Digital Maturity Model for Management Consultant Firms

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Digital Maturity Model for

Management Consultant Firms

Digital Technologies within a

Standardized Management Consulting

Process and Performance Improvements Master Thesis

Author: Mariángeles Bravo Guerrera, Annie Snöberg and Laurie Tetzlaff Examiner: HF

Tutor: HF

Co-judger: Hana Hulthén Term: VT20

Subject: Degree Project in Business Process Control and Supply Chain

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Abstract

Title: Digital Maturity Model for Management Consultant Firms: Digital technologies within Standardized Management Consulting Process and Performance Improvement

Authors: Mariángeles Bravo Guerrera, Annie Snöberg and Laurie Tetzlaff

Background: Digitalization and digital technologies are of significance to management consultant firms since they influence them and force them to reshape and improve their business processes. For that purpose, firms can use Business Process Improvements methods, such as process mapping, benchmarking and maturity models.

The authors identified multiple gaps in literature which this thesis aims to address.

One gap was the lack of literature focusing on the industry of professional service firms, like management consultant firms. Another gap was on a standardized management consulting process, and the knowledge of how digital technologies are being used within it. There was also a lack of knowledge about what performance improvements management consulting firms can expect when using digital technologies in their management consulting process. Lastly, there is no digital maturity model that is focused towards management consultant firms, that can identify management consultant firms' level of digital maturity.

Purpose: The purpose of this study was to explore, describe and explain digital technologies used within a standardized management consulting process, expected performance improvements through the use of digital technologies and the levels of digital maturity through a maturity model for management consultant firms.

Method: A qualitative study with an exploratory and explanatory research strategy was used. An abductive approach was applied for this multiple case study, which was based on semi-structured interviews with nine different Case Firms.

Findings and Conclusion: The results of the study developed a standardized management consulting process including following activities; initial contact, background research, current status assessment, present project plan, developing solution, closing project by implementing solution and follow up, feedback and lessons learned. Across the standardized consulting process, the firms used technology types of artificial intelligence, analytical applications, cloud computing, automation and web-based applications. The digital technologies are used for collecting information, collaborative presentations, communicating, collaborating to develop consulting solutions, storing and managing documents, managing projects, tracking time, analysing information, developing and sharing insights, and customer relationship management. Seven key categories of performance were identified,

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which could be improved through the use of digital technologies. The categories include improved knowledge transfer, increased efficiency, measurable value based impact to client, increased quality of consulting, increased competitive advantage, improved knowledge management, increased client reaction and satisfaction. Each of these categories of performance improvements can be further categorized into improvements of efficiency and effectiveness for the consulting process.

Improvements in efficiency relate to improvements in the utilization of resources.

Improvements in effectiveness relate to improvements in quality of the resulting client solution. The study defined four levels of digital maturity for management consultant firms, including researchers, beginners, adopters and leaders. The key dimensions for assessing their digital maturity include culture, strategy, organization and operations, technology and insights. Across the dimensions there are 15 sub-dimensions and 52 statements for self-assessing digital maturity. The maturity model may be applied by future management consultant firms for benchmarking position within the industry and identifying gaps, opportunities and vision for improvement in their own consulting process and performance through leveraging digital technologies.

Key words

Consulting Process, Digitalization, Management Consultant Firms, Maturity Model and Process Improvements.

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Acknowledgments

The authors of this thesis wish to thank all the people involved that have helped achieve the outcome of this thesis. First of all, we would like to thank our tutors Hana Hulthén and Helena Forslund for their advice and guidance of the main topic and direction of the thesis throughout the work in process. Moreover, a special thank you to Helena Forslund as our examiner, who provided fair feedback for improvements in each seminar.

We would like to thank all the interviewees that participated in this research and provided us with the necessary empirical material that led to our results and conclusions, as well as, helping us test the validity of our results. We would also like to express our gratitude to interviewee, whom is referred to as Carlos to maintain anonymity, who read through the research information and helped review the technological interpretations made in this thesis. To Kaitlin Wilson that read through the final thesis version and provided structure and general understanding feedback.

Finally, to Dave Boodram for providing critical information for the development and practical validation of a digital maturity model.

Mariángeles Bravo Guerrera, Annie Snöberg and Laurie Tetzlaff

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Appendices

Appendix A: Interview Guide

Appendix B: Detailed descriptions of each Case Firms Digitalized Consulting Process Appendix C: The Process of Creating Standardized Activity Names

Appendix D: Interpretation and Categorization of Consulting Process Activities Appendix E: The Process of Linking Digital Technologies to Consulting Activities

Appendix F: The Process of Identifying and Validating Management Consultant Firm Performance Areas

Appendix G: The Process of Linking Digitalization’s Impact on MC Firm Performance Areas and Process Activities

Appendix H: The Digital Maturity Model

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Figure List

Figure 1.1 Different process types imply different volume-variety characteristics for the

process. (Slack et al., 2015, pp.122). ... 3

Figure 1.2 Relationship between Research Questions. Author illustration. ... 8

Figure 1.3 Outline of this thesis. Authors illustration. ... 10

Figure 3.1 Knowledge management strategies in the consulting project cycle. Authors illustration based on Ambos and Schlegelmilch (2009, pp.502). ... 30

Figure 3.2 McKinsey’s proposed consulting process. Authors illustration based on Davis et al. (2007). ... 31

Figure 3.3 Consulting Business Process Framework. Authors illustration based on Nissen and Seifert (2008, p.1666). ... 32

Figure 3.4 SCOR Processes in Management Consulting Service Concept. Authors illustration based on Giannakis (2011, p.350). ... 33

Figure 3.5 Operationalization of MC firms consulting processes based on author’s interpretation of significant theory. Authors illustration. ... 34

Figure 3.6 Some common process mapping symbols. Authors illustration based on Slack et al. (2015, pp.152). ... 37

Figure 3.7 Process flow of case firm A’s MCP and its used digital technologies. Authors illustration. ... 39

Figure 3.8 Process flow of case firm B’s MCP and its used digital technologies. Authors illustration. ... 40

