reduction”—along with links to articles peddling fake treatments (e.g.

“Oregano Oil Proves Effective Against Coronavirus” had been shared more than 2,000 times). Whether out of malice, fear, or misunderstanding, users can easily share and reinforce disinformation and misinformation in real time, complicating the work of doctors and government officials in the midst of a public health crisis (Romm, 2020).

Facebook claims to have responded by partnering with fact checking organizations and leveraging its artificial intelligence system to search for misinformation, labeling the inaccuracies in the posts while also lowering the posts’ rank in users’ daily feeds, and ensuring it will not be included in recommendations or predictions when users are searching within Facebook (Caron, 2019). Twitter started steering U.S. users

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searching for coronavirus related hashtags to the Centers for Disease Control and Prevention. Google-owned YouTube said its algorithm prioritizes more credible sources so people searching for news see authoritative sources first (Romm, 2020).

Despite these efforts, regulators and health professionals do not believe the tech giants struck the right balance aiming to ensure digital debates do not cause real world harm.

This is further complicated by the fact that tech companies adamantly argue against acting as “arbiters of truth” as Facebook chief executive, Mark Zuckerberg, has said regarding deciding what users can say online (Romm, 2019).

In the midst of this, world stock markets were plunging, unemployment was skyrocketing, and companies were going bankrupt as it became clear that this public health crisis was morphing into the worst economic crisis since the Great Depression (Goodman, 2020; Malkani and Torgerson, 2020). Although there is no way to measure precisely how much misinformation exacerbated this debilitating economic ripple effect, it stands to reason, from an international business perspective, the ways in which coronavirus was communicated greatly impacted the financial and operational performance of global companies.

This case is a timely demonstration of global challenges at the intersection of international business, public health, and how big data is communicated. It reveals the vast web of stakeholders who maintain competing priorities, including politicians, government administrators, health authorities, news and media groups, multinational corporations, and more who are communicating health, and healthcare related data across the globe. As illustrated in this case, these actors, who may not have any education or training in data analysis and the interpretation of scientific information, are messaging information derived from COVID-19 data to their respective audiences.

This can lead to serious economic, health, and safety consequences.

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1.2 What is big data?

Multinational corporations have a long history of producing, storing, interpreting, and subsequently, utilizing significant quantities of data. However, big data differs from traditional corporate information and knowledge management due to its high volume, velocity, veracity, and variety. Wiencierz and Röttger (2017) explain that big data information assets consist of very large, complex, and variable amounts of data (volume); concepts, technologies, and tools that are required for fast and systematic storage, administration, and analysis of the heterogeneous data, in order to enable the retrieval of the information within seconds (velocity); the measured data must be reliable and accurate in order for corporations to make sound business decisions on the basis of such data (veracity); and diverse in formats, structures, and semantics such as text comments, videos, or data generated from wearables (variety). Subsequently, these datasets are generated through computer and storage systems in a way that makes these assets manageable and usable for organizations and individuals (Wiencierz and Röttger, 2017). This understanding of big data, however, is a recent development, despite the fact that its foundations have been evolving for decades, or even centuries.

Scholars cite the beginning of big data when society started analyzing and storing information in physical documents and platforms, including the Library of Alexandria as the largest data collection of the ancient world (López-Robles, 2019). At that time, knowledge creation was seen as exclusively for academics (López-Robles, 2019).

Similarly, the emergence of statistics, which began as an academic discipline in 1660, had a profound influence on data analysis as an application and tool for business strategy (López-Robles, 2019). In 1865, the concept of business intelligence was coined in the Encyclopedia of Commercial and Business Anecdotes, referring to information analysis relevant to business from a structured and optimized approach (López-Robles, 2019). This is acknowledged as the first application of data analysis for commercial purposes. The concept of big data as it is known today emerged as a result of the information and communication revolution, the development of the Internet, and subsequent digital storage platforms (World Economic Forum, 2015). By

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the early 2000s, central processing unit (CPU) technologies were overwhelmed by data storage. Thus, this IT crisis prompted the development of enhanced capacity, speed, and intelligence of big data systems, which also brought down costs and became more affordable for users (Russom, 2011).

This drastic increase of computing data was originally predicted by Gordon Moore, co-founder of Intel, in 1965 (Sainz, 2015). He observed that the number of transistors on integrated circuits doubles approximately every two years, and thus, this influenced processing speed, products prices, storage capacity, and size of pixels in digital images (see Appendix B) (Roser and Ritchie, 2015). As a result, the technology industry adopted Moore's Law as a measurement of the product evolution and rate of competition among tech-competitors. Moore's Law marked a significant societal turning point from prohibitively expensive computing devices to affordable laptops, and subsequently, smartphones (Sainz, 2015). This revolution occurred together with the development of new Information and Communication Technologies (ICTs) as well as rapidly expanding data collection and storage innovation. This is essential as big data has reached exponential growth rates able to generate over two and a half quintillion bytes daily (World Economic Forum, 2012) and forecasts project data growth will increase by forty percent annually (United Nations, 2016). With the tools, technology, and expertise required to collect, store, and process big data, companies can finally transform data into useful information and trends. Thus, this can be used to facilitate decision making within MNCs and is seen as one of the most powerful assets within contemporary organizations (Roser and Ritchie, 2015; Sainz, 2015).

