KPIs for Digital Accelerators: A Critical Review

Abstract

This paper proposes a conceptual framework for studying the KPIs of digital accelerators. Therefore, a critical review was developed and we derived clear recommendations in terms of KPIs that researchers must consider when evaluating digital accelerators applicable to startups. Digital startup businesses must focus on product, external factors, process, capability, organizational factors, and market to achieve sustainability. The proposed framework asserts that profitability, growth, productivity, and size are key categories that should be taken into consideration while grouping KPIs. These categories should be grouped into three different dimensions, economic, technological, and organization/client. The proposed KPIs can help the accelerator program evaluate its own performance and make the necessary adjustments to improve the program and eventually measure the startup’s success.

1. Introduction

Recent advances in digitalization have deeply restructured traditional businesses, providing exceptional opportunities for laying new forms of entrepreneurship (Markus & Loebbecke, 2013). Among the most important forms of entrepreneurship is digital entrepreneurship (Griva et al., 2023), which is always supported by some type of digital accelerator.

Accelerator theory is an economic claim that investment spending increases when either demand or income increases. Speed to market is correlated with higher profits as sales and profits are achieved sooner (Cooper, 2021). The theory also states that when there is excess demand, organizations can either reduce demand by raising prices or increase investment to meet the level of demand. Organizations generally choose to increase production, and thus increase profits, to achieve their fixed capital to production ratio (Cohen & Hochberg, 2014).

The literature describes an accelerator as a classic venture capital instrument that supports a new business (startup) in developing a business idea in the shortest time possible and turns it into the lowest-cost prototype (Konyuhov et al., 2020). The literature argues that initially accelerator programs were generalist, while nowadays the programs are diversified into industry-focused and vertically focused programs (Cohen & Hochberg, 2014). Early definitions pointed out the usage of these accelerators as a way to help organizations to define and shape their initial products or services, detecting promising customer segments and at the same time securing resources (capital and employees).

Digital technologies have transformed entrepreneurship forever by creating opportunities for new ventures based on the information revolution (Sharma & Meyer, 2019). Specifically, digital acceleration is a business strategy that determines the future of the company. It shifts the focus from digitizing a process to adopting the latest technologies, such as business process automation, data visualization, Machine Learning, and Artificial Intelligence. So, a digital accelerator refers to the use of digital technologies to transform a company, its business model, strategy, customer experience, and other areas of the business as quickly as possible (Márquez, 2022). In this sense, new technologies tend to transform prototype development into a less expensive, faster, and easier task. This improvement can be used for customer feedback and the validation of new products, such as using virtual and augmented reality techniques (Cooper, 2021). How can these accelerators be evaluated, measured, or even assessed?

Key performance indicators (KPIs) are a set of metrics designed to evaluate the performance of a business or organization, including startups (Marr, 2012; Parmenter, 2015). In this sense, startups can be defined as companies, partnerships, or temporary organizations intended to convey new ideas to the market and transform them into economically sustainable enterprises; they operate as new ventures seeking competitive advantages via innovation in products and services (Blank & Dorf, 2012; Kristin et al., 2022; Silva Júnior et al., 2022; Slávik et al., 2022). In the context of digital accelerators, KPIs can be used to track the progress and success of startups and other technology-based businesses as they work to grow and scale up their operations. As a startup, it is common to be distracted by attractive opportunities in potential markets and lose focus on the strategic vision. Key performance indicators (KPIs) are therefore the most frequently used tools to manage this process (Sharma & Meyer, 2019).

To clearly present the research problem, it was identified that despite, the proliferation of digital accelerator programs, their efficiency is far from clear, and their outcomes are heterogeneous (Cohen & Hochberg, 2014). Although research covers various acceleration methods and impacts, there is still a gap regarding accelerated innovation (Cooper, 2021). Additionally, Chen et al. (2005) and Langerak (2010) suggested that being the first to develop or introduce a new product is not always advantageous. Sometimes, the second entrant can succeed by learning from the first mover’s mistakes. There is a lack of effective research on the application of KPIs designed to measure the outcome of digital accelerators, creating a gap in the literature. Cohen and Hochberg (2014) already argued in 2014 that descriptive research was limited and that the research needed to go beyond simple inquiry and explore multiple drivers of multiple outcomes.

We follow the future research questions presented by Cooper (2021): Does the accelerated innovation really work? Which proposed methods work the best, why, and under which conditions? On this basis, we attempt to propose a conceptual framework for studying a set of KPIs that could be an accurate model to measure the impacts of digital accelerators application on startup organizations. This critical analysis is organized along with the following research questions:

• How do digital accelerators impact startup economic and operational performance?
• Which economic and client-focused KPIs can effectively measure digital accelerator impact on startup success?

