Organizational Buying Behavior in the Pharmaceutical Industry

Table of Contents

Abstract

Table of Contents

List of Figures

List of Tables

Chapter 1: Introduction
1.1 Background
1.2 Research Questions
1.3 Research Objectives
1.4 Thesis Outline

Chapter 2: Literature Review
2.1 Organizational Buying Behavior
2.2 The Organizational Buying Process
2.2.1 Factors Affecting The Organizational Buying Process
2.2.2 Purchase Decision Drivers
2.3 Participants in The Organizational Buying Process – Buying Center
2.3.1 Decider Typology
2.3.2 Buying Center-Map
2.4 B2B Marketing Approaches
2.4.1 Content Marketing
2.4.2 Thought Leadership

Chapter 3: Methodology
3.1 Guiding Paradigm and Research Design
3.2 Data Collection
3.3 Sample Selection
3.4 Data Analysis and Interpretation
3.5 Ethical Issues
3.6 Quality Criteria in Qualitative Research
3.6.1 Validity
3.6.2 Reliability

Chapter 4: Findings
4.1 Brand Awareness
4.1.1 Direct Vendor-Customer Interaction
4.1.2 Indirect Vendor-Customer Interaction
4.2 Deciding Factors
4.2.1 Product Features
4.2.2 Soft Factors
4.2.3 Economic Factors
4.3 Decision-Making Unit
4.3.1 Scientific Personnel
4.3.2 Non-Scientific Personnel
4.4 Buying Process
4.4.1 Need Identification
4.4.2 Team Building
4.4.3 Evaluation
4.4.4 Decision-Making

Chapter 5: Discussion & Conclusions
5.1 Discussion
5.1.1 The Buying Center
5.1.2 The Buying Process
5.1.3 Deciding Factors
5.2 Marketing Implications
5.3 Conclusions
5.4 Limitations and Future Research

References

Appendix I: Interview Guide

Abstract

The rapid technological advent along with the growing need to increase R&D efficiency in the pharmaceutical industry has brought scientific software into focus. For scientific software vendors to successfully tap into that niche market, it is crucial to grasp the complexities of the dynamic decision-making involved in such buying processes. However, the body of organizational buying literature does not address this niche market. Hence, this qualitative interview study aims at providing insights into this process, its participants, and factors driving their decision-making. A thematic analysis reveals that, for investments involving low six-figure sums and below, the buying process generally progresses through six sequential phases eventually resulting in a purchase followed by a single post-purchase phase. What this study highlights is the importance of scientific users, in spite of their hierarchical level, in the decision-making process. The actual buying decision, which is made by consensus, is positively influenced by a set of three deciding factors: product features, soft factors, and economic factors. Based on these findings, scientific software vendors are encouraged to strive for becoming a thought leader by following a science-heavy content marketing strategy.

List of Figures

Figure 1. Günter's (1993) interactive process model displayed in a circular fashion

Figure 2. Simplified Industrial Adoption Process Model (Ozanne and Churchill, 1971)

Figure 3. Buying Center-Map (Homburg and Jensen, 2004)

Figure 4. The three qualities of thought leadership (Crestodina, 2021)

Figure 5. Thematic Map

Figure 6. The identified buying center composition within this study

Figure 7. Conceptual framework for scientific software acquisition in informal teams

Figure 8. Deciding factors

Figure 9. The relative importance of deciding factors to different buying center members

List of Tables

Table 1. Purchase criteria (Rolfes, 2007, p. 26)

Table 2. Characteristics of content marketing (Hilker, 2017, p. 5)

Table 3. Overview of this study's respondents

Chapter 1: Introduction

1.1 Background

The first step in a pharmaceutical company’s value chain has been coined drug-discovery, and at its core lies the identification of new molecular entities (NMEs) for the treatment of human diseases (Properzi et al., 2020). The overall journey, however, from identifying promising NMEs in early discovery efforts to the successful commercial launch of a drug, is notoriously expensive and time-consuming (Edwards cited in Heid and Ostovich, 2020). In fact, the cost of bringing a new drug to the market has been estimated to be about $2.6 billion (DiMasi, Grabowski, and Hansen, 2016), and R&D costs seem to steadily increase (Properzi et al., 2020). Combined with reduced average forecast peak sales, this development will result in lower return on investment for bringing drugs to the market (ibid.). Hence, pharmaceutical leaders are under pressure to improve R&D productivity while at the same time cutting costs (Ford et al., 2020; PwC, 2020).

