Digital service innovation in B2B markets

Techno-demarcation perspective

Service innovation is a multidimensional construct that can be understood from several perspectives. When delving into the realm of digital transformation, techno-demarcation emerges as a valuable perspective, emphasizing the pivotal role of technology. Moreover, it underlines the notion that service innovation is fundamentally different from product innovation (Miles, 2016). Barras' (1986) seminal work on the “reverse product cycle” is a case in point. According to conventional product innovation theory, during the initial stages of innovation, companies focus on developing new and unique products to explore emerging technology. As the technology matures and products become more standardized, the emphasis shifts to process innovation, as companies seek to improve manufacturing processes, supply chain management and other operational aspects to gain a competitive advantage (Utterback, 1994). However, Barras (1986) proposes an alternate trajectory for services. When new technology is introduced, it is first used to optimize existing service processes and improve service delivery efficiency. Technological applications are then used to enhance the quality of these services. Eventually, this leads to the creation of entirely new or significantly transformed service offerings. Hence, techno-demarcation implies that service innovation should not be treated as a special case of product innovation; the unique characteristics of services require innovation concepts, models and processes different from those in manufacturing (Hipp and Grupp, 2005; Song et al., 2009).

While the techno-demarcation perspective has faced criticism for its exclusion of non-technological forms of innovation and its underestimation of the innovative intensity within service activities (Gallouj and Savona, 2009), it is worth remembering that this research reflects a time when manufacturing was considered the primary driver of the economy, with services often viewed as “innovation laggards”. Therefore, studies on the role of technology in service innovation have often focused on the diffusion of innovations derived from the manufacturing sector (Pavitt, 1984). Today, however, we find ourselves in a distinctly different landscape where digital innovations have become general-purpose technologies (Arora et al., 2004, 2013) for value creation, opening up previously unprecedented possibilities for service innovation. Consequently, techno-demarcation reflects the fundamental role of digital technology in connecting resources and service actors on an almost ubiquitous scale (Ehret and Wirtz, 2017; Vandermerwe and Erixon, 2023).

In light of this, we revisit the techno-demarcation perspective to outline how digital technologies profoundly influence the core dimensions of service innovation and to delineate research priorities. By adopting this perspective, we underscore the transformative potential of digital technologies in driving innovation across core service offerings, processes and the broader ecosystem. We recognize the distinctive attributes of services, including their intangibility, non-ownership nature and the central role of customer interaction (Lovelock and Gummesson, 2004). Consequently, our focus extends beyond mere novelty in tangible technological products, emphasizing the multi-faceted dimensions of innovation in services.

Service innovation dimensions

Although the service innovation concept is widely used, few studies have explicitly defined it (Witell et al., 2016). We build on the conceptualization by Gustafsson et al. (2020), which characterizes service innovation as a new offering and/or process that is adopted by stakeholders in an ecosystem and that creates value for them. This view of service innovation is particularly useful in understanding the role of digital technologies. First, it shifts the focus from the output delivered by the service provider to the value created for the beneficiary, consistent with new socio-technical processes accompanying digitization (Lusch and Nambisan, 2015). Second, it recognizes the beneficiary as an active participant in the innovation process, highlighting the pivotal role of digital technologies in facilitating connections among diverse stakeholders within an ecosystem (Smedlund, 2012). Gustafsson et al.’s (2020) conceptualization brings to the fore three distinct dimensions: service offering, service process and service ecosystem.

Service offering–focused research typically emphasizes measures tied to output, such as new service features and service products introduced, success rate, profitability or sales impact (Cooper and de Brentani, 1991; Hull, 2004; Wirtz et al., 2021). It perceives service innovation primarily as an economic concept that should bring benefits to its developer (Toivonen and Tuominen, 2009).

Service process–focused research examines the dynamic nature of activity and time within service activities, influencing value creation. This approach involves changes in employees' and customers' roles, competencies, practices, norms and behaviors (Helkkula et al., 2018). Recognizing the active involvement of customers, interdependence patterns and work divisions can vary considerably across different service processes (Larsson and Bowen, 1989). Process innovations can also impact customers' willingness to participate and their level of engagement (Chase and Apte, 2007).

Service ecosystem research focuses on the integration of resources among stakeholders, departing from the traditional emphasis on individual services (Rubalcaba et al., 2012). It recognizes that individual firms cannot successfully launch a new offering or orchestrate the required networks without connecting to multiple stakeholders and resources in an ecosystem (Helkkula et al., 2018). Consequently, service innovation entails changes in institutional arrangements governing resource access and integration, enabling beneficiaries to create value by including new stakeholders, redefining roles and reframing resources within service ecosystems (Koskela-Huotari et al., 2016).

