Master’s thesis Business Administration: Digital Business
Changing in a data-driven world.
Exploring the applicability of current change management models on change
efforts in a data-driven context.
Author: Jelle Paul Koning Student number: 10190163
Supervisor: prof. dr. H.P. Borgman Date: 20-07-2018
Statement of Originality
This document is written by Student Jelle Koning who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of Contents
2.
Theoretical Framework
7
2.1 Change Management
7
2.1.1 Change Typology
7
2.1.2 Change Management Model
8
2.2 Resistance to Change
9
2.3 Big Data and Data Analytics
11
3.
Research Design & Case Description
12
4.
Cross-Case Analysis & Discussion
19
4.1 Characteristics of change efforts in a DDC
19
4.2 RP1: types of change
22
4.3 RP2: Kotter’s Accelerate model (2012)
26
4.4 RP3: RtC
29
5.
Future Research
33
6.
Conclusion
34
7.
Limitations
36
8.
References
37
9.
Appendix
43
Appendix I: Interview checklist
43
Appendix II: Interview protocol
44
Appendix III: NVivo nodes
47
Appendix IV: Analysis characteristics change effort in a
DDC
49
Abstract
Many organizations attempting change efforts in a data-driven context fail, which is due to the unique characteristics of the data-driven environment. These characteristics revolve around the speed with which the environment changes, which results in more agile competitors and rapidly changing customer preferences. This research explores
characteristics which are specific to the data-driven context (DDC) and change efforts in a DDC. Furthermore, this research tests the applicability of current change management theories and models. The results of this research indicate that 1). change in a DDC falls in between the change type dichotomy of Weick and Quinn (1999), that 2). current change management models are only partially able to explain change efforts in a DDC and that 3). for resistance to change, specific characteristics due to the data-driven context are apparent. Subsequently, these characteristics are supported by previous literature and ways of handling these characteristics are explored. While in the process of analysis the way is paved for proposed avenues of future research. As such, a conceptual model is developed, which is built upon the model by Kotter (2012) and several theories on change, resistance to change, decision-making processes and ways of working.
1. Introduction
Only one third of business executives claim they are successful in the “business adoption of data initiatives”, according to Davenport and Bean from NewVantage Partners (Davenport & Bean, 2018, p. 11). Another managerial report even states that less than one fifth of business executives perceive their business as ‘very’ effective in leveraging digital technologies to further their cause (Harvey Nash & KPMG, 2017). This indicates that successful adoption of data technologies is still hard to accomplish. Meanwhile, in academia, empirical research is called for which explores how companies develop and transition toward data-driven business models (Engelbrecht, Gerlach, & Widjaja, 2016; Günther, Rezazade Mehrizi, Huysman, & Feldberg, 2017; Sorescu, 2017). It is stated that transitions and change efforts in a data-driven context (DDC) are different from traditional change efforts due to the agility of the digital era, which results in: 1). unclear end goals and visions; 2). rapidly changing markets; and 3). transformation decisions based on hype (Davenport & Westerman, 2018). Furthermore, change efforts in a DDC are perceived to be challenged by resistance to change (RtC) (Davenport & Bean, 2018; Harvey Nash & KPMG, 2017). In a DDC, this is explained through uncertainty regarding the urgency and vision behind a change effort and rapidly changing market, which result in a lower sense of control, a higher fear of change and higher levels of RtC (Bordia, Hobman, Jones, Gallois, & Callan, 2004; Gill, 2002; Kotter & Schlesinger, 1989; Piderit, 2000).
Combining the calls for research regarding the exploration of the transitions towards data-driven businesses and the findings of managerial reports, leads to the following research objective: to explore the characteristics of change efforts in a DDC and test the applicability of current change and RtC models and theories in a DDC. Therefore, the resulting research question is.
“How are current change and resistance to change models and theories applicable to change efforts in a data-driven context?”
To test the applicability of current change management models on change efforts in a DDC, this research considers where a change effort in a DDC fits in the change type dichotomy of Weick and Quinn (1999). Assigning the right type of change is important to understand the nature of the change, for different types of change require different theories and models. Furthermore, Kotter’s Accelerate model (2012) is considered, for it is his most recent iteration on his eight-steps model (Kotter, 1995), which is among one of the most popular change models among practitioners, due to its actionability and intuitivity (Appelbaum, Habashy, Malo, & Shafiq, 2012). Furthermore, its academic popularity is observable as it is one of the most cited works in change management (Hughes, 2016). Next, the proposed accelerators work in tandem, in contrast to the sequentiality of his eight steps (Kotter, 2012), where this sequentiality is a critique to his previous model (Appelbaum et al., 2012; Hughes, 2016; Kotter, 2012). These simultaneously working accelerators are proposed to be better suitable for the aforementioned agility of the digital era. For RtC, the paper of Ford, Ford and D’Amelio (2008) is considered, because the theory aligns itself with Lewin’s definition of RtC. In this definition, RtC is the interplay of “roles, attitudes, behaviours, norms” of all actors in a system (Dent & Goldberg, 1999, p. 31). As such, RtC is the outcome of this interplay, in contrast to being inherent to the human nature. Next to the possibility that RtC resides in a person, it is stated that the source of resistance is due to other factors within the system (Dent & Goldberg, 1999; Kotter, 1995).
This research employs a holistic multiple case study design, because no logical sub-units exist, and the considered theories have an holistic nature (Yin, 1994). A multiple case study design allows this research to seek out similarities and differences between cases, which culminate in new insights. Six case studies are specified in which data is collected through semi-structured interviews, which are conducted at the role of change agent and change recipient. All the companies where the case studies are conducted are in the process of becoming data-driven. The interview protocol is designed around the research propositions and characteristics deducted from the considered model and theories.
