Dataset on performance management systems' design in project-based organizations

This data article presents the supplementary material for the paper “A configurational explanation for performance management systems' design in project-based organizations” [1]. The article introduces a dataset on 15 project-based organizations (PBOs) in the management consulting industry in the Netherlands. The dataset includes organization-level conditions at PBOs, such as perceived environmental uncertainty, organizational size, innovation strategy, opportunity strategy, and performance management system design. The dataset is prepared for a fuzzy-set Qualitative Comparative Analysis (fsQCA). Combinations of conditions are expected to be related to a mechanistic or an organic performance management system design. This article includes the original dataset with quantitative scores and a qualitative motivation for each score, calibrated data, and fsQCA truth tables.


Data
The data was collected in 15 PBOs in the management consulting industry, by means of an interview with a top manager or highly informed middle manager, and a document study (for a case description, see Ref. [1] -Appendix A). Each interview contained a semi-structured part, a structured part, followed by another semi-structured part (for the supplementary interview outline, see Appendix A). The first semi-structured part served to collect data on the mechanistic and organic controls used by the PBO. Performance management system design, the outcome variable, was measured as the PBO's proportion of mechanistic controls relative to their organic controls, as outlined by Ferreira and Otley [5]. The structured part, based on earlier validated questionnaires, served to determine the case scores on each condition. Perceived environmental uncertainty was measured by means of 4 items (7-point Likert scale) developed by Miller [6]. Organizational size reflects the turnover of the PBO. Innovation strategy was measured by means of 3 items adopted from Jansen et al. [7]. Opportunity strategy was measured by means of 3 items derived from Naman and Slevin [8]. The final semi-structured part of the interview served to validate and motivate each case score, as displayed in Table 1. Table 2 presents threshold values for data calibration, while Table 3 features the calibrated data itself. Tables 4 and 5 exhibit the Truth Tables for the mechanistic/organic performance management system design.

Experimental design, materials and methods
To facilitate the educational use of the data and potential replication studies, the data has been calibrated [9] into fuzzy scores in the interval between 0 and 1. Defining threshold values is key for Specifications Table   Subject area Business, management and accounting More specific subject area Strategy and Management Type of data Table  How data was acquired  Interview, questionnaire, document study  Data format Descriptive, coded, calibrated, processed

Experimental factors
The sample includes 15 managers of project-based organizations in the management consulting industry. The authors collected data on perceived environmental uncertainty, organizational size, innovation strategy, opportunity strategy, and the outcome variable performance management system (mechanistic vs. organic).

Experimental features
The interviews started with a semi-structured part, to obtain data on the organic and mechanistic controls used in the PBOs performance management system (outcome variable). Thereafter, the interviews continued with a structured part, to determine case scores on each condition by means of validated questionnaires. The final part of the interview was semi-structured (including document study) to validate and motive each case score. Case scores were calibrated and configurations of conditions were presented in fsQCA truth tables.

Data source location
The Netherlands Data accessibility Data is included in this article Value of the data The data can be used by managers to support the process of designing a performance management system, based on the combination of organization-level characteristics of their PBO (see Ref. [1] for interpretations).
The data can be used as a benchmark for research on performance management systems of PBOs in other research settings (for a categorization of PBOs, see Ref. [2]) The data can be used to compare the explanatory power of fsQCA as a method of analysis, relative to other methods, including linear additive approach (for an example, see Ref. [3]) The data can be used as teaching material for fsQCA (see Refs. [1,4] for a discussion on the methodology). 4.5 The organization introduces a new product or radical change in a product almost every year. It is thus quite proactive. At the same time the organization scores rather low on risktaking.