Big data, as with many innovations, however, can be a double-edged sword (Buytendijk and Heiser, 2013). It brings the possibility of significant benefits by allowing organizations to personalize their products and services on a massive scale;

it fuels new services and business models; and it can help mitigate business risks (Buytendijk and Heiser, 2013). At the same time, there can be serious consequences if consumer data is misused. This can be harmful for consumers, as well as for organizations who can face reputational damage due to an inadequate understanding

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of data privacy issues. Governing and legislative institutions around the world are starting to investigate data protections. In 2016, as a measure to address some of these concerns, the European Union implemented the General Data Protection Regulation.

These regulatory measures lead to more consumer privacy protection, but it also makes data gathering and use of personal data more challenging for the private sector (GDPR.eu, 2016). Moreover, the consequences of not following GDPR protocols are significant—fines up to 20 million euro or 4% of global total revenue of the preceding year (whichever is greater) (GDPR.eu, 2016). As a result, big data can be both a powerful organizational asset and an organizational threat if companies misuse it (Buytendijk and Heiser, 2013).

1.3 Digital transformation in the healthcare industry

Global market overview

Big data is transforming many industries. However, it has the potential to make one of the greatest impacts in the healthcare sector, particularly because every healthcare company around the world generates, stores, and analyzes big data. Recently, one of the main reasons for such a robust volume of data is that healthcare systems have largely become digitized, by implementing electronic health records in hospitals and clinics (Hersh, 2014). Patient medical records can include a wide variety of data including clinical notes, lab reports, pathology images, radiology scans, and more (Dash, et al., 2019). Healthcare companies also yield big data from medical equipment utilization reports, online patient communities or forums, Internet of Things (IoT) health and wellness-related devices, mobile applications, biomedical and scientific research, clinical trials, nationalized patient registries, payer (e.g.

insurance companies) records, and more (Dash, et al., 2019; Luo, et al., 2016).

Therefore, not only is the volume of data difficult to manage, but the variety of data formats from unstructured text in clinical notes to images to lab results to invoices or financial-related data makes the storing, management, and analysis even more complex. This requires both highly sophisticated technology and employee expertise to identify useful and reliable information (Luo, et al., 2016). An integral component

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of big data being a functional tool and resource for companies is the ability to derive meaning and interpret information from the raw datasets. This process of translating data so that it becomes information has been thoroughly studied by information science scholars. Data is not of use to key decision makers or practitioners if it is not analyzed and interpreted, thereby becoming information (Kayyali, et al., 2013). Only just recently has technology finally advanced to a degree where it is easier to not only collect and store data, but also analyze it, and most importantly for the

healthcare industry, analyze datasets from multiple sources and in multiple formats to create meaningful information and insights (Kayyali, et al., 2013).

By harnessing the power of biomedical and healthcare data, modern healthcare organizations intend to revolutionize existing medical therapies and care systems (Dash, et al., 2019). With data of this scale, variety, accuracy and availability, companies are able to better equipped to conduct disease research, enhance hospital administrative process automation, design early illness detection mechanisms, prevent unnecessary doctor’s visits, develop disease prediction tools, discover new drug and treatment options, personalize patient healthcare experiences, and more. In order to understand the broader impact of big data in the healthcare industry, it is necessary to acknowledge the complex ecosystem of the healthcare, life science, and biotechnology market in which multinationals in these industries operate within. At present, the market includes systems which aim to promote health, prevent disease, and provide patient care (Dash, et al., 2019). Health and care systems are defined as broader than hospitals and clinical environments, but also encompassing public health and social care (European Union, 2018). The various components of a healthcare system are deeply interrelated within the system network and thus, a variety of exchanges and relationships exist. For example, primary care providers (e.g. physicians and healthcare professionals) provide healthcare services to patients; insurance companies provide insurance; reimbursement funds provide reimbursement; employers contribute benefits; pharmaceutical companies create essential medicines; the government is responsible for planning and managing healthcare infrastructure and

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regulatory matters; and the media plays a significant influencing role in the public sphere (European Union, 2018).

Across the globe, spending on health and long-term care is steadily rising and expected to continue (European Union, 2018). Today, aging populations, multi-morbidity (i.e.

multiple chronic conditions or illnesses), healthcare workforce shortages, increasing preventable, non-communicable diseases caused by risk factors such as tobacco, alcohol, and obesity, as well as the growing threat of infectious disease due to antibiotic resistance and new, or re-emerging, pathogens (Trafton, 2020; European Union, 2018) pose serious threats to healthcare systems across the globe. However, this also creates unique opportunities for MNCs to contribute to reforms and innovative solutions that address these challenges and create a more resilient, accessible, and effective healthcare system.

Healthcare systems around the world see big data as a mechanism to navigate this current landscape. For example, in Europe, the European Union is aggressively pursuing digital solutions, which yield enormous amounts of data, for cost effective health and care in order to increase the well-being of millions of citizens and radically change how services are delivered to patients (European Union, 2018). The EU intends to take action in three key areas: 1) Provide citizens secure access to and sharing of healthcare data across borders; 2) Develop better data to advance research, disease prevention, and personalized health and care; 3) Design digital tools for citizen empowerment and person-centered care (European Union, 2018).