Question 1 aims to shed some light on the efficacy of the impact of digital accelerators on startups, specifically concerning economic and client performance, by assessing their impact and outcome addressing the concerns presented by Cohen and Hochberg (2014) and also by Cooper (2021). Question 2 has the objective of identifying and explaining which KPIs must be defined to measure how the startups are affected by the digital accelerators, since as Chen et al. (2005) and Langerak (2010) argued there are still some disagreements on how to set up such KPIs. Both questions follow the future research line presented by Cooper (2021).

The present paper is divided into 7 main sections. A background is presented for the main topic. This is followed by an overview of the methods applied, especially the critical review methodology. As evaluation and KPIs set the main scope of the chapter, 2 sections are presented that address these topics. In the last sections, the conceptual framework is designed by clustering KPIs into three dimensions, conclusions are drawn, and finally the future research questions are presented to the readers.

2. Theoretical Framework

Recently, accelerator programs were crucial to develop the new COVID-19 vaccine, putting a new spotlight on the product development through accelerators programs (including digital accelerators) (Cooper, 2021). The first accelerator, Y-Combinator, was founded in 2005 in Cambridge, Massachusetts by Paul Graham, who later moved the accelerator to the San Francisco Bay Area. Several other accelerators were founded slightly later. Accelerators perform the function of helping the business startup and connecting the business to sources of capital and connections to industry and technology. They typically take a stake in the venture as part of this process and may perform many other functions for a venture (Sharma & Meyer, 2019, p. 52). Digital startups can be considered as a critical support that can significantly boost economic growth and job creation (Chae, 2019).

Most accelerators operate as a business, and so they have expenses and revenues. In this sense, Sharma and Meyer (2019) argued that when an organization starts an accelerator, they assume that they will lose money for the first few years and then start to recover the investment later as many products/startups/ideas in their portfolio start to mature and they are able sell their stake in those companies. As also argued by Lofsten (2016), the capability of a startup to innovate in products and services is the source of differentiation, leading to a competitive advantage in relation to other competitors. The author asserts that innovation capabilities are necessary, but do not guarantee business benefits and success.

Digital accelerators are widely connected to the use of digital technologies and as such it is possible to establish a connection with the term “digital transformation”. This means a combination of several digital innovations, which are set together to transform existing rules of engagement for and among organizations (Hinings et al., 2018). The current growth of digital technologies has improved business processes, but since those processes are inserted into a competitive market, it is necessary to achieve a competitive advantage (Bergmann & Utikal, 2021) by resorting to digital business transformation. For most organizations, digital transformation has become a central topic, as they need to deal with new digital tools and their business impact daily. Only a small number of organizations have their business processes sufficiently under control to realize the full value of digital technologies (Kirchmer & Franz, 2020).

As argued by Cooper (2021), many digital tools are available, accelerating the development of new products. Also, recent research (Geissbauer et al., 2019) shows that many organizations expect their investments in the development of digital products to increase efficiency and performance by 19% over the next 5 years and yield a 17% reduction regarding the time-to-market. It is important to note that digital accelerators can be applicable in startup organizations and in more mature organizations, depending on an organization’s goals. Additionally, the existence of innovative startups is fundamental as they can produce economic development, employment, technological advancement, and impact in society (Onesti et al., 2022). Also, the early stages of startups are frequently discussed in the business research literature (Laitinen, 2017). As Fried and Tauer (2015) argued, it is important to adopt tools to measure the performance of the startups to determine the drivers of successful ventures, as they typically operate under a lack of resources and in particularly uncertain contexts (Unterkalmsteiner et al., 2016). So, metrics can help startups make the right decisions at the right time.

This is where KPIs are critical to the success of startup based on digital accelerators. The KPI should be known and part of the team’s operating discipline. These KPIs need not be fixed in stone; rather, the KPIs should reflect the trends of the venture, both from the perspective of the investors and from the perspective of the leadership. Simply put, coin price or stock price should not be the only measure of progress (Sharma & Meyer, 2019). Additionally, the KPIs should be in place and routinely measured. Silva Júnior et al. (2022) describes how Industry 4.0 technologies along with other factors have been used to measure the competitiveness of startups and thus can be set a KPI to evaluate the performance. In this matter, it is important to clarify that Industry 4.0 is supported by ten enabling technologies: simulation, big data and analytics, the Internet of Things, cloud computing, cyber–physical systems, cybersecurity, collaborative robotics, augmented reality, additive manufacturing, and systems integration (Dalmarco et al., 2019).

3. Materials and Methods

Literature reviews can be seen as wide-ranging, dense papers that are usually used to describe and discuss the state of the art of a specific subject from a theoretical or contextual standpoint. An explicit and transparent approach based on the principles of methodologically pluralistic research, or a mixed method, should be used in any rigorous literature review (C. Barker & Pistrang, 2005; Frost, 2009). Thus, the critical review method is employed to refine existing hypotheses or models, or to create new ones. It involves a systematic examination and synthesis of the relevant literature to provide a comprehensive understanding of the current state of knowledge in a particular field.