Prior research examines types of innovation in isolation rather than in an integrated manner. In practice, service innovation opportunities and challenges can be better addressed by jointly drawing on offering, process and ecosystem dimensions. For instance, the maritime industry has traditionally been regarded as a laggard in terms of service innovation, with limited connectivity to vessels offshore and traditional, product-centric mindsets among ecosystem actors. However, this is changing as the following example from the Swiss-Swedish engineering group ABB illustrates. Its marine advisory system equips vessels and fleets with integrated marine solutions (software and sensors) for optimal reliability, flexibility and performance, providing customers with a wide range of service modules for energy efficiency and safer voyages (i.e. service offering). Using weather and loading data to plot the safest and most efficient route, ABB offers its customers smoother operation and better fleet control through a single interface. It enables fleet managers and ship officers to have better navigation with the support of 24/7 remote assistance from onshore operation centers (i.e. service process). Finally, through its technology platform, the firm orchestrates its global service network, digital operations centers, third-party cloud and activities of vessel owners and operators in what is a complex service ecosystem (Helkkula et al., 2018). This case demonstrates how digital technologies like IoT, IA and digital platforms can provide novel, potentially disruptive service opportunities. Next, we discuss how such technologies foster service innovation in B2B markets.

Internet-of-things technologies

IoT connects the global information infrastructure to a growing range of physical resources through sensors that offer real-time information on physical resources and environments, and actuators that allow global remote control of such resources (Ng and Wakenshaw, 2017). It has enabled novel business models through innovative services that leverage previously unconnected resources. For example, it facilitates mobility services by connecting vehicles, enables automated management of office facilities and enhances industrial services by equipping machines with sensors or actuators (Ehret and Wirtz, 2017).

As the world moves toward an era of IoT, Ng and Wakenshaw (2017) argue that the process of evolving physical goods into connected, transportable and dynamically reconfigurable service systems is disrupting at a Schumpeterian level that is just beginning. As a system, IoT consists of interconnected constituents that are uniquely identifiable and capable of virtual representation and virtual accessibility. This Internet-like structure enables the constituents' remote locating, sensing and operating. Real-time data and information flow between these constituents, empowering the system to be dynamically augmented, thereby broadening the range of outcomes in a dynamic and agile manner (Ng and Wakenshaw, 2017).

IoT technologies offer numerous opportunities for service innovation as they enable providers to connect equipment directly within end-user firms' premises, collect and analyze large sets of product usage and process data and remotely perform monitoring and control activities over customers' operations (Paiola and Gebauer, 2020). Key components of IoT, which are instrumental for delivering their advanced applications and envisaged benefits, are information protocols and middleware, sensors and actuators (i.e. components of automated systems that drive movement and change) and information technology-driven services such as big data analytics (Ehret and Wirtz, 2017). Thanks to cloud computing, massive amounts of data generated by relatively inexpensive sensors and actuators embedded in products can be easily stored, accessed and processed.

IoT facilitates market and customer information and opens new sources of innovation through the interaction between manufacturing assets and service markets. First, IoT enables new non-ownership services that transform negative uncertainty of asset ownership and operation for customers into opportunities for manufacturers or specialized service providers, who can remotely monitor and control assets in real time. A key characteristic that differentiates any service from goods businesses is the delivery of benefits without transferring ownership (Lovelock and Gummesson, 2004; Ehret and Wirtz, 2010). While services have always been part of human economic activity, digital technology has opened up previously unknown capabilities for specifying service benefits. Service contracts serve as an illustrative example. They play a central role in service businesses as each transaction needs to entail customer specifications for defining the promised service and related implications for service quality and fulfillment. Providers and clients had historically closed service agreements with analog communication, including handshakes and paper documents (Pine, 1993). With global infrastructures and technologies providing real-time data, providers and clients have unprecedented power to specify, negotiate, control and monitor service benefits, operations, performance and quality.

Second, firms can use IoT to unlock machine information and develop resources and capabilities to exploit data and gain intelligence and knowledge of business processes and operations (Ehret and Wirtz, 2017). For example, John Deere can help farmers make more informed decisions by providing machine and crop data, and IBM Watson’s AI system can help customers catalog and evaluate tens of thousands of suppliers. Since the value of information increases when aggregated and shared, firms aiming for IoT-based service innovation need digital systems for retrieving and analyzing data (Chesbrough, 2011).

The separation of information from a physical object, which allows information to be effortlessly transmitted and reconfigured in numerous ways, is called liquification (Normann, 2001). Liquification can enhance the abilities of digitized objects and mobilize the optimal combination of resources for a particular context. For example, it has allowed the separation of physical and information components of supply chains. Service innovations in supply chain management have resulted in new service offerings and processes through increased outsourcing and offshoring of information technology and transformed service ecosystems through the growth of information intermediaries (i.e. infomediaries; Hagel and Rayport, 1997). Liquification and subsequent processing and analyses to support decisions and actions are fundamental drivers of IoT (Ng and Wakenshaw, 2017). While the full potential of an IoT era has not yet materialized, integrating IoT with IA and other technologies holds the potential to unlock unparalleled levels of connectivity, efficiency and service innovation.