Figure 3.9 Process flow of case firm C’s MCP and its used digital technologies. Authors illustration. ... 40

Figure 3.10 Process flow of case firm D’s MCP and its used digital technologies. Authors illustration. ... 40

Figure 3.11 Process flow of case firm E’s MCP and its used digital technologies. Authors illustration. ... 41

Figure 3.12 Process flow of case firm F’s MCP and its used digital technologies. Authors illustration. ... 41

Figure 3.13 Process flow of case firm G’s MCP and its used digital technologies. Authors illustration. ... 41

Figure 3.14 Process flow of case firm H’s MCP and its used digital technologies. Authors illustration. ... 42

Figure 3.15 Process flow of case firm I’s MCP and its used digital technologies. Authors illustration. ... 42

Figure 3.16 Nr. of Case Firms per Process Activity. Authors illustration. ... 46

Figure 3.17 Standardized Consulting Process. Authors illustration. ... 46

Figure 3.18 Types of digital technologies applied by the firms. Authors illustration. ... 47

Figure 3.19 Specific digital technologies applied by the firms. Authors illustration. ... 48

Figure 3.20 Types of digital technologies applied by the firms across the standardized consulting activities. Authors illustration. ... 49

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Figure 3.21 Specific digital technologies applied by the firms across the standardized consulting activities. Authors illustration. ... 49 Figure 4.1 Authors summary of visualization of interpreted MC Firm performance areas

most impacted by digitalization of processes. ... 63 Figure 4.2 Distribution of empirically stated performance areas to validated categories

impacting MC firm performance. Authors illustration. ... 79 Figure 4.3 Digital technologies used in MC firm consulting processes and associated

improved performance categories. Authors illustration. ... 80 Figure 4.4 MC Firm Consulting processes performance categories impacted by digital

technologies. Authors illustration. ... 81 Figure 4.5 MC Firm standardized consulting processes and improved performance

categories. Authors illustration. ... 84 Figure 4.6 MC Firm standardized consulting process and improved efficiency and

effectiveness across each of the firms. Authors illustration. ... 85 Figure 5.1 Dimensions and Sub-dimensions of MC Firm Digital Maturity Model. Authors

illustration. ... 114 Figure 5.2 Range of case firm’s ranks across the dimensions. Authors illustration. ... 126 Figure 5.3 Comparison of case firm’s ranks across the dimensions. Authors illustration. .. 127 Figure 5.4 Order of Digital Maturity of the case firm’s based on average overall rank.

Authors illustration. ... 127

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Table List

Table 1.1 Matrix identifying overlap between pairs of Key Words to identify gaps in

literature. Authors illustration. ... 5

Table 2.1 List of participating Case firms. Authors illustration. ... 18

Table 2.2 Information of each respondents at the Case Firms. Authors illustration. ... 18

Table 2.3 Strengths and weaknesses. Authors illustration based on Yin (2018, pp. 157). .... 20

Table 2.4 Authors summary of methodological choices. ... 26

Table 3.1 Authors table of the MC markets seven major segments and its focus. (Based on FEACO, 2019, pp. 18). ... 28

Table 3.2 Authors summary table of operationalized key words for RQ1. ... 39

Table 3.3 Definitions of Standardized Activity Names. Authors illustration. ... 44

Table 3.4 Authors summary of case firm and its activities. ... 45

Table 4.1 Summary of Objectives and Measurements proposed in the Consultant's Scorecard. Authors illustration based on Phillips (2000). ... 56

Table 4.2 Industry Digitalization Measurements. Authors illustration based on Manyika et al. (2015, p.30). ... 59

Table 4.3 Authors summary of performance measurements classified as impacting efficiency or effectiveness. ... 61

Table 4.4 Authors summary of Digitalization Impacts on Processes and Measurements. .... 62

Table 4.5 Authors summary matrix of areas of digitalization impact on processes and on MC firm performance. ... 63

Table 4.6 Authors summary table of operationalized key words for RQ2. ... 66

Table 4.7 Authors summary of case firm stated areas of digitalization’s impact on processes and on MC firm performance. ... 77

Table 4.8 Authors summary of Digitalization’s impact on MC Firm Performance Categories ... 78

Table 5.1 The Digital Maturity Model 4.0: Assess your digital maturity against global best practices. Authors illustration based on Gill and VanBoskirk (2016, pp.4). ... 93

Table 5.2 Similarities between the different Maturity Model’s dimensions. Authors illustration. ... 98

Table 5.3 Justification for the meaning of the Developed Maturity Models Dimensions. Authors illustration. ... 99

Table 5.4 Authors summary table of the different Maturity Models’ levels. ... 113

Table 5.5 Statements for self-scoring in the dimension Culture. Authors illustration. ... 116

Table 5.6 Statements for self-scoring in the dimension Strategy. Authors illustration. ... 117

Table 5.7 Statements for self-scoring in the dimension Organization and Operations. Authors illustration. ... 119

Table 5.8 Statements for self-scoring in the dimension Technology. Authors illustration. 121 Table 5.9 Statements for self-scoring in the dimension Insights. Authors illustration. ... 122

Table 5.10 The scoring range per maturity level and dimension. Authors illustration. ... 123

Table 5.11 Maturity level definitions for each dimension and its percentage of the total scoring. Authors illustration. ... 125

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List of Abbreviations

AI Artificial Intelligence

BPI Business Process Improvement

HR Human Resources

ICT Information and Communication Technology IP Intellectual Property

IT Information Technology

KM Knowledge Management

MC Management Consultant

MCP Management Consulting Process PSF Professional Service Firms ROI Return on Investment

RQ Research Question

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1 Introduction

This introduction chapter begins with the background of this project, where the different research areas are defined and linked to each other. It begins with an introduction to the digital technologies and digitalization, leading into business process improvement, where the concept of maturity models is presented. The background then provides an introduction to the service industry where management consultant firms are presented as the primary industry discussed in this thesis.

Following this is the problem discussion which highlights the current gaps in the literature and available research in the field. The purpose of this study and guiding research questions are then described. The introductory chapter ends with a presentation of the outline of this master thesis.

