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Corporate Venturing Performance Measurement - Empirical Insights

Collaborations between corporates and start-ups become more and more important. In this research Steven Rottmann, M.Sc. presents empirical insights into the evaluation of CV collaborations during the collaboration phase.

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In a disruptive and fast developing world, companies become more and more collaborative by engaging into corporate venturing (Busi, Bititci, 2006; Keil, 2000; Eisenkopf, 2008; Schildt et al.  2003; Sena Ferreira et al. 2012; Toschi, 2009;). To fully understand business collaborations in a CV setting, the mechanisms and structures need to be analysed in detail – especially the  collaboration phase itself, which is the period after the contract has been signed and before the CV activity has ended. Only with in-depth understanding of the value adding collaboration phase, CV activities can be managed properly.

Since corporates pursue defined goals when engaging in CV, the management of CV activities becomes crucial. Companies strive to fulfil their stakeholders needs and to achieve their goals in a CV collaboration. Thus, one question is prevalent: “How can a CV activity be evaluated early in the collaboration phase in order to decide  whether to continue or end the collaboration with the respective CV partner?”. Consequently, a CV collaboration evaluation tool is needed (Benson, Ziedonis, 2009; Ernst et al. 2005).

However, to the knowledge of the author, there is only very limited CV research on the management of CV activities during the collaboration phase itself. The collaboration phase is the phase after the contracting and before the finalization of the CV activity. Existing literature focuses on the phases before and after the collaboration phase, e.g. by analysing selection criteria to identify suitable partners or the evaluation of CV projects, after they ended. A framework or method that indicates whether a collaboration should be continued or ended during the CV collaboration phase, does not exist.

Even existing performance measurement literature focuses on traditional performance measurement methodologies and indicators. These are designed to measure performance of a single business or supply chains (Westphal et al. 2010; Philbin, 2008). They do not consider the specific requirements of CV collaborations and rather focus on intra-company management (Westphal et al. 2010; Philbin, 2008). To the knowledge of the author, none focuses directly on the evaluation of CV collaborations (Westphal et al. 2010). Such a  performance measurement system could be used to manage the relationship and thus the output of CV activities. Performance evaluation, based on a profound scientific evaluation framework, enables fact-based decisions – independent from gut feelings.

Consequently, the most interesting question from corporates regarding CV shifts from “with which start-up should we collaborate with” to “how can we decide whether a CV collaboration with a start-up is successful and whether to continue or end it?” (Bititci et al. 2012). Yet, the evaluation of collaboration performance respectively the collaboration success is challenging (Busi und Bititci 2006; Bititci et al. 2012). Not only do boundaries between organization hamper CV collaboration evaluation, but also the fast changing and disruptive environment influences the performance. Still, as stated by Busi and Bititci, a deeper understanding of CV collaborations and its evaluation measures is needed (Busi und Bititci 2006). Based on that, this paper addresses the research questions:

What indicators do corporates use in order to evaluate at an early point of time, whether CV collaboration activities with a start-up should be continued or ended?

This paper presents empirical insights into relevant performance evaluation dimensions and practically applied indicators for the evaluation of CV activities during the collaboration phase. The indicators are derived from case studies with 2 organization from different industries. The case studies highlight the most relevant indicators for deciding whether to continue or end a collaboration with a startup at an early point of collaboration from the corporate’s perspective.

The paper is structured into five chapters. After the introduction part, a condensed literature review on CV and basic information regarding performance measurement will be presented. In chapter three, the applied method and used material are made transparent. Chapter four reveals the findings and most important insights. Finally, chapter five concludes on the practical use of indicators regarding the evaluation of CV activities during the collaboration phase.

Literature Review

What is corporate venturing?

Corporate venturing has been discussed in literature every now and then. Three major peaks of attention can be identified so far. In 1960 there was the first peak, when over 25 % of the Fortune 500 businesses engaged into corporate venturing (Baldi et al. 2015). In 1980, the second attention peak was reached, when corporates strived for diversification. The third peak emerged in the 1990s. New technological trends, market opportunities and an improved legal environment with less regulation and tax incentives fuelled the engagement in CV (Dauderstädt, 2013). Even today, the attention on CV is quite high (Birkinshaw, Hill, 2005), what might be based on the high complexity, fast development and disruptive changes in the environment (Maula, 2001; Keil, 2000).

