Chapter 2: Conceptual Models, Frameworks, and Theories in New Service Development

Introduction

When developing new services---whether in hospitality, healthcare, consulting, or digital platforms---organizations benefit from theoretical models and frameworks that offer systematic guidance. Unlike ad hoc approaches, formal models help innovators understand the multifaceted processes, coordinate internal and external stakeholders, and increase the likelihood of generating viable, value-enhancing services.

This chapter presents a range of theoretical constructs and frameworks fundamental to the field of New Service Development (NSD). We start with the overarching Service-Dominant Logic (S-D Logic), which we introduced in the previous chapter. We then explore process models that detail the stages of NSD projects, discuss the role of design thinking and dynamic capabilities, and examine frameworks that illuminate the ecosystemic and networked nature of services. We conclude with practical methodologies, such as agile and lean approaches, that bring structure and responsiveness to NSD projects.

1. Revisiting Service-Dominant Logic as a Foundational Theory

Conceptual Foundation

As we learned previously, S-D Logic (Vargo & Lusch, 2004) posits that value emerges from use, context, and interactions among actors. Rather than viewing services as intangible “addons” to products, S-D Logic sees service---defined as the application of competencies for the benefit of another---as the fundamental basis of all exchange.

In NSD, S-D Logic implies that:

• Customers and providers co-create value.

• Resources like knowledge, skills, and relationships matter as much or more than physical goods.

• The process of NSD is iterative and interaction-focused, not simply linear.

Mathematical Notion of Value as an Emergent Property

From a modeling standpoint, consider an NSD project with multiple stakeholders. Let:

• $X = {x_1, x_2, \dots, x_n}$ be a set of resources (e.g., technologies, staff expertise, brand equity).

• $Y = {y_1, y_2, \dots, y_m}$ represent customer inputs (knowledge, feedback loops, contextual information).

• $f(X,Y)$ describe a value creation function determined by how these sets interact.

In line with S-D Logic, value $V$ might be expressed as:

What’s different from a pure G-D perspective is that $f(\cdot)$ cannot be reduced to a simple input-output model. Instead, it must incorporate feedback loops, adaptivity, and learning. In practice, this could be represented by a model that updates with each iteration of service prototyping and customer feedback (e.g., Bayesian updating or reinforcement learning algorithms).

2. NSD Process Models: From Sequential to Iterative Approaches

Sequential Models

Early NSD frameworks, inspired by product innovation models, proposed a series of linear steps: idea generation → concept development → service design → testing → launch. Such stage-gate models (Froehle & Roth, 2007) assume that each phase must be completed and approved before moving on. While useful for highly regulated industries (e.g., certain financial or medical services), linear models can be inflexible.

Cyclical and Iterative Models

More contemporary NSD approaches recommend cyclical, iterative processes that continually refine the service offering based on real-time feedback. Iterative models often resemble a cycle of:
Ideation → Prototyping → Testing → Refinement → Re-Testing → Scale-Up

Mathematical Modeling of Iterative Learning

Consider a simplified iterative model where the performance of the service concept after the $k$-th iteration is $P^{(k)}$. Suppose the improvement from iteration $k$ to iteration $k+1$ depends on learning from feedback ($L$) and incremental investment in capabilities ($I$):

Here, $\alpha$ and $\beta$ are parameters indicating how effectively feedback and investments translate into performance gains. Over multiple cycles, if feedback loops and capabilities are well-managed, the service performance $P^{(k)}$ converges to an optimized solution. In practice, firms must calibrate $\alpha$ and $\beta$ by understanding how responsive the service is to improvement efforts.

3. Edvardsson & Olsson’s NSD Framework and its Refinements

Conceptual Basis

Edvardsson & Olsson (1996) proposed an early structured framework for NSD emphasizing the importance of service concept creation, service system design, and service process design. The framework highlights:

• Service Concept: The “what” of the service---its core value proposition.

• Service System: The “how” of the service, including infrastructure, technology, people, and routines required.

• Service Process: The sequence of activities through which the service is delivered.

When combined, these components ensure that NSD isn’t just about having a great idea---it’s about ensuring that the operational processes and organizational systems can deliver that idea effectively.

Mathematical Representation of Service Complexity

We might model the complexity $\Gamma$ of a service system as a function of the number of interacting components and activities:

• Let $n$ be the number of activities in the service process.

• Let $d$ be the average degree of interdependence (how many other activities each activity depends on).

Then a rough proxy for complexity could be:

The higher $\Gamma$, the more intricate the coordination needed. NSD frameworks often help reduce $\Gamma$ by structuring and standardizing parts of the service process, making it more manageable to innovate, implement, and refine.

