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Digital Age Organizations: Uncovering Over-the-Air Updates in the Smart Product Realm

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Abstract

Over-the-air (OTA) updates are gaining traction across various industries such as manufacturing, healthcare, and automotive. However, there is currently no consistent conceptualization of OTA updates in place. Also, we know little about what organizational capabilities are needed to turn OTA updates into customer value and thus infuse digital business models. Against this backdrop, this work follows two goals. First, to conceptually delineate what constitutes OTA updates and second, to pinpoint the organizational capabilities required to successfully manage OTA updates. Building on a large-scale survey of 360 product managers in manufacturing companies, we suggest that organizations follow two strategic steps when building OTA capabilities. First, they build the required technical competence and leverage it through their ability to create new innovative services. Second, performant companies deliver and monetize their value proposition in new ways by refining their business models.
Over-the-Air Updates in the Smart Product Realm
Forty-Second International Conference on Information Systems, Austin 2021
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Digital Age Organizations: Uncovering Over-
the-Air Updates in the Smart Product Realm
Short Paper
Introduction
“Software update in progress. Please wait.” It is the message we all dread receiving on an electronic device
while trying to complete an important task. However, these negative consumer experiences with over-the-
air (OTA) updates miss the bigger picture. In the last two years, the auto manufacturer Tesla has delivered
more than 20 updates to the Model 3 in a variety of domains, including safety, autopilot, or in-vehicle
entertainment - each designed to optimize or enhance the customer experience while seamlessly delivered
over the air. While OTA updates, i.e., the remote and wireless transfer of new software to products is already
standard practice in electronic devices such as smartphones or personal computers, they are on the rise in
various other domains such as manufacturing, healthcare, and automotive (Koster et al. 2021). This
progress is being driven in particular by the rapid digitization of individuals through smart products and
the internet of things (Turel et al. 2020). Such smart products, i.e., products that have built-in digital
components such as sensors, actuators, or connectors, allow data to flow freely between discrete devices
and manufacturing companies without customer intervention (Atzori et al. 2010; Raff et al. 2020). So far,
consumers typically experience these OTA updates as mere security updates or bug fixes that offer little
fresh value (product optimization). But like Tesla, companies such as Audi or BMW are increasingly using
OTA updates to delight their customers by offering additional features or functionalities that boost the
customer experience in use (product enhancement). This way, traditional one-off interactions become
dynamic, tailored relationships that constantly change and adapt to consumer needs, continuously
generating new revenue streams.
It is against this background that OTA updates are gaining momentum in theory (e.g., Raff et al. 2020;
Verganti et al. 2020) and practice (e.g., Koster et al. 2021; Yokoi 2020). However, there is currently no
uniform understanding of OTA updates and their two main manifestations (product optimization vs.
enhancement). Nor do we know much about the organizational capabilities needed to build OTA
capabilities and turn them into value for the customer (Appio et al. 2021; Raff et al. 2020). With that in
mind, this work, which is a spin-off of a larger research agenda in the context of smart products business
models, addresses the following two research questions: (1) How can the two key forms of OTA updates -
product optimization and product enhancement - be distinguished at a conceptual level?; (2) What
capabilities are required to implement OTA updates, and how can organizations create value with OTA
updates? The remainder is organized as follows: In the subsequent section, we derive a deeper
understanding of the conceptual characteristics of OTA updates. Next, we address the second research
question by outlining exploratory findings from a large-scale quantitative dataset of >300 managers of
smart-product companies. We close with our key implications and an outlook of our future research.