Critical reviews differ from systematic reviews since the systematic reviews tend to answer quantifiable questions. This method assesses existing research and competing ideas, serving as a foundation for conceptual development (Hyett et al., 2014). The aim is to describe an author’s hypothesis or conceptual model based on the main literature on a given subject (Grant & Booth, 2009). The same authors identified one of the goals as being to demonstrate that the reviewer has a strong understanding of the literature to the point where they can extrapolate conceptual frameworks or hypotheses on the research topic, going beyond the level of detailed description of the existing literature.

In this sense, one proposes specific areas of the literature to be reviewed in this paper, mainly the KPIs and metrics applied to digital accelerators, critical success factors, and outputs resulting from the application of correct KPIs in startups.

3.1. Exclusion Criteria

Regarding the inclusion criteria, all peer-reviewed articles on accelerator KPIs, startup performance metrics, and digital transformation indicators published from 2015 to 2024 were considered. Exclusion criteria included non-English publications, gray literature without peer review, and studies focusing solely on traditional (non-digital) accelerators. In terms of quality assessment, all the selected articles were evaluated regarding their methodology rigor, sample size adequacy, and their theoretical contribution to the proposed review.

3.2. Search Strategies

The search was conducted by resorting to several databases, namely Web of Science, Scopus, Business Source Premier, and IEEE Xplore. It applied a combination of keywords, such as “digital accelerator”, “startup KPIs”, “performance indicators”, and “innovation metrics”, to narrow the results under the proposed scope. Also, supplementary searches were conducted by reference list screening and forward citation tracking. The critical appraisal process was conducted, considering the methodological quality assessment of included studies, the identification of theoretical frameworks and their limitations, the analysis of conflicting findings and their implications, and finally gap identification in current measurement approaches.

4. Evaluate Accelerators

4.1. Metrics and Indicators

As already discussed, accelerators, in the context of business or organizational development, refer to programs or initiatives designed to speed up the growth and success of startups, projects, or individuals. Thus, evaluating accelerators involves assessing their effectiveness in achieving their goals and delivering value. In this sense, metrics and indicators play a crucial role in this evaluation process.

As argued by Griva et al. (2023), it is difficult to understand startup growth because young firms have limited financial performance and their growth cannot easily be measured by metrics such as customers, revenues, profits, and turnover. When evaluating the growth in early-stage startups, Griva et al. (2023) argues that the organizations should focus on human capital, their abilities, and culture, and not so much on the financial indicators and other actual metrics. So, the literature presents several research articles evaluating different outcomes (e.g., Audretsch, 2012; Coad et al., 2016; Patil et al., 2019), such as survival, funding, resources, and capabilities, to evaluate startup growth.

Onesti et al. (2022) emphasized the need to evaluate the performance of innovative startups, considering special financial statement ratios, since most startups might report financial issues at the beginning of their life, when it is more difficult to realize large sales and profitability. The author proposes a new composite index to assess the performance of the innovative startups and to examine their performance (in terms of profitability and employees’ productivity). An important indicator for digital accelerators is revenue growth (Parmenter, 2015), measuring the increase in revenue generated by startups in the accelerator program over time. This can be measured monthly or quarterly and indicates the accelerator’s effectiveness in helping companies grow. Another important KPI is customer acquisition, measuring the number of new customers a company/startup can acquire in each period. This can be measured by tracking the number of new leads or the number of new customers a startup was able to acquire through its marketing and sales efforts. Another important KPI is the number of successful exits, measuring the number of startups that successfully exit the accelerator program and subsequently achieve significant growth and success. This can be measured by tracking the number of companies/startups that are able to secure funding, win customers, or achieve other key milestones.

Additionally, Griva et al. (2023) identified studies from several disciplines that define growth, identifying three categories: (i) descriptions that qualify growth, (ii) descriptions that quantify growth, and (iii) descriptions that follow a mixed approach. Directly or indirectly, all definitions indicate the importance of resources and capabilities, as well as those traditional financial measures (e.g., turnover, profit, etc.). Sales, the number of customers, and the number of employees are growth dimensions.

Table 1. Performance indicators.

Dimension/Categories	Indicator/Measurement
Profitability	EBITDA
	EBITDA/sales
	Return on asset (ROA)
	Return on investment (ROI)
	Return on sale (ROS)
	Revenue generated
	Return on equity (ROE)
Growth	Turnover
	Number of successful exits
	Customer acquisition
	Profit
	Resources
	Capabilities
Productivity	Turnover per employee
	Value added per employee
	Turnover/staff costs
Size	Production value
	Revenues from sales of goods and services

Source: Parmenter (2015); Onesti et al. (2022) and Griva et al. (2023).

summarizes the indicators proposed by the literature, where profitability, growth, productivity, and size are categories that should be taken into consideration when defining a set of indicators to evaluate a specific startup based on a certain digital accelerator.

