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Digital “is” Strategy: The Role of Digital Technology Adoption in Strategy Renewal

Nicolas Van Zeebroeck , Tobias Kretschmer , and Jacques Bughin

Abstract—As digital technologies emerge and improve rapidly, firms face changing tradeoffs in terms of their technology infras- tructure and strategic direction. Hence, many of them adopt new digital technology and develop new business models and strategies. The literature on strategic alignment of IT suggests that firms need to synchronize these different domains of choice. We therefore, ask how far firms renew their strategy as they adopt new technologies. In this article, we study this question empirically by assessing if the adoption of new digital technologies is associated with, or even leads to, changes to firm strategy using a detailed survey-based dataset on firms’ strategy renewal and their adoption of digital technologies. We observe a strong positive association between the extent of strategy change and the stage of adoption of advanced digital technologies overall, suggesting a tight coupling between (technological) structure and strategy. Further, using instrumental variable regressions to disentangle the two effects, we find that the adoption of new technologies may lead to a large and robust effect on strategy change: the more extensive the adoption, the larger the change in strategy. This result is robust to various specifications and across industries. However, we notice substantial differences across technologies, potentially pointing at heterogeneity in their strategic nature or maturity level.

Index Terms—Digital strategy, digital transformation, strategic organization design (SOD), strategic renewal, technology adoption.

I. INTRODUCTION

THE emergence of new digital technologies over the last decades has coincided with a wave of strategic change

initiatives by firms and an increase in the perceived stress from these widespread technological changes. While these trends may simply coexist, it seems likely that they are at least to some extent interdependent, not least because digital technologies enable certain forms of strategy change through the adoption of new technologies.

Yet, little is known to date about the interdependence and timing of technology adoption and strategy change [31].

Manuscript received 15 April 2020; revised 26 October 2020 and 6 April 2021; accepted 29 April 2021. Date of publication 11 June 2021; date of current version 6 July 2023. The work of Kretschmer was supported by the Advanced Studies at LMU. Review of this article was arranged by Department Editor T. Ravichandran. (Corresponding author: Tobias Kretschmer.)

Nicolas Van Zeebroeck is with the iCITE, Solvay Brussels School of Eco- nomics and Management, Université Libre de Bruxelles, 1050 Brussels, Belgium (e-mail: [email protected]).

Tobias Kretschmer is with the Institute for Strategy, Technology and Organi- zation, Ludwig-Maximilians-University of Munich, 80539 Munich, Germany, and also with the Centre for Economic Policy Research, London (e-mail: [email protected]).

Jacques Bughin is with the Solvay Brussels School of Economics and Man- agement, Université Libre de Bruxelles and Machaon Advisory, 1050 Brussels, Belgium (e-mail: [email protected]).

Digital Object Identifier 10.1109/TEM.2021.3079347

Despite the well-established result that investments in IT require organizational adaptations [6]–[8], [14], the channels through which the availability of new technologies affects firm strategy and organization have been studied in less detail. And although strategic renewal has received wide attention [2], [19], [22], how the concept applies to digitally enabled renewal is still understudied. Hence, we ask how firms respond to the emerging reality of increased digitization and the rapid emergence and introduction of novel technologies. Do firms change their strat- egy to deal with the threat of digitization for their core business and/or to take advantage of the opportunities afforded by digital technologies, or are these technologies simply adopted without corresponding changes in firm strategy?

We propose a simple conceptual framework in which two complementary domains of strategic choice [26], business strat- egy and digital technology, lead to different possible config- urations with different costs and benefits. We argue that firms choose the most profitable configuration. In the long run, the two domains are kept in sync and coevolve as the “right foot follows the left” [36]. However, if one of the two domains experiences rapid changes in costs, possibilities, or returns, the tradeoffs for firms may change and adoption of one technology, for instance, may prescribe a new optimal configuration for the other domain (in this case strategy). The past decade has been such a period of rapid changes in the costs and capabilities of digital technolo- gies. In such conditions, our conceptual framework suggests that firms adopting the new digital technologies are more likely to also renew their strategy. We further expect this synchronization to occur gradually.

We seek empirical evidence on these interdependences of corporate-level strategy change and within-firm digital technol- ogy adoption and diffusion. Our empirical analysis uses data from a unique survey led by McKinsey and Company in 2017. The survey looks at digitization (focusing on the adoption of new generations of technology) and how firms have adapted their strategy to the threats and opportunities created by these technologies. The sample covers a wide range of firms (in terms of size, ownership structures, and geographies) and industries to give a broad view of the phenomenon.

Our results show the following points. 1) Digital technology adoption is positively associated with

strategy renewal. 2) This association exists at the extensive (i.e., any strategic

change) and the intensive (a greater degree of change) margins in that the more widespread a company has adopted a certain technology within the firm, the more

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likely it is to be engaged in more fundamental strategy change.

3) There are likely situations in which technology adoption inspires and drives strategy renewal.

4) The strength of this association depends on the technology itself.

These results are robust to a battery of robustness tests, includ- ing the inclusion of a control for the level of perceived stress from digitization, as well as to the use of instrumental variables (IV). We replicated our analysis with a second survey-based dataset with a different sample of firms and a different set of digital technologies (this survey was exclusively focused on artificial intelligence (AI) technologies) and obtained consistent results.

More generally, our results support the view that digital technology at large and specific technologies, in particular, is indeed strategic. They have implications for our understand- ing and the management of digitization and emphasize the close interdependence between strategic renewal processes and technology adoption. Thus, we contribute to the literature on strategic organization design (SOD), which posits that strategy and (technological) organization go hand in hand, specifically by documenting related strategic and technological adjustment processes in a period of rapid technological change.