Even though the relevance of CV has been acknowledged in literature and in practice, the theoretical understanding is still in its beginnings (Keil, 2000; Maula et al. 2009). Existing literature is highly fragmented and lacks consistency (Reimsbach, Hauschild, 2012). For example, even the term “corporate venturing” is used in many different ways - in academic as well as in practice (Banik, 2011).

In a nutshell, CV follows the idea that a large, well-established organization (the “corporate”) engages into collaboration with a small autonomous business (the “venture” or “start-up”) with high potential for growth and innovation. Whilst engaging in this collaboration the corporate pursues financial and strategic objectives (Reimsbach, Hauschild,  2012). The most common strategic objective is to gain insight into innovation and to identify new technologies (Kann, 2000; Reimsbach, Hauschild, 2012).; Ernst et al., 2005, Chemmanur et al., 2014, Sadowski, 2001, Faisst, 2005, Dushnitsky, Lenox, 2006, Toschi, 2009). Consequently, CV plays an important role in the development of new technologies and innovations (Baldi et al. 2015).

According to Maula, Chesbrough, Keil and others, CV is the umbrella term for entrepreneurial activities regarding collaborations of established companies with new and innovative businesses (Maula, 2001; Chesbrough, 2002; Keil, 2004; Seeliger, 2004; Dushnitsky, Lenox, 2005; Freese, 2006; Banik, 2011). Alongside others collaboration forms, like alliances, joint ventures or mergers and acquisitions, CV focuses on establishing mutual beneficial relationships (Wadhwa, Kotha, 2006; Lee, Kang, 2015; Seeliger, 2004; Schildt et al., 2003). CV collaborations represents the closest form of a relationship (Parung und Bititci 2006).

CV can be structured in intern and external corporate venturing (Dauderstädt, 2013). Internal CV refers to internal entrepreneurial activities. These activities can be anchored by establishing a new business unit or by the foundation of a new venture inside the corporate’s boarder (Dauderstädt, 2013). In contrast, external CV focuses on entrepreneurial activities outside the corporate’s border (Dauderstädt, 2013).

The present research focuses on external CV only. The above presented  definition of CV serves as basis for this paper.

What are the goals of corporate venturing?

As discussed above, CV represents a mean to enforce corporation’s entrepreneurial behaviour in order to meet shareholder’s needs (Siegel et al., 1988). Various authors discussed the goals of CV. Based on a financial perspective and having the shareholder interest in mind, Siegel et al. assumed at an early point in time, that CV’s main target is to realize financial returns (Siegel et al., 1988). However, during the course of time other scientists substantiated,that corporates do not focus mainly on financial benefits, but rather on strategic ones (Covin, Miles, 2007; Dushnitsky, Lenox, 2006; Chesbrough, 2002; Faisst, 2005). The table underneath gives an overview of the different goals, corporates pursue by engaging in CV:

Rottmann Table 1 1Rottmann Tabel 1 2

Even though the goals are formulated differently by author, it can be summarized that CV targets the following goals:

  • Beneficial financial effects
  • Insight into new technologies and markets
  • Spill-over effects
  • Fast market entries
  • Positive reputational impacts

In literature many authors argue, that although an organization might follow different goals, the “window on technology” or the insights into new technologies is the most important
one (Chemmanur et al., 2014, Sadowski, 2001, Faisst, 2005, Dushnitsky, Lenox, 2006, Toschi, 2009). 2.3.

What is performance measurement?