Case 3 57.89
Has a mechanistic performance management system. Uses narrow controls and targets to control output, results, and behavior. It also uses organic controls like sophisticated integrative mechanism and strategic interactive controls to focus on customer satisfaction and innovative capacity.
5.67 Finds itself in a rather unpredictable market. Product demand changes often and suddenly. Faces "unfair" competition. The only thing that is more predictable is technology.
V 3M 4.5 Introduces product innovations once or twice a year.
5.33 One of the more progressive organizations in the sector, i.e., voluntaristic orientation. Next to that, organization takes risks and is a market leader in some segments.
(    This organization radically changed its business model over the past years. Hence, they acted highly voluntaristicly. They also take relatively large risks. The organization does not take into account whether it follows or leads the competitors.
Case 12 29.63 The service level is the most important factor in this organic performance management system. Everything that contributes to that goal is encouraged. The employees bear the responsibility for achieving the goal and are supported through training, coaching and knowledge clusters.
2.38 This organization finds itself in a certain environment. The only aspect that makes it less predictable is the technology in the long run.
The innovations are mainly focused on adapting the service to the customer. The organization does not engage in radical changes itself. The supplier is the one who delivers the radical changes.
3.66 This organization is more on the deterministic side of the scale. The firm has not changed their service range dramatically over the last period. Although they are the ones followed by their competitors, they have products with both high risks and low risks.
Case 13 57.69 This mechanistic performance management system is driven by output and result control. The firm monitors employees in terms of generated turnover, achieved impact and customer portfolio. Next to that, they manage by communicating the company's vision and organizing monthly meetings to encourage knowledge sharing.
4.33 There are both predictable and unpredictable factors in this organization's environment. The demand and technology are uncertain, but resources and competition are more on the predictable side.
This organization tries to come up with many new services. Although they do not perceive them as radical (anymore), the examples indicate explorative innovation.

6
In the past 6 years, this organization changed its business model and became a front runner in the sector, which makes it voluntaristic. Furthermore, they take quite high risks, although keeping them within the possibilities of the organization.

Case 14 43.75
This organic performance management system is relatively small. The firm encourages intense contacts among the employees and knowledge exchange. Additionally, it sets turnover targets, the progress on which is monitored every 3 months. 3 The environment is quite certain, but the economic situation makes it somewhat more uncertain.

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1.5 Firm's services change incrementally. In the future it aims to become a bit more radical, but for now the change in services is slow. 3 The accumulated slow changes in the services resulted in a substantial change in services over the past 5 years. However, all projects have low risks.
Case 15 80 This mechanistic performance management system is primarily build around accounting controls such as financial reports. The firm expects its employees to report in a strictly predefined format and check those reports. 6 The resources availability and the demand are highly unpredictable. The factor that makes the environment somewhat more predictable are the type of questions that customers ask.

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1 This organization is specialized in one service and customizes this service to various clients, without radically changing the service. 2 The strategy is mainly deterministic, even though the type of clients changed somewhat over the last years.     determining the degree to which a case belongs to a condition, fully in (1), fully out (0) or maximal ambiguous (0.5 e case-crossover point). Based on the case score motivations and the scales used to measure each variable, we determined initial threshold values. We verified the threshold values by means of a cluster analysis (for further details, see Ref. [1]). The threshold values are presented in Table  2. The data reveals two clusters of PBOs, one cluster of 8 PBOs using predominantly organic controls (on average 66.4% organic controls; min. 56.3% max. 90.5%), and the other cluster of 7 PBOs using predominantly mechanistic controls (on average 62.4% mechanistic controls, min. 51.4% max. 80%). The calibrated data is displayed in Table 3.
The calibrated scores allow for conducting fsQCA on the combinations of conditions (pathways) that in conjunction either relate to organic or mechanistic performance management system design. In short, fsQCA examines combinations of conditions leading to a specific outcome, instead of examining conditions in isolation. It allows for different pathways to the same outcome (equifinality), as well as distinct pathways to opposite outcomes (asymmetry). For an elaborated discussion on fsQCA as methodology, see Refs. [1,4]. For identifying configurations of conditions, a Fuzzy Truth Table Algorithm was used. The truth tables (Tables 4 and 5) were derived with the fs/QCA software, by using a consistency cutoff value of 0.8 and a minimum of 1 case per solution term. The actual solution terms are presented and discussed in De Rooij et al. [1]. They can be replicated by means of the 'standard analysis' option of the fs/QCA software, having all prime implicants marked. The fsQCA reveals a transparent two-path solution per outcome (mechanistic/organic performance management system design), which makes the data particularly useful for educational purposes.