II. THEORETICAL BACKGROUND AND CONCEPTUAL MODEL

We conceptualize the SOD decision faced by firms with the following simple formalization: Assume that an organization consists of different “domains of strategic actions” [26] that are complementary to each other, i.e., the strategic actions taken in one domain affect the marginal benefit of the actions in the other domain. For simplicity, consider an organization with two strategic domains: Strategy (S) and Structure. Given our empirical setting and the goal of our study, we focus on digital technology (D) as part of the organizational structure [35].1 The complementarity between the two suggests that there is one “best” configuration of S and D that maximizes an organization’s profits (net of the cost of implementing the two activities). These net profits can be firm-specific; for example, a firm with a high absorptive capacity [18] or extensive IT support staff will face a lower cost of running a state-of-the-art technological system (such as AI) or smaller firms may expect lower benefits (at equal cost) of a process technology (such as Internet of Things) with a fixed cost of implementation. On the strategy side, the cost of implementing a customer-oriented online sales strategy will differ between an already sales-driven organization and a former state-owned monopolist employing former civil servants. This has two implications: First, organizations will strive for a configuration that maximizes their profits, and second, this optimal configuration may differ across organizations [29]. In a steady state, therefore, we will likely observe outcomes with clusters of (sufficiently similar) firms choosing the same or similar configurations [31].

Now suppose that one of the domains, D, experiences a shift in the cost of some actions in it, for example, because a digital

1The technology used in a firm is part of the firm’s organizational design as it guides the way work is organized, delegated, and monitored within the firm [21], [30].

Fig. 1. Numerical example of two-domain, two-action organization.

technology matures and its cost of implementing declines, or, by similar logic, a new digital technology becomes available in the first place. An example of a digital technology maturing and be- coming commercially viable in our sample is three-dimensional (3-D) printing (or additive manufacturing), which reached a stage where it could be used for mass production and a variety of purposes instead of just rapid prototyping in the 2010s. Similarly, image and pattern recognition (part of our supplementary AI survey) was virtually useless for commercial purposes, while the recent combination with deep learning and complex optimiza- tion has led to commercial applications emerging, most notably the ongoing push toward autonomous driving. A similar pattern is evident in mobile applications, which rose from awkward niche offerings to ubiquity in recent years. Conceptually, such a shift in the cost or benefits of digital technology will have two immediate consequences: First, it will lead to a new steady-state configuration for some organizations. These organizations found implementing the specific digital technology D prohibitively costly prior to the shift such that they did not choose the bundle of S and D including the new digital technology, but they may now. The second consequence is that the firm may end up changing its strategy S in response to the cost shift in D. This is not because the benefits or costs of the actions in S have changed, but rather because the shift in costs of D has changed the optimal configuration of S and D for the organization.

Fig. 1 illustrates this in a simple numerical example: In the “Pre” phase, configuration S1D1 offers the highest net

benefits (6 – 2 – 2 = 2). In the “Post” phase, technology D2 gets cheaper (we could easily assume that it was not available before and, thus, had an infinite cost), which leads to the two changes

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VAN ZEEBROECK et al.: DIGITAL “IS” STRATEGY: THE ROLE OF DIGITAL TECHNOLOGY ADOPTION IN STRATEGY RENEWAL 3185

we predicted: First, the new optimal configuration is S2D2, and second, the change in technology (D1 → D2) triggered a change in strategy (S1 → S2).

Based on the above, we can distinguish between two “states.” The first is the steady state in which both elements S and D are closely aligned and affect each other “like each foot follows the other” [36]. In a steady state, therefore, the statements “struc- ture follows strategy” [16] and “strategy follows structure” [25] are not meaningful because a specific strategy would not have been chosen without the best (digital) structure in mind, and vice versa [21], [23], [39]. The second state describes the transitional state in which one of the two domains has experienced a shock to its relative costs and, thus, triggers a causal chain: First, the domain in which a change in costs has occurred will change, selecting actions that have become comparably cheaper than the previously chosen one. This in turn will lead to an adjustment of the other domain to settle on the new optimal configuration. In this state, a shock to one activity domain can lead to a successive adjustment of other parts of the system.

In terms of the theory of strategic alignment, this concep- tualization simply implies that the optimal configuration may change as a result of a shift in cost or benefit of one of the domains, i.e., that a new configuration may become optimal. The SOD perspective deserves some qualification here: while the suggestion that Strategy and Structure are necessarily chosen jointly and with a view on the overall profits may be accurate in steady state, it may not hold in situations of flux and uneven shifts in the cost of some of the activities. Here, the “affected” activity may lead the “unaffected” both chronologically, but also causally as unaffected activities will only be changed once the affected ones have been tried and tested. Finally, the transition from one aligned configuration to another may require that changes occur in a stepwise fashion, which has two advantages: First, the “misalignment” between Strategy and Structure at any one point of the transition is comparably small since the changes in each activity are small. Second, the transition can still be reversed at a relatively moderate cost as the organization does not fully implement a new solution for one activity that would be hard to reverse, but rather performs a series of smaller commitments to avoid a costly failure [15].

One important nuance, however, stems from the potential impact of each technology on businesses. Agarwal and Helfat [2] proposed that a resource can be considered as strategic if it affects the long-term prospects of a firm. Our theoretical devel- opment inspires an empirical test for the strategic importance of a given (digital) resource: a resource that is not strategic in Agar- wal and Helfat’s terms will not significantly affect the long-term benefits of the firm and so changes in the costs of adopting this resource (D) would not change the returns of possible strategies (S) for the firm. In contrast, a digital technology that does trigger strategic change is one that is affecting the future profits of the firm enough to encourage a different strategic configuration. A strong positive association between the adoption of one specific digital technology and strategy renewal would, therefore, be empirical evidence of the strategic character of the technology.

While the theory of strategic alignment and our conceptual framework developed above could be applied to many other

technologies, we note two idiosyncrasies of digital technologies. First, digital technologies have proven to carry much more “transformational potential” than other technologies, i.e., they are more likely to be strategic in Agarwal and Helfat’s [2] terminology. The fact that digital technologies are often highly modular and can support many existing processes in the firm [13] and can be reproduced at virtually zero marginal cost enables, and even stimulates, wide-ranging deployment and scalability [24]. Moreover, customer-facing digital technologies permit a completely new way of interacting with consumers, including product cocreation [17] or targeted advertising [38]. This has opened up opportunities for new business models and processes in ways that conventional technologies rarely do. Second, the way in which digital technologies are utilized is often affected by the capabilities of the firm [42], as indicated above. Thus, firms may choose different levels of implementation (and ultimately strategic importance) of the same digital technology, which in turn may trigger different levels of strategic change.