The EU acknowledges data as a key enabler for digital transformation and sees digital tools as a way to translate scientific knowledge, help citizens remain in good health, and ensure they do not turn into patients (European Union, 2018). The aim is that these tools will also enable better use of healthcare data in research and innovation to support personalized healthcare, better health interventions, and more effective health and social systems. Because data is often not available to the patients, public health authorities, medical professionals, or scientific researchers, the EU perceives this as a

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hinderance in delivering effective diagnosis, treatments, and personalized care (European Union, 2018). Thus, health systems lack key information to optimize their services, and providers find it hard to build economies of scale to offer efficient digital health and care solutions and to support cross-border use of health services. Market effective, and integrated approaches to disease prevention, care, and cures (European Union, 2018). Today, the EU is developing high performance computing, data analytics tools, and artificial intelligence to design and test new healthcare products to provide faster diagnosis and better treatments. However, a key contingency in the success of these initiatives is the availability of high quality, high volume data. The EU is currently evaluating regulatory frameworks that will safeguard the rights of the individual and society, as well as stimulate innovation (European Commission, 2018).

With Europe setting its sights on developing digital infrastructure and data driven health systems, the United States is also driving digital health solutions forward by targeting personalized healthcare. The United States has the largest healthcare system in the world—11 percent of American workers are employed within the healthcare sector (Bureau of Labor Statistics, 2020), accounts for 24 percent of government spending (Center for Medicare & Medicaid Services, 2020), and is responsible for 17.7 percent of U.S. GDP (CMS.gov, 2018). Moreover, the U.S. healthcare industry is expected to grow up to 7% annually from $103 billion in 2018 to $173 billion in 2026 (Lineaweaver, 2019). Despite this enormous economic engine, from a public health perspective, the United States spends more than other countries without obtaining better health outcomes (Papanicolas, et al., 2018).

Unlike Europe, the United States has a system that consists of private providers and private insurance to pay for healthcare. As of 2018, 34 percent of Americans received their healthcare via government insurance or direct public provision (Berchick, et al., 2019). Without unified national healthcare infrastructure, patients have become active participants in their healthcare by demanding transparency, convenience, access and personalized products and services (Burrill, 2019). The U.S. home healthcare model, based on telehealth, delivers quality remote care and has lowered cost, lowered

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readmission rates, and increased patient satisfaction rates (Lineaweaver, 2019).

Telehealth is one example where digital transformation in healthcare is being led by the need for predictive and preventive care. The outcome is a digital health system responding to consumer and patient demands that also results in cheaper, more precise and less invasive treatments and therapies than traditional models (Burrill, 2019).

In comparison, to Europe and North America, the market environment in Asia and Africa is less developed. In Asia, the health system is characterized as fragmented and diverse with wide variations in healthcare policies and reimbursement systems across Asia (Tham, et al., 2018). Practitioners and researchers are calling for an integrated healthcare system with a collaborative and coordinated model of care across stakeholders in healthcare settings. Less developed societies depend on development assistance for health and on private insurance due to limited public assistance. Tham, et al. (2018) acknowledged that in developing regions such as in Africa and Asia, an integrated care model is meant to encourage benefits in sustainable health systems and relieve the healthcare burden, they also recognize the crucial support from international non-governmental organizations in developing and resource-limited areas from Asia and Africa (Tham, et al., 2018).

In accordance, the World Health Organization's Regional Office for Africa, are:

improvement of the health security, strengthen national health systems, special attention to health-related Sustainable Development Goals, address the social determinants of health, and turn the WHO secretariat in Africa into a responsive and results-driven organization (Pheage, 2017). Yet, technology innovation is disrupting the future of healthcare in Africa as well, as an example, CareAI, an European Commission project, is an artificial intelligence-powered computing system that together with blockchain is able to diagnose infectious diseases, such as tuberculosis, malaria, and typhoid fever within seconds. This “AI doctor” uses anonymous distributed healthcare data to provide personalized health services to patients anonymously, under useful contextual information, waning risks to the wider society.

However, African policymakers and overall health-related institutions and healthcare

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professionals will need to structure a new health framework to ensure patients privacy and a secure global healthcare system; unnecessary to mention the main priority of major developing regions, resources such as: accurate electricity infrastructure, clean water system and available drugs (Ekekwe, 2018).

International business trends in healthcare

The synergy between healthcare and technology has risen a new spectrum of business opportunities, top tech companies are integrating medical functions in order to obtain health and wellbeing data. Google is striving to diagnose types of cancer as well as heart attacks at early stages, while Apple is aiming at developing sensors to monitor blood through the skin or glucose levels through tears (Todor and Anastasiu, 2018).

Likewise, Samsung has partnered with medical professionals at the University of California to launch validation and commercialization of new sensors, algorithms and digital health technologies (Todor and Anastasiu, 2018). This development is shifting healthcare and biomedicine towards a coordinated management of the healthcare system, which has a powerful potential impact on all its stakeholders: patients, medical practitioners, hospital operators, pharma and clinical researchers and healthcare insurers. However, the uneven development of healthcare on a global basis, as well as the general public’s willingness to provide personal health data is complicating the innovation process (Todor and Anastasiu, 2018). Likewise, regarding privacy and security regulations protecting patient’s data, privacy policies are unevenly developed around the world, which puts into consideration the legitimacy of the acquired information. In both the private and public sector, innovative digital solutions can improve health, boost quality of life, and enable more efficient ways of organizing and delivering health and care services. For this to happen, they must be designed to meet the needs of people and health systems and be thoughtfully implemented to suit the local context (European Commission, 2018).