It is possible to set a relation between these categories and the economic, technological, and organizational dimensions:

(a)

Productivity and profitability categories comprise metrics measuring the efficiency of resource utilization and operational effectiveness, like Customer Acquisition Cost (CAC), Cost Per Conversion (CPC), and burn rate efficiency. So, the relationship is clear in relation to the economic dimensions, as the metrics are primarily economic indicators that assess resource optimization.

(b)

Growth category aggregates metrics to track expansion and development over time, like Monthly Recurring Revenue (MRR) growth, Network Expansion Rate, Innovation Pipeline strength. This category spans all dimensions, measuring progressive improvement.

(c)

Size category comprises metrics indicating the scale and reach of operations like Customer Retention Rate (CRR), Total Active Users, and market share. So, the relationship is clear towards organizational dimension as the metrics are primarily organizational/client-focused, measuring operational scale.

It is essential to recognize that organizational culture plays a vital role in the context of a new venture (Fuller & Unwin, 2005). Therefore, building on its culture, a company needs to develop and acquire different capabilities, which will enable it to effectively develop in its field of application, as per Griva et al. (2023).

It seems clear that the literature argues that metrics and indicators are crucial to evaluating the accelerators applied to certain startups. It is also true that startups need to be measured and evaluated throughout a series of KPIs to ascertain their success. But what are the critical success factors and KPIs for digital accelerators applied to startups?

4.2. Critical Success Factors and KPIs

Taking into consideration the previous question, this section intends to identify the critical factor that might ensure startup success and identify all relevant KPIs that should be evaluated during the accelerator application phase. Chorev and Anderson (2006) argued that there is no single dominant factor influencing a startup venture’s destiny and that several dimensions shape the probability of success. So, it is not possible to identify a specific success factor that ensures the startup survival. Along these lines, Sharma and Meyer (2019) identified five questions that all investors are asking to determine whether a specific company has what it takes to survive. These questions might also be applicable when organizations are investing in new technologies or into new digital acceleration programs.

• What resources does a target startup still need to ensure success?
• Given the size of the potential market, what is the ratio between the cost of those additional resources and the potential revenue over time?
• How quickly will we be able to tell if things are going off track?
• How soon will we reach a confidence level high enough to trigger further investment?
• At what points will this company be more valuable as a private company, a public company, or an acquisition target?

Kristin et al. (2022) produced a systematic literature review to identify the critical success factors in the development and sustainability of digital startup businesses. The authors found 42 critical success factors; these were grouped into six latent variables: product, external, process, market, capability, and organizational (

Table 2. Literature proposed critical success factors.

Latent Variable	Critical Factor
Product	Digitalization
	Novelty/innovativeness
	Product-oriented
	Value creation of product
	Functionality of product
	Profitable
	System quality
	Efficiency of digital product
	Effectiveness of digital product
	Platform
	Pricing
	Product benefit
	Scope
	Service quality
	Usability of mobile application
	The value proposition of the product
External	Network
	Economic impact
	Environment impact
	Business ecosystem impact
	Cognition
	Competitor
	External pressure
Process	Learning
	Accelerator/incubator capability
	Accelerator/incubator
	Business process
	Incubation process
	Mentoring
Market	Market-oriented
	User satisfaction
	Marketing and promotion
	Customer loyalty
	User experience
Capability	Skill and experience
	Resources
	Self-efficacy
	Behavioral intention
Organizational	Funding
	Internal management
	Risk assessment
	Employee satisfaction

Source: Kristin et al. (2022).

). The authors concluded that products, external, and processes are the key fields for the development and sustainability of digital startup business. Market, capability, and organization support the development and sustainability of digital startup businesses. Digital startup businesses must focus on product, external factors, process, capability, organizational aspects, and market to achieve sustainability.

Taking into consideration the CSFs presented, it is crucial to relate them to the KPIs already identified.

Table 3. CSFs links to KPIs.

CSF	Linked KPIs	Rationale
Product CSF	Technical debt (TD), conversion rate (CR), innovation adoption rate.	Product quality directly impacts user experience and market acceptance.
External CSF	Funding success rate, network diversity index, partnership formation rate.	External relationships provide essential resources and opportunities.
Process CSF	Time-to-prototype, idea-to-implementation ratio, operational efficiency metrics.	Process optimization affects speed and quality of deliverables.
Market CSF	Customer satisfaction (CSAT), net promoter score (NPS), market share growth.	Market understanding drives customer-centric performance.
Capability CSF	Skill Development Assessment Score, Mentor Satisfaction Score, R&D Productivity.	Organizational capabilities determine execution effectiveness.
Organizational CSF	Employee Engagement, Retention Rates, cultural assessment scores.	Internal organizational health affects overall performance.