1.1 Background

Digital technologies are changing current working methods within firms and forcing them to reshape their business processes (Denner, Püschel & Röglinger, 2018). Given the rapidly changing nature of the digital landscape, firms need to digitalize their business processes much faster (Markovitch & Willmott, 2014). Since all firms have operations that consist of processes (Slack et al., 2015), the shift to digital technology can be concluded as necessary for all firms. Bakotic and Krnic (2017) and Slack et al.

(2015) describe that a business process consists of a set of repeating and linked activities that are transforming inputs into outputs with the goal of satisfying client needs or meeting other strategic goals (Ibid). Business processes are key for firms’

performance and their ability to implement their business strategy in a successful way (Bakotic & Krnic, 2017). Carlos, one of the research participants, claims that digitalization is key for the development of a firm’s operations and growth because it can improve the performance of firms and business processes, which in turn increases speed and output efficiency and decreases the risk of human errors.

Digitalization is of significance both in value creation and organizational performance (Martinez, 2019). Firms that integrate digital technologies into their business models gain a competitive advantage and can significantly increase their margins. One way of approaching this integration is by process digitization, which is when business activities are conducted in digital form instead of in traditional form (BarNir, Gallaugher & Auger, 2002). Process digitization can include using the Internet for internal and external communication, for conducting sales activities, for supporting administrative tasks and to gather information (Ibid). The objective of digitization is generally to reinvent business processes, reduce the number of steps and documents required within processes and to automate decision making (Markovitch & Willmott, 2014). Various terms are used across literature; digitization, digitalization and digital transformation, for clarity within this paper the term digitalization will be used and

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will refer to the use of digital technologies based on Seblin, Truedsson and Cronemy (2019), Denner, Püschel and Röglinger (2018), Parida, Sjödin and Reim (2019).

When a firm reshapes their business process due to changes within the business environment, such as technological changes, it is called business process improvement (BPI) (Zellner, 2011). BPI is a requirement to remain competitive in the market (Bakotic & Krnic, 2017) and to increase business processes’ effectiveness and efficiency (Zellner, 2011) which are key measures for performance (Bakotic & Krnic, 2017). There are different methods that can be used to facilitate BPI, like process mapping and benchmarking (Siha & Saad, 2008), and maturity models can be used as support to BPI methods (Santos & Martinho, 2019; Simon, Schoeman & Sohal, 2010). A maturity model is a framework for measuring and improving the performance of a firm through appraising the current position against established levels of maturity within a specific discipline. Maturity models operationalize the factors, skills and practices believed to lead to successful firms (Simon, Schoeman &

Sohal, 2010).

As mentioned above, all firms have operations that consist of business processes and linked activities with the purpose of fulfilling clients’ needs (Slack et al., 2015). There are different types of processes within manufacturing and service firms, the process type indicates the firm’s position on the volume-variety scale, as well as the process complexity and flow scale, see Figure 1.1 (Ibid). Management consultant (MC), on which this thesis is premised, is a profession that is classified as professional service (Slack et al., 2015). Professional service firms (PSF) consist of processes by high variety and low volume, complex tasks and intermittent flows, and provide high levels of customization, with emphasis on the service delivery processes (Ibid). Professional service firms, specifically MC firms, are described as providing tailor-made, knowledge-based and problem-solving services (Skjølsvik, Breunig & Pemer, 2018).

MC firms are characterized as being knowledge intensive but not capital intensive (Von Nordenflycht, 2010).

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Figure 1.1 Different process types imply different volume-variety characteristics for the process. (Slack et al., 2015, pp.122).

There is now an emergence of digital technologies that enable professional service firms, like MC firms, to further improve and augment their offerings of expertise based and tailor-made knowledge-intensive services (Skjølsvik, Breunig & Pemer, 2018). Some PSFs have software and applications that support specific services, for example technologies for contract management, process mapping and workflows, and through automation and artificial intelligence (AI), which eliminates simple tasks that were otherwise conducted by junior or administrative employees. New tools with cloud computing enable improved access to information and expanded networks, which has eliminated the need for extensive and costly physical resources while also improve access to available information sources. Cloud based virtual tools also enable improved work life balance for employees as well as aid global expansion with reduced investment (Ibid). In addition to cloud computing, Parviainen et al. (2017) and Kirchmer et al. (2016) highlight analytical applications of AI and automation as a key technology for digitalization. The focus of this paper for digitalization being applied by MC firms, will relate to newly emerging digital technologies influencing professional services firms, specifically relating to cloud computing, AI, automation and analytical applications.

1.2 Problem Discussion

Digitalization, a key feature within the umbrella concept Industry 4.0, has been overlooked by researchers even though digitalization is important for the development of a firm’s business processes (Pfohl, Yahsi & Kurnaz, 2015; Rojko, 2017). The International Data Corporation estimates that at the end of 2019, spending on the digital transformation of business practices, products and organizations have reached $1.7 trillion USD globally and $2.3 trillion by 2023 (IDC, 2019). The growth

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of business practices digital transformation in 2019 was at 17,9 %. It is forecasted to continue to grow during the year of 2020, however, the forecast is estimated to only grow up to 10,4%. One of the reasons for the decline in growth are the pandemic COVID-19, which effects the entire world (IDC, 2020).

1.2.1 Management Consulting Process and Digital Technologies

Research Question (RQ) 1: How are digital technologies used within a standardized management consulting process?