Conceptual Framework of OTA Updates
Although the term OTA update is increasingly used in research and practice, it persists as a fuzzy umbrella
term for inherently different forms of virtual adaptations to smart products. However, to enable fruitful
research, it is critical to ensure that the epistemic object is properly conceptualized (Yadav 2018). To create
clarity at the conceptual level, we therefore organized a workshop with six researchers with expertise in
topics related to digital transformation and digital business models. This involved intensive discussions of
various real-world examples of OTA updates and categorizing them according to their distinguishing
characteristics. In this process, two central dimensions emerged that can be differentiated - OTA updates
for (1) product optimization and (2) product enhancement. OTA updates for product optimization are
usually pushed out to customers and designed to optimize the existing product so that it continues to
function as anticipated, safely, and securely. That is, for example, through bug fixes or routine security
updates. In contrast, OTA updates for product optimization are typically pulled by the customer and
enhance the existing value propositions by adding or customizing functions or services. In the following, we
outline our workshop results (see Figure 1). Due to limitations, we mainly describe the impact of OTA
updates for manufacturing companies like Tesla and link them with some of the implications for customers.
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Figure 1. Conceptualization of OTA Updates and Their Implications
OTA Updates for Product Optimization
Efficient service and repair. As cars become infused with smart components and software, OTA
updates help car manufacturers like Tesla to solve malfunctions and security bugs in a faster, cheaper, and
more scalable manner when disintermediating the dealers. Interestingly, software updates over the air can
even be used to optimize hardware modules. For instance, in 2018, Tesla improved the braking performance
of its Model 3 when Consumer Reports criticized its stopping distance (Olsen 2018). In B2B contexts, where
downtime is even more critical, OTA updates also come with the advantage of not being time and place-
bound to service technicians’ deployment. For example, the German engineering and technology company
Bosch enables original equipment manufacturers to update off-road vehicles operating in rugged terrain
such as mining and agricultural machinery, or, in an extreme case, cargo ships.
Accelerated innovation cycles. In the automotive sector, new model versions are launched every five
to eight years. In contrast to traditional car manufacturers, Tesla does not apply model years. They currently
have four model variants and update them every four to five weeks in response to technological advances
and changing customer requirements. The unique software-focused architecture allows to deploy updates
frequently and enables them to react rapidly to rules and regulations of a dynamic market. This provides
Tesla with a considerable advantage in fields such as autonomous driving and emission detection and
reduction. From the customers perspective, the products they buy improve in performance over time, so
they can be confident that their product is always up to date and remains valuable even in a fast-paced
environment.
Extended and intensified customer relationships. OTA applications can also intensify and extend
the relationships between manufacturing companies and their customers. The frequent deployment of
updates and the disintermediation of dealers and workshops ushers in more frequent and direct interaction.
Tesla, for instance, controls the customer experience from test-drive to after-sales and service. Thus, all
customers face consistent messaging and relationship management when interacting with Tesla at any
given touchpoint. Tesla’s vertical approach also ensures that all usage data and customer feedback are fed
back into product development to leverage highly user-driven features and designs.
Product Optimization Product Enhancement
Manufacturer
Implications
Efficient service and repair
Accelerated innovation cycles
Extended and intensified
customer relationships
Customized product
functions and services
Additional revenue streams
Customer
Implications
Convenience in service and repair
Reduced downtime
Efficiency gains
Extended product lifetime
Reduced depreciation
Personalized product experience
Flexibility through up- and
downgrading of product
functions and services
Manufacturer push Customer pull
OTA Capability
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OTA Updates for Product Enhancement
Customized product functions and services. Audi, BMW, and Tesla enable car owners to purchase
so-called on-demand functionality, such as advanced navigation or entertainment options, battery capacity
extension, or improved headlight function. While Tesla provides system-critical functions in-house, non-
critical functions can also be delivered by third parties such as Waze (navigation and traffic guidance) or
Spotify (streaming music). Offering such functions on demand comes with the important opportunity to
customize products in the usage stage. This allows manufacturers to respond to changing customer
requirements throughout the product lifecycle and provide ad-hoc enhancement of customer experience.