Source: own elaboration.

maps the relation between the CSFs and the performance indicators.

Many accelerators base themselves on a particular form of technology, becoming “AI Accelerators” or “Analytics Accelerators”. Regarding the specific function of accelerating the use of narrow technology, the accelerator is an excellent practical application (Sharma & Meyer, 2019, p. 55).

Kupp et al. (2017) argues that successful corporate accelerator programs critically depend on goal alignment between the sponsor in the established organization, the management team, and the accelerated startups. The overall objective of the acceleration program will ultimately define the appropriate measurement indicators, divided into 3 dimensions: financial, technological, and cultural. To measure the competitiveness, Silva Júnior et al. (2022) proposed several KPIs, divided into dimensions. For the propose of the present work, 3 dimensions are considered: organizational, human, and Industry 4.0 technologies. The authors developed the competitiveness measurement system, considering conceptualizations of KPIs suggested by Toor and Ogunlana (2010) and Parmenter (2015).

As Cooper (2021) stated, economic payoff resulting from digital accelerators is not easy to measure and poses a problem regarding the economic value of the hypothesized benefits. In other words, it is difficult to set KPIs that would provide an undoubtable score regarding the economic dimension. Despite this fact, some research attempts to empirically set several metrics to evaluate the digital accelerator results. Development costs are a reliable measure but not a valid metric since the goal is not to save money but to achieve a completive advantage and market share. The relationship between cycle time and product quality is also unclear since higher product quality might be related to decreases in cycle time (Langerak, 2010), but the opposite is also argued by Griffin (2002). Other authors (e.g., Tritoasmoro et al., 2022) argued that startup progress assessments are regularly carried out by managers and mentors. In general, this involves qualitatively taking into consideration several business metrics: PS fit, MVP, and business model.

Attractiveness, team agility, pivoting ability, and early adopter status are important. Slávik et al. (2022) showed that the key factor can be important when strategizing regarding a startup, but it is not parameterized and related to specific growth indicators. As such, the author argued that the viability of startups can be measured by indicators of growth and sales volume.

Table 4. KPIs proposed in literature.

Dimension	KPI/Metric	Reference
Economic/Organizational	Profit and sales	Jørgensen (2018) Silva Júnior et al. (2022) Langerak (2010) Tritoasmoro et al. (2022) Slávik et al. (2022)
	Mean sales growth and mean yearly sales
	Development costs
	Cycle time and product quality
	Innovative profile
	Intellectual property protection
	R&D
	Available resources
	Absorptive capacity
	Financial capability
	Technological capacity
	Dynamic capacity
	Value creation
	Competitive strategies
	Organization quality
	Organization culture
	MVP
	PS fit
	Business model attractiveness
	Team agility
	Pivoting ability
	Early adopter
Technological/Industry 4.0 Technologies	Simulation	Silva Júnior et al. (2022)
	Big data and analytics
	Internet of Things
	Cloud computing
	Cyber–physical systems
	Cybersecurity
	Collaborative robotics
	Augmented reality
	Additive manufacturing
	Systems integration
Cultural/Human	Employee’s education level	Silva Júnior et al. (2022)
	Founder characteristics
	Employee’s satisfaction
	Capital invested by the entrepreneur
	Founding team experience
	Employee’s commitment

Source: own elaboration.

summarizes all the KPIs proposed in the literature.

It can be argued that digital accelerators provide startups with a holistic support system that includes financial assistance, mentorship, resources, and networking opportunities. This comprehensive support accelerates the growth trajectory of startups and enhances their chances of long-term success in the dynamic digital landscape. Anyway, several combinations are possible when choosing which CSFs to consider and which KPIs to evaluate.

The review conducted reveals significant contradictions in digital accelerator effectiveness, with studies showing that while accelerated startups are 3.4% more likely to raise venture capital and achieve 2.7 times faster sales growth than non-participants, other evidence demonstrates that many accelerator programs fail to accelerate startup development and may even be detrimental in some cases (World Bank, 2025; Gali, 2021). The average success rate across all accelerators globally remains only 25%, highlighting substantial variability in program outcomes. Key measurement challenges persist, including selection bias where high-potential startups may succeed regardless of acceleration, attribution problems due to brief program durations, and inconsistent evaluation methodologies that often rely on self-reported data without rigorous control groups (Gov.UK, 2019; Assenova & Amit, 2022). Furthermore, accelerator research suffers from fundamental gaps in understanding contextual factors, with effectiveness varying significantly across industries, geographical regions, and program designs, yet most studies fail to specify boundary conditions under which different approaches succeed or fail (Assenova & Amit, 2022). The literature lacks the systematic identification of what works versus what does not work, with inadequate critical examination of the 75% of accelerated startups that do not reach funding despite program participation. These contradictory findings and methodological limitations underscore the need for more rigorous comparative studies that explicitly acknowledge both successful and failed accelerator outcomes while identifying the specific conditions that determine program effectiveness.