A concept that encompasses the entire value chain and has a special service-oriented focus is Industry 4.0, despite that it tends to be associated with the manufacturing firms and its application to MC firms and other professional service firms is rarely acknowledged (Ibarra, Ganzarain & Igartua, 2018; Rennung, Luminosu & Draghici, 2016; Rojko, 2017). Professional service firms, like MC firms, are identified as one of the industries that will lead the way in digital transformation in upcoming years, within both the United States and Western Europe (IDC, 2019). Although the effect of digitalization on such firms is expected to be prominent, there are very few empirical studies supporting that statement; therefore, the actual changes are unknown (Skjølsvik, Breunig & Pemer, 2018). Consequently, the need for more professional service firms focused research is apparent. According to Kronblad (2017) the current literature concentrates mainly on PSFs (Kronblad, 2017), thus, there is a lack of research specifically within MC firms. According to Moscati and Engström (2019), digitalization within MC firms is encouraged but uncritically examined and described by researchers. On the other hand, Hofmann and Rüsh (2017) explain that there is a lack of knowledge within the practical application of digitalization within management consulting processes (MCP). Rind Christensen and Klyver (2006) suggest that the lack of adoption and research of new technologies within MC firms might relate to high-cost perception in relation to short term goals for the firms. In addition, Ambos and Schlegelmilch (2009) argue that it can be difficult to standardize a MCP, since every consulting project is different, unique and affects different employees within the MC firm.

The literature reviewed reflects an identified gap between the umbrella concept, Industry 4.0, and the key search terms; Process Improvement, Digitalize Business Process, Digital Technologies, Service Industry, Professional Service Firms and MC.

To further assess if a gap between the key terms was prevalent, the following Table 1.1 was prepared to compare literature counts of pairings of each key word from business management databases.

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Table 1.1 Matrix identifying overlap between pairs of Key Words to identify gaps in literature. Authors illustration.

Table 1.1 illustrates the gap in literature supporting a link between topics of

‘Digitalization’ of ‘Business Processes’ for ‘Process Improvements’ of ‘Management Consultants’ which are the key concepts within this paper.

1.2.2 Digital Technologies and Management Consulting Firm Performance RQ 2: What performance improvements could management consulting firms expect when using digital technologies within their consulting process?

Bakotic and Krnic (2017) implies that BPI methods lead to increased productivity and reduced costs which results in improved overall performance in order to reach the firm's business goals in a more effective way. Conversely, Siha and Saad (2008) argue that not all BPI initiatives lead to increased profitability. Many firms have achieved significant individual process improvements, however, not a significant improvement in overall performance. Approximately 50-70% of process improvement initiatives do not succeed to achieve their goals. That being said, Zellner (2011) explains that BPI methods may increase the performance of business processes, specifically relating to increases in the effectiveness and efficiency. As mentioned earlier effectiveness and efficiency are key measures for performance, according to Bakotic and Krnic (2017), and the basis of BPI methods is to measure performance. Without measuring performance, it is not possible for a firm to determine the economic sense of an improved process.

According to Denner, Püschel and Röglinger (2018) the digital technologies continue to influence firm's daily work and require them to redesign their current business processes as well as processes implemented in the future. Despite this, firms lack important knowledge about digital technologies and how to identify which one(s) to implement for improving their respective business processes. According to Parida, Sjödin and Reim (2019) and Denner, Püschel and Röglinger (2018), the range of digital technologies that indicate process digitalization is extensive. Kuzin (2019) claims that the research of digitalization including practical business perspectives and for education is still within its early stages. Although in recent years more empirical data and best practices have started being collected and researched in-depth. Kotarba (2017) describes that in order to evaluate the firms’ benefits of digitalization, the firm’s level of digitalization must be measured. The respondent Carlos consider digitalization as important to the MC firm he works, since most of their processes contain digitalization to different degrees. Carlos describe that digitalization helps

One Search EBSCO Emerald One Search EBSCO Emerald One Search EBSCO Emerald

Services Industry 51,965 16 61,794 229,584 209 52,508 1,316 36 1,528

Professional Service

Firms 4,646 172 20,657 40,833 1 22,760 230 0 585

Management Consultants 3,491 368 11,575 63,313 15 18,964 228 2 514

Digitalize Business Process Process Improvement

Industry 4.0

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their firm run more efficiently, thus, other firms in the market should look into implementing it as well.

Ambos and Schlegelmilch (2009) state that no MC projects are similar to one another, they are all unique and according to Swart and Kinnie (2010) need to be adapted to each client. MC requires integration of IT systems, but it is a major challenge to succeed with the integration and the adoption to it remains slow (Ibid).

1.2.3 Digital Maturity Model for Management Consultant Firms

RQ 3: What are the dimensions and levels of digital maturity for Management Consultant Firms and the significance of digital technologies and performance indicators?

Maturity models are a recognized tool for firms aiming to improve business processes through comparison of one’s positions against more mature and highly performing firms within the same industry (Andersson et al., 2018; Simon, Schoeman & Sohal, 2010). However, there remains a gap in process maturity models specific to the consulting services industry (Simon, Schoeman & Sohal, 2010). The research of Szelagowski and Berniak-Wozny (2019) is arguably the only in-depth analysis and application of business process management maturity models to a knowledge economy context. Szelagowski and Berniak-Wozny (2019) claims that while there are an increasing number of business process maturity models, there are still some challenges for both practitioners and researchers to be able to take full advantage of them. Some of these key challenges are; no currently research data that empirically support the validity and usefulness of maturity models, the knowledge economy has limited applicability on traditional business process management, and there is no clear difference between the maturity model and the assessment model being used for evaluating the maturity level (Ibid).

Firms and industries are at different levels of digital maturity and the topic has attracted more attention by researchers and industry professionals (Andersson et al., 2018; Catlin, Scanlan & Willmont, 2015; Kuzin, 2019; Rader, 2019; Sehlin, Truedsson & Cronemyr, 2019). Although the concept of digitalization for businesses is better understood, there remains a lack of knowledge of how to develop and implement digital strategies, which negatively impacts many firm’s ability to implement digitalization (Andersson et al., 2018). From both an academic and consulting perspective, research and practical guidance for digitalization for firms remains limited (Ibid).

There is also a gap in research associating the actual relationship between digital maturity and corresponding growth and economic value add that is achieved by firms.

Often firms are driven to strategically respond based on their competitors’ digital positions, although to develop a strategy for digitalization, it is also critical to understand the timeline required to do so, as technology often transforms faster than

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the timeline to implement (Rader, 2019). It is important that digitally mature firms share expertise and experience to stimulate and drive the overall digitalization of an industry (Andersson et al., 2018; Rader, 2019). According to Rader (2019) there is a knowledge gap in practical applications regarding the benefits that can be obtained per level of digital maturity (Ibid).