Additional revenue streams. Business customers of KONE elevators can select from a number of
features and services to customize their elevators in use and upgrade elevators over the air with advanced
floor scheduling, access management, infotainment functions, and third-party integrations such as smart
home applications or delivery robots. Such individualization of the product offering also generates new
revenue streams for manufacturing companies. For example, customers may be charged for features and
services booked, and third parties may be charged for integrating their services in a smart product offering.
Now that we have built a clearer understanding of over-the-air updates at a conceptual level, we will next
take a deeper look at what capabilities are required on the part of the organization to successfully design
and manage such OTA update offerings for optimization and enhancement. We do so by analyzing
quantitative data from a large-scale survey that revolves around issues related to the development and
management of smart products.
Organizational Capabilities for Managing OTA Updates
In the context of an international research project on industries that manufacture or employ smart
products, we recruited 360 product managers from the US, Germany, Austria, and Switzerland for
participation in a large-scale quantitative survey. Industries covered include, for example, automotive (e.g.,
Daimler), industrial machinery (e.g., General Electric), electrical equipment (e.g., Hexagon), or medical
devices (e.g., Olympus). Against the backdrop of our aim to better understand how OTA capabilities are
built and used effectively, we applied exploratory post hoc analysis to the collected data. We employed the
partial least squares approach to structural equation modeling (PLS-SEM) with SmartPLS (Version 3.3.3.)
due to its strength in theory development and its ability to handle complex models with single-item and
multi-item constructs as well as reflective and formative measurement models (Hair et al. 2014; Hair et al.
2019). Figure 2 depicts our research model and respective path coefficients.
Measures and measurement model. The constructs were measured as follows: A firm’s technology
stack was measured using the reflective-reflective second-order IT capability construct by Lu and
Ramamurthy (2011; with IT infrastructure capability, IT spanning capability, and IT proactive stance as
first-order constructs). Agile product development was measured as a reflective-formative second-order
construct using the first-order scales on agile hardware development and agile software development
from Schulz et al. (2021). Service innovation was measured with an established construct from Chen et al.
(2011). To operationalize OTA capability, we used a single-item construct (“We are able to remotely update
our products over the air”; 7-point Likert Scale). The business model innovation construct was based on
Osterwalder et al. (2010). The perceived customer value was measured using an established scale by Tu et
al. (2001). Finally, we controlled for firm age, firm size, R&D intensity, customer focus, customer
proximity, industry, and country.
To examine the reliability and validity of the first-order reflective measurement models, we followed
guidelines by Hair et al. (2019). All requirements concerning indicator loadings (loadings above .7), internal
consistency reliability (Cronbach’s alpha and composite reliability above .7), convergent validity (AVE
above .5), and discriminant validity (HTMT below .85, .90 for conceptually similar constructs) were met.
Following Sarstedt et al. (2019), we opted for a disjoint two-stage approach to estimate and validate the
higher-order constructs. We created and estimated a model without the higher-order constructs in stage
one to generate latent variable scores of the lower-order components, namely, IT infrastructure capability,
IT spanning capability, IT proactive stance, and agile hardware development and agile software
development. In stage two, we used these scores as indicators of the higher-order constructs technology
stack and agile product development in the final model. For the reflective higher-order component
technology stack, we found indicator loadings of .90, .94, .93, Cronbach’s alpha (.91), and composite
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reliability (.94) at satisfactory levels, an AVE value of .84, and discriminant validity with HTMT values
below .85. In contrast to the technology stack, the second-order construct agile product development is
specified as a formative measurement model. Consequently, it needs to be assessed based on
multicollinearity and the relevance and statistical significance of the indicator weights. We found that
multicollinearity is not an issue as the VIF values of 1.89 are far below the conservative threshold value of
3. Our analysis also shows that both indicators (agile hardware development: .53: agile software
development: .56) have a substantial and significant (p < 0.1) effect on agile product development.