Our theoretical logic is anecdotally illustrated by many re- cent cases for which digital transformation occurred alongside strategy transformation. In the extreme, entire industries and dominant business models can be transformed, as, for example, the shift of many conventional firms toward platform-based business models demonstrates by Hagiu and Altman [46]. Aided by digital technologies, firms as diverse as Intuit, a financial services firm, Lawson, a convenience store chain, and Walmart, the retail giant, have recently opened up their core product to third-party suppliers offering complementary or even competing services.

In another example, BBVA, a Madrid-based financial con- glomerate, built data analytics and data management tools that were so powerful and efficient that it was able to spin them off as a new subsidiary called BBVA Data and Analytics [3]. Originally aimed at selling new data-based products externally (such as anonymized payment statistics), the subsidiary quickly turned into a powerful transformational force for the entire bank, guiding internal improvements to operations and inspiring new digital product features and experiences. From a limited-scope technology-driven initiative, the technology capabilities created ended up driving a larger strategy renewal at the bank level.

In a very different industry, Netflix’s business model was also dramatically reshaped by new technology. When its original business of DVD rental by post was no longer sustaining its growth, the company shifted to streaming technology as a new opportunity to distribute content. But the game changer came when big data technology and AI could lead to enhance user choice, which led Netflix to scale its strategy as a heavily customer-centric subscription model from the U.S. to world- wide.

The transformation of Ping An, the Chinese insurance con- glomerate, was perhaps even more radical. Its early incur- sions into digital channels and mobile applications enabled it to add a classified ads digital platform to its P&C insurance products and start building an entire fintech ecosystem. Ping An’s entire business scope got redefined as a consequence of the new technologies and capabilities acquired along the way.

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The common element in these examples is the driving role of new digital technology to inspire changes of varying degrees in the firm’s strategy. Here, digital technology is not simply an operand resource supporting the firm’s business strategy, but an operant resource that triggers new business innovations and ultimately renewal [34], [44]. In effect, the opportunities brought by new technologies open up avenues for new products or services and new business models. Of course, (technology) structure and strategy inform one another, but more often than not in these examples, strategic renewal2 was triggered by new technological capabilities.

These examples illustrate potential ways in which new techno- logical possibilities may drive strategic renewal. One plausible mechanism underlying this ripple effect is the notion of learning or cognition. As shown by Kaplan [28], senior management cognition is key to strategic responses to new technological developments. Technology experimentation and adoption in lo- cal initiatives may well raise top management awareness and cognition about the technological possibilities. Anecdotally, the CEO of a multinational manufacturing company based in Belgium recently indicated that he had overlooked the potential of digitalization until he had seen some of its potentialities in the form of pilot new products and applications, opening the door to new delivery and service models.3 Only after the pilot and proof of concept did he realize that digital technology could well be the catalyst for the strategic renewal he felt was necessary. In his own eyes, technology experimentation had been instrumental to cognition, which then led to strategy renewal.

Another underlying mechanism potentially at play is sug- gested by Lusch and Nambisan [34]. They suggest that new digital technologies may create new ways for other resources to be deployed and create value or may create new operand re- sources themselves. Xiao et al. [44] suggest for instance that “the introduction of e-commerce platforms could trigger resource reconfiguration and process reengineering and eventually create innovations in business operations.”

III. DATA AND EMPIRICAL APPROACH

A. Estimation Model

To empirically investigate our theoretical considerations out- lined above, we estimate the likelihood of a firm implementing a strategy change Si as a function of its actual adoption of digital technologies (Ai, reflecting an attempt at identifying or seizing the upside), after controlling for the firm’s perceived stress (Ei), firm characteristics (Xi), and industry and region effects (Ii), as

Si = c+ αAi + βEi + δXi + θIi + εi. (1)

This model only captures an aggregate effect of adoption on strategy change. It does not capture possible differences across firms in the degree of adoption and their differential links to different degrees of strategy change. Absent longitudinal data,

2Agarwal and Helfat [2] define strategic renewal, the most extensive form of strategic change, as “includ[ing] the process, content, and outcome of refresh- ment or replacement of attributes of an organization that have the potential to substantially affect its long-term prospects.”

3Based on an interview with one of the authors in March 2018.

we run our model along different margins of strategy change (from ad hoc tactical changes to major strategy renewal) and/or different margins of digital technology adoption (from experi- mentation to local adoption to large-scale diffusion). Assuming firms first experiment with technology before adopting it in one specific (often localized) use case until they diffuse the technology at scale, we can, for instance, assess whether the different stages of adoption correlate differently with a specific margin of strategy change.

Intuitively, if structure and strategy influence each other, then more advanced stages of technology adoption should be correlated with higher degrees of strategy change. This is the main assertion we want to test empirically. If confirmed, it will support the view that these technologies are “strategic” in that they inform a different strategic configuration to maximize the future profits of the firm.

We use a probit model to estimate (1) and subsequently run a series of robustness tests using different versions of dependent and independent variables, different sets of controls, and dif- ferent specifications.4 We also use IV regressions (explained in more detail in the following section) to assess potential bias in our baseline estimations due to unobserved heterogeneity. Our results are robust to all those changes.

B. Addressing Omitted Variable Bias and Endogeneity

Despite our large list of controls (including industry and geography dummies), our empirical strategy is not immune to potential omitted variable bias or reverse causality. One such concern, in particular, is that some firms might be prone to experimentation and that this propensity to explore the space of possibilities might drive a higher rate of experimentation and strategy change without implying any direct relationship between the two. Although this might hold true at lower margins of technology adoption and strategy change, it is unlikely to affect our core results at the technology diffusion and strategy change levels, which serve as our baseline estimates. A firm might indeed experiment with various technologies and make tactical changes to its course of action on a frequent basis, but strategy renewal as defined in our empirical setting could not happen overnight or every other month since it is changing the long-term strategy of the firm. Most likely, this fickleness would also not be profitable for the firm.