So, taking into consideration the previous review, it seems important to conceptualize a framework that might provide a path to ensure the startup success by applying digital accelerators.

5. Conceptual Framework

As already mentioned, the concept of “digital acceleration” has become critical for organizations looking to remain competitive in a constantly competitive market and especially for the startup companies. Thus, companies are adopting digital accelerators to update/innovate or start their operations, improve their customer experience, and optimize their overall performance. But not all the companies achieve the desired success. To ensure success, performance must be regularly measured, resorting to KPIs to measure and assess the progress towards achieving the established objectives.

According to the present review, and taking into consideration that profitability, growth, productivity, and size are categories that should be utilized when defining a set of indicators with which to evaluate a specific startup based on a certain digital accelerator, it is possible to propose a set of KPIs that can be part of a conceptual framework applicable to all startups using digital accelerators with which to assess the impact on the companies’ success (Figure 1). It is also crucial to bear in mind that digital startup businesses must focus on product, external factors, process, capability, organizational factors, and market to achieve sustainability, the critical success factors that must be considered.

5.1. Economical Dimension

The first proposed KPI is Customer Acquisition Cost (CAC). Setijono and Dahlgaard (2007) developed existing methodologies to measure customer value during acquisition and use, and Smetanková et al. (2020) use this KPI to analyze and quantify the impact of building information modeling on the reduction in total costs. This indicator refers to the total cost of acquiring a new customer, including all marketing and sales expenses (Livne et al., 2011). This metric plays a crucial role for digital accelerators, as it provides valuable insights into the effectiveness of the customer acquisition strategy, providing insight regarding the amount of money a startup spends on acquiring a new customer. A low CAC is vital for startups to ensure they are not overspending on customer acquisition and is an indicator that the company is efficiently converting leads into customers, indicating that the company is operating efficiently. This proposed KPI is part of the economic dimension being measured under the productivity and profitability category.

Another important KPI in the same dimension and category is Cost Per Conversion (CPC). CPC refers to the cost of acquiring a lead or making a sale (Cotter, 2002). This metric is important as it can provide insight into the efficiency of the company’s marketing campaigns. A low CPC score is an indicator that the company is operating efficiently, while a high CPC score can indicate that the company needs to optimize its marketing strategy to reduce costs (Saura et al., 2017). Still in the same dimension and category, Monthly Recurring Revenue (MRR) reveals the amount of revenue a startup generates from its customers each month. A steady and increasing MRR is crucial for startups to ensure they are building a sustainable business model. MRR is a KPI suitable for startups that operate on a subscription-based business model, such as SaaS (Software-as-a-Service) companies. MRR refers to the predictable, recurring revenue a company receives each month from its subscribers. It is an important metric for startups because it can help to predict revenue growth and ensure a steady cash flow. MRR can be used to track revenue growth over time and to predict future revenue. By analyzing MRR trends, startups can identify growth opportunities and areas that need improvement. For example, a decreasing MRR trend could indicate that customers are canceling their subscriptions, which would require further investigation to determine the cause of the problem and take corrective measures. Investors and analysts also use MRR to evaluate the financial health of a startup. In fact, many investors consider MRR to be a key factor when deciding whether to invest in a startup or not. A steady and increasing MRR trend indicates that a startup has a predictable revenue stream and a sustainable business model, which is attractive to investors. Return on investment (ROI) is another KPI proposed under the same category and dimension. The profit margin ratio is only based on profit and revenue flows, without paying attention to the assets of a specific startup; these assets are assessed using the ROI ratio, considered the most widely adopted measure of profitability (Laitinen, 2017). This is a measure of the profitability of a startup’s investment in marketing or other initiatives. A high ROI is important for startups to ensure they are making wise investment decisions (Migliaccio & Pavone, 2020). This specific KPI is especially helpful for investors since their main concern is the percentage ROI factor, the period, and the investment rounds needed to achieve an exit (Cánovas-Saiz et al., 2020).

The last proposed KPI for this, the economical dimension, and the productivity and profitability category is the burn rate (BR). This metric is a commonly used financial indicator for newly created startup companies (Yun et al., 2016). Burn rate can be described as the amount of money a startup needs to pay its monthly bills, or the size of the cash-out per time. Almost all startups track their BR to ensure that they have enough time to reach the next development stage. This is the rate at which a startup is spending its available funds (Ripsas et al., 2018). In other words, the metric reveals negative cash flow as quoted by cash spent per month, and it measures how fast a company spends its venture capital to finance above before generating positive cash flow from operations (Yun et al., 2016). A low burn rate is crucial for startups to ensure they are not spending money faster than they are making it, which can lead to financial difficulties. At the beginning of the venture, these kinds of financial metrics are those that secure economic survival.