Figure 2. Results of Structural Equation Modeling with Path Coefficients, R2 and Q2
Structural model. To test the significance of loadings and paths in the structural model, we applied the
bootstrapping procedure with 4,999 subsamples (Henseler et al. 2016). Figure 2 and Table 1 summarize the
results of the PLS-SEM analysis. While agile product development directly and significantly affects OTA
capability (.14, p <.05), the direct effect of the technology stack on OTA capability is not significant (.02,
p >.10). To further explore the mechanisms through which service innovation drives a firm’s OTA
capability, we performed a mediation analysis following Cepeda-Carrion et al. (2018). The results displayed
in Table 1 highlight the mediating role of service innovation, both for the relationships between technology
stack and OTA capability (full mediation) and between agile product development and OTA capability
(partial mediation). OTA capability does not directly affect the perceived customer value (.02, p <.10);
however, the indirect effect via business model innovation was significant (.09, p >.01). Hence, business
model innovation is an indirect-only meditator that fully mediates the relationship between OTA capability
and perceived customer value (Zhao et al. 2010).
To assess the model fit following Hair et al. (2019), we started by calculating the VIF values for each
construct to detect whether collinearity biases the regression results (all below the threshold of 5). Next, we
evaluated the in-sample explanatory power with R2 and the effect sizes of the predictor variables with
Cohen’s f2. Due to the research’s exploratory nature, relatively weak R2 values (see Figure 2) and f2 values
(between .05 and .16) can be considered satisfactory (Hair et al. 2019). To incorporate the out-of-sample
explanatory power, we applied a blindfolding procedure with an omission distance of 7 to calculate Geissers
Q2 values (see Figure 2, all > 0). Similar to the PLSdirect results for the key endogenous constructs (not
reported here: high predictive power for OTA capability, low predictive power for perceived customer
value), these findings indicate an acceptable model fit.
Technology Stack
Agile Product
Development
OTA Capability
R2= 0.19; Q2 = .09
Business Model
Innovation
R2= 0.12; Q2 = .06
Perceived
Customer Value
R2= 0.20; Q2 = .11
.02
.14**
.32*** .38***.24***
Service Innovation
R2= 0.36; Q2 = .09
.32***
.25***
Significant Path
Insignificant Path
Note:*p ≤ .10; **p ≤ .05; ***p
≤ .01. All tests are two-tailed.
.02
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Table 1. Results of Structural Equation Modeling Analysis with β-Coefficients
Discussion
The results of our exploratory analysis suggest that organizations should ideally bring two strategic
capabilities to the table for successful OTA capabilities. First, they need to build the required technical and
innovation competencies (technology stack and agile development capabilities) and leverage them through
their ability to create new innovative services. Second, companies need to deliver and monetize their value
propositions in new ways by refining their business models. In the following, we will elaborate on these
capabilities in more detail and discuss them in light of existing literature and practice.
STEP 1: What It Takes to Implement OTA Updates
Technology stack. To develop OTA updates, manufacturing companies need information technology
capabilities that allow them to equip products with the latest technology (e.g., operating software, sensors,
connectivity modules, etc.) and the technological infrastructure to connect with the user’s products to
collect, store and analyze product data, and to run applications on the products (Porter and Heppelmann
2014). In this regard, managers need to develop and follow a clear vision of how technology contributes to
the overall business and product strategy and plan IT investments accordingly. Tesla, for instance,
considered itself a technology company from day one, creating a shared understanding among employees,
suppliers, and customers of the value of digital innovation. Following the vision to build fully autonomous
cars, they equip cars with so-called “silent components” like cameras and sensors that are inactive at the
time of sale (Verganti et al. 2020). However, once Teslas autonomous driving algorithm is sufficiently
reliable, the silent components can be activated over the air to enable full self-driving for the entire fleet.
The touch screens in the center console, which replace traditional mechanical controls and displays in the
car, are another example of Teslas visionary IT planning process. By reducing the number of such physical
controls results in more flexibility in the virtual product enhancement via features and services.