Similarly, diffusing a new digital technology at scale within the organization requires a strong commitment and significant investments in complementary forms of capital (typically human and organizational) that take time to adjust. Because of that, unobserved heterogeneity in this case might drive a correlation between technology experimentation and low levels of strategy change but it is unlikely to drive a correlation between adoption at scale and strategy renewal.

Still, to address the risk of omitted variable bias, we have in- cluded in (1) the level of stress from digitally enabled disruptions perceived by the focal firm as a control. We do this to control for

4All our estimates were also performed with a logit model. The results were not affected.

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the importance of scaling versus experimenting, as stress may be a strong driver of strategy renewal (see, e.g., [27]).

To further mitigate the risk of reverse causality, we ran IV regressions in which we endogenize our core measure of digital technology adoption. In this effort, we are limited by our data constraints, which prevents us from drawing on secondary data. We, therefore, make use of the widespread and more basic nature of two of the technologies in our survey (web applications and cloud-based services), which are excluded from our measures of adoption in our main estimates. We use the adoption of these two foundational technologies as instruments for our core explanatory variable (digital technology adoption). To qualify as valid instruments, these variables need to be correlated with the adoption of current waves of digital technology but unrelated to current levels of strategic change.

The former condition is satisfied because web and cloud tech- nologies are highly generic, and act as enabling or prerequired foundations for the successful adoption of the more advanced technologies that are otherwise considered in our survey. This claim is supported by earlier works (see [4], [9], [11], [12]).5

The latter condition is supported by a time argument. Indeed, most of the strategic changes brought about by basic web and cloud technologies must have been implemented long ago, e.g., an internationally harmonized web presence or online shopping (for web technologies), and improved collaboration opportu- nities across the firm and locations (for cloud technologies). Adoption of these technologies (web and cloud) is, therefore, unlikely to be driving current strategy renewal (i.e., strategy change occurring in our time period of analysis) but should be predictive of the current adoption of new technologies.

Our econometric analysis will report the results of a battery of validity tests for these instruments. They all support our approach and intuition. The first-stage equation to be estimated in our IV model is the following, in which Wi and Ci reflect the adoption of, respectively, Web and Cloud technologies by the focal firm i:

Ai = c+ α1Wi + α2Ci + βEi + δXi + θIi + εi. (2)

C. Data

We make use of a unique and novel dataset. The data form a cross section of firms across a wide range of characteristics, industries, and geographies and stem from a survey run by TNS Sofres on behalf of McKinsey in the first half of 2017 toward a list of CxOs. This list of CxOs forms a representative database of 12 000 C-level executives, cutting across a wide range of regions and industries. They represent organizations from all sectors (including nonprofit) and firm sizes (from less than 10 employees to more than 10 000 employees), although the vast majority of respondents come from North America, Europe, and Asia. Note that the database of C-level executives to which the survey is distributed has no connection with McKinsey, it is

5In our data, we find that nonadopters of new digital technologies are almost four times more likely to be nonadopters of web or cloud technologies than adopters. Clearly, the two generic technologies act as precursors for more advanced technologies.

maintained exclusively by TNS Sofres for conducting business surveys like these.

To ensure the highest possible quality in the responses, questions and answer options are systematically randomized across respondents, and anonymity is guaranteed to minimize overstatements. Responses may include missing values, as re- spondents may skip questions where they do not have good knowledge of answers. More broadly, the survey procedure has been validated in multiple studies (see among others [10]). Standard tests did not reveal any systematic common method bias.

The survey looks at digitization at large and contains 1619 responses, a 13.3% response rate. These data are remarkable as it is very rare to obtain information jointly on the adoption of digital technology and new strategies among firms, especially with responses coming exclusively from C-level executives. The main downside of these data is the anonymity guarantee that prevents our access to the identities of the firms and exact values for their main characteristics. We are, therefore, limited to the categorical values provided to us.

Summary statistics are provided in Table I and correlations in Table II. Due to missing values (responses were not mandatory in the survey, as a way to avoid “noise” in answers), our final analysis sample size is 956. Note that we have also run our analyses with a full sample, either imputing the missing values or omitting the control variables with missing values and the results hold.

For robustness purposes and to mitigate some of these limi- tations, we also ran our analysis on a second dataset, coming from another McKinsey-led survey, performed in the same year (2017). The sample here comes from a different database of CxO’s maintained by McKinsey, although not necessarily McKinsey clients, with a response rate of about 15%. This alternative survey included the same strategy question as our main dataset, but adoption questions were asked on a narrower set of digital technologies belonging to the broad field of AI. This “AI” alternative dataset includes more observations (3073 responses, of which 2453 are complete and exploitable for our analysis) and offers a more granular control for firm size (albeit still in the form of a categorical variable). In turn, other firm controls (such as diversification, ownership, or incumbency) are not available in this alternative dataset. Summary statistics for our alternative dataset are reported in Table I, next to the main dataset, and results are given in the Appendix.

1) Measuring Strategy Renewal: Our measure of strategy change is built from a unique question included in our survey: “How, if at all, has your organization adapted its corporate strat- egy to address the digitization-related changes it has experienced in the past three years?” Respondents were asked to pick their response among the following graduated list of options.

1) We have not yet responded. 2) We have responded through ad hoc initiatives and actions. 3) We have developed a coordinated plan to respond to the

changes but have not changed our longer term corporate strategy.

4) We have changed our longer term corporate strategy to address the changes.