Intelligent automation

IA refers to the application of automation technologies to operate, optimize and expand service processes and tasks, helping organizations augment or automate service operations. Such technologies are used to improve efficiency and support the quality of contract fulfillment with real-time information and control of service operations (Bornet et al., 2021). While many scholars and managers tend to view automation and augmentation as mutually excluding trade-offs (Raisch and Krakowski, 2021), IA aims to leverage automation in a way that enhances human capabilities (Bornet et al., 2021; Spohrer et al., 2022), thereby empowering people by either relieving them from certain activities or enabling them to perform novel services.

IA integrates several technologies, especially those related to vision (e.g. computer vision, character recognition and biometrics), language (e.g. natural language processing), thinking and learning (e.g. analytics and machine learning) and execution of tasks (e.g. robotic process automation, smart workflows and physical robots), aiming to design streamlined, simplified and scalable service processes (Bornet et al., 2021). In particular, many information processing-type services (think of any service delivered over the phone, email, an app, or a website) and even entire service offerings (e.g. FinTechs and HealthTechs) can be end-to-end (E2E) automated with no human service employee involved. For example, Google serves billions of customers (including paying advertising clients such as SMEs) of which the vast majority will never interact with operational service employees. Google-type services are the exception today, but we expect such services to become mainstream and apply to many information-processing services globally. IA will enable firms to fully automate services and/or shift services to smart self-service solutions. Analyzing big data using methods such as machine learning, neural networks and deep learning can permit not only the automation of repetitive and routine tasks but also the processing of data to arrive at new conclusions or decisions (Huang and Rust, 2017).

The rapid pace of technological innovation, exemplified by machine learning and innovations such as distributed ledger technologies, points to a central role for technology in future B2B service innovation. For instance, the mining and shipping industries are shifting toward more autonomous service systems and operations, forming new industrial ecosystems with software and hardware experts, systems integrators, manufacturers, telecommunications providers and other specialized firms. These autonomous systems largely operate without human intervention. In light of these developments, Breidbach et al. (2018) provide a valuable distinction between traditional and autonomous systems based on their handling of variability. Specifically, traditional systems aim to reduce variability, often exemplified by user interfaces with limited options. Conversely, autonomous systems embrace variability, evident in practices like AI-based financial services autonomously making investment decisions. This distinction carries implications for digital service innovation, including how to design cost-effective processes across organizations. We believe future digital service innovations will accelerate such decision-making capabilities across companies.

Sophisticated algorithms, software, robotics, processing power and sensor technology breakthroughs enable increased autonomy across industrial and service applications. IA is particularly vital for B2B firms, given their more industrialized service processes (Hofmeister et al., 2023). By using industrial robots equipped with software, sensors, algorithms and cameras, companies like ABB provide paint process automation services to automotive clients. This enhances accuracy, quality, workplace safety and productivity while optimizing space utilization.

We also see that IA will have further disruptive power for B2B industries and markets. Service firms will not produce much of the technology required for IA (e.g. chatbot solutions, digital agents, smart SST and service robots) in-house. Rather, they will procure IA technologies from leading, globally operating vendors specializing in digital agents, warehouse solutions and airport solutions for information counters, and so on (Bornet et al., 2021; Paluch et al., 2022). This is akin to banks not building ATMs but procuring them from specialist equipment providers. It seems likely that such technologies will be increasingly sourced from global vendors that have cutting-edge R&D capabilities and the scale required to deliver high-quality and cost-effective solutions (cf., Ehret and Wirtz, 2017). Examples may include vendors of AI-powered self-service technology (SST), retail solutions based on service robots (e.g. SoftBank’s Pepper), embedded speech recognition (e.g. Nuance’s conversational AI solutions), customizable platforms for digital agents (e.g. ANZ Bank’s digital agent Jaime was developed by Soul Machines, a vendor of “digital people”) and chatbots (e.g. IBM’s Watson Assistant, a leading provider of conversational AI technology) that can be tailored to the specific needs of client firms (Paluch et al., 2022). Vast new markets are likely to emerge for B2B players. However, to seize such opportunities, firms must engage in market-shaping, which means considering a larger ecosystem of relevant stakeholders, understanding the institutional arrangements governing their behaviors and fostering new resource linkages across the ecosystem (Nenonen et al., 2019).

Digital platforms

Digital platforms bring together “individuals and organizations so they can innovate or interact in ways not otherwise possible, with potential for nonlinear increases in utility and value” (Cusumano et al., 2019, p. 13). Platforms can hence enhance the scale and scope of service innovations, thereby enlarging service markets using digital infrastructures.

Today, the largest service firms in terms of market capitalization are essentially digital platform businesses (e.g. Apple, Microsoft, Alphabet, Amazon, Meta, Alibaba and Tencent), and “by now, nearly every executive has navigated at least one discussion about whether his or her organization should strive to become a platform” (Brown, 2016, p. 2). However, most platforms fail, with only 3% of platform strategies succeeding (Yoffie et al., 2019). Furthermore, this failure rate seems particularly high in B2B contexts as firms tend to be more reluctant about being locked into another firm’s platform ecosystem than consumers. As such, while platforms offer exciting service innovation opportunities, it is important to understand better what platforms are and what it means for platform businesses to operate in B2B markets (Perks et al., 2017).