It has been emphasized that leveraging computational methods in R&D programs, indeed, may reduce time and costs (Genheden et al., 2017). The importance of computational chemistry in the pharmaceutical industry has been stated before (Selzer, cited in Future Medicinal Chemistry, 2011), and recently the pharmaceutical company Bayer noted “of approximately 20 small-molecule new chemical entities from Bayer HealthCare currently being tested in Phase I clinical trials, at least five originate or carry a definite signature of computational design, and more than half have benefited from it to some extent” (Hillisch, Heinrich, and Wild, 2015). Unsurprisingly, a quarter of pharmaceutical leaders stated the improvement of R&D efficiency as the primary goal of their digital investments in 2020 (Ford et al., 2020).

Considering the rapid advent of computing power, emerging digital technologies such as artificial intelligence or quantum computing have received much attention recently and are hailed as the probable “saviors” of the pharmaceutical industry’s R&D productivity (Properzi et al., 2020; Heid and Ostovich, 2020); this, however, has not happened yet, and it is up for debate when this might be the case (Bender and Cortés-Ciriano, 2021). Computer-aided drug design (CADD) is not nearly limited to these buzzword technologies but comprises a broad range of computational techniques using a vast number of pieces of scientific software.

The market for scientific software in the field of CADD is a niche market. In the commercial space, there are some larger players offering sophisticated platform solutions which cover the whole drug-discovery cycle, and a relatively small variety of highly specialized SMEs.

Within the context of this work, scientific software denotes any software which is used in early discovery programs in the medicinal and computational chemistry departments of large pharmaceutical companies for CADD-purposes. CADD has traditionally been the domain of computational chemists/cheminformaticians, but lately medicinal chemists become involved evermore (Ritchie and McLay, 2012).

Looking at these developments through the lens of a scientific software vendor (SSV), it becomes imperative to understand the intricacies of organizational buying behavior in pharmaceutical companies when it comes to scientific software acquisitions. It is therefore aim of this thesis to understand the organizational buying behavior (OBB) in this context by examining the buying process, identifying its participants, and factors influencing the buying decision. Based on the gathered insights, informed recommendations will be given, how an SSV’s marketing activities should be designed to effectively target its business-to-business (B2B) customer base and why. In doing so, this thesis shall contribute to the wider body of OBB literature.

1.2 Research Questions

Selling organizations in B2B markets, such as SSVs, have a vested interest to understand the external stimuli to which organizational buyers respond. Considering the gap within the OBB literature concerning the pharmaceutical industry, this study aims to understand the buying process of scientific software within this context. So, the overall research question which is to be addressed can be stated as follows:

How should a scientific software vendor design its marketing activities to effectively boost its sales numbers taking into account the multifacetedness of organizational buying behavior in the pharmaceutical industry?

To answer this broadly defined question, more specified questions have to be phrased. Thus, this research question can be broken down into the following questions:

- How to picture the buying process of acquiring scientific software and factors affecting the buying decision in pharmaceutical companies?
- Who, i.e., which roles within a pharmaceutical company, are the participants and key influencers in scientific software acquisitions?
- To whom, i.e., to which roles within a pharmaceutical company, should an SSV tailor its marketing efforts, and how?

1.3 Research Objectives

Based on the preceding discussion the following four research objectives have been formulated:

- To examine the intricacies of organizational buying behavior in the context of pharmaceutical companies when acquiring scientific software.
- To identify whether and how certain roles within a pharmaceutical company should be targeted individually in a scientific software acquisition process.
- To develop a link between objective 1 and 2, and literature-known theories.
- To draw conclusions from the prior stated research objectives to make informed recommendations regarding SSVs’ marketing efforts.

1.4 Thesis Outline

This study is divided into five chapters. Chapter 2 sets out the theoretical fundament of this work. In Chapter 3 the methodological approach, which this thesis followed, is outlined. Chapter 4 presents the findings of this study in a thematic map. And lastly, Chapter 5 discusses these findings by making use of literature-known theories, eventually concluding in marketing implications and limitations of this study.

Chapter 2: Literature Review

This chapter presents the theoretical foundations of this research. It starts by highlighting the concept of organizational buying behavior, and then explores in-depth the organizational buying process, the buying center concept, and ends by briefly touching content marketing and thought leadership.