A significant international business concern for all is the many local, transnational, and foreign laws and regulations healthcare MNCs’ products and services must

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maintain compliance with. Because laws in this area can vary from country to country, this further complicates the potential for success with launching new products in new markets. In the United States, the Food and Drug Administration (FDA) regulates the launch of new medical devices and pharmaceutical drugs. It also regulates the manufacturing and labeling and record keeping procedures for healthcare products (Lamph, 2012). Receiving marketing approval for new healthcare products and drugs from the U.S. FDA is expensive and time consuming. Likewise, in Europe, Conformité Européenne (CE) marking indicates that a product meets the essential requirements of all relevant European Medical Device Directives and is a legal requirement to market a device in the European Union (Lamph, 2012). In India, the Department of Health under India’s Ministry of Health and Family Welfare is responsible for the regulation of medical devices (Lamph, 2012). In China, the State Food and Drug Administration (SFDA) regulates the introduction of new medical products in the Chinese market (Lamph, 2012). Thus, MNCs must comply with regulations governing product standards, import restrictions, packaging and labeling requirements, tariff regulations and tax requirements. Non-compliance with the regulations and laws or failure to maintain, obtain or renew necessary licenses and permits could ultimately impact the company’s operations and financial performance.

The global regulatory environment is a critical function of the success of any healthcare company’s product marketing and sales strategy.

1.4 Corporate communications and the healthcare industry

Historically, corporate communication has fulfilled the critical role of disseminating business information to a variety of internal and external stakeholders. All MNCs have a variety of internal and external audiences they must communicate with; however, healthcare is particularly complex. One of the ways in which corporate communications is unique within the healthcare sector is due to both the volume and the diverse range of stakeholders. A stakeholder is any person, or group of persons, with which the company has, or wants to develop, a relationship (Dogramatzis, 2002).

Thus, the interconnection of stakeholders including employees, public and private

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payers, providers and suppliers, comprise the healthcare network ecosystem. Within the ecosystem, there is a clear differentiation between internal and external stakeholders. Internal audiences include every healthcare organization employee, working directly or indirectly, as a business unit, committee, team, or union. Whereas the external stakeholders are even more diverse and can be categorized into three differentiated areas: Inputting, Mediators, and Consumers (Dogramatzis, 2002).

Dogramatzis (2002) indicates inputting audiences include: regulators, lawmakers, politicians, reimbursements funds (e.g. payers and insurers), and suppliers. Audiences who function as mediators are prescribers, scientific and medical key opinion leaders, pharmacists, healthcare practitioners (e.g. doctors, nurses, etc.), and health system administrators (Dogramatzis, 2002). Consumer audiences are perhaps most far- reaching and include: patients, patient families or care takers, activists, the general public, media, investors, competitors, and non-governmental organizations (Dogramatzis, 2002). This means communicators in the healthcare industry must have a thorough knowledge of each of these stakeholders including their distinct characteristics and needs. Moreover, careful attention must be given to develop relationship strategies, targeting messages effectively, and evaluating their performance (Dogramatzis, 2002).

International business scholars often conceptualize corporate communications within the marketing mix—falling in the promotion segment, which utilizes marketing tools such as advertising, personal selling, sales promotion, and public relations to communicate with customers (Kotler, 2000). However, because this study investigates communication strategies beyond just customers (see Table 1) and encompasses both internal and external communication, it is necessary to also seek out concepts and definitions from communication science literature beyond traditional marketing. Van Riel (1995, p. 25) defines corporate communication as “an instrument of management by means of which all consciously used forms of internal and external communication are harmonized as effectively and efficiently as possible, so as to create a favorable basis for relationships with groups upon which the company is dependent.” Much like

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how data in the healthcare industry differs from the data a traditional MNC in another industry would have access to (e.g. Pfizer has very different data assets than IKEA), corporate communications in healthcare is similarly unique and differs from other industries. This can be attributed to the enormous complexity of the healthcare industry ecosystem. More so than any other industry, healthcare companies operate in a heavily regulated environment where MNCs interact extensively with government authorities, regulators, and politicians. Likewise, their stakeholders go far beyond the individual consumer who buys their product or service (see Table 1). The complex ecosystem of the healthcare industry is mirrored in each communication sphere’s key stakeholders and audiences. This complex ecosystem of stakeholders subsequently is mirrored within the healthcare company itself and its organizational structure.

Healthcare companies are heavily matrixed organizations, which is necessary to operate with many stakeholders internally, as that is how they operate externally. As a result, this is reflected in the corporate communications structure, which is also matrixed (Dogramatzis, 2002).

