Scholars have widely explored the role of consultants and tools in market-making (Chiapello & Gilbert, Reference Chiapello and Gilbert2019; Gond & Brès, Reference Gond and Brès2020), highlighting how markets do not emerge in isolation but are actively shaped by various instruments and frameworks (Ahrne et al., Reference Ahrne, Aspers and Brunsson2015; Geiger et al., Reference Geiger, Mason, Pollock, Roscoe and Ryan2024). Research emphasises the performative effects of such tools, particularly in how they structure decision-making and influence market trajectories (Muniesa et al., Reference Muniesa, Millo and Callon2007; Callon, Reference Callon2021). One increasingly influential but underexamined force in this process is hype. Hype has become a defining feature of market-making, particularly in new technology markets, where it amplifies attention, fuels expectations, and directs investment flows (Grodal & Granqvist, Reference Grodal, Granqvist, Ashkanasy, Zerbe and Härtel2014). It channels market actors toward ‘hot markets’ (Pontikes & Barnett, Reference Pontikes and Barnett2017) – fields perceived as highly promising and potentially lucrative – while amplifying their fear of missing out (FOMO). However, while scholars have explored the role of tools in structuring markets, the specific tools designed to interpret and navigate hype – and their influence on market-making – remain largely unexamined.

Navigating hype, as Chapter 3 argued, has become a crucial yet complex challenge for market actors across industries (Garud et al., Reference Garud, Lant and Schildt2021). The difficulty stems from the rapid rise of hyped markets and their inevitable downturn, a pattern commonly referred to as a ‘hype cycle’ (van Lente et al., Reference Van Lente, Spitters and Peine2013). Moreover, hype operates at the intersection of calculative and affective dimensions, shaping decision-making by introducing new information while simultaneously triggering strong emotions such as excitement and fear – what Beckert (Reference Beckert2016), following Keynes (Reference Keynes1936), terms ‘animal spirits’. For example, a manager evaluating an emerging technology may feel exhilarated by the potential for first-mover advantage, driven by the promise of competitive gains. At the same time, the manager may hesitate, fearing premature investment in an unproven innovation (Arnold et al., Reference Arnold, Breitenmoser, Röth and Spieth2022). This tension highlights the central challenge of timing market entry. As Kumaraswamy et al. (Reference Kumaraswamy, Garud and Ansari2018, p. 1030) observe, determining when and whether to enter a hyped market requires actors to ‘cultivate the capacity’ to interpret the ‘weak signals’ embedded within hype and act ‘before it is too late’.

The growing significance of hype in market-making has led consultants and analysts to develop specialised tools – what, in this chapter, we term ‘hype tools’ – to help stakeholders navigate hype cycles and strategically respond to hype as a market-making force. These frameworks have also become commercial offerings, showing how the task of taming hype is packaged and sold as part of a business of future-oriented advice. Hype tools are an example of the kind of promissory product described in Chapter 3 – crafted frameworks that structure uncertainty and market expectations. Yet, despite their increasing influence, the design and operationalisation of these tools remain largely unexplored, particularly the tensions that arise at the intersection of hype’s calculative and affective dimensions. Existing research has examined how hype fuels market momentum, creating high-growth markets while simultaneously rendering others ‘passé’ (Pontikes & Barnett, Reference Pontikes and Barnett2017). Yet, little is known about how these cycles are actively mediated by the very tools that claim to map them. As Beckert (Reference Beckert2016) suggests, examining these tools is crucial, as they do not merely reflect hype but actively shape how it unfolds, influencing market actors’ perceptions, decisions, and strategies.

To advance this enquiry, we theorise how analysts transform hype into a structured market-shaping framework that market actors can interpret and act upon. By developing hype tools, consultants and analysts aim to strike a balance between excitement and calculation, enabling technology adopters and investors to navigate hype cycles effectively. However, in designing these tools, consultants themselves must contend with the tensions inherent in hype’s dual nature – its affective intensity and its calculative dimensions. To examine how they manage this challenge, we draw on Latour’s (2012) concept of ‘purification’, which captures the effort to reconcile and structure the interplay between hype’s emotional and more calculative aspects. This perspective leads us to ask: How do consultants design and operationalise tools to enable market actors to leverage hype in markets for novel technology?

To address this question, we conducted a qualitative study of the original design and subsequent production and maintenance of the Hype Cycle Chart (HCC), a widely recognised tool for assessing how emerging technologies progress through a hype cycle and shaping how market actors evaluate the timing of investment in new technology markets (van Lente et al., Reference Van Lente, Spitters and Peine2013). The HCC has ‘gained substantial attention from practitioners’ (Dedehayir & Steinert, Reference Dedehayir and Steinert2016, p. 34), ‘facilitated strategic investment decisions’ (van Lente et al., Reference Van Lente, Spitters and Peine2013, p. 1615), and particularly influenced ‘large companies’ R&D decisions’ (Steinert & Lefier, Reference Steinert, Leifer, Kocaoglu, Anderson, Daim and Bayus2010, p. 2). Based on this analysis, we propose a model that highlights three mechanisms – balancing, reconciling, and timing – through which analysts frame hype not as speculative noise but as a structured and staged phenomenon that market actors can engage with. We term this ‘hype purification’, illustrating how analysts manage the interplay between excitement and calculability to help market actors navigate hyped markets.

6.1 Tools and Market-Making

Scholars widely acknowledge that ‘markets do not emerge out of a vacuum’ (Ahrne et al., Reference Ahrne, Aspers and Brunsson2015, p. 9) but are actively ‘made and shaped by actors of all kinds’ (Geiger et al., Reference Geiger, Mason, Pollock, Roscoe and Ryan2024, p. 6). Specifically, research has highlighted how tools, defined broadly as ‘frameworks, concepts, models, or methods’ (Jarzabkowski & Kaplan, Reference Jarzabkowski and Kaplan2015, p. 538), often play a leading role in facilitating market-making processes through ‘equipping’ actors (Callon, Reference Callon2021) with the means to navigate markets (MacKenzie & Millo, Reference MacKenzie and Millo2003) and structuring decision-making, particular decisions aimed at the future (Miller & O’Leary, Reference Miller and O’Leary2007). Building on this foundation, several key themes have emerged. One prominent theme explores how tools do more than support rational decision-making; they engage in ‘qualculation’ (Cochoy, Reference Cochoy2008) – a process that blends qualitative judgements with numerical calculations to shape market realities (Callon & Law, Reference Callon and Law2005). Another theme has explored how tools act as ‘market devices’, defined as the ‘material and discursive assemblages that intervene in the construction of markets’ (Muniesa et al., Reference Muniesa, Millo and Callon2007, p. 2). A further theme has developed around the ‘performative’ role of market devices, where tools are not merely passive representations of markets but actively shape and influence them (Callon, 1998; Muniesa et al., Reference Muniesa, Millo and Callon2007). For example, MacKenzie and Millo (Reference MacKenzie and Millo2003) examined the mathematical models underpinning financial derivatives, demonstrating that such tools are not external to markets but fundamental to their very construction.

However, while researching tool performativity remains important, scholars caution that focusing exclusively on performative effects risks neglecting the equally critical question of how tools are initially designed and constructed in the first place. As MacKenzie and Spears (Reference MacKenzie and Spears2014) argue, prioritising performativity alone ‘occludes attention’ to the processes through which tools are developed, potentially obscuring the underlying assumptions embedded in their design and designers’ intentions in shaping market dynamics. Following this approach, Gond and Brès (Reference Gond and Brès2020), studying the development of the Corporate Social Responsibility (CSR) market, highlight consultants’ dual role as both ‘tool designers’ and ‘market builders’, demonstrating how they crafted tools to reframe concerns initially seen as ‘peripheral’ within markets and organisations (CSR products and services), ultimately transforming them into ‘mainstream’ priorities. This shift from tool deployment to tool construction is also evident in Pollock and D’Adderio’s (Reference Pollock and D’Adderio2012) examination of Gartner’s Magic Quadrant tool. While their research underscored the tool’s performative role in shaping market behaviour, it also revealed how its ‘format and furniture’ – the design principles and classificatory mechanisms embedded within it – were deliberately engineered to structure market competition from the outset.

6.2 Hype and Market-Making

Scholars have long linked hype to adjacent ideas – passionate interests (Hirschman, Reference Hirschman2013), fads and fashions (Abrahamson & Fairchild, Reference Abrahamson and Fairchild1999), contagious stories (Shiller, Reference Shiller2019), projective storytelling (Garud et al., Reference Garud, Gehman and Giuliani2014), speculative bubbles (Goldfarb & Kirsch, Reference Goldfarb and Kirsch2019), and fictional expectations (Beckert, Reference Beckert2016). Earlier chapters reviewed this foundational work in depth; here we focus on what that literature leaves unexplored: the role of hype in early market formation.

Recent studies highlight hype’s pervasive role as an ‘integral part of the life’ of all emerging technology markets (Rip, Reference Rip, Fisher, Jones and von Schomberg2006, p. 355) and an ‘important element’ of market emergence (Grodal & Granqvist, Reference Grodal, Granqvist, Ashkanasy, Zerbe and Härtel2014, p. 142). Innovators and entrepreneurs often employ hyperbolic claims to generate enthusiasm among investors and early adopters, with van Lente (Reference Van Lente2012) theorising hype as a ‘resource’ that legitimises emerging technology markets and justifies their support. In this sense, hype builds excitement and channels the movement of key actors – such as investors and innovators – into a market (Valliere & Peterson, Reference Valliere and Peterson2004). As Wüstenhagen et al. (Reference Wüstenhagen, Wuebker, Bürer, Goddard, Truffer, Markard, Wüstenhagen and Wiek2009, p. 123) note, ‘[a]ttracting both talent and capital seems to be easier in industries or firms where expectations are high’, a dynamic echoed by Valliere and Peterson (Reference Valliere and Peterson2004, p. 15), who found that investors are ‘drawn to where the largest growth opportunities could be found’, with ‘hype serv[ing] as an indicator of these areas’.