2.1 Organizational Buying Behavior

As Armstrong et al. (2017, p. 218) put it, organizational buying “is the decision process by which business buyers determine which products and services their organizations need to purchase and then find, evaluate, and choose among alternative suppliers and brands”. Business-to-business marketers are faced with the task to effectively tailor their marketing activities to respond to these organizational buyers’ needs and wants. Hence, an understanding of organizational buying behavior is vital in order to succeed as a selling organization. However, the “multi-phased” and “multi-person” nature of organizational buying render achieving such an understanding a difficult task (Möller, 1985).

OBB has long been a topic of interest to academic scholars and practitioners alike, and, thus, has been subjected to decades of research endeavors. In the late 1960s, an increasing interest in the field of organizational buying processes emerged after Robinson, Faris, and Wind (1967) published their seminal work “Industrial Buying and Creative Marketing” (Johnston and Lewin, 1996). Soon thereafter, Webster and Wind (1972) came up with a “General Model for Understanding Organizational Buying Behavior”. These two important works, together with Sheth’s (1973) “Model of Industrial Buyer Behavior”, are often referred to as the conceptual groundwork in the field of organizational buying behavior (Johnston and Lewin, 1996). Since the publication of these “original models” of OBB, as Johnston and Lewin (1996) termed them, a plethora of academic scholars rely in their OBB studies on them as a theoretical backbone; even recent studies bear upon the aforementioned original models (Barclay and Bunn, 2006; Rajala and Tidström, 2017). Yet, it has been argued that the original models of OBB are outdated (Tanner, 1999). Sheth (1996), for example, noted a shift in OBB from transaction- to relation-focused; hence, he predicts a majority of OBB-related research to “become obsolete” eventually. Nonetheless, this notion is controversial among academic scholars. Wind and Thomas (2010) are convinced of the validity of the original models even in today’s interdependent business world. Instead of leaving the conceptual models behind, Tanner (1999) proposes to utilize them in the analysis of “transaction histories that make up relationships”.

2.2 The Organizational Buying Process

Between a business buyer’s initial need recognition to acquire a certain product or service and the final buying decision, a complex multi-phased process may take place (Havaldar, 2009). The academic literature offers many models of varying complexity, describing this phenomenon (Wind and Thomas, 1980; Juha and Pentti, 2008). While Webster’s model (1965) distinguishes merely four distinct phases, Wind’s (1978) model incorporates twelve phases. What the majority of these models share, is the rather rational, systematic, and formal treatment of such buying processes as compared to consumer buying processes (Homburg, 2020, p. 160). The so-called buygrid framework, introduced by Robinson, Faris, and Wind in 1967, suggests the number of buyphases is inextricably linked to the type of buying situation (or buyphase). Three types of buying situations were identified, each of which exhibits particular characteristics: new task, modified rebuy, and straight rebuy. Classification of buying situations into types took place according to the following three factors (Anderson, Chu, and Weitz, 1987):

- the purchase situation’s “newness” to the buying decision makers
- the buying decision makers’ “information requirements”
- the degree of “consideration of new alternatives” by the buying decision makers

Depending on the type of buying situation, up to eight sequential buyphases can occur, i.e., both the number as well as the “activities within each phase” might differ significantly between the different buying situations (Wind and Thomas, 1996). This eight-phase model has been criticized for lacking interactive features, thereby not taking into account buyer-seller relationships (Günter, 1993, p. 200). Hence, Günter (1993, p. 201) proposed a modified eight-phase model which emphasizes the interactive nature of the buying process (Figure 1).

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Figure 1. Günter's (1993) interactive process model displayed in a circular fashion.

When Ozanne and Churchill (1971) studied the diffusion and adoption of innovations they noted “this adoption process is nothing more than a five-stage decision process leading to the purchase of an innovation” (Figure 2). Their conceptual model of the process of adoption incorporates five dimensions: activating factors, which initiate the process, purchase-directing factors, which influence the final buying decision, the duration of the decision-making process, the consideration of alternatives, and the use of information sources. While this framework resulted only in weak statistical correlations, it highlights a noteworthy link between the buying decision process and the adoption of innovation.

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Figure 2. Simplified Industrial Adoption Process Model (Ozanne and Churchill, 1971).