Wiencierz and Röttger (2017) maintain that in order to understand the potential and limitations of big data applications in the context of corporate communications it is necessary to consider its three distinct and separate component spheres: marketing communications, public relations, and internal communications. Marketing communications is primarily responsible for corporate identity. It also drives brand, customer, and product communications, but does so collaboratively with public relations (Wiencierz and Röttger, 2017). Public relations is focused on reputation management and external communication activations with the media, investors, politicians, regulators, patient advocacy groups, and more (Wiencierz and Röttger, 2017). Internal communication is tasked with organizational communication from business unit leaders to employees (Wiencierz and Röttger, 2017). Table 1 illustrates the communications responsibilities per each component sphere. It is important to note that although each component sphere has its own roles and responsibilities, the three units are highly integrated and dependent on one another even when their

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communication responsibilities do not overlap (Van Riel & Fombrun, 2007). It is essential that all three are aligned in order to communicate coherently and cohesively to their many stakeholders (Van Riel & Fombrun, 2007).

Table 1. Systematization of corporate communication's fields of activity (Wiencierz and Röttger, 2017)

Internal communications Marketing communications Public relations Corporate identity

Employee communication Brand communication

Management communication Customer communication

Product communication

Media relations

Investor relations/finance communications Community relations

Public affairs/lobbying Issue management Crisis management

Corporate social responsibility communication CEO communication

With the integration of digital channels and tools, a shift has occurred in how companies reach their key stakeholders. Mainly, audiences are more accessible, so thus companies have had to adjust their communications strategies, develop new ways of messaging, and learn to leverage social networks and automated communication platforms (Goodman, 2019; Wiencierz and Röttger, 2017). An outcome of utilizing digital communication tools is that they are often built with mechanisms for tracking information and gathering data (Goodman, 2019). Furthermore, as digital organizational communication has broadened organizations’ stakeholders, companies have had to adjust their strategies, developing new languages and narratives, leveraging social networks and automated communications. These new business practices have led business practitioners as well as academic researchers to study new challenges and opportunities in digital communication, in order to understand and theorize these changes, as well as perceive future trends and developments of new applications (García-Orosa, 2019).

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1.5 Theoretical problematization and research gap

In the past, corporations have struggled to harness the power of big data because they lacked data storage infrastructure as well as advanced analysis techniques and methodologies for effectively analyzing the relevant data sets (Micu et al., 2011).

Today, companies have the hardware, software, and data processing tools, techniques, as well as human capital expertise to store, organize, and interpret the data. This means that companies are eager to communicate the information derived from data in order to advance their business priorities and sustain competitiveness. The mechanics of how to transform big data into information that can be communicated to many stakeholders is sparse. Additionally, there is minimal existing literature on how communicators leverage data into marketing communications, public relations, and internal communications. Wiencierez and Röttger (2017) conducted a systematic literature review to assess existing publications on the application of big data in corporate communications and found the majority focus on marketing communications, whereas the amount of research studies on public relations is significantly low, and internal communication hardly exists. In addition, the systematic literature review illustrated the lack of research in strategic big data usage in corporate communication from a holistic and integrated perspective. They found there were no studies assessing corporate communication as a whole investigating the synergy of marketing communication, public relations, and internal communication working altogether (Wiencierez and Röttger, 2017). García-Orosa (2019) agrees on the limitations of single-channel studies and points out the need for new terms and methods to study corporate communication in the context of big data.

Despite the lack of literature, the topic is timely and has pertinent implications for practitioners as big data poses significant challenges to communicators who need to synthesize the information and apply the data in multiple channels and contexts to meet business requirements. These concepts are made all the more complex when attempting to communicate big data across diverse geographies and cultures within a complex regulatory environment within the healthcare industry. Communications

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practitioners who are responsible for delivering information derived from big data are responsible for not only developing messages with complex information, but also, must ensure their interpretation and dissemination of the data is compliant with cross- border legal protocols and global regulatory requirements of data privacy. Beyond the field of communication science, there is also a dearth of management literature related to big data. Top tier business journals, such as the Academy of Management Journal, Strategic Management Journal, and Journal of International Business Studies, have published minimal, if any, articles related to this topic. This further validates the research gap this study is seeking to address and the need to position the business imperatives and international management implications for big data communications.

Measuring what matters and translating big data into business planning and decision making are key priorities for corporations and management teams (Loebbecke and Picot, 2015). Likewise, communications and big data is a significant international business challenge. Effective communication of complex data enables strategic decision making and enhances market positioning, which is necessary for MNCs to sustain global competitiveness. Additionally, a research gap exists regarding the intersection of communication and international business and big data. Current organizational communication and international business literature lacks research reflecting the intersection of these competencies. Few studies bring together previously disparate streams of work in the fields of communication science and information systems with respect to big data applications in corporate communication.

This complexity is intensified when it comes to international business, since international performance and international information behaviors are characterized by a greater diversity of elements, where additional variables and a higher level of dynamism is present unlike it happens in domestic markets (Leonidou and Theodosiou, 2004). Likewise, the healthcare sector is particularly relevant from an international business strategy lens as every person, in every region of the world, is a consumer of healthcare products and services. Big data offers healthcare MNCs the opportunity to better understand this enormous customer base, develop effective

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communication strategies for reaching each of their audiences, and subsequently enhance competitiveness and future growth strategies. However, empirical findings are currently not addressing this phenomenon.