Hype derives its effectiveness from its ‘collective’ nature (van Lente, Reference Van Lente2012). The more diverse the stakeholders who engage with and amplify hype, the more momentum it gains as a persuasive force, shaping market perceptions and attracting investment. When hype attains widespread attention, it fosters what Grodal and Granqvist (Reference Grodal, Granqvist, Ashkanasy, Zerbe and Härtel2014, p. 143) describe as ‘collective excitement’, where self-perpetuating promissory narratives ‘give rise to fads, hypes, and bubbles’. Moreover, Pontikes, and Barnett (Reference Pontikes and Barnett2017) highlight how the rapid escalation of hype can fuel the emergence of ‘hot markets’ – nascent industries that gain widespread attention due to their perceived potential and profitability. These markets serve as ‘attractive points of entry’, drawing in market actors particularly enticed by the surrounding ‘buzz’ (Pontikes & Barnett, Reference Pontikes and Barnett2017, p. 147).

Navigating hyped markets is inherently challenging, as hype can distort decision-making by blurring the line between genuine opportunities and inflated promises. As Grodal and Granqvist (Reference Grodal, Granqvist, Ashkanasy, Zerbe and Härtel2014, p. 143) observe, hype constrains market actors’ ability to ‘make decisions’, particularly in the early stages of an emerging technology market when uncertainty is at its peak. To mitigate risk, some actors adopt a ‘waiting game’ strategy (Robinson et al., Reference Robinson, Le Masson and Weil2012; Endenich et al., Reference Endenich, Hahn, Reimsbach and Wickert2022), delaying investment to avoid what Kumaraswamy et al. (Reference Kumaraswamy, Garud and Ansari2018) term ‘errors of commission’ – prematurely investing in overhyped technologies that ultimately fail. However, while early-stage investments carry the highest risk of failure, they also offer the greatest potential for profit and growth (Knight, Reference Knight1921). To avoid ‘errors of omission’ – missing out on transformative innovations (Kumaraswamy et al., Reference Kumaraswamy, Garud and Ansari2018) – early adopters must often commit before clear evidence of a technology’s success emerges. These competing risks underscore hype’s dual role in market decision-making, raising a critical question: Does it obscure judgement or provide valuable strategic insight?

Research has begun to examine hype as a key driver of decision-making, yet its role remains highly contested. Many studies link hype to irrationality and incalculability, arguing that it distorts decision-making processes. For instance, Shi and Herniman (Reference Shi and Herniman2023, p. 6) highlight that while hype can be ‘instrumental in decision-making’, it often presents ‘less accurate information about an innovation’, making it difficult for actors to make well-informed choices. Similarly, Wüstenhagen et al. (Reference Wüstenhagen, Wuebker, Bürer, Goddard, Truffer, Markard, Wüstenhagen and Wiek2009) argue that hype fosters non-reflective thinking and ‘herd behaviour’, where actors follow market trends without rigorous evaluation. As a result, hype is frequently positioned in opposition to rational decision-making, often dismissed as ‘communications that lack factual, rational substance’ (Kiefer & Hunt, Reference Kiefer, Hunt and Laskin2017, p. 168).

Despite its association with uncertainty and speculation, a recent study in the Market Studies literature (Geiger et al., Reference Geiger, Mason, Pollock, Roscoe and Ryan2024) has highlighted the powerful affective reactions that hype can generate, demonstrating that it remains a significant force in shaping markets and driving technology investment. With its ties to passionate interests (Hirschman, Reference Hirschman2013), excitement, and future-oriented expectations rooted in technological promises, hype can be seen as a crucial yet underexplored mechanism in the ‘vascularisation’ (Gond & Brès, Reference Gond and Brès2020; Callon, Reference Callon2021) of the economy – pumping energy, attention, and resources into emerging fields. For instance, scholars argue that hype fuels economic activity by creating an ‘evocative overload’ of what Beckert (Reference Beckert2016, p. 13) terms ‘fictional expectations’ – projected futures that drive present-day investment decisions. Drawing on Keynes’ (Reference Keynes1936) concept of ‘animal spirits’, it is suggested that emotions such as ‘excitement’ and ‘fear’ can spur bold decision-making and high-stakes risk-taking in uncertain environments, even when such actions might appear irrational.

6.3 Role of Consulting Tools in Navigating Hype

Despite growing recognition that hype is not just a by-product but a crucial driver of market-making, existing research has largely overlooked how consultants actively mediate hype through specialised tools. To manage the challenge of navigating hype, market actors increasingly turn to tools like the HCC, which claim to structure hype by offering frameworks for distinguishing between exaggerated expectations and genuine opportunities. While the Sociology of Expectations has emphasised the impact of tools in shaping technological expectations (Borup et al., Reference Borup, Brown, Konrad and van Lente2006; Konrad & Alvial-Palavicino, Reference Konrad, Alvial-Palavicino, Konrad, Rohracher and von Schomberg2017), far less attention has been given to their actual design and operationalisation. Existing studies acknowledge that tools like the HCC provide structured mechanisms for action (Dedehayir & Steinert, Reference Dedehayir and Steinert2016). However, while these studies highlight the role of hype tools in shaping expectations, they do not examine how these tools are developed, maintained, and refined in response to market dynamics.

To address this gap, our research shifts the focus from merely acknowledging the existence of these tools to examining their design and operationalisation. To explore this, we draw on Latour’s (2012) concept of ‘purification’, which describes the attempt to categorise and separate domains – such as science and society, or in this case, calculation and affect – to maintain the illusion of distinct, independent spheres of action. Purification is central to how consultants frame their role in managing hype. For instance, consultants claim that the HCC can ‘separate hype from the real drivers of a technology’s commercial promise’ (Gartner, Reference Gartner2018), positioning it as an instrument of neutral evaluation.

However, Latour’s (2012) thesis is that purification is never entirely successful – in an enduringly non-modern ‘hybrid’ world, purification efforts are constantly confronted by a parallel process of ‘hybridisation’, where there is the simultaneous mixing and entanglement of domains that purification attempts to separate. Little empirical research has explored how consultants grapple with this duality – balancing the affective pull of hype with its calculative dimensions – when designing these tools. This leads us to ask: How do consultants design and operationalise tools to enable market actors to leverage hype in markets for novel technology?

6.3.1 A Note on the Hype Cycle Chart (HCC)

The concept of a ‘hype cycle’ is a relatively recent innovation, first introduced in 1995 by Jackie Fenn, a Gartner consultant, during her first year at the firm. Fenn coined the term and developed the tool to illustrate the dramatic rise in excitement and subsequent disillusionment surrounding emerging technologies, framing it as a predictable and repeatable process. She observed a recurring pattern among emerging technologies and ‘drew a graph illustrating the ups and downs of this cycle’ (Fenn & Raskino, Reference Fenn and Raskino2008, p. 8). She then mapped out each stage of the process and populated the curve with examples of technologies (Burkhardt, n.d.).

In developing the HCC, Fenn drew inspiration from two key sources. To name the cycle’s stages, she turned to theological fiction, particularly John Bunyan’s The Pilgrim’s Progress (Fenn, interview). She compared the trajectory of hype cycles to a journey, beginning with an ‘Innovation Trigger’, that sparks attention and generates hyped, often unrealistic expectations. When these expectations fail to materialise, they collapse, moving from the ‘Peak of Inflated Expectations’ into the ‘Trough of Disillusionment’.

Fenn also drew on economic theories, particularly Kondratiev Waves and Schumpeterian creative destruction (Grodal et al., Reference Grodal, Krabbe and Chang-Zunino2023). While traditional economic cycles involve a series of ‘consecutive up and down movements’ (Fenn & Raskino, Reference Fenn and Raskino2008, p. xiv), Fenn argued that hype cycles feature a distinct stabilisation phase. As technologies mature, they enter the ‘Slope of Enlightenment’, where gradual improvements accumulate through experience. Finally, when the broader benefits of these innovations become widely recognised, they achieve the ‘Plateau of Productivity’ (see Figure 6.1).

Figure 6.1

The Hype Cycle Chart (HCC).

Figure 6.1 Long description

A line graph of expectations versus time plots a curve with five labelled stages. 1. Innovation trigger: marked by a steep rise terminating in a peak. 2. Peak of inflated expectations: the termination peak of innovation trigger. 3. Trough of disillusionment: the peak of inflated expectations drops to a trough. 4. Slope of enlightenment: The trough of disillusionment proceeds to a concave down increasing curve labelled slope of enlightenment. 5. Plateau of productivity: The concave down increasing curve of enlightenment plateaus.

Source: Author representation

The HCC evolved organically as other Gartner analysts ‘used the framework to put their own sets of technologies, their own domains on it’ (Fenn, interview). Its visibility surged when Alexander Droibik, a fellow Gartner analyst, incorporated the HCC into his 1999 e-business analysis to ‘predict the 2001 dotcom bust’ (Fenn, interview). Fenn describes this as a ‘really nice validation’ of the tool and a pivotal moment that propelled the HCC into global prominence (Fenn, interview).

The HCC is primarily designed for Gartner clients, particularly organisational technology adopters, who seek to navigate the evolving business solutions landscape and identify the most promising opportunities among emerging innovations that remain untested or underdeveloped. Gartner faces the challenge of effectively engaging this audience and demonstrating the value of the HCC and its advisory services in guiding strategic investment decisions. Given that Gartner’s business model relies on expanding its subscription services through personalised advisory support, client retention becomes a critical priority (Pollock & Williams, Reference Pollock and Williams2016). As a result, ensuring continued subscriptions directly influences how Gartner analysts develop and refine tools like the HCC, shaping them to align with clients’ decision-making needs and feedback, thus reinforcing the perceived value of their insights.

Today, the HCC has become one of the most recognisable and widely referenced tools in the digital economy and beyond. With over 100 distinct iterations mapping nearly 2,000 technologies (Hashemi et al., Reference Hashemi, Gallay and Hongler2021), it has evolved from a proprietary tool for Gartner’s clients into a broadly circulated and influential benchmark. As Bourne (Reference Bourne2024, p. 757) notes, the HCC is ‘now deeply embedded in promotional knowledge, reproduced in client pitches, industry presentations, and textbooks’.