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TABLE I SUMMARY STATISTICS

TABLE II CORRELATIONS

∗Correlation coefficient is significant at the 1% probability level

5) We initiated at least some of the changes in the industry. Note that, as formulated, the question explicitly refers to a

response to digitalization, thereby inducing a causal chain of events from enabling digital technology to strategy renewal, which motivated the formulation of (1) with strategy renewal as a dependent variable, We code our dependent variable (strategy renewal) as a dummy equal to 1 if the focal firm has changed its long-term corporate strategy (i.e., levels 4 and 5 on the survey response scale) to address the changes, but we test the sensitivity

of our results to different margins of change, i.e., including response (3), having at least developed a coordinated plan, and response (2), ad-hoc initiatives and actions. Overall, 46% of the respondents have at least changed their corporate strategy and 67% have at least developed a coordinated plan. We also run our model on the original responses on a scale from 1 to 5 to test the linearity of our effects along the intensive margin (i.e., are experimentation, local adoption, or diffusion associated with increasing levels of strategic change?).

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TABLE III ADOPTION RATES

2) Measuring Adoption: Which technologies (among a set of prelisted ones) the responding firm has already experi- mented with, or adopted in at least one functional area, or deployed at scale throughout the organization is at the heart of the survey. These questions have been asked for a set of ten broad families of digital technologies.6 We constructed different measures of adoption, either as binary variables (at least one technology has been experimented with/adopted locally/diffused at scale), as count variables (number of technologies experimented/adopted/diffused), or relative count variables (difference between the number of technologies exper- imented/adopted/diffused and in-sample median of the same). Table III reports adoption rates by technology for each of the three different margins of adoption.

In our data, traditional web applications and cloud-based services stand out as very largely adopted. Barely 3% of the firms in our sample have not even experimented with Web ap- plications (66% have it fully diffused at scale). For cloud-based services, these figures are 8% (no adoption whatsoever) and 44% (full-scale diffusion) respectively. Given their widespread adoption and diffusion, we exclude these two technologies from our independent variables.

These two technologies (web and cloud) are not just more widespread, they are also likely to be complementary to many of the other (more advanced) technologies in the survey (see [4], [9], [11], [12]). We take advantage of this feature to use their

adoption by the focal firm as an instrument for the focal firm’s adoption of other technologies (see Section III-B).

3) Controlling for Perceived Stress: One important potential confounding factor in our analysis could be the extent to which firms perceive threats in their environment due to the digitaliza- tion of their competitors or new entrants [27], [32], [45]. Such stress could indeed potentially drive both the adoption of digital technology and strategy renewal. We, therefore, need to control for firm perceived stress from digital technologies, which we capture through the following question: “If your organization took no action in the future to digitize any elements of its business, how much of its current revenue do you think would be at risk of being lost or cannibalized within the next three years?”7

6The categories are: big data and big-data architecture (e.g., data lakes), ad- vanced neural machine-learning techniques (e.g., deep learning), robotics (e.g., robotic process automation), AI tools (e.g., virtual assistants, computer vision, voice recognition), additive manufacturing (e.g., 3-D printing), mobile Internet technologies (i.e., devices that connect to the Internet and work individually, such as wearable technologies and Internet-enabled appliances), cloud-based services, traditional web technologies (e.g., social media, online meetings, video conferencing), augmented-reality (AR) technologies, Internet of things (i.e., devices that can communicate with each other as part of a network).

7In the alternative (AI) dataset, the question reads as “which of the following statements best describes the impact you think AI will have in your industry in the next three years?” with response options ranging from “Major negative impact” to “Major positive impact”. Our measure of stress is a dummy equal to 1 if the firm’s response is negative (minor or major). In an alternative specification, it takes value 1 only for “major negative impact.”

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TABLE IV MAIN REGRESSION RESULTS—TECHNOLOGY ADOPTION AND STRATEGIC CHANGE

In columns 1–4, the dependent variable is our baseline (binary) measure of strategy renewal. Columns 5 and 6 use a different margin of strategy change (tactical in column 5, plan only in column 6). Column 7 uses a discrete measure from 1 (no change) to 4 (renewal) and 5 (disruption). Standard Errors in parentheses. Coefficients significant at the ∗ 10%, ∗∗

5%, or ∗∗∗1% probability levels.

Answers to this question are recoded as a dummy variable indicating whether the focal firm has negative expectations about the impact of digital technologies, which we use as a proxy for perceived stress.8 A total of 51% of firms report comparatively high stress (relative to their peers) for the baseline version and 29% for the more restrictive version (based on the third quartile).

4) Other Firm Controls: We are limited by our data in the number of firm observables we have and can, therefore, use them as controls (the identities of our sample firms are unknown to us). In addition, the firm controls in our data are categorical variables. They enable us nonetheless to control somewhat for several key sources of heterogeneity at the firm level: region (at the country level), industry, size (proxied by dummy variables indicating revenues in excess of or below $1 billion), age (proxied by a dummy distinguishing between incumbent (established) firms and new entrants), and ownership (whether the firm is publicly listed or not). We include all these controls as dummy variables in our model. We further exploit information about the degree of diversification of the focal firm (B2C versus B2B, Product versus Service, Monoproduct versus Portfolio). This extensive set of controls—although categorical—is aimed at capturing as much heterogeneity at the firm level as possible. They are not as granular or detailed as one could hope, but if the latent variables they serve as a proxy for were confounding factors, then their inclusion or exclusion would significantly affect the coefficient of our core explanatory variable, which they do not. Nonetheless, since they may not entirely rule out all sources of unobserved heterogeneity, we develop our strategy to mitigate endogeneity concerns further in the next section.

IV. RESULTS

We start by estimating (1) on our sample, using the diffusion of at least one technology at scale within the firm (excluding

web and cloud) as the default measure of adoption. Results are reported in Table IV. Column 1 reports our baseline estimates of (1) using a probit model (our default specification).

A. Control Variables

Looking first at our control variables, Table IV (column 1) shows that perceived stress is indeed strongly and positively associated with strategy renewal. Its marginal effect (computed at the means of all covariates) corresponds to an 11.9 percentage point increase in the likelihood of strategy renewal. With a baseline likelihood of 45% in our analysis sample, this corre- sponds to a 26% higher renewal likelihood for firms perceiving comparatively high stress. This coefficient is remarkably stable across our different specifications, except when the dependent variable is based on a lower level of strategy change.