Ultimately, communicating effectively to customers and employees is one critical mechanism for how companies achieve business objectives. The introduction of big data offers communicators a new advantage and device to drive business priorities forward. Multinational corporations have always managed large flows of information and data. Similarly, corporate communications practitioners in the life science and healthcare industries have always needed to message highly technical and scientific information to internal and external stakeholders. However, neither have ever been required to manage data at the enormous volume, veracity, variety, and velocity as big data offers today. Thus, it is necessary to examine how this impacts the way MNCs are communicating. As big data disrupts traditional business operations, how does it subsequently affect corporate communications? Are the challenges communicators face when utilizing information derived from big data unique? If so, what are the challenges in utilizing big data in communications compared to technical information of the past? Does big data allow communicators to communicate with their myriad of internal and external stakeholders more effectively? Additionally, recognizing that most communication practitioners are trained in the field of communications, not data analytics, clinical research, or science in general, how do they ensure accuracy in translating highly technical data? The answers to these questions are not found in existing literature.

1.6 Research questions

• RQ1. How are multinational healthcare corporations communicating information derived from big data to internal and external stakeholders?

• RQ2. What challenges do communicators in the healthcare industry face when utilizing big data?

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1.7 Purpose

The purpose of the study is to understand how healthcare MNCs communicate information derived from big data. Healthcare companies have access to enormous volumes of data assets, yet they also operate in one of the most highly regulated business environments where data privacy and legal requirements vary significantly from one country to another. Thus, this sector offers a fruitful environment to study big data-related communications from an international business perspective.

Additionally, this study is pertinent from the communications discipline perspective because compared to other sectors, healthcare companies must communicate with significantly larger audience which pose unique challenges.

The aim of the study is to examine how big data impacts traditional corporate communications strategies reaching both internal and external audiences and how global healthcare companies are communicating big data across marketing communications, public relations, and internal communications to advance their strategic business objectives. In order to address the gaps in existing literature, the study intentionally seeks to understand the convergence of these three prongs of corporate communication together rather than examine one discipline’s use of big data independently. The study also seeks to understand what tools or methods communicators are utilizing to engage both key internal and external stakeholders with information garnered from data sources around the world to facilitate strategic decision making, spur innovation, enhance competitiveness, and achieve business goals.

1.8 Delimitations

The focus of the study will not include an assessment or review of tactics and methods for storing, managing, or processing data. The intent is to understand how the data is used after data experts, analysts, or scientists have synthesized and evaluated the data so it results in information. This study examines the application of this information,

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and particularly, how non-technical business practitioners utilize the data in communication strategies. The literature review was tailored to address the scope of the study. For example, because big data is a relatively recent phenomenon, and rapidly changing discipline, the literature review was limited to studies published within the past 10 years. The topic was further narrowed to one industry, healthcare, and one corporate function, communications.

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2 Literature review

The intent of Chapter 2 is to present existing scientific theories on big data, corporate communications, and international business in the healthcare industry. Examining existing studies and prior scholarly contributions subsequently informed the design of the conceptual framework. The quality of the literature review was maintained by the 5C criteria: concise, clear, critical, convincing, and contributative (Callahan, 2014).

To follow the 5C criteria, the authors developed a critical review procedure comprised of three analytical points: 1) methodology, 2) theory, and 3) key findings.

Additionally, in order to ensure relevancy, the authors tailored the journal scan to review only the past 10 years (2010-2020) of publications on the topic.

2.1 Journal scan

To conduct the review of literature, the authors drew upon the Scimago Journal Ranking list, which measures scientific influence of scholarly journals by accounting for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from, to select the top tier management and international business journals. The authors searched using a variety of relevant keywords (see Table 2), however, a notable discovery was that the highest ranked journals in this discipline, including the Academy of Management Journal, Strategic Management Journal, and Journal of International Business Management, had minimal, if any at all, articles addressing this topic. Thus, the authors expanded the scan to review data science, healthcare, and communications journals.

Table 2. Journal scan search keywords

Big data Communications International business Healthcare Big data, business

intelligence, data science, data analytics,

information systems, communication science.

Corporate communication, information and knowledge creation, MNCs.

International business, international markets, international performance.

Healthcare, healthcare system, biomedicine, wellbeing.

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2.2 Big data

As noted in Chapter 1, the study of big data is rapidly evolving and new insights, theoretical contributions, and research is ongoing. Due to its recency, complexity, and ability to be utilized across a myriad of sectors, big data definitions vary widely. As big data continues to evolve, grow, and change, so too does the many interpretations of what it is and how it is defined. Scholars have different definitions depending on their field. For example, in the Journal of Information Science, Gupta and Rani (2018) posit: “Big data refers to large datasets which require non-traditional scalable solutions for data acquisition, storage, management, analysis, and visualization, aiming to extract actionable insights having the potential to impact every aspect of health and life.” Beyond applied engineering and information science disciplines, communications scholars provide similar definitions. Wiencierz and Röttger (2017) explain that big data information assets consist of very large, complex, and variable amounts of data (volume); concepts, technologies, and tools that are required for fast and systematic storage, administration, and analysis of the heterogeneous data, in order to enable the retrieval of the information within seconds (velocity); the measured data must be reliable and accurate in order for corporations to make sound business decisions on the basis of such data (veracity); and diverse in formats, structures, and semantics such as text comments, videos, or data generated from wearables (variety).