Each HCC is updated annually, with new technologies added, existing ones repositioned, and others phased out. While designed for enterprise decision-makers, its online accessibility means it is used by technology vendors, venture capital investors, and other market actors seeking to position and evaluate emerging innovations. The release of a new HCC is eagerly awaited, and any adjustments send strong signals to investors, vendors, and market analysts, aligning adopter communities, vendors, and other experts, reinforcing the idea that a technology’s placement on the tool ‘can help make or break a given sector of the technology industry’ (Woodie, Reference Woodie2014).

Producing and updating an HCC involves a combination of quantitative and qualitative data, interpreted through a process that blends calculation with qualitative judgement – what Cochoy (Reference Cochoy2008) terms ‘qualculation’. Quantitative data includes financial forecasts evaluating innovations’ impact and market penetration. Meanwhile, qualitative data is drawn from vendor briefings and use cases (A1, interview). Analysts also routinely engage with clients, exploring their strategies for adopting emerging technologies and gaining insight into investment considerations. The final decision on where to position technologies within the HCC is described as a ‘judgment call’ (A1, interview) – one that reflects not only formal analysis but also the more nuanced, qualculative process of making sense of complex and often ambiguous signals. This process integrates insights from internal technology vendor assessments, media coverage, and even ‘looking at Google Trends’ (A1, interview).

6.4 Purifying Hype as a Market-Making Force

Deciding to invest in hyped markets was once a wholly unspecified process. Before the release of the HCC, no comparable tools, methodologies, or frameworks existed to help market actors navigate and interpret hype in emerging technology markets. Moreover, hype was perceived as random or relentless, moving in unpredictable waves rather than following a structured trajectory (Edgerton, Reference Edgerton1997). According to Jackie Fenn, it was disorganised ‘noise’ without much of a discernible pattern – there were merely constant ‘waves of hype’ (Fenn, interview). This situation began to change in the late 1990s. Initially published as a one-off research note, the HCC quickly gained traction with Gartner clients, who would approach Fenn during conference events to ask, ‘Hey, are you going to update that [HCC]?’ (Fenn, interview). Fenn attributes the HCC’s widespread appeal to its visual simplicity: ‘The shape just resonates.’ Once she sketched the HCC, its meaning required no extensive explanation. ‘Some models you need to present and say, “Well, this is how you use it.” But with the [HCC], people just saw it, and they get it’ (Fenn, interview).

6.5 Balancing: Bringing Together Different Rationales in One Tool

The HCC invites market actors to consider two very different curves – a process we characterise as balancing. First, a bell curve depicts ‘initial enthusiasm and disappointment driven by positive and negative hype’ (Fenn & Raskino, Reference Fenn and Raskino2008, p. 26), foregrounding how the projected claims surrounding emerging technology may not be fulfilled, which we label managing animal spirits. Then, an S-curve portrays ‘how an innovation’s performance improves slowly at first, then picks up steadily, and finally yields diminishing returns’ (Fenn & Raskino, Reference Fenn and Raskino2008, p. 26), labelled as encouraging calculative rationalities. We begin by discussing the HCC’s left-hand side (see Figure 6.2).

Figure 6.2

Bringing together different rationales in a single picture.

Figure 6.2 Long description

A 2-line graph of expectations versus time plots a left hand bell curve and a right hand S curve. The left hand bell curve, subtitled, managing animal spirits, shows initial enthusiasm peaking, and declining after, to a trough of disillusionment. The peak is labelled, peak of inflated expectations. The right hand S curve subtitled, encouraging calculative rationalities, shows a gradually rising innovation trigger indicated by a concave up rising curve, that inflects to a concave down increasing curve showing how the innnovation's performance improves, and subsequently plateaus. The plateau is labelled, plateau of productivity. The trough of disillusionment in the bell curve meets the improving performance of the innovation at a certain point.

Source: Author representation

6.5.2 Right-Hand Side: Encouraging Calculative Rationalities

Now, we turn to the right-hand curve, where the objective shifts. While analysts use the left-hand side to highlight risks, they are careful not to discourage market actors from engaging altogether. Jackie Fenn warned against the dangers of excessive caution. Waiting too long can be just as consequential as acting too early. ‘Serious threats to survival’, she stressed, ‘are probably cases when companies don’t move soon enough’ – leading to irrelevance or missed opportunity. As Fenn explained, some market actors may dismiss a technology as ‘no good’ or fail to recognise that competitors are already adopting it (Fenn, interview).

The right side signals when benefits can be gained from investing and adopting early. As Fenn points out, ‘so when something’s Peaking, adopters could say, ‘Ok, this may be of interest, and we should look at it, but let’s not invest at all costs just because everybody else is. Let’s really understand what it means for us’ (Fenn, interview). Her message to market actors is to capitalise on the opportunities presented within the HCC, even when things turn sour: ‘And then when the issues hit, and things start to be difficult, and people are telling you all the times it didn’t work, if it’s valuable to you, then you can and should pursue it anyway, even while it’s in The Trough’ (Fenn, interview).

Furthermore, drawing on well-established criteria for stock market orientation (Chiapello, Reference Chiapello2015; Birch & Muniesa, Reference Birch and Muniesa2020), the internal HCC methodology encourages analysts to categorise market actors as ‘Type A, B, or C’ based on their risk tolerance. A central message of the HCC is that entering a market should align with the adopter’s risk profile. ‘Aggressive’ adopters may explore and adopt emerging technologies earlier, as they are more willing to take risks, whereas ‘conservative’ adopters are advised to adopt early only if the potential benefits outweigh the uncertainties. Jackie Fenn explains the reasoning behind these classifications:

I found it in talking with companies to be a very simple way that organisations are prepared to self-identify. So, if you say, ‘well, you do think you’re Type A, B, C, you’re aggressive, moderate, conservative?’, they can immediately say, ‘Oh, yes, we’re definitely a Type C’. Or, ‘well, our Product Department is a Type A, but in IT we’re Type B or C’ or vice versa. So, it’s a very simple way that people identify where they are in terms of how willing they are to take the risk of new technologies and also the mismatches within their own companies.

(Fenn, interview)

Another analyst describes how he needs to find the correct balance between optimism and caution when providing recommendations to different market actors. As they see it, some should expect to reap significant rewards, whilst others should stay in line with best practices:

[I]f you’re Type A, stuff that’s early in the [HCC], that’s the kind of stuff you might want to be an early adopter of to create competitive advantage. But, if you’re Type B or Type C, we would probably say, ‘You want to stay away from that unless it’s absolutely transformational, and you’re going to lose out by not being part of it.’

(A1, interview)

For instance, Type Bs are told to invest at ‘The Trigger mid-point, right before it quite reaches The Peak’ (A1, interview). Investing after The Peak is considered a safer investment because,

We’re going to look at something as it’s fed out of the hype, so it’s made it through that. But we know now it’s going to be refined [and] bugs are going to be fixed as it goes down through The Trough of Disillusionment - maybe that’s where some companies want to invest.

(A1, interview)

Another analyst uses the example of Software-as-a-Service (SaaS), an emerging phenomenon at the time, to talk us through the way he advises market actors:

When you talk to a client, the Type B and Type C will say, ‘Should we go SaaS?’ And you can go, ‘No, it’s not for you, yet’. But for the Type As, you might say, ‘Yes, SaaS Financial Management or SaaS HR, it’s over The Peak, it’s going into The Trough of Disillusionment, so if you know what to watch out for then, yes, you can be an early adopter.’

(A4, interview)

The image above, widely disseminated in industry circles, illustrates this segmentation of market actors based on their capacity/willingness to support risk (see Figure 6.3).

Figure 6.3

When to invest?

Figure 6.3 Long description

The Hype Cycle Chart titled, When to Invest?, with the five stages labelled. A label reads, here? with a right arrow pointing to the peak of inflated expectations, followed by another one, that reads, or here? with an arrow pointing to the trough of disillusionment, and yet another one that reads, only now? with a n arrow pointing to the slope of enlightenment,

Source: Author’s representation of an image witnessed at an industry conference

6.6 Reconciling: Engaging Hype without Succumbing to It

We now examine how analysts determine when technologies enter, progress through, or exit a hype cycle. A key challenge for analysts in constructing the tool is engaging with hype without becoming over-influenced by it. To maintain critical distance, they must engage in a reconciliation process, where, to avoid falling prey to hype, analysts balance individual judgement with collective peer review. This process, which we theorise as negotiating where the dots go, ensures that decisions are not solely driven by speculative enthusiasm but are instead tempered through structured evaluation and collective deliberation.

6.7 Timing: Shaping Market Actors’ Investment Decisions

A final challenge for analysts is positioning technologies within the HCC in a way that aligns with evolving market dynamics – a process we term timing. However, this plotting process involves an ongoing balancing act as analysts navigate the risks of responding too quickly to speculative enthusiasm or delaying recognition of technological shifts. When hindsight reveals misjudgements, analysts must be adept at addressing missteps and performing repair acts.

6.8 Discussion: Towards a Processual Understanding of Hype Purification

How analysts create tools to help market actors interpret and respond to hyped markets plays a vital role in emerging new technology industries. Yet the extant literature on market-making has paid scant attention to how analysts construct and operationalise these tools. We conducted a qualitative study of the original design and subsequent production and maintenance process for the HCC, where our findings show that analysts attempt to transform hype from unstructured noise into an interpretable market force, theorised as a process of ‘hype purification’. This new understanding contributes to research on tools and market-making, adds to the hype literature, and extends the emerging concept of hype management.

Our notion of hype purification refers to analysts’ attempts to distinguish between hype’s affective and calculative dimensions when creating tools like the HCC. We identify three mechanisms that help analysts contend with the complexities, tensions, and contradictions of purifying hype – balancing, reconciling, and timing (see Figure 6.4).

Figure 6.4

Model of hype purification.

Figure 6.4 Long description

The diagram depicts a process called Hype Purification involving two main elements: Hype as disorganized noise and Hype tools. The central process involves a cycle with the following stages:

Balancing: Connects the left-hand Bell Curve (Managing Animal Spirits) and the right-hand S-Curve (Encouraging Calculative Rationalities). Hype Purification: The central concept of the diagram. Reconciling: Includes Collective Peer Review: Negotiating Where the Dots Go and Individual consultant assessment: Falling Prey to Hype. Timing: Includes Addressing Missteps and Performing Repair Acts.