Recent studies, however, often deviate from the view that the organizational buying process progresses linearly and rationally through a set of distinct buying phases. Makkonen et al. (2012) depict organizational buying as muddling through, which, in this context, is described as a situation where “buyers […] rather incrementally move away from a problematic situation that actually comprises a cluster of interlocked problems”. Grewal et al. (2015) on the other hand postulate that nowadays organizational buying comprises the simultaneous occurrence of four processes: implementation, evaluation, reassessment, and confirmation.

Most of the previously presented models neglect the selling party in the organizational buying process. Considering the increasing firm interdependencies taking a dyadic approach has already been proposed in the 1970s (Bonoma, Zaltman, and Johnson, 1977). In this context, the IMP Group’s interaction model emphasizes the importance of relationships, i.e., it explains organizational buying processes as being embedded in long-term business relationships (Homburg, 2020, p. 174). In this context, a long-term business relationship is defined as a series of consecutive transactions which are interconnected (Plinke, 1989, cited in Backhaus and Voeth, 2005, p. 510).

The specifics of software acquisition processes have only sparsely been studied (Palanisamy et al., 2010); however, it has been shown that the original models of OBB may be used to understand the enterprise software acquisition process (ibid.). Other scholars who have done research in this area, also based their conceptual frameworks on the traditional notion of a linearly progressing process comprising several phases (Rounds, 1992; Hlupic and Paul, 1996; Chau, 1999; Shainesh, 2004), which quite closely resemble those of the traditional models.

2.2.1 Factors Affecting The Organizational Buying Process

OBB is affected by a complex combination of various factors. Many studies, conceptual as well as empirical, have addressed these influential variables (Homberg, 2020, p. 180). In this context, Johnston and Lewin’s (1996) “integrated model of OBB” represents the most comprehensive work. This model combines nine “constructs” (environmental, organizational, group, participant, purchase, seller, conflict/negotiation, informational and process or stages) identified within the original conceptual models of OBB and extends these with two additional constructs (decision rules and role stress). Homberg (2020, p. 180) took a different approach and identified four key parameters affecting the buying process:

- the purchase situation’s newness
- the purchase situation’s or the product’s complexity
- the product’s economic importance
- the perceived risk of the purchase situation

In other words, any organizational buying situation can be characterized by these parameters (Lewin and Donthu, 2005). Given the rather large possible variation in each of these parameters, a myriad of different buying situations is imaginable; however, certain trends have been observed which proved empirically true for all of them (Homberg, 2020, p. 180pp). It must be noted, though, that a number of such correlations is based on weak statistics, and meta-analyses sometimes report contradictory results (Lewin and Donthu, 2005). Elucidating all of these correlations goes beyond the scope of this work, but two of them shall be highlighted here as noteworthy examples.

With increasing newness, complexity, economic importance, or perceived risk, the buying center’s information needs increase significantly (McQuiston, 1989; Bunn, 1993; Hunter, Bunn, and Perreault, 2006). Commonly this is associated with an active information-seeking behavior, making use of a plethora of information sources (Hutt and Speh, 2012, p. 84). Additionally, an increase in any of the four key parameters seems to spark the wish in organizational buyers to encounter a sales representative of the supplier (Nerdinger, 2001, cited in Homburg, 2020, p. 181p).

2.2.2 Purchase Decision Drivers

The notion of purchase decision drivers is of particular relevance to business marketers (Ozanne and Churchill, 1971), as knowledge of their nature and relative weighting of these allows tailoring marketing efforts to the customers’ specific needs (Rolfes, 2007, p. 26). It is generally assumed, that different members of a buying center each have their individual priorities when it comes to purchase decision drivers (ibid.). In Table 1 these drivers are presented a set of general purchase criteria.

Table 1. Purchase criteria (Rolfes, 2007, p. 26).

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Loebbecke et al. (2010) have studied the relative importance of information-related as well as feature-related purchase decision drivers in B2B software acquisitions. They have empirically found that the information-related drivers customer references and expert network recommendations as well as the feature-related drivers price performance, functionality, and sales team service positively influence the buying decision, while the information-related driver demonstration team presentation does not seem to have an impact at all.