Subsequently, these datasets are generated through computer and storage systems in a way that makes these assets manageable and usable for organizations and individuals (Wiencierz and Röttger, 2017). Several authors maintain the importance of the three (or, more recently, four) “Vs” which are key dimensions of big data: volume, velocity, variety, and often, veracity (Russom, 2011; Wiencierz and Röttger, 2017; Mikalef, et al., 2018). Volume refers to the size and complexity of big data compared to conventional databases. Variety acknowledges the heterogeneity of data, regarding formats, structures, and semantics, as texts or words, videos, images or the diversity generated from the wide range of technological items. Velocity depicts the ability to immediately store, administer and analyze heterogeneous data. Veracity alludes to the

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importance of being considered when making decisions based on big data analysis (Wiencierz and Röttger, 2017).

Beyond scientific scholarship, the “Vs” have been adopted, and modified, by many relevant industry institutions, such as the National Institute of Standards and Technology, a laboratory within the United States Department of Commerce dedicated to physical sciences, technology, engineering, and information systems, which define big data as “consists of extensive datasets—primarily in the characteristics of volume, variety, velocity, and/or variability—that require a scalable architecture for efficient storage, manipulation, and analysis (National Institute of Standards and Technology, 2018).” Additionally, Gartner (2015), one of the world’s largest research and advisory consultancies, defines big data as “high volume, high velocity, and/or high variety information assets that demand cost effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

Even in mainstream trade business publications big data definitions appear. For example, Internet governance and regulation scholar, Viktor Mayer-Schönberger, and technology journalist, Kenneth Cukier, defined big data, in their book, Big Data: A Revolution That Will Transform How We Live, Work, and Think, as referring to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and government, and more (Mayer-Schönberger and Cukier, 2013). Thus, beyond purely academic scholars’ interpretation of big data, many other relevant actors from government institutions, to the MNCs, to the media and journalists are defining and shaping the understanding of this phenomenon.

2.3 Data analytics

Beyond definitions and key characteristics of big data, much of the existing literature also describes tools and methods that are applied in order to understand the meaning of big data. The process by which big data is analyzed and organized into meaning, or synthesized into information, is called data analytics (Mikalef, et al., 2018). This is an

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essential component of big data’s impact within an organization because, tactically, raw data on its own is not useful to companies until it is transformed into information.

Friké (2009) defines information as relevant, usable, significant, meaningful, processed data. This concept is illustrated in the data-information-knowledge-wisdom (DIKW) pyramid (see Figure 1) which is derived from the information systems and knowledge management discipline (Friké, 2009). As it is understood in this framework, data is discrete facts without context. Rowley (2007) explains these facts can be structured, unstructured or semi-structured data from a wide range of sources.

Data becomes information when it is put into context or given meaning through the application of analysis. Thus, big data analytics harnesses analysis techniques, technologies, systems, practices, methodologies and applications to organize, structure, and critically analyze the data by identifying patterns and trends (Chen et al., 2012).

Figure 1. Data-information-knowledge-wisdom (DIKW) pyramid (Rowley, 2007)

2.4 Data intelligence

The output of data analytics is subsequently data intelligence. Data intelligence is the tool through which the analysis and the interpretation of information transform information into knowledge, the next level of the DIKW pyramid (Rowley, 2007).

Data intelligence is an important concept in management literature because the intent is to create strategic knowledge in order to make precise, high impact business decisions and improve organizational decision making overall (Chen, et al., 2012;

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Saleem Sumbal, et al., 2017). Information derived from data can create valuable knowledge, which ultimately promotes organizational competitive advantage (Saleem Sumbal, et al., 2017). The objective of data intelligence is to improve business performance by optimizing and enhancing opportunity identification, organizational capabilities, trends forecasting, and eventually, decision making (López-Robles, 2019). However, several researchers remain uncertain of the degree of efficiency of big data on the organizational decision-making process (Ransbotham, et al., 2016;

Elgendy and Elragal, 2016; Miah, et al., 2017).

One way in which scholars question big data’s ability to facilitate effective decision making is attributed to organizational “data binges.” Bumblauskas, et al. (2017) conceptualizes a data binge in instances where data is simply gathered without being thoroughly or conscientiously handled, which then decreases data’s value as a tool for decision-making. This contends data quality over quantity, when data lacks objective analysis and knowledge craves action, the marginal value for organization is minimal (Bumblauskas, et al., 2017). The conversion process from data, to information, to knowledge, and to actionable knowledge, is essential (Bumblauskas, et al., 2017).

However, it is a complicated task when considering the interactions and relationships across industries, organizations, international cultures, and legal parameters.

Additionally, Côrte-Real, et al., (2017) observed that organizational competitive advantage and problem-solving capabilities diminishes with big data analytics as accurate technology and ample organizational resources are essential for the analysis to be effective and applicable. When analyzing big data application from a managerial perspective, big data analytics has been based on the knowledge-based view and the influence on dynamic capabilities, subsequently indicating a positive relationship between information technology and organizational agility (Côrte-Real, et al., 2017).

However, knowledge and information are not always beneficial for businesses, since it is not about how much organizations know, but rather how they use what they know (Côrte-Real, et al., 2017).