4. Integrating Design Thinking in NSD

Conceptual Foundations

Design thinking brings a human-centered, iterative approach to NSD. It encourages empathy, rapid prototyping, and testing with real users. Key stages of design thinking include:

• Empathize: Understand users’ needs, challenges, and contexts.

• Define: Clearly articulate the problem.

• Ideate: Generate multiple solution concepts.

• Prototype: Create tangible artifacts or service simulations.

• Test: Evaluate and refine solutions with user feedback.

Modeling the Impact of Empathy and Prototyping Speed

Let’s imagine the reduction in uncertainty about customer needs after each prototyping cycle. If $U^{(k)}$ is the uncertainty after the $k$-th prototype test, and each test reduces uncertainty by a factor related to the depth of empathy ($\epsilon$) and the speed of iteration ($\sigma$):

• $\epsilon$: The effectiveness of empathy-driven insights at clarifying customer needs.

• $\sigma$: The speed at which prototypes can be built and tested.

High $\epsilon$ and $\sigma$ mean more rapid convergence on a service that truly addresses user needs.

5. Dynamic Capabilities and Their Role in NSD

Conceptual Basis

Dynamic capabilities refer to an organization’s ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments (Teece, Pisano, & Shuen, 1997). In NSD, dynamic capabilities enable firms to sense new opportunities, seize them by allocating resources, and continuously transform their service offerings as technologies and markets evolve.

Mathematical Approximation of Capability Accumulation

Consider an organization’s dynamic capability $\mathcal{D}$ as a function of investment in learning ($L$), knowledge integration ($K$), and adaptive resource reallocation ($R$):

For simplicity, assume a linear form:

Where $\lambda_1, \lambda_2, \lambda_3$ are weights reflecting how sensitive the overall dynamic capability is to each factor. High dynamic capability ($\mathcal{D}$) correlates with more effective NSD, allowing quicker pivots, faster integration of feedback, and more resilient responses to competitor actions.

6. Value Constellation, Ecosystem Theory, and Actor-Network Theory

Value Constellations and Ecosystems

Instead of a linear value chain from supplier to firm to customer, service innovation often emerges in ecosystems or “value constellations” (Normann & Ramírez, 1993). Multiple actors---suppliers, partners, customers, regulators---co-create value. Understanding these interactions is vital. NSD frameworks that embrace ecosystem thinking help identify leverage points, potential collaborators, and new value creation opportunities across the network.

Actor-Network Theory (ANT)

ANT suggests that both human and non-human actors (technology, documents, platforms) form networks that shape outcomes. In NSD, ANT implies that the success of a new service depends not only on the firm and its customers but also on the relationships with software tools, data standards, and infrastructural elements.

Basic Network Representation

We might model the value potential of an ecosystem by considering a set of nodes $N = {n_1, n_2, \dots, n_q}$ representing actors, and edges $E = {e_{ij}}$ representing interactions. If each edge $e_{ij}$ contributes some value $v_{ij}$, then total ecosystem value $V_{\text{eco}}$ could be approximated by summation over all edges:

This simplistic model suggests that introducing new service features that increase interaction quality ($v_{ij}$) among key stakeholders can raise overall ecosystem value.

7. Front-Stage vs. Back-Stage Processes and Service Blueprinting

Conceptual Foundations

Service blueprinting is a technique to visualize and design the entire service delivery process. It distinguishes:

• Front-stage (Visible) Processes: Activities the customer sees and experiences directly (e.g., a waiter taking an order).

• Back-stage (Invisible) Processes: Internal mechanisms (e.g., kitchen staff preparing the dish, supply chain ensuring fresh ingredients).

In NSD, blueprinting clarifies roles, responsibilities, and dependencies. It reveals bottlenecks or opportunities for innovation that might not be apparent when focusing solely on front-stage interactions.

Equation for Process Efficiency

If we let $T_f$ be the total time customers spend in front-stage interactions and $T_b$ the total time spent in back-stage activities required to deliver a unit of service, we can measure a simple efficiency ratio:

A high ratio implies that most of the customer’s waiting or engagement time is value-adding, visible interaction rather than back-stage delays. NSD efforts might aim to minimize $T_b$ via process redesign or automation, increasing perceived efficiency and customer satisfaction.

8. Open Innovation Models in NSD

Conceptual Basis

Open innovation suggests that knowledge and ideas for NSD need not come solely from within the firm. Partners, customers, universities, and crowdsourcing platforms can provide critical insights. Incorporating external knowledge reduces development costs and risks, and broadens the range of possible innovations.