The second striking result among our controls is the signif- icantly negative coefficient associated with monoproduct and monoservice firms. This may be because diversified firms are more versatile, making pivots or shifts among product portfolios less costly than in highly specialized and less diversified firms.

B. Main Effect of Technology Adoption

Turning now to our main explanatory variable, we first find that technology adoption is positively and significantly asso- ciated with strategy change. The estimated coefficient of the standalone term (in column 1) corresponds to a 19 percentage point higher incidence of strategy renewal among firms that have

8This dummy is equal to 1 if the share of revenue at risk is above the median (which is around 25% of the revenues at risk), and 0 otherwise, reflecting more negative expectations relative to other firms in our sample. Our results are robust to an alternative construct in which the stress variable is set to 1 when revenues at risk exceed the third quartile (roughly 50% of revenues at risk or more) instead of the median.

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adopted at least one technology (marginal effects computed at sample means). Given a baseline incidence of strategy renewal of 46% (see Table I), this marginal effect represents a 41% increase in strategy renewal from technology adoption.

C. Exploring Gradual Effects

In the subsequent columns of Table IV, we use the more fine-grained nature of our survey responses regarding our key independent and dependent variables. We run regressions using the different extents of technology adoption first one by one (columns 2 and 3) and then jointly (column 4). In column 2, the core explanatory variable takes value one if the focal firm has experimented with at least one technology (and has gone beyond experimentation with no other), and zero otherwise. In column 3, it is similarly set to one if and only if the focal firm has adopted at least one technology locally (but has not diffused any). The coefficient for both variables is significant and negative, indicating that strategy renewal is less likely when a firm has only experimented with or started adopting locally one or more tech- nologies. In column 4, it appears further that the coefficient in- creases with the stage of adoption. Condition on having diffused at least one technology, being at experimentation or adoption stage with at least one other technology, is positively associated with renewal, but the coefficient is not significant at conventional levels for experimentation and local adoption. The same pattern is observed in column 7, which uses a discrete version of the dependent variable reflecting the level of strategy change.9

These patterns suggest that firms who try out new or emerging digital technologies are likely to dip a toe in the water to see whether they should develop a strategy around it. Firms advanced in their adoption of technologies are significantly more likely to have engaged in a strategy change than those at the experimentation stage, suggesting that technology adoption and strategy change follow each other.

Columns 5 and 6 test our full specification against two alter- native versions of the dependent variable. Column 5 corresponds to the lowest level of response (ad hoc (tactical) initiatives only). Column 6 uses the intermediate level of reaction (having a coordinated plan but no effective change to the long-term strategy yet). Comparing these two columns with our baseline (strategy renewal, in column 4), we find that the lower levels of adoption (experimentation and adoption) correlate positively with the lowest level of response (ad hoc initiatives, column 5). In contrast, the coefficient associated with the highest level of adoption (diffusion, our default) on the lowest level of reaction (ad hoc initiatives) is negatively signed. Although they are not significant at conventional levels, the coefficients of adoption stages on different degrees of strategic change are consistent with our core assumption that more advanced levels of adoption are associated with higher orders of strategy change.

D. Instrumental Variables Regressions

In order to test whether our baseline results are subject to omitted variable bias, we run our baseline estimates with IV,

9From 1 = no change to 4 = renewal and 5 = disruption.

using the diffusion at the scale of web and cloud technolo- gies within the focal firms as instruments for that same firm’s adoption of the other (more advanced) technologies. The results of our second-stage least squares estimates are reported in the first column of Table V. First-stage results are reported in col- umn 2. We comment on the validity of our instruments in the following.

The first-stage results clearly support the enabling role of cloud and web technologies as they both strongly predict the adoption of other technologies. The second-stage results are qualitatively consistent with our non-IV estimates: technology adoption is still strongly and positively associated with strategy change. Strikingly, the coefficient of technology adoption gets three to four times larger in the IV compared with the corre- sponding non-IV results.10 This is partly due to the fact that most control variables are associated with technology adoption (as suggested by most probit models of adoption in the litera- ture, sometimes referred to as “rank effects”) and their effect on strategy renewal is therefore channeled through technology adoption itself.

Interestingly, most of our control variables are significant in the first-stage regression, but not in the second-stage IV regres- sion. This suggests that some of the effects of our technology adoption variables are picked up by the control variables unless we run IV regressions.

E. Instrument Validity

We employ the standard machinery of tests for weak, under- , and overidentification. First, we test for underidentification using a Lagrange-multiplier test based on the Kleibergen-Paap [48] rk statistic to check whether the instruments explain enough variance in the endogenous regressor on top of the other controls. Our test rejects the null hypothesis of underidentification at the 1% level. This means that our instruments hold enough explanatory power and are indeed predictors of the current adoption of new technologies.

Next, we test for weak identification using Stock and Yogo’s [47] characterization. The Kleibergen-Paap Wald [48] rk F- statistic has a value of 63.19, clearly above the cutoff values of Stock and Yogo (7.25 at 25% maximal IV excess bias and 19.93 at 10% maximal IV excess bias). This suggests that our instruments are not just weakly correlated with our endogenous regressor and the correlation between the instrument and en- dogenous variable is sufficiently strong.

Finally, we test for overidentification using Hansen’s J statis- tic. The p-value of 0.23 does not let us reject the validity of overidentifying restrictions, which again supports the validity of our instruments.

In sum, our diagnostic tests do not reveal evidence of weak, under-, or overidentification and, therefore, support the formal

10To avoid comparing apples and oranges, we need to compare IV results to non-IV results obtained using a similar regression model. This implies comparing column 1 of Table IV (probit estimate with the default set of controls) with column 6 of Table V (IV Probit estimate with the same set of controls) and column 1 of Table V (a 2SLS IV estimation with the default set of controls) with column 7 of Table VI (an OLS estimate with the same set of controls).