2.3 Participants in The Organizational Buying Process – Buying Center

As has already been mentioned above, the organizational buying process is not only of multi-phased nature but also involves multiple participants. The amalgamation of people involved in this process was coined an organization’s buying center by some (Robinson, Faris, and Wind, 1967; Johnston and Bonoma, 1981; Lilien and Wong, 1984), while others call it the decision-making unit (Wilson, 2000). Such buying centers, unlike common organizational departments, are loose inter-departmental units which are not listed in an organization’s organigram (Kotler et al., 2020, p. 179). Notably, early research works have treated buying centers as static entities, but it is now commonly accepted that buying centers’ “composition and functioning are fluid” (Ghingold and Wilson, 1998). Members of a buying center can slip into a number of roles within the context of the buying process (Webster and Wind, 1972):

(1) Gatekeepers are the ones who control the flow of information. While they only have an indirect bearing on the buying decision, gatekeepers have some sort of filtering function by deciding which information from the outside, e.g., business marketers, pass through to the other members of a buying center (ibid.).
(2) Users are the ones who will eventually use the product or service under scrutiny in their day-to-day jobs. Frequently, the user role sets a buying process in motion (ibid.). Their expert knowledge usually gives them a pivotal role in determining certain specifications (Foscht and Swoboda, 2004, p. 263).
(3) Influencers are participants in the buying process who influence the buying decision by providing information and criteria to assess a product or service (ibid.). They do not necessarily belong to the part of the work force which will be using the product or service, but in most cases, influencers belong to the technical personnel (Webster and Wind, 1972).
(4) Deciders are those persons within a buying center, who, independent of the “formal allocation of competences”, have the authority to make the final buying decision (Foscht and Swoboda, 2004, p. 263). Depending on the type of buying situation, deciders commonly belong to the purchasing department (routine purchases) or senior management (large-scale investments) (Garrido-Samaniego and Gutiérrez-Cillán, 2004).
(5) Buyers have the formal authority to select vendors, negotiate terms, and complete the purchase (Webster and Wind, 1972). Yet, their scope of decision-making is rather narrow due to the influence of a buying center’s other members (Rolfes, 2007, p. 42). Buyers are, in many cases, associated with procurement.

Rolfes (2007, p. 43) argued that compared to users and buyers who can be identified relatively effortless, gatekeepers, influencers, and deciders are much more difficult to identify following the higher level of abstraction in their definitions. While users and buyers are defined based on their organizational tasks, the other roles are defined by their responsibilities in the buying process and their formal authority. What further complicates this issue is the fact that one person can have multiple roles in a buying center at the same time, and one role can be taken on by multiple persons at the same time (ibid.).

Bonoma (1982) suggested the addition of a sixth role to Webster and Wind’s (1967) original role concept of the buying center: the initiator.

(7) Initiators are those persons within an organization who first note the need for a new investment, and subsequently initiate the buying process (Foscht and Swoboda, 2004, p. 263). It is not uncommon that the initiator role is taken on by a user (Garrido-Samaniego and Gutiérrez-Cillán, 2004).

As an extension to the decider role, Bagozzi et al. (2000, cited in Rolfes, 2007, p. 50) defined the approver role.

(8) Approvers authorize the buying decision made by deciders taking into account the concerns of influencers and users (ibid.).

When looking at the actors of a buying center, another differentiation between so-called promotors and opponents can be made (Homburg, 2020, p. 157).

(9) Promotors actively support the purchase of a certain product or service (ibid.).
(10) Opponents hamper and decelerate the buying process (ibid.)

It is useful to further divide promotors into different types according to the underlying reasons for their authority in the buying center (ibid.).

- Promotors by power are influential in the buying center due to their hierarchical position (Homburg, 2020, p. 157).
- Promotors by know-how are influential in the buying center due to their technical expertise. Their hierarchical position is insignificant (Homburg, 2020, p. 158).

2.3.1 Decider Typology

Strothmann (1979, cited in Gaggl, 2014, p. 18) classified buying center members according to their information behavior, which is a non-task-based typology but rather relies on personal traits (Rolfes, 2007, p. 58). Based on how buying center members look for information, three different characters are described:

The “literary-scientific”-type works in a scientific and systematic manner; hence, this type demands detailed written information from professional journals and similar impersonal information sources (Kohr, 2000, p. 96). Conversations with sales representatives of suppliers will only be considered when written information are analyzed exhaustively (Rolfes, 2007, p. 58).

The “objective-judging”-type is pleased with only an acceptable level of information from a variety of different sources, either personal or impersonal (Kohr, 2000, p. 96).

The “spontaneous-passive”-type has an aversion against searching systematically for information; thus, settles for a lower depth of information without actively seeking more details. This type’s preferential sources of information are personal, e.g., conversations (Kohr, 2000, p. 97).