In chapter 2.2. the goals of corporate venturing were discussed. Obviously, a mechanism to monitor and manage the goal achievement is necessary (Seeliger, 2004). This mechanism can be represented by performance measurement. It aligns the organization’s goals with the organization’s strategy and actions (Seeliger, 2004). Performance measurement serves to steer the organization towards achieving its objectives and to evaluate the degree of objective realisation (Seeliger, 2004). Performance measurement: Performance measurement (PM) represents a systematic approach to plan, measure, monitor, assess, reward, and control the performance of organizations whilst using suitable methods and tools (Camarinha-Matos et al., 2009; Hilgers, 2008; Krause, 2006; Kaack, 2012). In general, PM represents a learning system, which is constantly optimized and refined to enhance its information and steering function (Seeliger, 2004). More specifically, performance measurement is a method to plan and conduct data collection regarding goal achievement (Westphal et al., 2010). PM comprises two main elements (Westphal et al., 2010):

  • The definition of performance measurement systems (PMS) to describe how performance measurement could be set-up and conducted
  • The definition of dimensions and key performance indicators (KPI) to evaluate the business performance

Performance measurement systems: A performance measurement system (PMS) respectively performance measurement framework is a system to measure and steer the multi-dimensional strategic and operational performance in balance with its alternating interdependences based on a cybernetic process and elements of organizational learning (Grüning, 2002; Collier, 2005). As Sena Ferreira et al. summarize, a PMS is the bundle of different but linked performance measures (Sena Ferreira et al., 2012). A PMS serves three functions: measure performance, grant rewards or impose sanctions and assign responsibilities and rights (Anderson, Dekker, 2005). PMS represent tools for the management to monitor, control, steer and influence employee’s behaviour to achieve the organization’s goals (Grüning, 2002; Collier, 2005). PMS received a lot of attention by practitioners and researches (Dauderstädt, 2013). Thereby, the discussion about organizational control is based on systems theory. It states that businesses of all kinds are goal-oriented, and the control mechanisms are installed to use resources effectively and efficiently to achieve these goals by influencing behaviour (Collier, 2005).

The focus of PMS changed during time. Originally, formal and financially measurable information was collected to support management decision making (Collier, 2005). Today also external information concerning customers, markets, competitors and especially nonfinancial information plays an important role (Collier, 2005). Solely using financial measures is discussed critically, because strategic goals and effects get lost (Seeliger, 2004).

Performance and performance indicators: The discussion about performance measurement and achieving goals is coined by the terms “performance”, “performance measures” and “key performance indicators” (Dauderstädt, 2013). In this paper performance is defined as an organization’s capability to achieve goals and thereby fulfilling stakeholder’s expectations (Grüning, 2002). The status of goal realization and consequently the resulting performance can be transferred into indicators (Sena Ferreira et al., 2012; Grüning, 2002). These indicators refer to different dimensions: Efficiency and effectiveness, internal and external measures as well as financial and nonfinancial measures (Sena Ferreira et al., 2012). Financial and non-financial performance measures are also defined as quantitative and qualitative performance measures. Literature discusses the value of both, financial and nonfinancial KPIs.

In general, it is proven, that businesses with a higher PMS intensity outperformed their counter-parts with lower PMS intensity (Davila et al., 2010). Consequently, differences in performance can be attributed to differences in the business’s ability to conduct performance measurement (Keil, 2000). The impact on business’s performance by using a suitable PMS and KPIs is widely acknowledged in literature (Dauderstädt, 2013).

What is the state-of-the-art of performance measurement in CV?

The biggest challenge in the management of CV relationships is less the identification of suitable business partners, but more the management of the relationship itself (Banik, 2011). Research states that the management of CV activities is complex and difficult (Burgers et al., 2009). This might be one of the reasons why research concerning the CV collaboration evaluation is extremely limited from both, the theoretical and the empirical perspective (Maula, 2001; Banik, 2011; Toschi, 2009; Westphal et al., 2010; Basu et al., 2011; Weber, 2009). Not only are empirical studies focussing on inter-organizational management missing, but also there are only very few studies dealing with performance evaluation of CV activities during the collaboration phase (Keil, 2000; Kollmann, Kuckertz, 2010; Strauss, Corbin, 1998; Faisst, 2005; Fulghieri, Sevilir, 2009; Kuratko et al., 2009). The existing literature focuses on traditional performance measurement methodologies and indicators. Yet, the usage of established measures – not developed for CV – has its limitations (Banik, 2011; Keil, 2000). The frameworks and KPIs are designed to measure the performance of a single business or output of supply chains (Westphal et al., 2010; Philbin, 2008). Most of them focus on intra-company management, but only very few focuses directly on the evaluation of CV activities during the collaboration phase (Westphal et al., 2010). The limited CV performance measurement research available, almost exclusively analyses overall success determinants or CV strategies (Seeliger, 2004). Standardized and formalized PMS for CV are missing. Systematically structured operational KPIs, which evaluate the strategic outcome and success do not exist (Dauderstädt, 2013; Faisst, 2005; Schween, 1996; Kollmann, Kuckertz, 2010; Strauss, Corbin, 1998). To make CV more transparent and effective, a CV performance evaluation framework is crucial (Faisst, 2005). Since some CV specific requirements need to be met, the is an extensive need for a customized CV collaboration-oriented performance measurement framework (Westphal et al., 2010; Faisst, 2005).