The Hype as disorganized noise lists the following: Distorting decision making Difficult to decide Creating wait-and-see strategy

The Hype tools are as follows: Structuring market perceptions and expectations Guiding investment decisions Impacting competitive dynamics

There are two graphs: Top Graph: A typical hype cycle graph with Innovation Trigger, Peak of Inflated Expectations, Trough of Disillusionment, and Slope of Enlightenment labeled on the curve. Bottom Graph: A hype cycle for Artificial Intelligence 2023 and 2024, showing Edge A I in 2024 and Cloud A I Services in 2024.

The first mechanism, balancing, refers to how analysts structure the relationship between hype’s emotional (excitement, speculation) and calculative (evidence, feasibility) dimensions in a two-sided graph. Rather than fully separating these elements, they seek to balance two rationales – what we label ‘animal spirits’ and ‘calculative rationalities’ (Beckert, Reference Beckert2016) – and navigate the tension that arises from their interaction. The left-hand side of the tool is dominated by animal spirits – the surge of excitement and speculation accompanying emerging technologies. This curve indicates the volatility of early-stage hype and the potential risks associated with premature investment. As enthusiasm and attention build, market actors are prompted to view emerging technologies as ‘hopeful monstrosities’ (Mokyr, Reference Mokyr1990) and reflect on the risks and consequences of entering a market too soon.

Conversely, the right-hand side of the tool illustrates the transition to calculative rationalities as more evidence accumulates regarding a technology’s feasibility and commercial potential. This shift encourages market actors to adopt an investor mindset (Chiapello, Reference Chiapello2015; Birch & Muniesa, Reference Birch and Muniesa2020), weighing risks and opportunities in a more structured manner. By visually distinguishing between these two dimensions, the tool does not eliminate hype but reframes it as an interpretable and structured phenomenon.

However, as Latour (2012) argues, purification efforts are always associated with multiple hybrids – messy interconnected realities that require management by deploying several complementary mechanisms.

The second mechanism, reconciling, shows that analysts are embedded in the promissory forces they seek to manage and could, for instance, become emotionally ‘attached’ (Hochschild, Reference Hochschild2011) to technologies, which we label as falling prey to hype. For example, analysts consistently retained specific technologies in the tool year after year (‘perennial favourites’), thereby delaying the recognition of newer innovations and leading to ‘errors of omission’ (Kumaraswamy et al., Reference Kumaraswamy, Garud and Ansari2018), which caused market actors to overlook emerging opportunities. To remedy this, the analysts’ judgements were subjected to collective scrutiny – a process we label as negotiating where the dots go.

The final mechanism, timing, involves moving technologies strategically from left to right on the tool, flagging technologies that may be transitioning between phases. However, while analysts sought to reinforce the idea that there are more and less favourable moments to invest, they could sometimes advance technologies through the tool too soon, contributing to ‘errors of commission’ (Kumaraswamy et al., Reference Kumaraswamy, Garud and Ansari2018), which encouraged market actors to enter markets prematurely.

We show that addressing missteps was critical as errors could become visible and consequential. Supporting prior findings that the tool’s ‘plausibility’ (Rindova & Martins, Reference Rindova and Martins2022) is essential for sustaining legitimacy, analysts engaged in repair acts, which involved codifying and refining ‘tool-based practices’ (Gond & Brés, Reference Gond and Brès2020). This suggests that while the HCC presents itself as a structured forecasting tool, it is, in reality, an evolving framework – one that adjusts to market shifts but does so silently and selectively, ensuring that its authority remains intact.

6.9 Theorising Hype Tools as a Market-Making Force

Our chapter contributes to the Market Studies literature (Geiger et al., Reference Geiger, Mason, Pollock, Roscoe and Ryan2024) by introducing a distinct and previously unexplored category of market device (Muniesa et al., Reference Muniesa, Millo and Callon2007) – ‘hype tools’. We defined hype tools in Chapter 2 as material and technical frameworks that shape hyped markets by guiding the timing of decision-making processes and market actions. Prior research has established that markets do not emerge autonomously (Ahrne et al., Reference Ahrne, Aspers and Brunsson2015; Geiger et al., Reference Geiger, Mason, Pollock, Roscoe and Ryan2024) but are actively constructed by various actors, including consultants and their tools, which play a pivotal role in organising and structuring markets (Pollock & D’Adderio, Reference Pollock and D’Adderio2012; Gond & Brès, Reference Gond and Brès2020).

However, while scholars have explored how tools shape market emergence and organisation, far less attention has been paid to how hype – an important market force – is structured, mediated, and sustained through tools. Specifically, we examine the HCC, one of the most influential and widely referenced tools in technology markets, which Beckert (Reference Beckert2016, p. 12) suggests is a ‘constitutive element of capitalist dynamics’. While previous studies have acknowledged the HCC’s role in shaping how market actors engage with and respond to hype (Pontikes & Barnett, Reference Pontikes and Barnett2017; Logue & Grimes, Reference Logue and Grimes2022; Wadhwani & Lubinski, Reference Wadhwani and Lubinski2025), the tool itself has yet to be systematically examined in terms of how it is constructed, maintained, and adjusted over time. In this chapter, we address these gaps by providing an in-depth analysis of the HCC, shedding light on its development, adaptation, and influence on market dynamics.

Moreover, prior research has pointed to the performativity of tools, suggesting that they do not merely describe market trends but actively produce and reinforce them (Callon, 1998; MacKenzie & Millo, Reference MacKenzie and Millo2003). However, rather than focusing solely on their performative effects, we have argued for a closer examination of how hype tools are designed and operationalised to exert influence in the market. As MacKenzie and Spears (Reference MacKenzie and Spears2014, p. 394) suggest, ‘[tools] do indeed have effects, but … exclusive attention to their effects occludes attention to the processes that shape [tools] and their development’. We extend this argument by shifting attention from the presumed performativity of hype tools to examining how they are continuously redesigned, thus offering one of the first empirical studies to capture the iterative, negotiated, and often contested processes involved in embedding, shaping, and reconfiguring a key tool to ensure that it remains performative by sustaining the attention of market players.

Scholars have highlighted that hype – and its institutionalisation through structured hype cycles – is expanding (Garud et al., Reference Garud, Snihur, Thomas and Phillips2023), creating a pressing need to understand better the mechanisms that govern how market actors navigate hyped markets (Pontikes & Barnett, Reference Pontikes and Barnett2017; Garud et al., Reference Garud, Lant and Schildt2021; Logue & Grimes, Reference Logue and Grimes2022). While existing research has explored how hype acts as an engine of economic dynamism (Beckert, Reference Beckert2016), a critical unanswered question remains: how do market actors interpret the ‘high hopes’ (Beckert, Reference Beckert2016) surrounding new markets and determine when they are ‘hot’ and when they are ‘passé’ (Pontikes & Barnett, Reference Pontikes and Barnett2017)? Previous studies have primarily explained these dynamics through ‘herding behaviour’ (Wüstenhagen et al., Reference Wüstenhagen, Wuebker, Bürer, Goddard, Truffer, Markard, Wüstenhagen and Wiek2009) or ‘self-perpetuating’ promissory narratives (Grodal & Granqvist, Reference Grodal, Granqvist, Ashkanasy, Zerbe and Härtel2014; Beckert, Reference Beckert2016).

However, these perspectives largely overlook the critical role of hype tools in structuring market perceptions. They do not fully account for how analysts actively monitor, categorise, and selectively amplify hype, shaping expectations, guiding investment decisions, and constructing trajectories of market development. Our chapter shows how the HCC introduces a shared language, framing hyped technologies within a structured trajectory that encourages early-stage caution while positioning later phases as business opportunities. Through directing attention and expectations, the HCC actively helps market participants navigate hyped markets by getting actors to consider their risk appetite in judging when to respond to market signals.

Finally, we highlight that, rather than a neutral mapping of technological trends, the HCC operates as a selective and highly curated tool, in which analysts deliberately ‘filter’ and ‘prioritise’ which technologies to include (Pollock & D’Adderio, Reference Pollock and D’Adderio2012). This filtering process ensures that only technologies deemed strategically relevant to clients are included. In other words, we show that hype is a ‘business issue’ that analysts actively manage and monetise, transforming the tool into a ‘sellable product’ that sustains client engagement and advisory services (Gond & Brès, Reference Gond and Brès2020, p. 721).

Furthermore, we reveal how analysts themselves are not immune to the forces of hype and may prematurely advance certain technologies, reinforcing promissory cycles or developing attachments to ‘perennial favourites’, keeping them on the tool well beyond their relevance. These tendencies can lead to feedback loops that shape investor sentiment and technological momentum, fostering what Patvardhan and Ramachandran (Reference Patvardhan and Ramachandran2020) term ‘artificial evolution’ – a process that propels market trajectories in unexpected, sometimes contradictory, directions. By conceptualising these dynamics, our chapter responds directly to Gond & Brès’ (Reference Gond and Brès2020, p. 721) call for deeper engagement with how consultants and their tools actively structure and recalibrate the ‘trajectory of markets’.

6.10 Hype Tools Balance Calculative and Affective Aspects

Our concept of hype tools advances existing research on hype by showing that it is not solely an emotionally charged phenomenon but also one in which distinct ‘calculative’ dimensions are constructed and enacted. Existing literature has prioritised accounts of hype’s affective dimensions, mainly the excitement and speculative fervour surrounding emerging technology markets (Grodal & Granqvist, Reference Grodal, Granqvist, Ashkanasy, Zerbe and Härtel2014; Geiger & Gross, Reference Geiger and Gross2017). For instance, Beckert (Reference Beckert2016, p. 32) describes hyped expectations as a ‘cognitive and emotional force that helps orient and animate the capitalist economy’, while Geiger and Gross (Reference Geiger and Gross2017, p. 451) highlight the ‘strong affective valence of hype’, noting how it fosters ‘feverish anticipation, expectations, and hope’. Yet, other research points to the tension between speculative enthusiasm (animal spirits) and structured decision-making (calculative rationalities) (Hirschman, Reference Hirschman2013). For instance, as Zaloom (Reference Zaloom2009, p. 246) argues, much contemporary business knowledge is organised around the dual influences of calculation and affect. However, existing research offers limited insight into how these two influences are managed and mediated in practice.