Agile product development. While linear product development can work well when the requirements
are fixed in the production stage, it is ill-suited for smart products that are meant to be updated in the usage
stage (Paluch et al. 2020). Therefore, manufacturers need to design and build long-lasting hardware
platforms for smart products (e.g., with silent components) that can further evolve through updates while
β-values
SIC
BMI
PCV
Direct Effects
Technology Stack
.32***
(-)
(-)
Agile Product Development
.32***
(-)
(-)
Service Innovation
(-)
(-)
OTA Capability
.24***
.02
Business Model Innovation
.38***
Indirect Effects
Technology Stack
(-)
.02
.01
Agile Product Development
(-)
.05**
.02
Service Innovation
.06**
.03
OTA Capability
(-)
.09***
Business Model Innovation
(-)
Total Effects
Technology Stack
.32***
.02
.01
Agile Product Development
.32***
.05**
.02
Service Innovation
.04
.05
OTA Capability
.24***
.11**
Business Model Innovation
.38***
Note: *p ≤ .10; **p ≤ .05; ***p ≤ .01. All tests are two-tailed. Control Variables included but not shown.
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in use (Raff et al. 2020). As mentioned earlier, Tesla uses one generation of hardware and continuously
updates it in short intervals rather than annual cycles as traditional competitors do. This approach is the
essence of agile product development (Beck et al. 2001). The continuous delivery and deployment of
software updates and features is based on customer usage data and feedback (Olsson et al. 2012). In this
regard, Tesla monitors the product usage of its entire fleet. When validating new feature architectures, they
can even conduct experiments with designs, features, or functionalities by releasing them to a small number
of users to learn from a unique sample before doing the full rollout. Thus, what digital players like Amazon,
Dell, and Airbnb have long done via rapid updates and on-the-fly user testing is being deployed in smart
product settings to drive further product development. From an organizational perspective this requires an
experimental and data-driven culture backed by open-minded customers.
Service innovation. The digitization of products and their ecosystems fosters the transformation from
‘making and selling products’ towards providing ‘products as a service’. While some manufacturing
companies such as General Electric are following a servitization strategy for many years now, smart
products come with increasing potential to offer services beyond traditional maintenance and repair
solutions (Lenka et al. 2017). To create a competitive advantage, manufacturers continuously develop new
software-based services using their wealth of machinery and usage data. As an example, agricultural
machinery manufacturers such as John Deere or CLAAS create value for their customers by delving deeply
into their business processes. By offering smart farming services, they empower their customers to collect
and analyze contextual data, namely, soil, crop, and machinery data, and optimize a farmer’s machinery
settings accordingly to minimize inputs and increase crop yield. Here, OTA updates are used to provide
farmers with individual solutions such as specific steering models for larger fleets to improve performance
when several machines work on the same farmland at the same time.
In sum, our research shows that successful OTA companies start with agility in product development as
well as basic technological product competence and then layer on associated service innovations. We
assume that such service orientation can be adopted by all types of companies and is a natural step in the
evolution towards a successful OTA strategy. Moreover, service orientation is the key that enables
companies to redefine the value relationship between business and customer, making the leap to OTA-
based product optimization and enhancement. It is important to note that for a sustainable customer
delight through OTA updates, a change of the general business model is necessary. We will discuss this
aspect in the next section.
STEP 2: What It Takes to Create Value Through OTA Updates
Business model innovation. Companies that can update their products over the air must also adapt
their business model. This involves developing value propositions, delivering modes, and monetization
strategies that account for the continuous stream of optimizations and enhancements created from smart
products, thus enabling companies to extract continuous revenue streams from their offerings (Raff et al.
