How to build data-driven Strategy Maps? A methodological framework proposition

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Abstract

The Strategy Map is a strategic tool that enables companies to formulate, control and communicate their strategy and positively influence their performance. Introduced in 2000, the methodology for developing Strategy Maps has evolved over the past two decades, but still relies exclusively on human input. In practice, Strategy Map causalities – the core elements of this tool – are determined by managers’ opinions and judgments, which can lead to a lack of accuracy, completeness and longitudinal perspective. Although authors in the literature have pointed out these problems in the past, there are few recommendations on how to address them. In this paper, we propose a methodological framework which uses operational data and data mining techniques to systematize the detection of causalities in Strategy Maps. We apply time series techniques and Granger causality tests to increase the efficiency of such strategic tool. We demonstrate the feasibility and relevance of this methodology using data from skeyes, the Belgian air traffic control company. 1

Introduction

Introduced in 2000, Strategy Map (hereafter SM) is a performance management tool widely adopted by organizations. After the creation of the Balanced Scorecard (hereafter BSC) in 1992, Kaplan and Norton developed the SM to establish causal relationships among the indicators of the BSC. A SM collects an organization’s key indicators and groups them into four perspectives: Financial, Customer, Internal Business Process and Learning and Growth. SMs are a visual representations of the indicators which help understand the side effects of a change in an indicator. This concept of causality, discussed in more detail in Section 2.1, distinguishes a SM from a simple performance measurement scorecard [1].

SMs’ usefulness for companies has been demonstrated in the literature [2]. They are used by organizations to formulate, control [3] and communicate [4], [5] their strategy. In addition, managers can use them as tools for decision-making and decision-rationalizing [6]. SMs have a decision-facilitating effect for managers to assess the relevance of external information as well as to evaluate whether a strategy is appropriate [7].

Human input intervenes in the development of SMs through the manager’s experience and intuition. Managers are experts of the organization and their knowledge is considered sufficient to judge whether an indicator should be included in the SM and whether there is a causal relationship between a pair of indicators. However, the experts’ opinion raises critical issues, which are discussed in more detail in Section 2.2, that may be detrimental to the tool and consequently to the organization. Although isolated alternative methods have been explored and are presented in Section 3, there is no overall framework in the literature that uses business data to counteract subjectivity arising from human input in the context of SM. However, it is known that companies that make decisions based on data perform better [8]. Therefore, in Section 4, we propose a methodological framework for the use of data and the application of data mining techniques in the creation process of SM. In Section 5, we apply our proposed methodological framework to the Belgian air traffic control company to demonstrate its relevance and feasibility. Section 7 discussed the results of application of our proposed framework. Finally, Section 8 highlights some limitations in our framework and Section 9 concludes the paper with further research directions.

Section snippets

Causalities in Strategy Maps

In the SM authors have investigated various aspects of the practical development of such models in companies, which we can divide into three stages:

  • 1.

    The selection of the indicators to put in the four perspectives

  • 2.

    The identification of causalities between chosen indicators

  • 3.

    The validation of the identified causalities

We insist on the distinction between causality identification and causality validation, considering that of the companies that create their SM, only a small number seek validation [2].

Related work

In our proposed framework, we suggest to use Granger causality tests to test and validate causalities in the Strategy Map. To the best of our knowledge, the use of Granger causality tests to detect causalities was first mentioned in a paper in 2005 in the context of studying the cause-and-effect relationships of the BSC tool [10]. Since then, very few applications of SM causalities validation have been explored. Some of them look for generalities in the causal relationships of the tool [3],

Methodological framework

In order to build Strategy Maps using hard data, we propose a methodological framework of five phases (see Fig. 2). This framework does not report on the preliminary steps of defining mission and vision, but focuses on practical data analysis steps. We describe each phase of our proposed framework hereafter.

Application to an air traffic control company

To illustrate how the application of our protocol would look like, we report initial tests on the 5 phases of our proposed solutions. For this purpose, we use operational data provided by skeyes. Skeyes is the Belgian air traffic control company that employs 891 people and is responsible for five Belgian airports and two radar stations. In 2019, it controlled more than one million flights and generated revenue of 245.2 million euros.

Evaluation

In order to validate the methodological framework and application developed in this paper, we decided to perform an evaluation workshop. Indeed, we unfortunately could not find any type of evaluation protocol for this type of scientific production in the literature. Thus, we organized a Feedback Workshop with skeyes’ stakeholders – the performance and strategic managers of the organization – and we evaluate our proposed framework through a collection of opinions, comments and suggestions.

The

Results interpretation and managerial implications

The purpose of this paper is to propose a methodological framework to build SMs using hard data of the organization. The final SM, presented in Fig. 5 depicts the validated causal linkages that exist between the eight indicators of our initial sample. Thanks to the linkages validation with hard data, this strategic tool can be used as a support for informed decision-making. The arrows, which represent the side effects between indicators, serve as guide for performance and strategy managers by

Limitations

The case study application presented in Section 5 demonstrates the feasibility of our proposed framework and leads to the production of a SM. The methodological framework we propose uses hard data and quantitative methods to build SMs. Validation of causal relationships between indicators is carried out through VAR models and Granger tests. These analyses impose many constraints in terms of data and analytical capacity of the organization which apparent as limitations.

First, the data included

Conclusion

In this paper, we present our vision of a methodology for developing SM based on operational data and data mining. This methodology would allow solving problems related to human input in the process. The preliminary results show that we can identify and validate causality in the sense of Granger between indicators selected for the SM. Although the results for the eight indicators selected for this study look relatively clear, the problem becomes quite complicated when an organization faces more

CRediT authorship contribution statement

Lhorie Pirnay: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing, Visualization, Validation. Corentin Burnay: Conceptualization, Investigation, Writing – review & editing, Supervision, Validation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

We would like to sincerely thank skeyes for providing the necessary data for the practical application of our methodological framework and, more specifically, Bertrand Gallez for his availability and guidance throughout this paper.

Lhorie PIRNAY is a Ph.D. researcher in the Business Administration Department at the University of Namur, Belgium. She is working in Performance Management and Data Analysis areas. More specifically, her current research interests focus on the detection and validation of cause-and-effect relationships in Strategy Maps for Balanced Scorecards. Lhorie Pirnay is the corresponding author and can be contacted at: [email protected].

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  • Lhorie PIRNAY is a Ph.D. researcher in the Business Administration Department at the University of Namur, Belgium. She is working in Performance Management and Data Analysis areas. More specifically, her current research interests focus on the detection and validation of cause-and-effect relationships in Strategy Maps for Balanced Scorecards. Lhorie Pirnay is the corresponding author and can be contacted at: [email protected].

    Corentin BURNAY is Assistant Professor of Strategic and Information Management in the Business Administration Department of the University of Namur, Belgium. He completed his Ph.D. in Economics and Management Sciences from University of Namur in 2016. His research interests focus on the design and implementation of self-service decision support systems. He has published over 20 research papers on these topics within the fields of requirements engineering, business analysis, and conceptual modeling of information systems. In 2014, he received the Distinguished research paper award at the International Conference on Advanced Information Systems Engineering (CAiSE). He is co-organizer of the MORE-BI and B4IS international workshops hosted in the same series of conference.

    1

    This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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