Our chapter addresses this gap by examining how analysts actively construct, operationalise, and sustain the interplay between affect and calculation – not as opposing forces but as co-constitutive elements that shape market dynamics. Rather than merely visualising hype cycles, the tool creates a negotiation space where affective excitement and calculative assessments are present and strategically balanced. By visually separating and contrasting hype’s emotional and calculative aspects in a structured framework such as a two-sided graph, the hype tool studied plays a critical role in framing hyped technology markets as viable spaces for intervention and investment. While the emotive dimension of hype fuels enthusiasm, it could also generate scepticism among decision-makers, perhaps leading to a wait-and-see approach (Endenich et al., Reference Endenich, Hahn, Reimsbach and Wickert2022).

However, the hype tool does not simply reflect this tension but actively shapes it by introducing a structured distinction between affect and calculability, which serves a dual rhetorical function. First, hype tools establish ‘affective credibility’ (Comer, Reference Comer2024) by recognising the emotional force of hype as a key driver of engagement with emerging technologies. Excitement, anticipation, and the imaginative dimensions of technological futures are crucial for resonance. A hype tool may struggle to connect with its audience without these elements. It risks losing relevance and credibility among market participants if analysts ‘over-purify’ their tools – stripping away speculative energy in favour of pure calculability. Second, it confers ‘calculative legitimacy’ (Doganova, Reference Doganova2024) by incorporating structured, measurable, and commensurable elements that frame hype as a navigable and structured phenomenon. By introducing notions of calculability, these tools reframe hype from speculative noise into an organised and interpretable process that market actors can strategically engage with.

6.11 Hype Tools Extend Processes of Hype Management

Scholars typically characterise hype and hype cycles as ‘unbounded’ and emerging spontaneously and unfiltered from within innovation communities (Garud et al., 2014). However, recent scholarship has introduced the concepts of ‘expectations management’ (Konrad & Alvial-Palavicino, Reference Konrad, Alvial-Palavicino, Konrad, Rohracher and von Schomberg2017) and ‘hype management’ (Logue & Grimes, Reference Logue and Grimes2022), reflecting a shift in how hype is understood – not merely as a destabilising influence but as a force that, under certain conditions, can be shaped and leveraged. As research on hype management gains prominence (Ometto et al., Reference Ometto, Lounsbury, Gehman, Felin, Foss and Zenger2023; Heupel et al., Reference Heupel, Fonseca, Rutherford and Edwards2024; Wood et al., Reference Wood, Dwyer and Scheaf2024; Wadhwani & Lubinski, Reference Wadhwani and Lubinski2025), scholars increasingly recognise the need to examine how market actors engage with hype and channel it as a strategic resource. While existing studies primarily focus on entrepreneurs as primary articulators of hyped expectations (Logue & Grimes, Reference Logue and Grimes2022), our chapter broadens this perspective by examining analysts and their role in designing tools that actively structure hype trajectories. This shifts the focus of hype management beyond individual and organisational actors to the industry-wide tools that sustain, regulate, and legitimise hype.

Our chapter builds on the hype management perspective by examining how analysts develop tools and practices that provide a ‘threshold of institutionalisation’ (Power, Reference Power2015, p. 48) to hype cycles. By framing hype as a staged and repeatable process, these tools embed a prevailing interpretation of hype cycles into market discourse, shaping how market actors perceive, engage with, and respond to hype, reinforcing expectations that hype cycles follow a structured trajectory – even though, in practice, technological developments remain contingent and uncertain (see van Lente et al., Reference Van Lente, Spitters and Peine2013).

However, while analysts seek to impose structure on hype, our findings reveal that hype management is anything but straightforward. Rather than working as authoritative guides (Porter, Reference Porter2004), hype tools are precarious instruments (Jackson, Reference Jackson, Wajcman and Dodd2017). Errors, misjudgements, and ‘discrepancies’ (Dedehayir & Steinert, Reference Dedehayir and Steinert2016) frequently emerge in the classification and positioning of technologies, requiring ongoing adjustments to sustain their credibility. This aligns with research on ‘invisible work’ (Justesen & Plesner, Reference Justesen and Plesner2024), highlighting how seemingly stable systems and frameworks rely on continuous, often unseen maintenance work. Our findings suggest that hype management is not solely about predicting technological trajectories; it is equally about repairing and recalibrating expectations to sustain the ‘plausibility’ of these tools (Rindova & Martins, Reference Rindova and Martins2022). (We return to the hype management concept in Chapter 9.)

6.12 Research Opportunities for Studying Hype Tools

We have only begun to scrape the surface of hype cycles and their role in market-making. While this chapter and book expand our understanding of how analysts and their tools actively shape the trajectory of hype in new technology markets, they also reveal opportunities for further research. Future studies could investigate further how hype tools actively shape competitive dynamics by channelling attention and investment into specific areas while marginalising others. This asymmetry in speculative interest warrants closer scrutiny, particularly in its role in market structuring. Research should more fully explore the extent to which those who judge the timing of a technology’s entry or exit from a hype cycle wield significant influence over industry resource allocation. Future work might examine in more detail how powerful consultants, in constructing and disseminating a ‘coherent field frame’ (Lo & Rhee, Reference Lo, Rhee, Lo and Rhee2022), strategically amplify or suppress innovations – decisions that can have far-reaching consequences. Studies might throw more light on whether analysts, by prematurely promoting a technology in a tool like the HCC, may fuel excitement and drive speculative investment, or by quietly ‘removing’ it, might introduce doubts about its viability – potentially leading to its ‘stigmatisation’ (Garud et al., Reference Garud, Snihur, Thomas and Phillips2023).

Future research could expand the scope of enquiry to encompass the broader ecosystem of actors and mechanisms involved in generating, filtering, and evaluating hype. A key area of interest is the role of other hype tools that shape organisational decision-making in uncertain technological landscapes. While the HCC remains the most prominent, other influential tools – such as McKinsey’s Tech Trends, WEF’s Top Ten Emerging Technologies, and Forrester’s Top Emerging Trends to Watch, to name a few – may also play a crucial role in shaping market expectations. Digitalisation has also given rise to algorithmic tools that track and quantify hype in real-time, including Google Trends, Brand24, HypeIndex, and Buzz.

Our study only had limited engagement with market players regarding how they utilise these tools. We explored how vendors respond to these tools but have not yet studied extensively how technology adopters act on them. Future research might address an intriguing range of issues. For example, how do these tools shape adopters’ perceptions of opportunities for action? What new practices and strategies emerge as market actors develop more sophisticated responses to hype? Exploring the extent to which the development of hype tools encourages market actors to build proactive or reactive approaches to hype would offer valuable insights into the evolving dynamics of hype tools and market-making.

The HCC is just one example of how analysts attempt to reshape market actors’ disposition towards hype. Chapter 7 builds directly on the above analysis by turning to another critical dimension of this taming process: the categorisation of emerging technological trends. If Chapter 6 demonstrated how analysts guide market actors on when to act, Chapter 7 addresses how they help them decide what to act upon. Analysts provide a structured map of the innovation landscape by developing, revising, and retiring technology categories. These category frameworks, much like the HCC, are not passive reflections of technological developments but active devices for shaping market attention and organising vendor narratives.

The introduction – and often rapid adjustment or abandonment – of market categories has become a defining feature of the digital economy. In a context marked by extraordinary technological change, industry analysts have created hundreds of market-defining categories over the past few decades to help clients navigate shifting landscapes. While some categories endure for years, many prove far more transient (Wang, Reference Wang2010), with some disappearing after only a few months (Pontikes & Kim, Reference Pontikes, Kim, Durand, Granqvist and Tyllstrom2017).

There is a clear gap in Information Systems (IS) research regarding why a market gatekeeper, such as an industry analyst, launches a category and subsequently adjusts or abandons it shortly after. When IS researchers and others discuss these ephemeral terminologies and classifications, they depict them as part of the rhetoric accompanying new technologies (Barrett et al., Reference Barrett, Heracleous and Walsham2013), as fads and fashions (Baskerville & Myers, Reference Baskerville and Myers2009; Hirschheim et al., Reference Hirschheim, Murungi and Peña2012; Cram & Newell, Reference Cram and Newell2016; Piazza & Abrahamson, Reference Piazza and Abrahamson2020; Heusinkveld et al., Reference Heusinkveld, van Grinsven, Groß, Greatbatch and Clark2021), or organising visions (Swanson & Ramiller, Reference Swanson and Ramiller1997; De Vaujany et al., Reference De Vaujany, Carton, Dominguez-Péry and Vaast2013; Davidson et al., Reference Davidson, Østerlund and Flaherty2015; Liao, Reference Liao2016; Wang, Reference Wang2021). Yet, these broad-brush approaches tend to leave the complex topology of digital economy product markets underspecified and tell us little about what prompts this gatekeeper to permit, and in some cases, foster an ever-changing category system. However, it is essential to understand these developments, as even short-lived categories herald significant technological and market changes (Wang, Reference Wang2010).

To shed light on this puzzle, we turn to Category Sudies, which has provided deeper insights into how market gatekeepers categorise technologies (Durand et al., Reference Durand, Granqvist, Tyllström, Durand, Granqvist and Tyllström2017). Scholars have begun to ask what spurs a market gatekeeper to create a category in the first place (e.g., Durand & Khaire, Reference Durand, Granqvist, Tyllström, Durand, Granqvist and Tyllström2017; Delmestri et al., Reference Delmestri, Wezel, Goodrick and Washington2020). However, there are still crucial gaps in this literature, particularly on the question of ‘how categories emerge and fall out of use’ (Kennedy & Fiss, Reference Kennedy and Fiss2013, p. 1139). Specifically, Category Studies has ‘overemphasised the stability of categories and the inertia of classificatory systems’ (Durand & Paolella, Reference Durand and Paolella2013, p. 1109). Moreover, the mechanisms and forms of organisation within gatekeepers that launch, modify, and truncate the career of the category have not been thoroughly investigated (Kennedy & Fiss, Reference Kennedy and Fiss2013). This prompts our research questions: How and why do gatekeepers continuously introduce, adjust, and abandon categories?