2.3.2 Buying Center-Map

For business marketers to know on whom in a buying center to concentrate their marketing efforts, Homburg and Jensen (2004) suggested the creation of so-called “decider profiles”. These profiles can be visualized in a buying center-map (Figure 3). This map shows various buying center members traits, such as their valuation of technical vs. economic arguments. “Value seekers”, for example, are promising targets of individually tailored marketing activities, while “purists” are commonly much more difficult to appeal to (Homburg and Jensen, 2004).

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Figure 3. Buying Center-Map (Homburg and Jensen, 2004).

2.4 B2B Marketing Approaches

Business marketers face the difficult task to grasp the dynamic decision-making process as well as the influence of individual members of the buying center. Therefore, ascertaining a buying center’s composition as early on as possible during a buying process, can offer huge advantages to a selling firm: individually tailored marketing activities addressing key members of the buying center will make optimal use of resources; thereby avoid wasting them on less influential buying center members (Ghingold and Wilson, 1988). Especially during the early stages of a buying process, buying center members are prone to be affected by marketing efforts (ibid.). Hence, a buying center’s initial composition is of relevance to business marketers. What renders this task extraordinarily challenging, are the buying center’s cloud-like boundaries and the varying influence of buying center individuals during the purchasing process (Spekman and Gronhaug, 1986).

While it has been recognized that marketing capabilities are vital to a software firm’s success (Li et al., 2010, cited in Parry, Kupiec-Teahan, and Rowley, 2011), research into the marketing of software is scarce (Parry et al., 2012). Hence, in the following two common B2B marketing approaches are illustrated.

2.4.1 Content Marketing

Marketing efforts in the B2B realm often resort to content marketing (CM), which can be, after Holliman and Rowley (2014), defined as “creating, distributing and sharing relevant, compelling and timely content to engage customers at the appropriate point in their buying consideration processes, such that it encourages them to convert to a business building outcome”. As the cornerstone of CM, content denotes all information with added value to potential customers which business marketers use strategically to engage with them (Wang et al., 2017). Both, CM as well as classical marketing instruments address customers’ needs and wants, but there is a significant difference: while classical marketing instruments merely promise fulfilling that need, CM actually fulfills that need by providing content with added value (Hilker, 2017, p. 3). Hence, CM is characterized by the absence of selling messages but having a customer-centric perspective (Holliman and Rowley, 2014). As such, CM is an example of the so-called pull strategy (Hilker, 2017, p. 5), i.e., customers are pulled to the product or service of the selling party (Table 2). Recognizing an added value, which is independent of a purchase, in the provided content, may raise the customer’s interest in the actual product (ibid.).

Table 2. Characteristics of content marketing (Hilker, 2017, p. 5).

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The rapid advent of the internet and the exponential growth of social media also drove business marketers’ interest in “producing and curating digital content” (Kilgour, Sasser, and Larke, 2015, cited in Wang et al., 2017), which has become known as digital content marketing. Indeed, CM is commonly associated with marketing efforts which take place only in the digital world. But in fact, content can be delivered not only in the digital environment via, for example, blog posts, white papers, and webinars, but also in the physical environment via conferences (Wang et al., 2017).

2.4.2 Thought Leadership

When examining CM, one eventually bumps into the term thought leadership (TL). According to Brennan and Croft (2012) CM is simply a means to position oneself as a thought leader. But the question arises: what is a thought leader? While this term is commonly used in practitioner books and blog posts, it has, so far, only gained limited attention in the academic community (Barry and Gironda, 2019). Just recently, however, a study by Harvey et al. (2021) substantiated the importance of TL by linking it to “reputation building, brand awareness, differentiation, visibility, and influence”. Employing a grounded theory approach, Barry and Gironda (2019) have been the first academic scholars who have operationalized TL. In fact, they suggest two dimensions of TL: “an outcome-oriented dimension (recognized for) and a competency dimension (recognized as)” (ibid.). Harvey et al. (2021) on the other hand, defined TL as “knowledge from a trusted, eminent and authoritative source that is actionable and provides valuable solutions for stakeholders”. Hence, being recognized as a thought leader is a strategic advantage, thereby converting a brand “from a commodity supplier to a trusted advisor” (Wizdo, 2013). According to Crestodina (2021) a thought leader has to master three qualities (Figure 4). The key takeaway is, to become a thought leader a company should not only offer expert insights, but also express a strong opinion, and develop a digital brand presence.

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