Digital platforms are explicitly designed to use digital technologies to enable business interactions among authorized members, fostering novel ecosystems and influencing the institutional arrangements governing the practices of its members (Rangaswamy et al., 2020; Wirtz et al., 2019). For over two decades, disruptive platform-based business models have accelerated service growth in B2B markets. Consider the example of Salesforce.com; more than twenty years ago, the company’s founder, Chairman and CEO, Marc Benioff, became an early proponent of the software-as-a-service (SaaS) model in an industry dominated by software sales and licensing. Salesforce.com today relies on a comprehensive platform and ecosystem of partners that serve as a powerful competitive advantage in its sector (Ulaga and Kowalkowski, 2022).

However, different types of digital platforms exist, and Rangaswamy et al. (2020) make an important distinction between business and technology platforms. There is a wide range of business platforms, including transaction platforms (e.g. Amazon Business and Shopify), service platforms (e.g. Vixxo), payment platforms (e.g. Visa and its Authorize.net gateway) and two-sided technology platforms (e.g. Apple’s App Store). In all these business platforms (1) the core matchmaking functions (i.e. linking one user to another on the platform) are executed digitally, (2) the platform promotes direct communications and business transactions among its users and (3) platform members are independent parties who retain residual ownership rights. On the other hand, pure technology platforms such as Salesforce.com play no or minimal role in matching buyers with sellers. Technology platforms are generally one-sided because the service offering comes from the platform company itself.

Furthermore, it is crucial to recognize the distinctive characteristics and considerations that set apart B2B markets from B2C environments when evaluating the requirements and prerequisites for successful platform implementation. First, B2B markets are generally more heterogeneous and complex, and platform members, which are legal entities, are more reluctant to share proprietary data with a platform company (Hein et al., 2019). Second, in contrast to B2C markets where the winner-takes-all or winner-take-most approach often prevails (Cusumano et al., 2019), the high switching cost of industrial assets as well as the generally narrower platform scope and size may encourage platform coopetition rather than platform competition (Jovanovic et al., 2022). This means that traditional direct and indirect network effects (i.e. the platform’s value increases with each new actor on the same or the other side of the platform; Rangaswamy et al., 2020) do not necessarily apply. Instead, data network effects may be more vital for B2B platforms; the more the platform learns from the data it collects, the more valuable the platform becomes to its members (Gregory et al., 2021).

Common for all digital platforms, however, is that they are based not only a technical architecture and other tangible resources (as in the case of traditional, firm-centric and product-centric platforms) but also on intangible resources. These form an “architecture of participation”: a set of organizational norms, rules and activities that its connected members use to coordinate and co-align their actions (Lusch and Nambisan, 2015; Nambisan and Sawhney, 2011).

Additionally, regardless of platform, the degree of openness is a key issue influencing the overall service experience. If a platform is too closed, it will prevent the leverage of innovations by third parties and the number of members may become too small to generate network effects. On the other hand, too much openness might bring value-destroying effects, such as poor-quality interactions and congestion, which cause members to defect (Rangaswamy et al., 2020). Overall, platforms can drive service innovation by enabling novel offerings, processes and knowledge not previously accessible to individual members or the broader service ecosystem.

Service offering innovation

The growth of services in B2B sectors is primarily driven by new digitized offerings that can be supplied virtually (Wolf, 2022), presenting opportunities for more effective outcomes due to the scalable and fungible nature of digital resources (Adner et al., 2019). The advent of the IoT facilitates continuous connectivity and data flows, giving rise to a range of non-ownership services and anything-as-a-service (XaaS) business models. Examples include remote advisory services, data management and integration, cloud infrastructure optimization and the “SaaS-ification” of software licenses into subscriptions. Additionally, IoT enables providers to monitor and control assets remotely, transforming uncertainty downsides for customers into opportunities (Ulaga and Kowalkowski, 2022). Hence, traditionally product-centric firms like Caterpillar are augmenting their existing portfolio of offerings with a host of adjacent, connected services.

Advancements in sophisticated algorithms, software, robotics, processing power and sensor technology are further driving the development of increasingly autonomous solutions across services and industries. This IA trend is expected to accelerate decision-making capabilities across companies further. For instance, the subscription model, which has gained traction in consumer markets, is now increasingly adopted in B2B. Examples include predictive analytics of vast datasets, intelligent document processing, automated human resources and talent management and end-to-end supply chain automation solutions that include traceability and smart contracts.