This chapter addresses these questions by drawing on empirical evidence from a long-term, in-depth qualitative study of the ‘Big Three’ industry analyst firms. Examining these firms provides insight into how they develop and promote categories that clients find helpful, and then modify or abandon those that detract from this goal. Furthermore, we link IS research with developments in practice-based category discussions (Granqvist & Ritvala, Reference Granqvist and Ritvala2016; Durand et al., Reference Durand, Granqvist, Tyllström, Durand, Granqvist and Tyllström2017; Granqvist & Siltaoja, Reference Granqvist and Siltaoja2020). This allows us to demonstrate how industry analysts attempt to make their expertise relevant to client decisions through a set of material and visual processes, which we theorise as category-work, figuring-work, and client-mapping. Together, these create ‘client-induced categories’, defined as temporary-probing mechanisms devised to help technology adopters navigate complex digital markets (Khaire & Wadhwani, Reference Khaire and Wadhwani2010).

7.1 Market Gatekeeper Priorities during Category Creation

Market categories have been depicted as ‘disciplining standards’ (Kennedy & Fiss, Reference Kennedy and Fiss2013, p. 1139) and defined as ‘devices that create order in markets’ (Beckert & Musselin, Reference Beckert2013, p. 7). Categories channel how consumers search for and evaluate products (Rosa et al., Reference Rosa, Porac, Runser-Spanjol and Saxon1999; Lounsbury & Rao, Reference Lounsbury and Rao2004). For instance, when an industry analyst issues a report on a new technological category, buyers will use it to find and compare vendors (Pontikes & Kim, Reference Pontikes and Barnett2017). Consequently, because they guide adopters in deciding what and where to buy, vendors must consider (and perhaps adapt to) categories to avoid illegitimacy discounts (Zuckerman, Reference Zuckerman1999).

For instance, when Gartner introduced its Enterprise Resource Planning (ERP) category, software vendors had little choice but to rebadge and redesign offerings under the ERP banner, as these solutions were sought by adopter organisations (Swanson, Reference Swanson2020). Alternatively, research has shown that if a vendor does not identify as part of a category, it could be ‘screened out’ of consideration (Zuckerman, Reference Zuckerman1999, p. 1399). For instance, Pollock and Williams (Reference Pollock and Williams2011) discuss the case of a software producer that failed to actively position itself in Gartner’s emerging customer relationship management (CRM) category and subsequently lost out in a procurement contest.

Technology vendors are especially keen to be included in categories produced by a market gatekeeper, such as an industry analyst, as these receive the most attention from technology buyers (Pontikes & Kim, Reference Pontikes and Barnett2017). Such gatekeepers emerge to help technology adopters choose and evaluate complex products. Conceived as neutral third parties (Aspers & Beckert, Reference Aspers, Beckert, Beckert and Aspers2011), gatekeepers are viewed as possessing the necessary expertise and having no stake or interest in market categories (Khaire, Reference Khaire, Durrand, Granqvist and Tyllström2017). When a gatekeeper launches a new category, it is thus assumed that they are providing consumers with a comprehensive and inclusive picture of market offerings (Rosa et al., Reference Rosa, Porac, Runser-Spanjol and Saxon1999, Reference Rosa, Judson and Porac2005; Lounsbury & Rao, Reference Lounsbury and Rao2004); that they faithfully represent the market. For instance, Lounsbury and Rao (Reference Lounsbury and Rao2004) hypothesise that a gatekeeper is spurred to launch a new category when ‘new entrants’ flood into an area, therefore expanding the ‘variety of product models’ and stretching the category boundary (p. 976). Such surges of new entrants typically reflect intense excitement around a technology, suggesting that gatekeepers launch categories to capitalise on that moment of heightened attention.

In contrast, more recent work describes gatekeepers as not passively reflecting markets (Lounsbury & Rao, Reference Lounsbury and Rao2004) but actively constituting them (Carruthers & Stinchcombe, Reference Carruthers and Stinchcombe1999; Beunza & Garud, Reference Beunza and Garud2007). Evidence has emerged that shows gatekeepers categorise vendor offerings around the decision-making concerns of clients, rather than providing complete pictures (Kodeih et al., Reference Kodeih, Bouchikhi and Gauthier2018). For instance, in her examination of film critics, Hsu (Reference Hsu2006) provides evidence that intelligible categories are ‘awarded attention and legitimacy by consuming audiences’ (p. 489). Similarly, Pontikes and Kim (Reference Pontikes and Barnett2017) describe how, because their clients ‘favour category clarity’, an industry analyst could ‘exclude a firm from a report’ that ‘complicates their narrative’ (p. 85).

Together, these studies support the notion that market gatekeepers shape category systems in response to client concerns. However, the processes through which gatekeepers use these concerns to construct categories remain poorly understood. For example, the creation of more selective or meaningful pictures (Hsu, Reference Hsu2006; Khaire, Reference Khaire, Durrand, Granqvist and Tyllström2017), where producer developments are excluded (Pontikes & Kim, Reference Pontikes and Barnett2017), contrasts with previous assumptions that gatekeepers built exhaustive knowledge of producer offerings (Carruthers & Stinchcombe, Reference Carruthers and Stinchcombe1999; Rosa et al., Reference Rosa, Porac, Runser-Spanjol and Saxon1999, Reference Rosa, Judson and Porac2005; Lounsbury & Rao, Reference Lounsbury and Rao2004). Indeed, a gatekeeper’s decision to omit significant technological developments could appear as an oversight, for which it could be challenged (Lounsbury & Rao, Reference Lounsbury and Rao2004; Navis & Glynn, Reference Navis and Glynn2010).

In this context, there are growing calls to study the pragmatic circumstances surrounding category construction (Durand et al., Reference Durand, Granqvist, Tyllström, Durand, Granqvist and Tyllström2017; Grodal & Kahl, Reference Grodal and Kahl2017), including the work practices and artefacts that produce categories (Blanchet, Reference Blanchet2017). Indeed, while Pontikes and Kim (Reference Pontikes and Barnett2017) note the specificity of industry analyst category formats – for instance, they are incorporated in ‘visual’ reports – they do not give this importance. However, it might be assumed that when the gatekeeper foregrounds client concerns, the figures utilised will have some bearing on the categorisation process (Delmestri & Greenwood, Reference Delmestri and Greenwood2016; Blanchet, Reference Blanchet2017). Thus, to move forward, we must focus on the material and, specifically, visual processes gatekeepers enact in the process of building categories to serve client requirements.

7.2 Material and Visual Processes for Foregrounding Client Concerns

For this purpose, we build on recent calls for research on what market gatekeepers do when they categorise entities in markets (Durand et al., Reference Durand, Granqvist, Tyllström, Durand, Granqvist and Tyllström2017) and aim to identify categories and categorisation processes (Kennedy & Fiss, Reference Kennedy and Fiss2013). While earlier research considered categories as ‘theoretical constructs’ (Rosa et al., Reference Rosa, Porac, Runser-Spanjol and Saxon1999, p. 64) or ‘conceptual tools’ (Negro et al., Reference Negro, Koçak and Hsu2010, p. 4), we build on later scholarship, which calls for practice-based investigations (Granqvist & Ritvala, Reference Granqvist and Ritvala2016) that forge an understanding of the various ‘acts of categorisation’ (Durand et al., Reference Durand, Granqvist, Tyllström, Durand, Granqvist and Tyllström2017, p. 13). Early literature suggested that simply ‘listing products’ (Lounsbury & Rao, Reference Lounsbury and Rao2004, p. 1974) was sufficient to spark a category system, but later contributions have described a more structured progression. For instance, Beckert and Musselin (Reference Beckert2013, p. 2) describe categorisation as involving processes of framing (i.e. the ‘construction of categories to which goods can be allocated’), allocation (i.e. how particular goods are ‘defined as belonging to this category’), and discrimination (i.e. ‘the establishment of quality differences within a product category’).

However, the sociomaterial mechanisms underpinning categorisation processes (see Wagner et al., Reference Wagner, Newell, Ramiller and Enders2018) are still under-researched. Such mechanisms include how categories are tied to material processes, directing our focus towards the visual character of schemata – a topic attracting increasing attention among category scholars (Delmestri & Greenwood, Reference Delmestri and Greenwood2016; Blanchet, Reference Blanchet2017). For example, Espeland and Stevens (Reference Espeland and Stevens1998) have demonstrated the importance of visualisations in related market processes, such as ‘commensuration’. Delmestri and Greenwood (Reference Delmestri and Greenwood2016) see ‘appearance’ as part of a more extensive process of ‘visual framing’, which is crucial in the social valuation of a category (p. 34). Further studies of visualisation complement Category Studies, which conceives category turnover as driven only by ‘linguistic’ (Vergne & Wry, Reference Vergne and Wry2014) or ‘discursive’ (Grodal & Kahl, Reference Grodal and Kahl2017) interventions. Delmestri and Greenwood (Reference Delmestri and Greenwood2016) argue that the ‘persuasive rhetoric of visuals’ may be more potent than the linguistic, as it provides for immediate kinds of visual apprehension (p. 34; see also Puyou & Quattrone, Reference Puyou and Quattrone2018). While these studies help us understand how figures persuade, they throw little light on our quest to uncover how gatekeepers go about making categories more valuable to clients.

Building on the above studies, we can refine our initial research question. First, if the market gatekeeper views its primary goal as increasing the value of categories for clients, what categorisation acts (Durand et al., Reference Durand, Granqvist, Tyllström, Durand, Granqvist and Tyllström2017) are involved? Second, to what extent are the resulting processes of material and visual display performing as constitutive elements in the construction of categories?