2020). In a recent interview, Hau Thai-Tang, Ford’s chief product platform and operations officer,
commented on their introduction of OTA updates for the Mustang Mach-E series, “I think we now have the
ability to make the vehicle physically better for the customer with these OTA updates, and that’s something
that’s game-changing in terms of the business model.” (Martinez 2020). Our analysis backs Thai-Tang’s
proposition and shows that the implementation of OTA updates only yields positive effects on the perceived
customer value when changes in the business model apply. For product enhancement, the approach is
straightforward. Product manufacturers can offer new product functions and add-on services on-demand.
To use them, customers either start a subscription (typical for services), or choose a one-off payment
(typical for software functions). For example, Tesla created a subscription service for premium connectivity,
which covers advanced navigation and entertainment options. After a free trial, car owners need to pay a
small monthly fee to keep features such as live traffic visualization or car karaoke. At the same time, Tesla
is offering a more expensive, one-time (about $2000) software update for its Model Y that boosts 0-to-60
mph acceleration by half a second. To offer customers a wide range of value-added applications, companies
can even take a platform approach similar to KONE and co-produce services with third-party providers,
while always being aware of the complex mechanisms underlying such digital service assemblages made up
of heterogeneous actors and artifacts (Eaton et al. 2015). While it appears simple for product enhancement,
it is more challenging to adjust the business model to capture product optimization through OTA updates.
In this regard, manufacturers may provide customers with outcome-based contracts, meaning that they
guarantee a specific outcome to customers and align the provision of products and services accordingly
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(Visnjic et al. 2018). While outcome-based contracts for smart products are feasible due to monitoring
possibilities, OTA updates can now help drive efficiency gains. Here, OTA updates enable smart product
manufacturers to improve their customers’ systems operations by continuously updating products and
services. Consequently, they co-create value and significantly influence the outcome delivery, which reduces
the risk of not fulfilling a contract. Agricultural machinery manufacturers, for example, could offer service
agreements related to yield performance. Instead of selling machinery (e.g., tractors, combines, balers),
they could turn their value proposition upside down and offer a yield-per-acre proposition, i.e., farming as
a service. In doing so, OTA updates promote the transition from such transactional, product-centric
business models to relational, service-centric business models.
Implications and Further Research
Taking stock, in this study, we provide empirical evidence that companies that successfully leverage smart
products and OTA applications do so primarily through their technological competence and agile
development practices that, in concert, allow them to create service innovations. In addition, our results
indicate that only companies that can adapt their business models to deliver value to their customers in new
ways - for example, by engaging with new partners or using new revenue models - can create customer
delight. Overall, our findings contribute to research in several meaningful ways. First, our work fuels the
cumulative knowledge creation in IS and beyond by developing a clear-cut conceptualization regarding the
two types of OTA updates, namely OTA updates for product optimization and product enhancement (Yadav
2018; Webster and Watson 2002). Second, we contribute to research on smart products and smart service
systems (Beverungen et al. 2019; Raff et al. 2020). Third, our research addresses emerging questions about
how organizations should organize and position themselves in the digital age, and thus connects to existing
work in the areas of smart product business models, digital infrastructures and platforms (e.g., Beverungen
et al. 2019; Yoo et al. 2012; Yoo et al. 2010).
Since the present project is a spin-off of a larger research agenda in the context of smart product business
models, it represents only a small excerpt of our work. Other research questions that we will address in the
context of this agenda are revolving around the following issues: What are the motivational drivers for
purchasing OTA enhancements of smart products (e.g., self-indulgence or performance gains)? When is an
offer perceived as fair or unfair (e.g., in some situations, customers might feel that the manufacturing
company is withholding something from them if certain product features are not enabled or unlocked by
default)? What are the optimum pricing models to best extract value from OTA updates (e.g., two-part
pricing, subscription-based pricing, or even freemium-based pricing models)?
In conclusion, we believe that the findings from the present project, as well as the answers to the above
questions in the context of our research agenda, shed light on important aspects that will allow digital age
organizations to develop viable digital business models by realizing the full potential of OTA updates.
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