5.2. Organizational/Client Dimension

As part of the organization/client dimension, measured under the size category, another essential KPI is proposed for digital accelerators—the Customer Retention Rate (CRR). This KPI refers to the percentage of customers who continue to purchase from the company over a given period of time (Valenzuela-Fernández et al., 2016), and it is presented as a metric for understanding customer retention patterns (Smith et al., 2000), establishing an association between customer retention outcomes and several management processes including customer retention planning and budgeting (Ang & Buttle, 2006). A high CRR score indicates that the company meets customer expectations, and that the product or service meets the customer’s needs. CRR is an important metric as it can provide a first glance into customer satisfaction and can help companies identify areas that need improvement.

Also, part of the Organization/Client dimension, measured under the size category, Customer Lifetime Value (CLV) is proposed as a critical KPI for digital accelerators. This metric is essential as it can help organizations to determine the optimal amount to invest in customer acquisition and retention. By understanding the CLV, companies can make informed decisions about their marketing budget and customer retention strategies, ensuring a healthy return on investment (Damm & Rodríguez Monroy, 2011), measured using a separate KPI. CLV refers to the total amount of money a customer is expected to spend over the course of their relationship with the startup. A high CLV is essential for startups to ensure they are making the most out of their customer base. CLV is customer value to a company over a period, with a simple formula: average annual customer profit x average customer retention (Jasek et al., 2018). Of course, for this dimension and category, customer satisfaction (CSAT) must be set as a critical KPI for digital accelerators. CSAT refers to the level of satisfaction that customers have with the company’s products or services (Pan et al., 2010). This metric is important as it can provide insight into the quality of the company’s products or services and can help identify areas that require improvement (Roche, 2006). The concept that the quality of interaction while procuring a product or service has a direct impact on customer satisfaction is now prevailing in marketing theory (Pan et al., 2010). A high score on CSAT indicates that the organization meets or exceeds customer expectations, while a low CSAT can indicate that the company needs to improve its products or services to meet customer demands. The costumer turnover rate (CTR) is the last KPI proposed for the organization/client dimension under the size category. What constitutes growth is significantly different for new digital ventures as compared with established firms (Griva et al., 2023). So, this KPI represents the percentage of customers who cancel their subscriptions or stop using a startup’s product/service. A low turnover rate is important for startups to ensure they are retaining their customer base and not losing revenue. Turnover or churn is defined in the literature as the inactiveness or deactivation of the customer towards the service or product (Vyas et al., 2018).

5.3. Technological Dimension

Conversion rate (CR) is proposed as an important KPI that needs to be measured, taking into consideration that a digital accelerator always relies on a certain digital tool and that a website is always available for interaction with the customers. CR refers to the percentage of website visitors who take specific actions, such as filling out a form, consulting a DB, or making a purchase. M. Barker et al. (2022) argued that this specific KPI is part of a set suited for steering digital business in product companies. Companies can use this KPI to manage their digital businesses more effectively and avoid measurement traps. A high conversion rate is an indicator that the company’s website is optimized for conversions, and that the user experience is streamlined and effective. This metric is crucial for digital accelerators, as it can help identify areas of the website that require improvement and increase the overall efficiency of the website. This proposed KPI is part of the technological dimension being measured under the growth category.

In the same dimension, and being measured under the same category, technical debt (TD) measures the amount of technical debt accrued by the digital accelerator startup. A higher level of technical debt can indicate that the startup does not effectively manage its technological infrastructure and could face significant challenges in the future. Rios et al. (2018) described TD as a conceptualization of problems that occur while developing a digital evolution, considering the tasks that are not carried out adequately during the development. The concept TD represents technical compromises that can yield short-term benefit to the project in terms of increased productivity and lower cost, which may affect the digital evolution (Avgeriou et al., 2016). Nielsen et al. (2020) argued that TD is important for the field of digital transformation, because TD can hinder the organizations in fully reaping the benefits of digitalization. Finally, net promoter score (NPS) is part of the technological dimension being measured under the growth category, referring to the likelihood that customers will recommend the startup product or service to others (Ickin et al., 2019). NPS is a widely used metric for measuring the level of customer loyalty to the product or service; it is crucial as it can provide insight into the company’s overall reputation and the likelihood of attracting new customers through word of mouth (Reichheld, 2003).