7.3 Helping Clients Understand the Complex Market of Offerings

Industry analyst informants from the ‘Big Three’ – Gartner, Forrester, and IDC – viewed their primary goal as creating categories to support and help their clients understand the complex market of offerings. In prioritising value categories given to clients, we found that industry analysts conducted three sets of processes. The first concerned how analysts constitute the market for offerings through category-work. These processes include framing, allocating, discriminating, and figuring.

7.4 Client-Friendly Pictures Lead to Category Segmentation

The second set of processes we identified in our data captures creating figurations, theorised as figuring-work, which involves depicting, splitting, and drawing in.

7.4.1 Depicting: Creating Figurations in a Way that Helps Clients Make Decisions

Depicting, primarily by creating figurations that told a story, was vital in helping the client towards a decision. Below, we examine the circumstances in which these figurations are seen to be optimally designed to provide information that can assist clients in procurement decisions. Here, there is a lifecycle element in that the influence of figurations varies across different moments in the evolution of a category. In the early stages, when scores of vendors are attracted to a promising application field, there are too many vendors to draw a meaningful figuration. A figuration with two hundred vendors on it, an analyst told us, would be ‘unreadable’ (A7, interview). Another analyst said: ‘If you look at the Magic Quadrant as it stands today … it can get a little bit cluttered, and … people have to spend a bit of time scrutinising it to read it’ (A8, webinar).Footnote ¹ Later, when a field is mature, with some products prevailing, and where applications are well characterised, procurement becomes less uncertain, and there may ultimately be little need for a figuration.

Over the years, however, analysts have recognised an ideal range or number of vendors that enhance the effectiveness of categories for clients. For example, according to analyst A7, the optimal number of dots on a Gartner Magic Quadrant was about 15 to 25. A similar heuristic existed surrounding the Forrester Wave, where they looked to have ‘10 to 12 vendors’ (A11, internal meeting). Likewise, according to A6 at IDC, MarketScape should contain around 10 to 15 vendors. An analyst described the rationale for why they sought to avoid ‘crowding’ on the figuration:

By providing more relative space between [dots], it’s much easier to read and view and take a snapshot of which vendor is where. You can see clearly the dot positions, and you can actually see the distances and the deltas between the dots a little bit more.

(A8, webinar)

Analysts are guided not only by a number range; there is also a market picture drafted for directing categorisation work. Two types of market pictures enhance the category construction process. One analyst told us how, when constructing figurations, they wanted to capture the ‘good market’ (A6, interview) and another how they sought ‘beautiful pictures’ (A7, interview). The words ‘good’ and ‘beautiful’ were a framing mechanism that pointed towards producing aesthetic figurations that were not so crowded as to confuse the client and not so sparse that they lacked complexity (see Figure 7.1). Additionally, it was necessary for the figuration to have a reasonable number of vendors and for there to be a clear message for the decisions faced by would-be adopters.

Figure 7.1

The ‘good market’.

Figure 7.1 Long description

A bubble chart plots vendors based on their capabilities (y-axis) and strategies (x-axis). The space is divided into four sections: Leaders at the top right, Niche Players in the upper middle, Contenders in the lower middle, and Participants at the bottom right. Several vendors are plotted as bubbles of varying sizes, with larger bubbles indicating a greater market presence. The vendors are distributed across all four sections, with a concentration in Contenders section. The largest bubble is in the niche player section. Participants and leaders sections have one moderately sized bubble each.

Source: Author representation

There were also diagrammatic devices to enhance how vendors appear on a figuration. For instance, analysts sought to arrange vendors within Waves to create a ‘rugby ball’ or ‘torpedo shape’ (A7, interview), which suggested a desire not just to fill the figuration randomly but also to discriminate between vendors in a way that gave it a structure with an implicit narrative of improvement: a torpedo meant vendors were not bunched within one part of the diagram (see Figure 7.2). Instead, there would be vendors with an even spread of capacities (e.g., challengers, contenders, strong performers, and leaders). In other words, the figurations were fashioned to allow analysts to create a set of ordered associations that helped guide clients, similar to how an artist leads a viewer’s eyes through a painting, which served to give the figuration significance so that the picture would indeed tell a story about the different capacities of vendors.

Figure 7.2

The ‘torpedo shape’.

Figure 7.2 Long description

The plot visualizes vendor positions across two dimensions: Current offering, along the y-axis, ranging from Weak to Strong, and Strategy, on the x-axis, also ranging from Weak to Strong. The plot is divided into three regions: 'Risky bets', 'Contenders', and 'Leaders'. Several vendors are plotted as hollow circles labeled 'Vendor' within these regions, with varying sizes and positions. Market presence is represented by the size of the circles, with larger circles indicating greater market presence. Leaders have the highest concentration of vendors followed by contenders, and risky bets.

Source: Author representation

7.5 Building Categories around Client Concerns

The third and final set of processes we identified in our data captures how industry analysts directly build categories around client concerns, theorised as client-mapping, which involves probing, cycling, and killing off.

7.6 Discussion: Fostering a Changing Category System

In addressing how and why industry analysts foster an ever-changing category cycle, we find that they want to ensure their expertise is seen as relevant to client decisions. Bringing together insights from IS research and Category Studies and studying the Big Three industry analyst firms, we found that analysts created and maintained categories that clients find valuable and adjust or abandon those no longer attracting attention. Furthermore, by deploying a practice-based category focus (Granqvist & Ritvala, Reference Granqvist and Ritvala2016; Durand et al., Reference Durand, Granqvist, Tyllström, Durand, Granqvist and Tyllström2017; Blanchet, Reference Blanchet2017), we show that analysts maintain a category system that seeks to engage clients through category-work, figuring-work, and client-mapping.

Category-work consists of framing, allocating, discriminating, and, by extending Beckert and Musselin’s (Reference Beckert2013) framework, figuring (i.e. creating a graphical figuration). Figuring-work reveals the constitutive elements of these figurations where the gatekeeper splits (i.e. segments) a category to allow the drawing of market pictures that tell the client a story. Client-mapping is where the gatekeeper probes for buyer preferences, cycling between frontroom and backroom categories and adjusting or abandoning categories no longer attracting client attention (see Table 7.1 for a summary of our findings). Together, these processes produce ‘client-induced categories’, defined as temporary-probing mechanisms devised to help technology adopters navigate complex markets in the digital economy. We will revisit this concept in Chapter 9, which reframes categories as part of moving from ‘hype in the wild’ to more managed hype systems.

Table 7.1Client-induced categories

Table 7.1 Long description

The table has three columns titled Process definition, Example quotes, and Implications for category change.

Row 1 column 1 reads. Process 1: Category-work.

Framing an emerging technology by giving it a name.

Allocating vendors to the new category.

Discriminating between vendors in the category.

Figuring the category through incorporating it in a graphical figuration.

Row 1 column 2 reads. We sometimes define the terminology that gets used to describe a particular innovation. Many people claim E R P, but Gartner was one of the original players behind the E R P term and trying to make that more widespread in use.

Row 1 column 3 reads. Analysts attempt to constantly detect and categorise new technological developments to help their clients understand technical change, which includes identifying and ranking major vendors in the category.

Row 2 column 1 reads. Process 2: Figuring-work.

Depicting figurations in a way that helps clients make decisions.

Splitting categories when figurations become overcrowded.

Drawing in clients when creating categories.

Row 2 column 2 reads. When we are getting too many vendors, which is the case for one of the categories that I am studying currently, we are getting too many vendors, so, I decided, in that case, to split the category in two.

Row 2 column 3 reads. Analysts sought to make categories understandable for clients by incorporating them in graphical figures, which included splitting categories to allow more meaningful market pictures to be drawn.

Row 3 column 1 reads. Process 3: Client-mapping.

Probing the client base to see if a category intervention is helpful.

Cycling between categories that are more/less of interest to clients.

Killing off categories no longer attracting client attention.

Row 3 column 2 reads. Because if, the market picture, drops to five dots, there’s five vendors in this market, it’s highly consolidated, so why would, clients, ring us? So, for example, Operating Systems, what’s the point of having a Magic Quadrant for Operating Systems?’

Row 3 column 3 reads. Analysts would ‘frontroom’ categories that clients appeared most interested in and ‘backroom’ or abandoned those receiving less attention.

In short, client-induced category creation exemplifies how hype interpretation has become part of the business of hype. The client-induced category should capture how the gatekeeper attempts to make things easier for its clients by providing them with selective and meaningful market pictures (Hsu, Reference Hsu2006; Pontikes & Kim, Reference Pontikes and Barnett2017). Although one might imagine that categories are created on their own terms – that is, separate from consumption – the notion of client-induced category captures how the gatekeeper introduces or adjusts categories to capture attention and client interest. If a category does not gain traction (and thus market value), it is quickly retired in favour of a new narrative. In other words, a category’s survival depends on its commercial relevance – analyst firms do not sustain a classification that fails to engage their paying audience. Instead, they pivot to new categories better aligned with client demand.

By continually tailoring and refreshing these classifications to match client interests, analysts ensure that hype is not only managed but also monetised – its interpretation packaged as a product in its own right. Thus, category creation is not merely an analytical exercise but a cornerstone of the business of hype, where interpreting and structuring hype for clients becomes a commodity. We therefore depict these categories as shaped by the dynamics of consumption. In practice, this means that as client interest (and hype) in an area swell, analysts carve the market into categories that reflect that excitement. This conception differs markedly from the accounts offered by Lounsbury and Rao (Reference Lounsbury and Rao2004) and Rosa et al. (Reference Rosa, Judson and Porac2005), for instance, who see gatekeepers as creating categories that are comprehensive and inclusive of innovation, simply reflecting the market.

Even though client-induced categories can be short-lived, this does not mean they are insignificant. Their fleeting nature does not make them insignificant – on the contrary, even brief client-induced categories help anchor moments of hype. As Kennedy and Fiss (Reference Kennedy and Fiss2013) argue, transient categories act as ‘disciplining standards’, directing attention towards which technologies are considered promising at a given moment. In doing so, they help shape the ‘social realities’ of innovation economies, even if only for a time (p. 1139).