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TABLE V INSTRUMENTAL VARIABLES ESTIMATES

Dependent variable is our baseline (binary) measure of strategy renewal. Standard Errors in parentheses. Coefficients significant at the ∗ 10%, ∗∗ 5%, or ∗∗∗1% probability levels.

TABLE VI ROBUSTNESS TO DIFFERENT SPECIFICATIONS

Dependent variable is our baseline (binary) measure of strategy renewal. Standard Errors in parentheses. Coefficients significant at the ∗ 10%, ∗∗ 5%, or ∗∗∗1% probability levels.

validity of our instruments. In other words, they fully support the view that our instruments are exogenous and strongly cor- related with our endogenous regressors, but not with the error terms.

To further explore our IV results, we also ran a battery of robustness regressions, reported in Table V. Columns 3 and 4 report IV estimates using only one instrument at a time (Web adoption in column 3, cloud adoption in column 4). Column 5 reports an estimation of the full IV model (with both IVs) using limited information maximum likelihood (LIML) instead of simple 2SLS, which is more robust to weak instruments.

Finally, column 6 reports an estimate of our full IV model using an IV Probit (instead of 2SLS). All results are consistent with our baseline (in column 1).

All these results provide strong supportive evidence of a direct influence of technology adoption on strategy change.

F. Exploring Robustness

Different robustness checks are reported in Tables VI and VII. In column 2 of Table VI, we drop the “stress” variable from the controls. This lets us check the consistency of our core

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TABLE VII ROBUSTNESS TO DIFFERENT MEASURES OF DIFFUSION

Dependent variable is our baseline (binary) measure of strategy renewal. Standard Errors in parentheses. Coefficients significant at the ∗ 10%, ∗∗ 5%, or ∗∗∗1% probability levels.

estimates when removing this potential source of heterogeneity. Our results hold as there is no statistically significant difference between the coefficients of our core explanatory variable be- tween this specification and our baseline, repeated in column 1.11 This is important as it indicates that stress perception does not mediate the technology-strategy relationship. In column 3, we introduce an alternative measure of stress, which is set to 1 when firms’ perceived level of stress is in the top quartile of our in-sample distribution (instead of the default measure based on the median value). Our core results are unchanged. In columns 4–6 of Table VI, we check whether our results are robust to different sets of controls (or no controls at all). Finally, column 7 reports the results of our baseline specification estimated with a linear probability model using OLS.

We report further robustness tests in Table VII. They exploit different margins of technology diffusion. We first use two alternative measures of diffusion. In column 1, we use the nominal count of technologies diffused at scale within the focal firm (instead of a dummy indicating “at least one” as we do elsewhere), thereby testing the intensive margin. Again, we find a positive association with strategy renewal. In column 2, we use a dummy equal to 1 if the number of technologies the focal firm has already diffused at scale is equal to or larger than the median. Columns 3 and 4 use two alternative versions of our core diffusion measure, including the two technologies we had excluded given their widespread adoption (traditional

11In this specification, we recover a substantial number of observations that had missing stress information. All our results were tested with both samples (with and without stress test) and they are fully consistent.

web applications and cloud-based services). Both are included in the diffusion variable in column 3 and only Web (the most widespread, with the highest rate of diffusion) is excluded in column 4. We also replicated all our estimates (see Table IV) with these versions of the key explanatory variable and all our results hold.12

G. Exploring Technology Differences

We finally turn to technology-specific effects. To this end, our baseline specification was tested with each technology-specific measure of adoption as an explanatory variable. The results are reported in Table VIII.

These technology-specific estimates offer some contrast to our baseline results. For all eight technologies, diffusion is positively associated with strategy renewal (except for additive manufacturing, where the coefficient is close to zero). How- ever, the magnitude and significance of the coefficients vary widely, from a low of 0.04 percentage point increase (marginal effect computed at the means) associated with AI and not significantly different from zero) to a high of 26.0 percentage points increase for big data. Three technologies clearly stand out as most strongly associated with strategy renewal: Big data, mobile Internet, and Internet of things. This is interesting as these technologies stand out in our set of surveyed technologies. First, compared to the other technologies in our sample, IoT, mobile Internet, and Big data (fully diffused in 12%, 22%, and 11% of firms, respectively) had already reached maturity and

12The results of these tests are available from the authors upon request.

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TABLE VIII EXPLORING TECHNOLOGY DIFFERENCES

Dependent variable is our baseline (binary) measure of strategy renewal. Standard Errors in parentheses. Coefficients significant at the ∗ 10%, ∗∗ 5%, or ∗∗∗1% probability levels.

were diffused more widely than, say, additive manufacturing or AI (2% and 6% full diffusion, respectively) in our survey period (2017). This suggests that firms had more time and opportunity to experiment with and develop use cases for the technology that can then be utilized to initiate and implement a major strategic change. This is in line with our conceptual considerations that for a technology to truly play a key role in facilitating strategic change, firms need to be able to integrate it into their organizational design, which takes time and exper- imentation. A second notable difference between IoT, mobile Internet, and Big data and the other technologies surveyed is that they can potentially be applied to a much wider range of use cases.

The popular press and industry reports typically emphasize the general-purpose nature of these technologies, pointing out that their application goes beyond a single industry or func- tion within the firm [13]. In contrast, some other technologies such as additive manufacturing or robotics are more limited to the manufacturing of physical goods of a certain type. While this can (and will) enable firms to redesign their production processes to become more efficient, the role these technologies play in realizing new business models and/or redesigning their organizational processes (beyond the manufacturing function) is limited. Hence, it is plausible that the three technologies most strongly associated with strategic change are the ones with the most general applicability, most resembling a general-purpose technology. We, therefore, speculate that for a technology to be closely linked to firm-wide strategic change, it has to be mature enough to permit sufficient experimentation and generic enough to support multiple use cases.

H. Exploring Industry Differences

Table IX reports the results of our estimations of (1) with industry subsamples. We organized industries in five clusters: manufacturing, financial services (including private equity), for- profit services (including retail, transport, and professional ser- vices), public and nonprofit services (including also healthcare and energy/utilities), and high-tech (including media and tele- com companies). The results do not reveal any strong differences across industries, suggesting that our results are not driven by one specific industry and use case.