Methods and Materials

Case research

This paper is based on one of the most powerful tools in management research – case studies (Pekkola und Ukko 2016). Case studies allow to generate understanding of a specific phenomenon, especially in the organizational context (Yin, 2009).The advantage of case
studies lies in three strength (Voss et al. 2002):

  • Case studies are applicable when, research objects and its elements as well as the phenomenon itself long for explanation
  • The phenomenon can be understood in its complexity whilst answering what, how and why questions regarding the object of analysis
  • The research is conducted in its natural setting and environment, what leads to a profound understanding of the real-life practice

Case studies not only represent a single research method, they rather combine various qualitative and quantitative methods. For example, a case study can include interviews, document analysis and questionnaires (Pekkola und Ukko 2016).

This research presents a multiple case study approach focussing on the evaluation of CV activities. In more detail, the investigated case studies focus on an early evaluation, whether the CV collaboration should be continued or ended. The continuation or ending decisions thereby is based on indicators from the perspective of a corporate. Only few literature offers insight into the practical evaluation of CV activities during the collaboration phase. This gap suggests an inductive case-based research approach to investigate the phenomenon. The evaluation of CV activities during the collaboration phase with the goal to decide about ending or continuing a collaboration is highly complex. Since this study analyses this complex matter, inducive case studies are most suitable. They allow to investigate the what and how, thus justifying the case study approach. Moreover, case studies are a suitable research method, when the case study uses revelatory cases (Yin, 2009).

The author investigates KPI systems by multiple cases, which were replicated in different organizations. The choice of multiple cases allows to generalize, when comparing with single case studies (Keil et al. 2008). The author used the case research model from Leonard-Barton as orientation (Leonard- Barton 1990). In this model a lead case study is investigated in depth in order to generate the grounding KPI system. Then, further case studies serve for replication and validation of the in-depth case study. Again, this helps to improve generalizability. The author conducted two case studies of CV performance evaluation in large organizations from the sectors automotive and chemicals during the period of April 2018 until January 2019. The investigated corporates are companies, that are well-established and collaborate with new businesses to gain insights into new technologies and innovation on a regular basis. The new businesses on the other hand are paid to deliver the innovation or insight. The new businesses are independent and not founded by the corporate. Consequently, a focus on external CV is given.

The case studies’ main material was interviews and secondary data. The collected data was used to validate and support the insights from the interviews, especially the evaluation actions and outcomes. The cases are chosen due to the researcher’s access to the corporates and due to the proven maturity regarding corporate’s experience in CV activities. All cases describe collaborations between corporates and start-ups that work together in a project setting to generate innovation. The present study can be classified as unique, because it reveals new information and insights into the evaluation of CV activities (Pekkola und Ukko 2016).

Data collection

Three elements were encompassed by the data collection for this study. Firstly, an initial understanding of the state-of-the-art and practical application of the evaluation of CV activities was established. Secondly, a potential category system for performance evaluation focusing on the needs of CV activities during the collaboration phase was built. Thirdly, a deep dive into the key factors which play a relevant role in the evaluation of CV activities during the collaboration phase was conducted.