6. Conclusions

KPIs can provide detailed reports on measurable performance requirements and allow new or mature companies to look for possible savings opportunities. It also enables users to determine supplier and distributor expectations. Overall, KPIs for digital accelerators are essential tools for measuring the progress and success of the startups and companies participating in the program. By tracking these metrics, accelerators can identify areas for improvement and make strategic decisions to support the success of the startups in their program. Apart from the KPIs proposed on the conceptual model, it is also important to define a set of KPIs for the accelerator program itself, such as the number of applications, the acceptance rate, the number of startups graduated, the number of mentors and partners involved, the amount of funding provided, etc. These KPIs can help the accelerator program evaluate its own performance and make the necessary adjustments to improve the program.

The proposed conceptual framework includes metrics that used to evaluate the survival, funding, resources, and capabilities of the startup, ultimately measuring their growth. On the other hand, the limited financial performance and growth cannot easily be measured for startups by metrics such as customers, revenues, and profits, and so these KPIs are automatically excluded from the proposed model. But some of them might be important when considered from a different angle. Regarding revenues, this metric is not be applicable to startups, but revenue growth might be a trend worth following since it measures the increase in revenue generated by startups in the accelerator program over time and is a key indicator of the accelerator’s success in helping companies scale and grow. Following the same idea, customer acquisition measures the number of new customers a company/startup can acquire in each period and tends to be a crucial indicator for the startups operating under a digital accelerator. Broadly speaking, in terms of program itself, the indicator “successful exits” plays a crucial role by measuring the number of startups that successfully exit the accelerator program and subsequently achieve significant growth and success. This indicator can be seen as a wider KPI, applicable to the organization that controls several startups operating on digital accelerators.

The present work focusses on a model that defines a set of KPIs, defined and placed under certain categories, such as profitability, growth, productivity, and size, these being the categories most relevant to the performance measurement of the company. Also, the accelerator applicable to the startup will ultimately define the appropriate measurement indicators, divided into 3 dimensions: economical, technological, and organizational.

Answering the research questions, the present review allows a conclusion regarding the following query: “How do digital accelerators impact startup economic and operational performance”? Digital accelerators can have a significant impact on startup companies by providing them with resources, mentorship, and networking opportunities crucial to allowing growth and success in the current digital age. They can offer startups access to experts in various fields, including business development, marketing, product design, and technology, helping the companies improve business plans, products, or services. By providing startups with resources, expertise, funding, and mentorship, digital accelerators can help startup companies to grow and scale faster than they would otherwise be able to, thus helping them to achieve their goals and become successful companies more quickly. Thus, the research question can be comprehensively answered through the proposed framework by providing measurable indicators across economic, technological, and organizational dimensions. The framework enables the systematic comparison of accelerated versus non-accelerated startups, the longitudinal tracking of performance improvements, and the quantification of digital acceleration’s unique value proposition. By implementing this measurement approach, researchers and practitioners can generate definitive evidence on digital accelerator effectiveness, addressing the current gap in understanding program impact and outcomes.

Subsequently, and answering the second research question “Which economic and client-focused KPIs can effectively measure digital accelerator impact on startup success?”, the proposed framework can measure the impact of digital accelerators on startups, taking into consideration different dimensions and, at the same time, mitigating the success factors critical to this kind of ventures. One contributes to the digital accelerators literature by proposing a conceptual framework of KPIs with which to measure the performance of early-stage startups. The comprehensive proposed framework of economic and client-focused KPIs provides accelerators with measurable indicators to assess their impact on startup success, enabling data-driven program optimization and stakeholder reporting. These metrics collectively address the second research question by offering concrete measures that demonstrate accelerator value across in both financial performance and customer relationship dimensions.

This paper contributes to practice, and managers would benefit overall from the conclusions presented. The research results might be used by top management or policy makers that are in the process of deciding which digital accelerator program to adopt and the best combination of guidance and support, especially in terms of performance measurement.

7. Future Research Directions

Digitalization, IT transformation, and Innovative Culture are all slogans that nowadays are being used in our day-to-day management activities. Managers and investors are more and more dependent on metrics and models to evaluate new business or to evaluate existing companies. Digital accelerators are being used to drive the innovation and to enable successful startups. The role and impact of digital accelerators on early-stage startups is a theme of great interest and might drive academics to invest greater investigation resources to analyze how the proposed framework empirically can be applicable to startup companies that are applying a digital accelerator program. We recommend the adoption of the present model to evaluate a group of startups, validating the framework and providing the practitioners with undoubtable guidance about how to measure the first part of the startup lifecycle. Also, it is important to identify that the added value of the proposed framework is centered on the practical application of new startups that mainly use digital accelerators. It is believed that following and evaluating the proposed framework may provide an answer regarding “how to ensure the success” in digital accelerator applications. Startups can implement the proposed framework by choosing the correct digital accelerators and then consider the critical success factors and constantly measure the startup performance in 3 different dimensions. In our opinion, productivity and growth play a critical role and all related KPIs must be measured through an information system that applies the metrics to a specific balance score card (BSC); alternatively, it is necessary to include the KPIs on the organizational BSC.