Finally, digital platforms enable the provision of novel services that were previously unavailable to individual members. Consequently, platforms are replacing traditional pipeline businesses and enable the management of subscriptions and other service contracts (Rangaswamy et al., 2020). For example, security company Axis Communications leverages machine learning and deep learning in its video surveillance systems to enhance real-time analysis, making its AI-enabled technology platform essential for scalable and cost-effective service innovation. Axis generates and integrates complementary assets by attracting third-party developers through its software development kit (SDK). Such digital interfaces provide standards and facilitate interactions in increasingly digital ecosystems (Hein et al., 2019). Overall, the abundance of generated data and the ability to analyze, curate and use large data sets open avenues for new services that emphasize continuous value creation over time through access- or performance-based contracts (Ulaga and Kowalkowski, 2022).

Service process innovation

Service process innovation refers to any change in the service creation process that influences the value created, including shifts in the roles, competencies, skills, practices, or behaviors of providers, customers and other stakeholders involved (Helkkula et al., 2018). Rather than the common belief that digitalization “changes everything,” Furr et al. (2022) point out that many firms innovate to improve their service processes rather than change their core offerings. A case in point was container shipper Maersk’s attempt to use blockchain “to do what it did before blockchain, namely to ship more efficiently around the globe” (p. 596). Blockchain makes it possible to create a digital ledger of transactions with information on their ownership, hence providing a higher level of trust than other shared databases (Bousquette, 2022).

IoT technologies enable real-time monitoring of equipment and service resources, allowing firms to develop, customize and integrate digital touchpoints to improve service processes and systems (Lundin and Kindström, 2023). Examples include apps, online systems and self-service abilities, which bring about operational improvements, better control, consistent service quality and cost and time savings. Companies can get closer to their customers and partners by designing and managing new digital touchpoints. For example, the digital agriculture enterprise Climate analyzes weather, soil and field data to assist farmers in optimizing resource usage and enhancing overall productivity by identifying potential factors that could limit crop yields within their fields.

Digital empowerment of human capabilities through IA further enhances service processes by making provider and customer employees more informed and capable of performing tasks. The augmentation and automation of tasks and decision-making contribute to an enhanced service experience and reduce the likelihood of errors and mismatches. Digitization allows services to be dynamically reconfigured, increasing service variability and adaptability. However, companies can also strategically leverage digital technologies to deliberately reduce variability, particularly for complex and costly B2B services (Wirtz and Kowalkowski, 2023). Applying an operations management approach, organizations can boost efficiency by reducing customer-induced variability through measures such as separating front-office and back-office operations, modularizing services and deploying self-service technologies, robots and AI. These steps build on one another, facilitating smooth implementation (Wirtz and Zeithaml, 2018; Wirtz et al., 2023a).

In addition, platforms play a crucial role in connecting providers, customers and complementors, forging digital linkages among geographically dispersed actors. This shift from intra-organizational processes (e.g. low-contact systems; Chase and Tansik, 1983) to inter-organizational processes involving multiple actors is driven by the digitization of data and services. By integrating resources and processes on a single platform, B2B firms can streamline workflows, eliminate redundancies and optimize the overall service delivery process. Platform-mediated resource integration also allows for real-time feedback and iterative improvements based on customer interactions and insights, leading to ongoing enhancement of service processes.

Service ecosystem innovation

Finally, digital technologies play a pivotal role in shaping innovation within service ecosystems where multiple stakeholders collaborate to enhance user experiences and improve the overall value-creating potential of the ecosystem (Helkkula et al., 2018). These technologies enable connecting people and things, data collection and processing and autonomous learning and decision-making, ultimately automating value creation (Beverungen et al., 2019; Breidbach et al., 2018). Service ecosystem innovation requires the collaboration of several actors who contribute a wide range of resources (Poeppelbuss et al., 2022). However, it also introduces greater uncertainty, both technologically and relationally (Ramirez Hernandez and Kreye, 2021).

Many B2B firms rely on external parties to perform various service activities across the customer journey, such as information provision, systems integration, training and field service provision (Witell et al., 2020). Understanding how digital technologies impact these activities, the evolving role of service partners and the response required for service innovation is therefore crucial. However, digitizing service processes may involve significant upfront investments for partners, impeding the pace of digital service innovation in many companies. Walmart’s challenge in onboarding farmers to their digital inventory software system serves as an example, highlighting the hurdles faced in this regard.

Ecosystem reconfiguration is often necessary for the successful implementation of new services. For instance, Michelin uses tire sensors, vehicle boxes and antennas to capture and transmit IoT data. However, the firm faced several technological and relational challenges with its service network when moving from selling truck tires to fleet solutions (Renault et al., 2010). The firm could not integrate and automate back-office processes and fully support its dealers until it implemented a new IT system that connected its network members.

Digital platforms empower firms to orchestrate the collaboration of customers, providers and complementors in the co-creation of services (Perks et al., 2017; Rangaswamy et al., 2020; Wirtz et al., 2019). By leveraging standardized tools like application programming interfaces (APIs) and SDKs, firms can integrate complementary assets on the platform, fostering liquidity and increasing resource density within the platform-enabled service ecosystem (Hein et al., 2019).