Data was collected from different sources: (1) quantitative and qualitative data through the conduction of semi-structured interviews; (2) the analysis of various CV documents from company sources. The author conducted 8 interviews with 5 experts to gain insight into the cases and to understand the goals and focus of the corporates for evaluating CV activities in an early stage. The interview strategy was laid out to access the strategic and operational level of CV collaborations. Interviewees differed in their closeness to the CV cases. This cross-level interviewee selection sharpened the understanding for the organization, the cases and the goals behind the CV activities and offered differentiated insight. Moreover, it reduced the ex-post rationalization bias. The researcher interviewed the CV experts with a semi-structured interview. These interviews served to confirm the identified research gap from a practical point of view and to gain understanding of the practical CV processes with focus on the collaboration phase. The interviews revealed the current state-of-theart regarding performance measurement in the context of CV, as well as desires of improvement. The interviews lasted about 60 minutes. Afterwards, the researcher analysed the interviews by using codes, oriented on research regarding the evaluation of a CV project after finalization.

At the beginning of each interview the author collected background information about the interviewee and her/his responsibility. Then, information about the organization’s history in CV was asked. Depending on the interviewee, strategic information regarding goals and decision making was demanded, whereas other were asked about the process, used evaluation indicators and detailed examples. If interviewees denied to tape, the author took notes of the most important remarks. In order to collect quantitative data on the researched cases, newsletter, emails and case specific documents, like meeting minutes and process documentation, were analysed with the goal to support and validate the interview’s insights (triangulation).

As described above, the extracted data was coded (Strauss und Corbin 1998). Firstly, the author assigned codes, deduced from a literature review and knowledge about CV. Secondly, the codes were extended by additional codes which emerged throughout the data analysis. Thirdly, the codes were clustered according to content-wise topics. Thus, codes which where conceptually similar got merged into one bigger code. Fourthly, on a third level, the codes were clustered into dimensions. These dimensions are codes, which built the overall categories for the indicators and the pillars for the indicator system.


The importance of CV, especially the value adding collaboration phase has been widely acknowledged. Yet, so far only few empirical researches have been published, focussing on the CV collaboration phase. Even less studies investigated the evaluation during the collaboration phase. Therefore, this research strives to present the current state-of-the-art regarding the performance evaluation of CV activities during the collaboration phase. The practically applied indicators are carved out from case studies. These indicators support organizations and managers to monitor and steer CV collaborations. Depending on these indicators, organizations decide whether to continue or end a CV activity. The case study findings are presented below.

Company A – Automotive

Company A is a large automotive organization. Having more than 100 years of experience, this company engages in CV in order to gain innovations and technological insights. Most often innovations and new technologies regarding cars have a need to be integrated into the product fast. It is crucial for this company to evaluate early and depending on reliable factor, whether to continue or end the CV collaboration. Engaging in about 20 new CV activities a year, this corporate gained quite some experience in the field of corporate venturing.Rottmann Table 2

The company relies on 10 indicators to decide, whether to continue or end a CV collaboration. These 10 indicators can be clustered into 4 dimensions: financial, collaboration, innovation and process. An overview of the indicators is presented in the table underneath.

Invested capital/planned capital: As can be seen, the financial dimension only is assigned with one measure – ratio between invested capital and planned investment. This measure serves to check, the investment status. It offers insight into the capital need from the start-up.

The idea is, that the needed capital should be proportional to the time, the project is running. For example, if 2/3 of the planned investment capital are consumed in the first quarter of the project, it can be assumed that the start-up devours more money than originally planned.

The collaboration dimension is resembled by the indicators: team stability/change, management support, response time and number of conflicts.

Team stability/change: Team stability or change describes the managers feeling regarding the stability of the start-up’s team. If team members are exchanged quickly, additional effort is required to get the new team up and running. Corporates even might assume, that the start-up withdraws experts. Yet, sometimes changes regarding the team might even increase the performance, since different experts might be needed in different project phases. Still, team stability is used as an indicator to evaluate, whether a project can be successful, even though this indicator is not quantified and only is based on the managers gut feeling.