This present study aims to address the aforementioned gaps in within the academic literature and contribute new knowledge to the field of management consulting. The RQs are built upon and related to each other, see Figure 1.2, which means that the problem discussion for RQ1 and RQ2 helps building the case for RQ3 as well.

Meaning, that this research have developed a digital maturity model, which also state the significance of digital technologies and performance indicators. This digital maturity model can be applied by MC firms for self-assessment. Moreover, within the practitioners’ interest, the study aims to provide a framework which can be applied within the industry. This will be expanded on further in the following section.

1.3 Purpose

The purpose of this study was to explore, describe and explain: (1) digital technologies used within a standardized management consulting process; (2) understand consultants/industry professionals’ perceptions and expectations of performance improvements through the use of digital technologies; and (3) identify levels of digital maturity of management consultant firms through the development and use of a maturity model and the significance of digital technologies and performance indicators. The developed digital maturity model is expected to support management consultant firms in the identification of their current level of digital process maturity, benchmark against the levels of digital process maturity within their industry and address existing gaps in the digitalization literature in order to help firms advance to more mature and disciplined digital processes.

1.4 Research Questions

RQ 1: How are digital technologies used within a standardized management consulting process?

RQ 2: What performance improvements could management consulting firms expect when using digital technologies within their management consulting process?

RQ 3: What are the dimensions and levels of digital maturity for Management Consultant Firms and the significance of digital technologies and performance indicators?

The following Figure 1.2 visualizes the relationship between the research questions guiding this study. Research question 1 first describes and standardizes the MCP and describes the how digital technologies are being used. Research question 2 is

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dependent on the understanding of MCP and digital technologies used from research question 1 in order to explore and explain the ways in which digital technologies may improve management consultant firm process performance. Finally, research question 3 explores and explains the key dimensions of MC firm digital maturity and significance of digital technologies and performance on MC Firm digital maturity.

Further, research question 3 then analyses and defines the rankings of participatory case firms and the resulting levels of digital maturity. As such, the outline of this thesis does not follow a normal academic paper structure which will be described in greater detail in the following subchapter

Figure 1.2 Relationship between Research Questions. Author illustration.

1.5 Outline of Thesis

An outline of this thesis is presented in Figure 1.3. There it can be seen that the thesis introduction (1.0) is followed by methodology (2.0), which includes the methodological choices made by the authors concerning the research philosophy, strategies, approach, design, population and sample selection, data collection method, data analysis method, research quality, ethical considerations, individual contributions, and a chapter summary.

This approach does not follow a normal standard frame because the research questions in this study are interdependent, as illustrated in figure 1.3. The intent of this was to make it easier for the reader to follow the authors’ thought processes throughout the development and implementation of this study, and to facilitate a greater understanding of the results presented in this dissertation.

The purpose of the following chapter (3.0) is to answer the first research question (RQ 1: Management consulting process and digital technologies, how digital technologies are used within a standardized management consulting process). This begins with a presentation of the theoretical framework use in this study (3.1), about management consultants, digitalization and digital technologies and process mapping. This is followed by empirical data (3.2) collected by the interviews with the case firms, which were then analysed in subsection 3.3. chapter 3.0 ends with the authors’ conclusions of RQ1 (3.4).

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Chapter 4.0 addresses the second research question (RQ2: Digital technologies and management consulting firm performance). This includes a discussion of the performance improvements management consulting firms can expect when using digital technologies within their consulting process. Therefore, this chapter begins with presenting the theoretical framework regarding management consultant performance measurement and digitalization measurement (4.1), followed by empirical data based on the collected interviews (4.2). data analyses are presented in subchapter 4.3, and the authors’ conclusions of the second research question are discussed in subsection 4.4.

Finally, the conclusions drawn for RQ1 and RQ2 will be tied together and used as supporting evidence for the third research question of what the dimensions and levels of digital maturity for management consultant firms are and the significance of digital technologies and performance indicators. This information is presented in chapter 5.0.

The chapter begins with the theoretical framework (5.1) regarding maturity models and digital maturity models. Following this is the empirical data concerning these issues (5.2), which is analysed in subchapter 5.3, along with supporting evidence from chapters 3.0 and 4.0. The last subchapter (5.4) for this section is the conclusion, where the answer to the third research question is discussed in detail.

Chapter 6.0 (Conclusion and Implications) provides brief summary of the overall results of this study (6.1-6.3), followed by a discussion of the theoretical and managerial implications (6.4) and suggestions about future research (6.5).

The authors of this study reflect upon their work and the process of conducting this project in the final chapter (7.0) of this thesis.

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Figure 1.3 Outline of this thesis. Authors illustration.

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2 Methodology

The following chapter describes and motivates the methodology choices that have been made in this study in order to fulfil the purpose of developing a digital maturity model specific to management consultants on a process level. Initially, this chapter describes the Research Philosophy, Strategies, Approach and Design. Then leading into the population and sample selection and data collection methods, where methods like literature review and semi-structured interviews will be discussed. This is then followed by a section where applied data analysis methods are argued for, and lastly research quality is discussed as well as ethical considerations that have been in mind during this study. This is followed by a chapter regarding the researcher's individual contribution to this thesis. The methodology chapter is then concluded with a summary of the methodology choices that have been made throughout this study.

2.1 Research Philosophy

Research philosophy leads the direction of the nature of a study through beliefs and assumptions regarding the gathering and creation of knowledge within a particular area of research (Bell, Bryman & Harley 2018; Saunders, Lewis & Thornhill, 2016).

According to Bell, Bryman and Harley (2018), by choosing a research philosophy the conducted research gains a different understanding of reality based on its fundamental beliefs (Ibid). This research aimed to develop new knowledge by studying previously identified gaps that relate to specific problems within several organizations, therefore, the research was framed within a research philosophy.