In the B2B realm, the tolerance for failure among customers is typically low, underscoring the criticality of selecting pilot customers and effectively scaling pilot initiatives. Moreover, ensuring the economic viability of service innovation within the ecosystem can be challenging, especially when engaging diverse stakeholders with varying priorities. This challenge is exemplified by the use of blockchain technology in transformative projects like asset tracking in complex supply chains. For example, in collaboration with IBM, Danish shipper Maersk launched its hyped TradeLens blockchain platform to digitize container shipping. Still, the platform encountered complexities and difficulties in enlisting participation from numerous companies (including other carriers) and nations, leading to its closure (Bousquette, 2022).

Theme 1: new recurring revenue models

Digital technology lays the foundation for novel recurring revenue models (Ehret and Wirtz, 2017; Varian, 2010). For example, subscription businesses have outperformed the S&P 500 index in the last decade and maintained growth throughout the pandemic (Zuora, 2022). In B2B, subscriptions span from software and connected equipment to various product-as-a-service offerings. With the power to monitor and control physical assets in real time through IoT technologies, industrial equipment providers also find lower thresholds for revenue models such as outcome-based contracts and innovative rental and leasing offerings (Ehret and Wirtz, 2017). These connectivity-based service offerings are generally enabled and mediated by digital platforms.

Overall, digital technologies and the associated data flows allow new services with a stronger focus on continuous value creation over time, requiring new service activities and processes. However, B2B and service research are just beginning to understand the profit implications of these recurring revenue models (Queiroz et al., 2020; Worm et al., 2017). Hence, there is limited guidance on how to build and deploy such service innovations. Managers and researchers have yet to agree on the structure, responsibilities, costs and liabilities associated with these revenue streams and the implications for order-to-cash cycle management. Thus, there are opportunities for future research to elucidate the financial architecture of these innovations.

Theme 2: service innovation in the metaverse

The metaverse is the latest chapter in the digital service revolution which will allow exciting innovation opportunities at the intersection between IA, IoT, digital twins and the metaverse. For example, equipment repair can be deskilled and improved through superimposed tags, explanations and analyses that allow for better, faster and cheaper repairs (Dwivedi et al., 2023). Deskilling may even facilitate self-service, allowing firms to bring repairs and maintenance in-house.

Digital twins are the cornerstones of the enterprise metaverse, being virtual representations of physical assets, systems or processes. They are increasingly used to detect, prevent, predict and optimize outcomes through data curation and real-time analytics (Rantala et al., 2023). This presents unique opportunities for innovating service offerings and processes, and for fostering novel virtual ecosystems. While most firms are in the early stages focusing mainly on the internal benefits of single twins, we see that the metaverse offers opportunities to establish multi-sided platforms around interconnected digital-twin networks. Research is therefore needed to explore the optimal use, implementation and implications of the metaverse within B2B contexts.

Furthermore, once services are scalable and incremental service costs are low, one might expect that today’s fragmented B2B service markets may become more concentrated and many small and medium-sized B2B firms will be pushed out of business. In many markets, “born digital” new entrants may be the disrupters. Questions such as how mirror worlds, augmented reality and entirely virtual worlds will change B2B service innovation, delivery and markets are largely missing but seems to offer exciting opportunities for service scholars.

Theme 3: scaling up service innovations

A general challenge for many B2B firms, especially those rooted in a product-centric engineering culture, is the ability to ramp up their service innovations. Often, it comes to a stall when firms want to launch and roll out the service offering (Kowalkowski and Ulaga, 2017). Despite digital opportunities, especially those facilitated by IA and platforms, scalability is still a major challenge for service processes and many digital service offerings are developed very individually as firms struggle to mobilize the wider ecosystem (Poeppelbuss et al., 2022). Platforms support scalability, yet the TradeLens example illustrates the difficulties for a platform owner (in this case, Maersk and IBM) in onboarding a critical mass of key ecosystem actors. While technologies like blockchain continue with proof-of-concept initiatives attract widespread attention, Jensen et al. (2019) note that very few of those initiatives have moved to real-world operating solutions.

Further, compared to incumbent firms, many digital firms are specialized rather than diversified, which Giustiziero et al. (2021) explain by hyperscaling and hyperspecialization. The ability to scale quickly at low costs in the digital world overwhelms the typical efficiencies from integration. So, when hypergrowth is possible, spending resources on anything other than the core business is a distraction. Such “first-scaler advantage” accrues not to the first firm that enters a market but to the first firm that serves that market at scale. Once a scale-up occupies the high ground in its service ecosystem, the network around it recognizes its leadership and attracts further talent and capital (Levie and London, 2018). We hence need to understand how incumbent service providers can better defend their market position through service offerings and platforms, offset the advantages of their digital challengers and examine the role incumbents' deep domain expertise and high-touch service capabilities can play.