Management support: Management support represent an important indicator. Management support is shown by the top management’s commitment, expressed by being benevolent towards the CV activity and attending meetings. If the top management ceases to attend meetings or top management decisions are postponed, the CV activity might lose grip. Top management support is important to receive decisions in favour of the CV activity and is the basis for a successful project. Yet, management support is a subjective indicator, without an objective method to measure it.

Response time: The same holds true for the indicator response time. Response time describes the time a start-up needs to respond to a corporate’s question/task. It does not need to present the solution right away, but at least an information, that the start-up is aware of the question/task. Unfortunately, the response time as an indicator is not defined clearly, but only based on subjective perception. It is neither measured, nor is a value for a “good response time” defined.

Number of conflicts: Number of conflicts embodies another indicator to decide whether to continue or end a CV activity at an early stage. On the one hand, this indicator describes social conflicts. On the other hand, it refers to number of problems escalated to a higher decision level. This is a relevant measure, because it indicates goal conflicts, which affect the performance negatively. The higher the number of conflicts, the worse. For this indicator there is no threshold, however a steadily increasing of number of conflicts triggers the investigation of the other indicators.

Number of impulses: Company A uses the number of impulses to monitor the innovativeness of the CV project. An impulse can be an idea or the proposal of a joint workshop. The goal of these impulses must be to improve the product, service or the collaboration. The higher the number of impulses, the higher the probability for a successful collaboration. Due to the fact, that this indicator does not have a predefined optimum, it must be monitored over the entire collaboration phase. If the number of impulses stagnates or decreases, it indicates that the collaboration performance decreases. When impulses go down, it is less likely to find a solution. Moreover, if the number of impulses stagnates or decreases it might be a sign that the venture’s interest and commitment in the CV activity decreases, too.

Number of qualified updates: Company A argues that the number of qualified updates represents a performance measure in CV activities. This indicator has a slightly different focus than the number of impulses. Qualified updates target not only the quantity of impulses, but also the quality. When using “number of qualified updates” as an indicator, the organization checks, whether specific data has been shared. For example, it can be measured how often documentation is updated or how often project management data is actualized. The qualified updates show how fast innovation is pursued. The number of actual qualified updates is compared with the number of all updates. The assumption behind is, that if the partners appreciate each other they are keen to keep the ratio from qualified updates to total amount of updates close to one. If the ratio decreases, it indicates that the performance and the commitment might decrease.

Number of not-on-time-deliveries: For company A, milestone plans are essential to steer CV activities and to achieve its goals right on time. These milestone plans have one specific objective – measuring whether features or products are delivered on time. Thus, on-time delivery represents a main indicator to measure CV performance. Important milestones are aligned with pre-defined deliverables. On-time delivery means that the deliverables are finished at the agreed point in time. If all agreed deliverables are finished at the respective milestone, the venture’s performance meets the corporate’s expectations. If the deliverables are repeatedly delayed, the corporate revaluates the CV collaboration. At company A the ratio of “number of on-time delivered features” to the number of all deliverables for a milestone is tracked. The ratio should be close to one or one in order to indicate great performance.

Number of information exchange sessions: The number of information exchange sessions is used as measure to evaluate CV collaboration performance as well. The information exchange meetings help to strengthen transferring information. These sessions can also be used for joint problemsolving. Yet, the number of exchange sessions has a flaw. On the one hand, the number of exchange sessions needed is project specific. Therefore, it is hard to tell a suitable number of sessions. On the other hand, there could be a lot of exchange sessions, however not all relevant people might show up.

Exchange session attendance rate: Only measuring the number of information exchange sessions might not be enough, since having information exchange meetings does not guarantee that relevant experts participate. Thus, the number of employees attending these sessions represents a useful indicator for collaboration success. A high attendance rate safeguards the information exchange between the organizations. Still, the number of participants in information exchange sessions does neither represent the quality, neither that the attendees share their new knowledge, nor whether the right people attended the session. All in all, company A’s CV performance evaluation system covers 4 dimensions referring to financial and strategic goals. Even though company A talks about KPIs and indicators, there is no KPI dashboard. During the case study it became obvious, that all except two indicators (invested capital/planned capital, number of not on-time-deliveries) are based on the expert’s subjective evaluation. There are neither target values nor statistical documentations about the manager’s assessment. After having presented the indicators used by company A, the chosen multiple-case study approach reaps its benefits by comparing the lead case with replication cases. Company B represents the chemicals industry. In order to integrate innovations, this organization engages in CV ever since. According to the interviewees, company B starts approximately 10 CV project every year. The used indicators from company B are quite similar to the ones from company A. An overview of company B’s indicators is presented in the table below. Since the used indicators are quite similar to company A, only the differing indicators shall be described.Rottmann Table 3