Saunders, Lewis and Thornhill (2016), state that there are five major research philosophies within business and management research, these are positivism, critical realism, interpretivism, postmodernism and pragmatism. Positivism is constituted by a scientific approach to research, where reality is scrutinized through a theoretical social science perspective, positivism claims that cause and effect is not constant but a variant possibility (Ibid). Critical realism comprises the belief that the perception of reality does not represent the actual reality, and that this perception is shaped by structures caused by events and circumstances (Bell, Bryman & Harley, 2018).

Interpretivism studies the meaning of situations assigned by individuals, interpretivism research focuses on context and new understandings of phenomena through the eyes of individuals (Bell, Bryman & Harley, 2018; Saunders, Lewis &

Thornhill, 2016). It is used to study organizations from the point of view of diverse groups of people (Saunders, Lewis & Thornhill, 2016). Postmodernism focuses on processes rather than entities, (e.g., organizing and managing instead of organization and management), it includes in-depth explanation of phenomena (Saunders, Lewis

& Thornhill, 2016). According to Bell, Bryman and Harley (2018) and Saunders, Lewis and Thornhill (2016) postmodernism challenges the truth of reality, claims that language fails to describe all perspectives of reality and it tends to emphasize aspects

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that are most commonly agreed on instead of all of the aspects that constitute reality (Ibid). Finally, pragmatism mainly focuses on a problem and practical solutions to it, this problem does not have to mean a problem per se, but something lacking or out of place within reality (Saunders, Lewis & Thornhill, 2016).

This study followed a pragmatic research philosophy, based on that, the purpose of this study was founded on a problem that arises due to research gaps in knowledge.

The research gaps were mainly related to digitalization of consulting processes, management consultancy firm’s processes and improvement of process performance using digitalization. The study also aimed to provide a solution to the identified gaps, through the development of a maturity model to identify the stages in digital process maturity and recognize the expected performance per stage of digitalization of processes. This maturity model was intended to highlight recommended practices that could improve a MC firm’s consulting processes. It also aimed to contribute with new knowledge, that will provide a clearer view of the topic within a specific type of firm.

This means that the study brings both a practical and theoretical solution. According to Saunders, Lewis and Thornhill (2016), a pragmatic philosophy encourages the usage of diverse data collection methods to provide a broader view of the situation.

Kelemen and Rumens (2008) state the data collection methods used should be reliable, well-suited and bring credibility to the study. For this reason, the data collection for this study was done through semi-structured interviews and review of relevant theory.

According to Bell, Bryman and Harley (2018) each research philosophy is shaped by philosophical assumptions. Saunders, Lewis and Thornhill (2016) identify three philosophical assumptions; ontology, which refers to understanding and interpreting reality based on the conducted research. epistemology, which addresses what is known by the researchers and in theory, and if the quality of this knowledge is acceptable. Finally, axiology, which determines if the values of the researchers would influence the research and results (Ibid). The conducted research counted with a mix of philosophical assumptions. Firstly, the ontological assumption of this pragmatic research was the practical application of its results and the process focus. The epistemological implications were both the general relevance of the researched problem and the theories and empirical knowledge that support actions within a specific context, which in this scenario would be the management consultancy firms.

Finally, the axiological assumption for this study were researcher reflexive results, where information presented was interpreted by the researchers.

2.2 Research Strategies

Bell, Bryman and Harley (2018) explain that the strategy of research describes how theory will be presented as one of the bases for the study and philosophical assumptions. Research strategies can be firstly divided into quantitative, qualitative and mixed methods (Bell, Bryman & Harley 2018; Saunders, Lewis & Thornhill,

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2016). Quantitative research inspects numerical variables and understands if they relate and how, data is standardized and compared (Saunders, Lewis & Thornhill, 2016). Qualitative research examines the meaning behind the research participants’

statements and interactions, analyses the outcomes and supports it with theory in order to make a significant contribution to the field of study. Mixed method research consists of a combination of quantitative and qualitative approaches to research, providing more in-depth descriptions and answers to the research questions (Ibid).

For the execution of this research, a qualitative method was the chosen one, in order to provide a real-world perspective of digitalization within a selected environment, management consultancy firms, by obtaining information from the people that experience on a regular basis the digitalized processes of these firms. Also, a qualitative approach allowed the use of diverse methods for the collection of the information, which was compared and analysed, making sure that the information sources were reliable and that it contributed to the research topic. The research process was highly interactive and non-standardized by encouraging the participants to guide the discussions and give subjective but relevant opinions. Moreover, the main idea was discussed in advance with the participants of the study, but the focus points and questions were not disclosed until interview and discussion sessions in order to reduce external influence on the results.

Saunders, Lewis and Thornhill (2016) explain that the research questions of a study can also define the strategies that should be adopted. Based on the purpose of the questions there are four major qualitative research designs; exploratory, descriptive, explanatory or evaluative research. Saunders, Lewis and Thornhill (2016) state that the aim of an exploratory research is to gain new insights of a problem that is not acknowledged or is still undefined within the field of research (Ibid). Therefore, this study opted for an exploratory research aiming to provide rich descriptions through semi-structured interviews of a highly under researched topic and setting. The rich descriptions and lack of structured data-collection methods supports the in-depth and subjective nature of a qualitative research approach. According to Saunders, Lewis and Thornhill (2016) an explanatory research main objective is to clarify and analyse the relationship between variables through statistical tests. The RQ2 of this study adopts an explanatory connotation, by trying to answer a causal relationship between two variables, digitalization of management consultancy firms processes and improved performance, although in this research the answer to this question was constructed based on qualitative data.