Theme 4: ecosystem innovations

There is a growing interest in service research that examines the interplay between technology and institutions, as well as the role of institutions and governance in organizing service ecosystems. Digitally enabled innovations bring about benefits that often have externalities for most ecosystem actors, even those not directly involved in the initial adoption or implementation of the service (Agrawal et al., 2022; Spohrer et al., 2022). Actors may face the challenge of balancing seemingly contradictory or paradoxical requirements, such as sharing access and resources, safeguarding unique capabilities, balancing automation and augmentation and making investments for collective benefits (cf., Ostrom, 2015). Drawing on our insights, we argue that many technologies have dual properties. For example, IA can be used to both empower humans through close collaboration with machines and shift human tasks to machines. Such interdependencies across time and space create paradoxical tensions (Raisch and Krakowski, 2021). However, research has only recently begun to explore these tensions and their impact on service innovation efforts at an ecosystem level (Tóth et al., 2022).

On a broader scale, digital service innovations disrupt social systems and present opportunities to stimulate social action. Hence, unlocking the action potential of digital service innovation requires a deeper understanding of its social dimension. However, our understanding of the interaction between technology and the social dimension of service is still in its early stages, both among information systems and service scholars. Here, soft systems design approaches have begun to explore the concept of constitutive rules in information systems, which are based on the collective intentions of communities regarding the use and meaning of technology (Lowe et al., 2020). Another promising avenue for exploration is the process of establishing constitutive rules that human actors agree upon when adopting digital technologies for service innovation. For instance, the concept of cryptocurrencies as a replacement for traditional monetary institutions is still in the process of gaining broader acceptance in society (Taleb, 2021).

Theme 5: power dependency and lock-in effects

Today, we are witnessing the emergence of increasingly intricate and dynamic ecosystems comprising technologies and collaborators. When it comes to service innovation, numerous firms, including global market leaders, rely on software and hardware companies to obtain essential resources and capabilities for data management, cloud services, cybersecurity, algorithms, AI and more. These include big tech companies like Amazon, Cisco, Microsoft and Nvidia, as well as various specialized local firms. As competition in the realm of services intensifies within the digital sphere, the dynamics of the industry are evolving, leading many B2B firms to become more reliant on these ecosystem actors and their platforms and IoT and IA technologies for new service offerings and process innovations. Consequently, the control over technologies within ever-evolving service ecosystems is gradually shifting away from individual firms while complexity becomes intricately interwoven with the interplay between technology and people. This necessitates the imperative of orchestration (Breidbach et al., 2018). Research should thus explore how lead firms can drive extensive change and coordinate activities across organizational boundaries by effectively orchestrating and governing the ecosystem’s infrastructure, technologies and human resources.

Moreover, incumbent firms often encounter challenges when it comes to scaling up (Theme 3) and navigating the complexities of digital infrastructure. As a result, they may rely on external digital innovators or systems integrators for service innovation, even resorting to utilizing these firms' white-label services that can be effectively reused on a larger scale (Poeppelbuss et al., 2022). However, such practices raise concerns regarding the potential asymmetry in the relationship, possible competition and issues of forward integration.

Additionally, the presence of platforms and other digital infrastructure often leads to lock-in situations, which may explain the failure of certain B2B platforms. Customers tend to prefer open standards and APIs, avoiding being tied to a proprietary supplier platform. Anecdotal evidence suggests that apprehension regarding dependence on TradeLens, with intellectual property jointly owned by IBM and Maersk, made many actors reluctant to participate as they sought an industry platform with broader governance (Hill, 2018). Further research should seek to examine how firms can mitigate such risks and identify approaches by which a platform can attract customers and other relevant stakeholders.

Theme 6: security and responsibility in digital domains

More research is needed to understand potential vulnerabilities in digital domains and how tools, technologies and governance mechanisms could be integrated into firms' service offerings to reduce such vulnerabilities (Ng and Wakenshaw, 2017). The increasing connectivity facilitated by IoT technologies, cloud computing and the interdependence of actors relying on digital technologies for sensitive data and mission-critical equipment and processes heightens the importance of cybersecurity. Moreover, as IA accelerates and firms' data accounts become more intricate, cybersecurity problems become increasingly difficult to predict. Effective management and mitigation of such risks have emerged as a differentiator for B2B firms and a source of opportunities for service innovation. For example, ABB offers cybersecurity services, working closely with shipowners, yards and classification societies to enhance the cyber resilience of ships and ensure compliance with international and state-specific regulations (Tronvoll et al., 2020). Research in this area should also delve into the sharing of digital governance protocols and practices among ecosystem partners to enable the development and adoption of new, more resilient services.

Furthermore, digital and AI innovations carry serious ethical, privacy and fairness risks for end users. A recent stream of research on corporate digital responsibility (CDR) explores these risks (e.g. Lobschat et al., 2021), especially in digital service ecosystems with their vast flows of money, service, data, insights and technologies between business partners (Wirtz et al., 2023b). Plenty of pressing research questions relate to CDR in B2B digital innovation. For instance, new vendors arise that deliver customer-facing digital technologies and their design and development of customer-interfacing technologies often determine how service firms interact with their customers. Consequently, CDR risks need to be mitigated between B2B vendors and their clients. For example, should biometric customer identification be a design feature of a particular service offering or process? We need to understand better how B2B suppliers can work effectively with technology partners and their clients to develop and implement new technologies that follow good CDR principles, what governance procedures are most effective in enhancing CDR compliance, and what role contractual agreements can play (e.g. penalty clauses and shifting the liability of CDR failures).