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Organizations can apply what they know from data analytics is through effective communications. One way to address this gap could be for both communicators and data users to complete data literacy training in order to enhance the quality of the collected data and information, and ultimately involve the whole organization through effective communication practices based on an active bottom-up strategy to boost the value of big data across the organization as a whole (Côrte-Real, et al., 2017).

2.5 Communication

In the academic and scientific literature, communications and data have historically functioned in an interdisciplinary way. This is particularly evident in the information management publications. For example, one of the cornerstones of today’s business communications methodology, Shannon and Weaver’s Model of Communication (1948) was developed first as a mathematical model. The model originally functioned to explain technical communication around signal processing, or the exchange between sender and receiver. This exchange is, on the most foundational level, the basis of communication. Berlo (1960) amended the model so it became applicable beyond the information technology discipline and into what is now the Sender- Message-Channel-Receiver (SMCR) Model of Communication.

Figure 2. Sender-message-channel-receiver (SMCR) model of communication (Berlo,1960)

The model is structured as a loop where the communication process moves through sender, encoding, message, channel, decoding, receiver, and feedback which is

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ultimately delivered back to the sender (see Figure 2). The sender is considered the start of the communication process and ultimately encodes, creates, and distributes the message to the receiver (Berlo, 1960). The sender is an individual, group, or organization who initiates the communication development process (Sanchez, 1999).

Sanchez (1999) posits the sender is responsible for the success of the message. The sender’s experiences, attitudes, knowledge, skill, perceptions, and culture influence the message (Burnett and Dollar, 1989). The message construction process is called encoding. Translating information into a message in the form of symbols that represent ideas or concepts (Sanchez, 1999). The symbols can take on numerous forms such as languages, words, images, or gestures. Symbols are used to encode ideas into messages that a broader audience can understand (Sanchez, 1999).

The process of encoding involves the sender first making a decision on what needs to be transmitted to the receiver (Burnett and Dollar, 1989). Part of this decision is understanding as much as possible about the receiver (Sanchez, 1999). What knowledge and assumptions does the receive already have? What information does the receiver want from the sender? What language or symbols is the receiver familiar with? Next, in order to transmit the message, the sender utilizes a communications channel. The channel is the mechanism for delivering the message. Selecting an appropriate channel is of equal importance as crafting the message itself (Burnett and Dollar, 1989). If a sender relays a message through an inappropriate channel, its message may not reach the right receivers (Sanchez, 1999). Selecting the right channel will assist in the receiver understanding the full scope of the message. Sanchez (1999) poses key questions to determine which channel is the best fit for a message:

Is the message urgent? Is immediate feedback required (i.e. bi-directional communication)? Is documentation required? Is the content complicated, controversial, or private? Is the message going to someone inside or outside the organization? In some cases, more than one channel is required to effectively reach the receiver.

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Once the appropriate channel(s) is selected, the message enters the decoding stage in the communication process (Sanchez, 1999). At this point, the sender is no longer active and the receiver is responsible for processing, examining, interpreting, and assigning meaning to the message. The communication process is considered successful if the receiver interprets the sender’s message as intended. However, Sanchez (1999) emphasizes that there are many factors that impact the extent to which the receiver will fully comprehend the message: how much the receiver already knows about the topic, their receptivity to the message, the relationship and trust that exists between the sender and receiver. All interpretations by the receiver are ultimately influenced by their experiences, attitudes, knowledge, skills, perceptions, and culture (similar to the sender’s relationship to the encoding process) (Burnett and Dollar, 1989).

The final phase of the process is feedback. After receiving a message, the receiver responds (Berlo, 1960). The signal can take many forms: spoken comment, body language/nonverbal cues, written message, an action, even no response at all, which is, in a sense, a form of response (Bovee and Thill, 1992). including Further, feedback is seen as highly important as it can reveal communication barriers: differences in background, different interpretations of language, words, terminology, or phrases, and differing emotional responses (Bovee and Thill, 1992).

With the integration of big data into the communications landscape, scholars have established theoretical models to describe the process of how to make big data manageable and useful for each of the component spheres of corporate communication. Wiencierz and Röttger (2017) designed a four-stage model in order to describe the process of incorporating big data into corporate communication (see Figure 3).

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29(110) Figure 3. Four phases of strategic big data usage in corporate communication (Wiencierz & Röttger, 2017)

According to Wiencierz and Röttger (2017), Phase 1 articulates the communications problems and objectives, as well as assesses whether big data can realistically address these aims. Phase 2 ensures the reliability of the data by examining the data generation process while also clarifying what type of data is used, how much data is available, the accessibility of the data, how quickly it is being generated, and the authenticity and integrity of the data. Phase 3 concerns the analysis of the big data. After collecting the data, Phase 3 guides the analysis of the data. Finally, Phase 4 evaluates and measures the value added by big data. Throughout Phases 2-4 the communications professional will also be seeking to obtain all relevant stakeholder buy-in and acceptance (Wiencierz and Röttger, 2017).

An essential element of Wiencierz and Röttger (2017) theoretical contributions is in order to understand the potential and limitations of big data applications in the context of corporate communications, it is necessary to consider its three distinct and separate component spheres: marketing communications, public relations, and internal communications.

2.6 Marketing communications

Contextualizing the healthcare system within the big data revolution, and analyzing the marketing decision-making process in healthcare organizations, results in

Communicating big data in the healthcare industry