Mathematical Representation of External Knowledge Flows

Let $K_{\text{int}}$ be the internal knowledge and $K_{\text{ext}}$ the external knowledge accessible through open innovation channels. The overall knowledge base $K_{\text{total}}$ could be modeled as:

Where $\theta$ (0 ≤ $\theta$ ≤ 1) represents the firm’s capability to absorb and integrate external knowledge (sometimes called “absorptive capacity”). High $\theta$ indicates effective collaboration and idea integration, strengthening NSD outcomes.

9. Lean, Agile, and Scrum Methodologies in NSD

Conceptual Foundations

NSD can learn much from software development methodologies like Lean, Agile, and Scrum. These approaches emphasize:

• Iterative Development: Break projects into small, manageable increments.

• Frequent Feedback: Regular customer check-ins and pilot testing.

• Continuous Improvement: Incorporate lessons learned quickly to refine the service.

Quantifying Iterative Efficiency

Consider the number of iterations $i$ needed to reach a satisfactory Minimum Viable Service (MVS). If the service performance target is $P^*$, and each iteration improves performance by a fraction $\rho$ of the gap remaining, we can model improvement as a geometric process:

If $\rho$ is large, the gap closes quickly, indicating that agile methods (frequent iterations and feedback) effectively accelerate the convergence toward the target performance.

10. Practical Anecdotes and Tips

• Anecdote: A large insurance company introduces a new online claims service. Initially, it followed a linear product-development-like approach, releasing a fully-fledged portal after a lengthy planning phase. Dissatisfied customers found it confusing. Embracing iterative models and design thinking, the firm deployed a series of prototypes, tested them with a small group of customers, and refined the service repeatedly. By incorporating open innovation (partnering with a UX consultancy) and applying blueprinting, they vastly improved the user experience. Over time, they integrated dynamic capabilities---such as rapid data analytics---to respond swiftly to emerging customer needs.

• Practical Tip: When starting an NSD project, try mapping it against two frameworks: a stage-gate model and an iterative model. Identify which elements need structured oversight (e.g., compliance checks) and which need iterative experimentation (e.g., user interface design). This “hybrid” approach can balance rigor with flexibility.

• Practical Tip: Use service blueprinting to uncover back-stage inefficiencies. Even incremental changes---like reorganizing workflows or introducing a simple software tool---can free up resources, allowing you to focus more on front-stage value creation and innovation.

Summary and Looking Ahead

In this chapter, we examined several conceptual models and frameworks central to NSD:

1. S-D Logic repositions value creation as co-creative and emergent, guiding the fundamental perspective on NSD.

2. NSD Process Models range from linear stage-gate approaches to iterative cycles that accommodate continuous feedback and refinement.

3. Edvardsson & Olsson’s Framework underscores the importance of harmonizing service concept, system, and process design.

4. Design Thinking introduces a human-centric, empathic, and iterative methodology that improves alignment with customer needs.

5. Dynamic Capabilities enable organizations to adapt and thrive in fast-changing service environments.

6. Value Constellations, Ecosystem Theory, and ANT broaden the focus from dyadic firm-customer interactions to complex, networked environments.

7. Front-stage vs. Back-stage Processes and Service Blueprinting clarify how to visualize and optimize all components of service delivery.

8. Open Innovation encourages leveraging external knowledge sources to expand the range of innovative possibilities.

9. Lean, Agile, and Scrum methodologies provide practical tools for quick iteration, incremental improvements, and responsive development cycles.

In the next chapter, we will move from these overarching frameworks to focus on the crucial role of the customer in NSD. We’ll explore how customers’ inputs, co-creation activities, and journey mapping shape the outcome of new service initiatives and drive continuous improvement.

References:

• Edvardsson, B., & Olsson, J. (1996). “Key Concepts for New Service Development.” Service Industries Journal, 16(2), 140—164.

• Froehle, C. M., & Roth, A. V. (2007). “A Resource-Process Framework of New Service Development.” Production and Operations Management, 16(2), 169—188.

• Normann, R., & Ramírez, R. (1993). “From Value Chain to Value Constellation: Designing Interactive Strategy.” Harvard Business Review, 71(4), 65—77.

• Teece, D. J., Pisano, G., & Shuen, A. (1997). “Dynamic Capabilities and Strategic Management.” Strategic Management Journal, 18(7), 509—533.

• Vargo, S. L., & Lusch, R. F. (2004). “Evolving to a New Dominant Logic for Marketing.” Journal of Marketing, 68(1), 1—17.

End of Chapter 2