Theoretical implications

Digital service innovation has profound implications for virtually all B2B firms, including technology- and engineering-oriented companies that focus on hardware and software development. While it is recognized that service innovations encompass non-technological aspects (e.g. organizational and business model dimensions), digital technologies offer new pathways for service design, innovation and performance. In addition to a research agenda, we provide the following contributions pertaining to the role of digital technologies in B2B service innovation.

First, this article explores how three signature technologies provide key opportunities for service offering, process and ecosystem innovation. Despite the dominant and growing role of technology-enabled services and service innovation, the innovation literature remains strongly biased toward products (Biemans and Griffin, 2018), which is demonstrated by extant research on digital product innovation (e.g. Wang et al., 2022; Lyytinen et al., 2016). As digital technologies are transforming traditional industries and blurring industry boundaries, digital services are becoming pivotal to the competitive advantage of B2B firms, regardless of whether their origin is service or manufacturing (Biemans and Griffin, 2018; Kowalkowski and Ulaga, 2017). Yet, we still lack a comprehensive understanding of digital service innovation in B2B as prior research tends to focus either on the idiosyncrasies of manufacturing (e.g. Paiola and Gebauer, 2020; Tian et al., 2022) or on various non-technological aspects of innovation (e.g. O’Cass and Sok, 2013; Salunke et al., 2011).

Second, we adopt a techno-demarcation perspective on service innovation to emphasize the transformative potential of digital technologies in driving innovation across core service offerings, service processes and the broader ecosystem. While traditional approaches to technological service innovation (e.g. Barras, 1986) have faced criticism for their reductionism (Gallouj and Savona, 2009), digital technologies now serve as the foundational architecture and infrastructure for service innovation systems. It is also worth noting that while early work emphasized digital technology’s importance in service innovation, the extent of recent digital innovation in the realm of service could scarcely be predicted (Barrett et al., 2015). Rather than trying to synthesize a wide range of technological and non-technological perspectives, which often leads to overly broad and abstract research (Witell et al., 2016), we have intentionally drawn on techno-demarcation to reflect this significant shift. In doing so, we bring forward a systems perspective on service innovation.

Finally, our study highlights the multidimensional nature of service innovation as it discusses the influence of technology on the “what” (the offering), “how” (the process) and “who/for whom” (the ecosystem). While any signature technology may primarily impact a specific dimension, we discuss the interdependence between them. For example, a digital platform can enable new interconnected and layered services based on fundamentally different service processes, linking data previously not combinable. It may further allow complementors to enter the ecosystem and build their own service innovations to make the overall customer value proposition even more compelling. Furthermore, to realize the potential of such service innovation, IoT and IA technologies may be pivotal. Overall, this research taps into the literature stream that treats the service innovation construct as multidimensional (e.g. Helkkula et al., 2018; den Hertog et al., 2010; Rubalcaba et al., 2012), although we specifically delineate the principal role of digital technologies.

Managerial implications

In addition to the theoretical implications of our research, there is also practical relevance. Today, service innovation has become a major growth engine for all types of B2B firms. Digital technology is the key driver enabling this growth. At the same time, research shows that in comparison to B2B firms primarily focused on products, services-focused firms tend to exhibit a lower degree of sophistication in their innovation practices. They often lack explicit management frameworks for innovation, maintain reduced expectations regarding innovation outcomes and display a preference for incremental innovation (Biemans and Griffin, 2018). By elucidating how signature technologies drive service innovation in B2B markets, we provide managers with valuable insights to enhance their capacity for identifying and capitalizing on opportunities in an era where such innovations represent a pivotal determinant of success.

The most imminent managerial implication lies in recognizing the paramount importance of business model design in harnessing the potential of service technologies. Simply integrating a sensor into a machine does not create a substantial impact on its own; instead, its significance lies in its role as a component of a performance-based or subscription-based business model. While there are often bold assertions in the public and in the management literature regarding the substitutive power of technology, the key determinant for success lies in aligning beneficiaries, business models and governance structures. Effective service implementation involves technologies that enhance and automate human capabilities rather than merely replacing them (Raisch and Krakowski, 2021).

Furthermore, managers should approach service innovation opportunities and challenges by considering all three dimensions of service innovation: that is, the service offering, service process and service ecosystem. While a given service innovation project might primarily prioritize one dimension, it is important to recognize that it will inevitably have varying impacts on the other dimensions (Helkkula et al., 2018). The same principle applies to the facilitating technologies. As a result, we recommend that innovation managers and business developers aiming to leverage the advantages of digital service opportunities should direct their attention to all three dimensions and consider the synergistic role of multiple signature technologies. This comprehensive approach ensures a more holistic and effective strategy for capitalizing on the potential of digital services.