2.3 Research Approach

Saunders, Lewis and Thornhill (2016) explain that the purpose and research questions of the study will guide the means in which theory is developed. According to Bell, Bryman and Harley (2018) the development of theory depends on its relationship with the research, either if the research is driven by theoretical foundations or if the theory

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is a result of empirical findings. Bell, Bryman and Harley (2018) and Saunders, Lewis and Thornhill (2016) identify three different research approaches, inductive, deductive and abductive. An inductive approach requires previous theoretical knowledge and research within the chosen topic in order to formulate the research questions and recognise concepts that should be looked in to (Saunders, Lewis &

Thornhill, 2016). However, in an inductive approach the relevant theory will be collected in order to explain the information obtained from the research participants (Ibid). A deductive approach consists of gathering theory on a topic and the research and research questions are made purposefully to test this theory (Bell, Bryman &

Harley 2018; Saunders, Lewis & Thornhill, 2016). A deductive approach entails collecting empirical data, to develop or adjust new theory that will be tested by collecting more empirical data (Ibid).

This research followed an abductive research approach. According to Saunders, Lewis and Thornhill (2016), an abductive approach combines induction and deduction, and it is commonly used for business and management research. Firstly, this research used some theoretical knowledge to identify gaps in the literature and used empirical data to justify the practical perspective and relevance of the identified gaps. Afterwards, more empirical data was collected and analysed to map the processes of different consultancy firms, cross reference it and make a standardized process map. Subsequently, theory was collected in regard to digitalization, business process improvement and how to measure it within MC firms, MC Firms’ processes and existing knowledge on maturity models. Finally, more empirical data was collected on the development and application of maturity models, as well as from the case firms to compare and analyse the theory to propose a digital process maturity model. Saunders, Lewis and Thornhill (2016) explain that abductive approach overcomes the weaknesses of the inductive and deductive approach by allowing the researchers to modify and adapt the theoretical framework of a study at all stages of the research process. This contributes to a more unified research design and hypothesis verification (Ibid). Williams, et al. (2019) recommend an abductive approach be selected to account for existing maturity model research, emphasizing the importance of primary data sources.

2.4 Research Design

Bell, Bryman and Harley (2018) outline multiple research designs, which are the frameworks for collection and analysis of data. The research design selected emphasizes the priority of specific dimensions of the research process and defines the criteria that are used to evaluate the quality of the research. The five primary research design alternatives are experimental (and its variants,) cross-sectional (also called social survey), longitudinal, case study and comparative (Ibid). Experimental design involves the creation of two groups and comparing the before and after state following manipulation of a variable to one group and not the other, although this design is

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rarely used in business research due to limited ability to achieve the control required for verifiable results. Cross-sectional design requires a minimum of two cases as the study objects in order to determine patterns of association with two or more variables, this design requires quantifiable data and is conducted over a single point in time (Ibid). Longitudinal design is used for mapping changes in business and management research, although is similar to cross-sectional design but also involves the time order of variables which enables causal inferences to be made. Case study design focuses on a singular case and involves intensive analysis and is intended to highlight the unique features of the focus case. Case study design is typically based on qualitative data and one of the most common designs, although in practice can incorporate more than one type of research design. Comparative research design involves a minimum of two cases and aims to seek explanations for similarities or differences or greater awareness or deeper understanding of cross-sectional contexts (Ibid).

This research followed the comparative design approach. Bell, Bryman and Harley (2018) state that comparative design can be applied to a qualitative research strategy in the form of a multiple case study. Main arguments in favour of the comparative design approach is for theory building and allowing theoretical reflection on key characteristics and contrasting findings of two or more cases (Ibid). Comparative design approach was selected for this study as this study creates a model which is dependent on multiple MC firms in progressive levels of digital maturity. Therefore, the design approach involved a qualitative comparison of multiple cases both within and across MC firms. Empirical data was collected for the development of theory on what is common in the consulting process across the MC firms, what is common and different in terms of the digital technologies used within the consulting service process, what contrasting performance improvements are identified through the digital technologies used and what differentiates the firms in terms of digital maturity.

The level of analysis applied was based on an organization level, as the study is based on data collection and analysis of the MC firms.

2.5 Population and Sample Selection

A population is defined as the “universe of units that a sample is to be selected” (Bell, Bryman & Harley, 2018, pp. 188). The population relevant to this study is every firm that provides management consultant services. The International Council of Management Consulting Institutes (ICMCI) represents the various management consultancy associations and institutes, and has almost 50 members, of which each of those associations would have individual MC firm members (ICMCI, 2020).

IBISWorld (2019) presents that as of 2019 there are approximately 774,000 management consultant firms located within the United States, with a significant portion holding a presence internationally. The European Federation of Management Consultancies Associations (FEACO) has members from 15 European countries and has approximately 3500 MC member firms (FEACO, 2020a). The Management

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Consultancies Association UK has over 50 members within the United Kingdom (MCA, 2020).

The sample is defined as the “segment of the population that is selected for investigation” (Bell, Bryman & Harley, 2018, pp. 188). A sample of the population is intended to generalize the sample and represent the findings for the larger population (Ibid). The sample for this study is the selected case firms that participate within the study. This study involves multiple comparisons in order to fulfil the purpose, including a comparison of management consultant firm’s service process, comparison of how digitalization is applied, and comparison of performance improvements achieved. According to Bell, Bryman and Harley (2018), when several comparisons are required, generally a larger sample is necessary. There are conflicting views on the minimum and maximum sample sizes for qualitative research to be effective, therefore it is ultimately the researchers that should determine an appropriate sample size that enables theoretical saturation for the intended research questions. Rather than focus on a minimum number of cases, focus more on the quality, detail and depth of the interviews (Ibid). Based on sample sizes and theoretical relevance presented in Bell, Bryman and Harley (2018), the researchers did not impose a minimum or maximum limit of interviews to generate the sample.