Number of network expansions: In the dimension “collaboration” company B uses an additional indicator – number of network expansions. The social network is crucial for an organization’s success, especially for new businesses Thus, the number of newly, successfully established relationships with people or departments is monitored by company B. If a start-up establishes new relationships, it gains strength through further supporters or partners with different knowledge. By having a great network, the venture can get information more quickly. Still, in some cases, e.g. development of specific features, it might not be necessary or even possible to expand the network. Since establishment of networks takes time, it should be kept in mind, that this indicator can only be measured over a longer period.

Number/speed of adaptions: In company B this indicator is used in absolute terms regarding the number and the speed of adaption. Closely connected to on-time delivery, speed of adaption means the time span from gaining new information until implementing actions to adapt to the new circumstances. Speed of adaption is important, because the longer any of both organizations operates in the wrong direction, the longer resources and energy are sapped. Yet, the speed of adaption is hard to define, because it is problem specific. Thus, this indicator is not defined clearly. The evaluation is solely based on the manager’s perception.

As mentioned before, both organizations apply quite similar indicators to determine whether to continue or end a CV activity. The differences are, that company A uses 10 performance measures in total, one of them being “exchange session attendance rate”. This indicator is not used by company B. Company B uses 11 indicators including the indicator “number of adaptions” and the indicator “number of network expansions”, which are both not used by company A.

This empirical research shows, that there is potential for improvement regarding the CV collaboration evaluation during the collaboration phase. In both organizations indicators are not defined clearly, and evaluations are not documented. Still, quite similar indicators are used to evaluate whether to continue or end a CV collaboration.


Even though CV has been discussed in literature for a while, there are still some white spots to investigate (Clercq et al., 2006; Dauderstädt, 2013; Husted, Vintergaard, 2004). Researchers have underlined the need for empirical insights into CV, especially the collaboration phase (Baldi et al. 2015; Busi, Bititci, 2006). Since an early evaluation of the CV collaboration activity seems to be the foundation for a successful partnership, this study addressed this need by presenting an overview of practically used indicators.

The study contributes to the existing literature by collecting empirical evidence of commonly used indicators to determine whether to continue or end a CV activity at an early point of time. Practically used indicators were extracted from case studies.

The research project contributes to the theoretical knowledge of CV and performance measurement by deepening the understanding of CV evaluation during the collaboration phase. Moreover, this study explains why and how these measures are used to evaluate CV collaborations.

This research contributes to managerial recommendations by giving insight into the evaluation of CV evaluation in an early stage of collaboration. It also contributes by supposing 4 dimensions which should be evaluated. By summarizing the most relevant indicators, clustered in dimensions this research gives orientation for developing a KPI system focusing on the collaboration phase in a CV setting. By understanding and learning from the examples and the relevant factors, managers can steer CV activities and choose to continue or end a CV collaboration at an early stage.

Still, the present research has some limitations. Since the case study’s goal is to investigate the what and how, the results are only generalizable in a limited scope. The collected empirical evidence is valid for the investigated organizations and might be different for other organizations in different branches. Thus, the decision of continuing or ending a CV collaboration based on performance evaluation indicators necessitates further research to develop and design a generalizable indicator system. It might also be interesting, to investigate whether the indicator system is transferable to other contexts, e.g. collaborations outside the CV  environment. Furthermore, empirical in-depth and quantitative research regarding the indicators system for CV performance evaluation during the collaboration phase might reveal insights. Finally, the research should focus on the impact of applying such an indicator system in the context of the CV collaboration phase.

List of references