We throw new light on how client-induced categories constitute markets in the digital economy. For instance, our study revealed that when there are many vendors in up-and-coming application areas, the gatekeeper will ‘split’ the category to allow meaningful market pictures to be drawn. In other words, we show that categories can be segmented not just because notable differences have emerged between vendor technologies but also to attract and capture clients (Cochoy, Reference Cochoy2007).

Finally, building on Khaire and Wadhwani (Reference Khaire and Wadhwani2010), the concept should capture how category emergence is ‘not always followed by stabilisation’ as client-induced categories endure only as long as they provide utility for primary audiences (p. 1283). After a short period, many of the categories no longer seem to achieve the same level of purchase with their intended audiences. When this happens, the gatekeeper will either backroom or abandon the category.

Below, we articulate how this theorisation of client-induced categories allows us to integrate and build bridges between IS and category scholarship and open up new research opportunities for studying markets in the digital economy.

7.7 How Organising Visions Relate to Category-Work

Few markets are as dynamic as that for digital solutions (Chiasson & Davidson, Reference Chiasson and Davidson2005). There is an almost continuous flow of new terminologies and classifications (Swanson & Ramiller, Reference Swanson and Ramiller1997). We contribute to this conversation by incorporating insights from Category Studies into our understanding of how the digital economy is constituted by ‘relatively transitory collective beliefs’ (Abrahamson & Fairchild, Reference Abrahamson and Fairchild1999, p. 709). IS scholars and others have usefully theorised transient terminologies as ‘organising visions’ (Swanson & Ramiller, Reference Swanson and Ramiller1997; De Vaujany et al., Reference De Vaujany, Carton, Dominguez-Péry and Vaast2013; Davidson et al., Reference Davidson, Østerlund and Flaherty2015; Liao, Reference Liao2016; Wang, Reference Wang2021), defined as a ‘focal community idea for the application of information technology in organisations’ (Swanson & Ramiller, Reference Swanson and Ramiller1997, p. 460). While some (e.g., Wang, Reference Wang2021) conceptualise the industry analyst categories described here as the same or similar to organising visions, we think it essential to differentiate these concepts as the former reveals processes not captured by the latter.

Industry analyst categories are not generic visions that attract and orient diverse players in forming new technological areas, like organising visions do. Instead, they are knowledge products that (re)make product-market boundaries as part of outputs designed to inform the actions and decisions of technology buyers. Our identification and theorisation of client-induced categories, therefore, articulate mechanisms not fully accounted for by existing concepts. We thus echo and give a new direction to the call by De Vaujany and colleagues (Reference De Vaujany, Carton, Dominguez-Péry and Vaast2013) to bring the abstract study of organising visions down to earth by showing how they relate to the concrete operation of markets and innovation alongside and in tandem with categorisation work, inviting further study of how the digital economy is constituted through the enactment of client-induced categories.

We consider the insights offered here necessary, not only because we identify the different dynamics underpinning client-induced categories but also because doing so deepens our understanding of how gatekeepers influence markets. Research in this space has usefully characterised organising visions as having ‘a career’, where many struggle to gain ascendancy before eventually being displaced (Ramiller & Swanson, Reference Ramiller and Swanson2003, p. 13). However, scholars have been less effective at explaining why specific organising visions take hold and others fade away (Liao, Reference Liao2016; Wang, Reference Wang, Grover and Markus2015). Our category lens suggests that scholars in this area have left underspecified the complex topology of digital product markets and have not given sufficient weight to the emergence of gatekeepers like industry analysts who exercise increasing influence over transient terminologies. The concept of the client-induced category moves this form of analysis forward and also responds to calls for more attention to be given to the role of ‘influential gatekeepers’ who ‘patrol’ category boundaries (Wang, Reference Wang2021, p. 414).

7.8 Creating Categories to Deliver Benefits to Clients

Our chapter further contributes by shedding light on a key but understudied question in the Category Studies literature: how and why categories emerge and fall out of use (Kennedy & Fiss, Reference Kennedy and Fiss2013, p. 1139). We identify and explain how this market gatekeeper creates, adjusts, and abandons categories and how this relates to their primary goal of delivering benefits to clients. Moreover, our chapter offers empirical depth and nuance to this conversation by revealing the different processes that shape industry analysts’ course of action. For instance, in theorising the notion of client-induced category, we build on the suggestion by Pontikes and Kim (Reference Pontikes and Barnett2017) that what matters to the gatekeeper is to provide clients with categories that they find valuable, and to this end, they will eliminate information and develop specific methods to manipulate categories.

While offering a point of departure for our study, we also provide a more nuanced theorisation of how and why gatekeepers might conduct these kinds of interventions, including revealing aspects that contrast with Pontikes and Kim’s (Reference Pontikes and Barnett2017) analysis. For instance, we find that the gatekeeper does not simply omit data during category creation, as they suggest, as this may appear as an oversight for which they could be challenged by clients and others (Lounsbury & Rao, Reference Lounsbury and Rao2004; Navis & Glynn, Reference Navis and Glynn2010). Instead, it operates a more dynamic system where new vendor developments and technologies are accounted for in further layers of the gatekeeper organisation and where specific categories are front and back roomed as client interests and buying patterns dictate.

7.9 How Figurations Impart New Dynamics to Categories

Our final contribution is to demonstrate how incorporating a category into a graphical figuration intensifies this cycle of category creation, adjustment, and abandonment. In our chapter, client-induced categories are linguistic, material, and, most importantly, visual (Delmestri & Greenwood, Reference Delmestri and Greenwood2016). We reveal how attempts by the gatekeeper to create market pictures that told a story for clients became a powerful mechanism shaping the creation and abandonment of a category. The Category Studies literature has not seriously examined how categories and graphic figurations interrelate and whether figurations might impart different dynamics to category systems. For instance, Category Studies suggests that shifts in ‘linguistic’ (Navis & Glynn, Reference Navis and Glynn2010) and ‘discursive’ (Grodal & Kahl, Reference Grodal and Kahl2017) frames intensify the cycle of category generation and exhaustion (see also Granqvist & Siltaoja, Reference Granqvist and Siltaoja2020). However, because in the contexts described in this chapter, the picturing of a category is seen to draw in an audience in ways that linguistic formats do not (Delmestri & Greenwood, Reference Delmestri and Greenwood2016), some categories might be considered ‘prelinguistic’ (Comi & Whyte, Reference Comi and Whyte2018). A further understanding of how a category develops through incorporation in a graphical figuration is essential because emergence through a market picture chronologically precedes linguistic developments, such as the introduction of new terminology (Comi & Whyte, Reference Comi and Whyte2018).

7.10 Research Opportunities for Studying Categories

The dynamics of markets in the digital economy take on a new hue when seen through a category lens, especially the notion of a client-induced category. Some assume it is simply innovation by technology vendors that intensifies the cycle of generation and exhaustion of categories (Kohli & Melville, Reference Kohli and Melville2019). However, our research reveals that this cycle is also a product of the gatekeeper category system and the business of hype. Our chapter invites future research to explore how the gatekeeper process of introducing, adjusting, and abandoning a category evolves. For instance, as recent research indicates (e.g., Pontikes & Kim, Reference Pontikes and Barnett2017), the lifespan of the categories described in this chapter is short – and getting shorter all the time. The interval between the industry analyst introducing and then adjusting or abandoning a category is diminishing from cycles measured in years to cycles measured in months, and future research might investigate why this is the case. For instance, one conjecture worth exploring is whether this faster category turnover results from the emergence of new industry analyst firms (Pollock & Williams, Reference Pollock and Williams2016), forcing the Big Three to compete more aggressively for the attention of audiences through launching evermore distinctive forms of categorisation (Bessy & Chauvin, Reference Bessy and Chauvin2013).

Further research is needed on the implications for technology vendors when categories are backroomed or abandoned. For instance, Pontikes and Kim (Reference Pontikes and Barnett2017) write that technology vendors have much to gain from becoming a ‘category king’, that is to say, dominating an industry analyst category. In such cases, the outcomes for a vendor are likely to be significant if an industry analyst decides to discontinue that category. Brandtner (Reference Brandtner2017), for instance, has written about the potential for powerful incumbents to suffer ‘status anxiety’ when there is no longer the same certainty about their category, as it will not be apparent ‘who is on top of the status order’ (p. 214).

Scholars could investigate whether client-induced categories have political ramifications (Bowker & Star, Reference Bowker and Star2000) or whether they disrupt traditional kinds of entrenchment (Durand & Khaire, Reference Durand and Khaire2017). For instance, it is widely thought that gatekeepers are ‘receptive to the interests of powerful incumbents’, such that they avoid ‘category reconstitution if a few incumbents dominated a category’ (Lounsbury & Rao, Reference Lounsbury and Rao2004, p. 991). Evidence presented here, however, indicates that the gatekeeper would no longer promote (and could even abandon) a category dominated by a few powerful incumbents, as it would be perceived as offering little value to its clients. Future research, therefore, might seek to understand whether client-induced categories act for or against influential vendors with entrenched product markets and if this invites hostility or resistance. It could also investigate whether these categories lead to a potential reordering of the composition and structure of markets, for instance, by killing off the competition between established vendors (Arora-Jonsson et al., Reference Arora-Jonsson, Brunsson and Hasse2020) or generating opportunities for other vendors to enter a category (Durand & Khaire, Reference Khaire, Durrand, Granqvist and Tyllström2017).

Finally, a further potential theme for development concerns how a focus on the socio-materiality of categories can be revealing. We found striking, for instance, that seemingly essential questions – when has a market emerged? – were prompted or settled through ‘small’ graphic props (e.g., analysts felt justified in launching a new category when there were fifteen to twenty-five dots in a figuration). This insight echoes research by McKendrick et al. (Reference McKendrick, Jaffee, Carroll and Khessina2003) on the number of vendors required before an area is recognised as a legitimate market. Similar issues arise as to when a category should be abandoned (e.g., fewer than five dots). This offers the ‘paper trail’ sought by Grodal and Kahl (Reference Grodal and Kahl2017) to study category decay, inviting further studies of how the competitive space is constituted through the material and visual enactment of categories.