Purposive sampling is used in qualitative research, where the cases for data collection and analysis are selected strategically and purposefully to ensure relevance to the research questions (Bell, Bryman & Harley, 2018). Researchers are required to first select the case of study, for example organizations or individuals and then select and sample units within the case (Ibid). The level of sample case selected for this study was at the organization level, as the study is on MC firms. Bell, Bryman and Harley (2018) explains that samples of purposive sampling approaches for qualitative research include extreme or deviant, typical, critical, maximum variation, criterion, theoretical, snowball, opportunistic and stratified (Ibid). Theoretical sampling is done in order to discover categories and their properties and suggest the interrelationships into a theory. Selection of cases and units for theoretical sampling is based on the quest for generation of theoretical understanding. Generic purposive sampling includes several of the sampling strategies, excluding theoretical, and is more open ended as used for generation of concepts and theories. Selection of cases and units for generic purposive sampling is based on identifying the cases needed to address the research questions (Ibid). This research followed the generic purposive sampling approach as the research questions are intended to address gaps within theory and generate new theory relating to MC firm consulting process, digital technologies used, performance improvements and levels of digital maturity. The following list outlines the required criteria for selecting the case firms needed to be relevant for and address the research questions:

● Must be a firm that offers management consultant services;

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● Firms strategically selected, if possible, based on their publicly presented position of digital maturity / innovation (Ex. Known digital leaders such as McKinsey) or lack of to support the collection of data across a broad range of maturity;

● No geographic limitation will be imposed on where the management consultant firms are located, as typically management consultant firms have a presence internationally and the intent of the study is not to compare levels of digitalization across countries just firms; and

● No size of firm limitation will be imposed on the management consultancy firm, as it is not clear if smaller or larger firms are more able to implement innovative digital solutions.

Table 2.1 presents the case firm sample for this study. The case firm’s industry has been classified based on the respondent’s information during interviews and streamlined to the MC market segments presented in sub-section 3 on management consultants. The case firms were researched and relevant individuals in each firm were identified by the use of the firms’ website information regarding their employees. These individuals were then approached via phone call or email and after a brief description of the background and aim of the thesis project they were asked if interested to participate in an online video interview. It was also explained in advance to the interviewees that the name of their company and themselves would be kept anonymous. If interested, then a time and date were scheduled for an interview.

Case Firm

Age of

Firm Industry Nr. of

Countries

Nr. of Employees A > 20 years Technology and Operations < 10 ≈ 2 500 B > 50 years Finance and Risk Management ≈ 130 ≈ 40 000

C < 5 years Operations 1 ≈ 20

D > 10 years Finance and Risk Management,

Strategy, Other ≈ 20 ≈ 500

E > 100 years

Finance and Risk Management, Operations,

People and Change, Strategy, Technology, Other

≈ 150 > 200 000

F > 40 years

4

continents ≈ 80 000

G > 90 years

≈ 150 > 200 000

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H > 100 years

≈ 150 > 200 000

I > 20 years Technology 2 ≈ 200

Table 2.1 List of participating Case firms. Authors illustration.

The following list outlines the required criteria for selecting respondents within the selected case firms in order to be relevant for and address the research questions:

• Interviews must be conducted with a consultant or manager consultant within the management consultancy firm; and

• Individuals strategically selected, if possible, based on their strategic importance and relevance to the firm, for example numbers of years within the position or firm.

Table 2.2 below, presents some information for the selected respondents within each case firm. The names in the column ‘data source’ are fictive names, since all respondents are kept anonymous due to ethical considerations that are mentioned later in chapter 2.9.

Case Firm

Data Source

Position in the Organization

Time

Employed Type of Interview A Carlos IT Consultant 1 year Online Video Interview B Steve Auditor/Consultant 12 years Online Video Interview

C George CEO/Senior

Consultant 3 years Online Video Interview

C Johanna COO/Senior

Consultant 3 years Online Video Interview

D Mario Consultant 8 years Online Video Interview

E James Auditor/Consultant 5 years Online Video Interview F Gabriel Junior Consultant 1 year Online Video Interview

G Cajsa Management

Consultant 3 years Online Video Interview H Frans Senior Consultant 4 years Online Video Interview

I Tony

ERP Consultant, Delivery Team

Leader

3 years Online Video Interview Table 2.2 Information of each respondents at the Case Firms. Authors illustration.

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Additional empirical information was also collected through an interview with Dave Boodram, the Director of Consulting for digital transformation business unit at a MC firm that will be kept anonymous. The purpose of this was to collect input regarding the development of the digital maturity model and to build an understanding of its practical applications. This MC firm is not considered as a case firm and Dave is not an respondent in this study, therefore, this is not considered part of the population and sampling selection.

2.6 Data Collection Method

2.6.1 Literature Review

While specific intents of conducting a literature review will vary, the general aim of a literature review is to conduct a competent review of the existing works of others, in order to establish the significance of the proposed research and affirm credibility as being knowledgeable on the presented topics (Bell, Bryman and Harley, 2018).

According to Bell, Bryman and Harley’s (2018) recommended process to search literature, firstly, it is important to define key words to develop a focused and relevant literature set. The first stage involves making notes from initial articles found that are related to the research question(s), note the key words used by the authors of this literature and continue to adapt the search until the most relevant terms are identified (Ibid). Key words were searched in various combinations of pairings to refine relevance, and using the databases; EBSCO Business Source Premier, Emerald Insight and Google Scholar. The filters applied to the search during the initial search were for ‘articles’ that were ‘peer reviewed’ and in ‘English’, which is consistent with Bell, Bryman and Harley’s (2018) initial relevant literature research methodology.

Initial key words were refined to Services Industry, Professional Service Firms, Neo- Classic Professional Service Firms, Management Consultants, Service Process, Industry 4.0, Process Improvements, Business Process Improvement, Digitization, Digitalization, Digital Transformation and Maturity Model. The key words were further refined and reinforced as relevant through collecting definitions from literature, business and management dictionaries and subject handbooks.

As outlined in sub-section 2.2 and 2.3, this study followed a qualitative research strategy and abductive research approach, which is a combination of inductive and deductive approaches. Bell, Bryman and Harley (2018) outline that narrative literature review types are suitable for qualitative and interpretive research, where the objective is to obtain an understanding of a topic area, identify principal research areas and assess theoretical value of the areas. A narrative research approach also enables researcher’s flexibility to modify the boundaries of the research as it evolves and as more data is collected, which can lead to unanticipated ways of understanding the research (Ibid). Conducting a qualitative based literature review involves two main processes; coherence and problematization, in order to guide how the literature

Digital Maturity Model for Management Consultant Firms