V. DISCUSSION AND CONCLUSION

How do firms respond to the emerging reality of increased digitization and frequent and often unforeseen introduction of novel technologies? Do they change their strategy to circumvent the threat (in a “bold retreat” approach suggested by Adner and Kapoor [1])? Do they simply digitize their core business without changing their corporate-level strategy? Or do they follow a combination of these two approaches? More gener- ally, do the two processes—technology adoption and strategy change—occur in sequence or in parallel?

Our conceptual model, in this article, suggests a distinction between a steady-state situation in which technology structure and strategy—two interdependent domains of strategic choice— are well aligned, which means that they have been chosen to jointly ensure the highest possible payoff. In this long-run equilibrium, strategy follows technology as much as technology adoption follows strategy, and it is very hard to disentangle a causal relationship. Our conceptual model predicts that sudden

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TABLE IX EXPLORING INDUSTRY DIFFERENCES

Dependent variable is our baseline (binary) measure of strategy renewal. Standard Errors in parentheses. Coefficients significant at the ∗ 10%, ∗∗ 5%, or ∗∗∗1% probability levels.

changes in the cost of the options in one domain (e.g., new technologies) will create new tradeoffs encouraging firms to experiment with new technologies, creating, in turn, new optimal configurations in the other domain (strategy). This implies that exogenous shocks in the availability or cost of new technologies as firms have been experiencing for a decade or so provide a particular situation where the technology may push firms into unchartered territories and reassess their strategic options as they experiment with the technology.

This leads us to empirically test the extent to which gradual changes in the firm’s technology are associated with gradual changes to their strategy, up to their potential renewal. This approach cogently offers an empirical test for the strategic nature of technologies, as evidence of a link between the adoption of a specific technology and firm renewal will suggest that the focal technology is affecting the long-term prospects of the firms enough to encourage a strategy renewal, and hence that it is a strategic resource (in Agarwal and Helfat [2]’s terms).

Our empirical analysis, based on data from a large survey of executives cutting across different geographies and industries, supports our conceptual model as technology adoption and strategy renewal clearly occur in parallel and in close connection with each other. Moreover, we find evidence in the particular context of our study that suggests a direct impact of technology adoption on strategy renewal.

Given the cross-sectional nature of our data, our estimates rely on interfirm variance in levels of technology adoption and in degrees of strategy change. We find a strong and robust positive association of the degree of strategy change with the level of technology adoption. Moreover, we find a strong and posi- tive association between the extent of strategy change and the

perceived stress from emerging digital technologies, suggesting that it is a potentially important source of motivation for a strategic renewal as predicted by the literature [32].

Overall, the main insight from our empirical analysis is that firms do not necessarily craft a new strategy simply by contem- plating emerging technologies, but some redefine their strategy more gradually as they experiment with new technologies. We, therefore, uncover one possible channel through which technol- ogy reshapes strategy [5], [37], [40]. Although our results sug- gest that not all firms respond in the same way to the emergence of new digital technologies, times of technological change are likely to coincide with episodes of widespread changes in firm strategies.

The gradually strengthening association between degrees of technology adoption and degrees of strategy change supports the view of a learning mechanism operating in this context. This speaks to the notion of cognition (see e.g., [28]) as tech- nology experimentation may reveal new strategic opportunities gradually and raise top management cognition gradually. More research would be welcome to further uncover these mechanisms and the role of dynamic capabilities in those processes.

Our results speak to two main bodies of literature: SOD [21], [30], [39] and IS Alignment [33], [41], [43]. To the former, our study shows that there exists a set of strategic renewal processes that may be driven by new technology experimentation and adoption. To the latter, our research backs up the view that adoption of digital technologies and strategy change are closely linked processes, thereby advocating for an integrated view of digitalization as a core embedded feature of the business rather than as a separate function that needs to be aligned with the core business [5], [20].

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APPENDIX

TECHNOLOGY ADOPTION AND STRATEGIC CHANGE: MAIN ESTIMATES USING ALTERNATIVE DATASET (AI SURVEY)

In columns 1–4, the dependent variable is our baseline (binary) measure of strategy renewal. Columns 5 and 6 use a different margin of strategy change (tactical in column 5, plan only in column 6). Column 7 uses a discrete measure from 1 (no change) to 4 (renewal) and 5 (disruption). Standard Errors in parentheses. Coefficients significant at the ∗ 10%, ∗∗ 5%, or ∗∗∗1% probability levels.

Our work also has important managerial implications. Specif- ically, we stress the role of technology experimentation in devis- ing strategic responses to digitalization. Our analysis suggests it is unrealistic for firms to build a new strategy based on a technol- ogy they have not yet experimented with. One may hypothesize that experimentation is needed as much to clarify the actual possibilities of a technology (cognition) as to start building the right skills and capabilities that would be needed to leverage the technology (creation of new operand resources). To paraphrase Mintzberg, a strategy needs structure as much as structure needs a strategy. Firms should, therefore, ensure close integration of their digital experimentation with their strategy function and processes to ensure they inform and reinforce each other.

Our study has some limitations. First, although our data offer a uniquely detailed insight into the experiences and attitudes of firms across a wide range of regions, industries, and sizes, this level of detail comes at a cost. Fixed- or random-effects models based on panel data would certainly help achieve better identification of causal relationships and uncover the actual timing and event dynamics within the relationships we identify.

The current pandemic may also offer a unique natural exper- iment to validate our conceptual model. During the pandemic, firms have been forced to adopt digital technologies in a short period of time (e.g., to enable remote work). This is, therefore, a unique context where technology adoption has been exoge- nously imposed. It will be interesting to study its impact on strategy renewal in the years to come. We hope that this article will help inspire some efforts in this direction.

ACKNOWLEDGMENT

The authors would like to thank McKinsey/Digital for supply- ing the data and M. Mickeler and A. Schulz and two anonymous referees for their very valuable comments and suggestions. All errors and omissions remain the authors’ alone.

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