The contributions of this research are of relevance to academia and practitioners, as stated by the aforementioned calls for future research and managerial reports. These contributions are as follows: first, characteristics of change efforts in a DDC are explored in order to find out in what way a change effort in a DDC is different from a traditional change
effort. Second, a change model and change and RtC theories are tested and areas of ‘misfit’ with a DDC are identified. Third, a new conceptual model is developed, that takes these areas of ‘misfit’ into account.
The following section of this research deals with defining the main concepts, constraints and research propositions (RPs). Section three explains the research design and describes the cases. The fourth section consists of the discussion of the cross-case analysis. The fifth section handles directions for future research, while the sixth section covers a conclusion. Finally, the seventh section handles the limitations of this research.
2. Theoretical Framework
In this section, the main concepts and constraints are described. Subsequently, relevant research propositions are developed. First, current change typology is described to fit change efforts in a DDC in a framework, allowing for a better understanding in the nature of the change efforts in a DDC. Second, the change management model considered in this research is described, allowing this research to build upon previous research. Third, the concept of RtC, a brief historic overview of RtC and the RtC theory used in this research are described. Fourth, a description of big data, data analytics and the data-driven context is given, which provides the context and constraints of this research.
2.1 Change Management
2.1.1 Change Typology
Regarding change management, two main types of change are distinguished: planned, episodic change and emergent, continuous change (Weick & Quinn, 1999). The main
characteristics of episodic change are: a clear beginning and end of the change process, where the triggers of change are external, causing the organization to reside in an unsustainable state. To move away from this state, intentional change is needed to reach a sustainable equilibrium, which happens in a planned manner. Due to this reaction to external factors, and organizations residing in an unsustainable state, episodic changes are considered to be transformational, where the old organization is replaced. To manage this kind of change, change efforts undergo a Lewinian process of unfreezing, transitioning and refreezing (Weick & Quinn, 1999). As far as change management models go, the root model of all planned change management models is that of Lewin, which is still relevant as of today, 70 years after Lewin proposed it (Burnes, 2004; Burnes & Cooke, 2013; Rosenbaum, More, & Steane, 2018; Weick & Quinn, 1999). As opposed to the episodic change type, the continuous change type’s main characteristics are that change is a constant factor within an organization and that these changes are small and incremental. These incremental changes can build up towards substantive change. Instead of planned, changes are emergent, triggered by contingencies and opportunities, and happen continuously from all levels of the organization. Continuous change is being managed by freezing (i.e. taking a snapshot of patterns), rebalancing (i.e, evaluating and empowering patterns) and unfreezing (i.e. restarting continuous change) (Weick & Quinn, 1999). In line with the characteristics of change efforts in a DDC (Davenport & Westerman, 2018), this research proposes change efforts in a DDC exhibit characteristics of both types of change. The agility of the digital era leads to unclear end goals and visions, which hinders the fruition of a truly planned, episodic change. This is strengthened by rapidly changing markets and transformation decision based on hype. Furthermore, change in a DDC is not a truly emergent, continuous change, for change is directed upfront in a Lewinian way, where the trigger to change is externally-driven. This leads to the first RP:
RP1: Change efforts in a DDC exhibit both characteristics of planned, episodic change and emergent, continuous change.
2.1.2 Change Management Model
This research considers the updated model of Kotter due to its popularity (Appelbaum et al., 2012; Hughes, 2016; Kotter, 1995, 2012). His updated model is a reiteration of his earlier model, proposed in 1995, where he proposed eight subsequent steps towards successful change efforts (Kotter, 1995, 2012). His 1995 model mimics Lewin’s model, where Kotter’s eight steps can be categorised in the original three steps proposed by Lewin (Rosenbaum et al., 2018). Instead of eight steps, his 2012 model transforms these steps into eight
accelerators which operate in tandem. Kotter stated there are three main differences between his eight steps and his eight accelerators: 1). while going through the steps, most often they are handled one after another, in a sequential way, while the accelerators work in tandem and without clear end. 2). The steps are driven by a relatively small coalition with sufficient power, while the accelerators attract people from all over the organization which results in a
relatively bigger volunteer army. 3). Whereas the steps are appropriate for a traditional hierarchy, the accelerators have a prerequisite of a network next to a hierarchy (Kotter, 2012). The eight accelerators consist of: 1). creating a sense of urgency around a big opportunity, 2). building and maintaining a guiding coalition (i.e. a group of change initiators), 3). vision formulation and the development of change initiatives around the big opportunity, 4). vision communication to create buy-in, 5). barrier removal by the group of change initiators, 6). celebration of reaching short-term goals to create buy-in, 7). reiterate change efforts, and 8). institution of changes (Kotter, 2012). Regarding the aforementioned change types (Weick & Quinn, 1999), the content of the accelerators is typical for managing episodic, planned change, while the notion that the accelerators work in tandem, without clear end fits the continuous characteristic of the respective continuous, emergent change type. In line with the characteristics of change efforts in a DDC (Davenport & Westerman, 2018), this research proposes that due to unclear long term visions, change effort structures and end goals, the accelerators of Kotter’s model (2012) are affected. Therefore, the second RP is:
RP2: In order to be aligned with the DDC, characteristics of Kotter’s Accelerate model (2012) require improvement.
2.2 Resistance to Change
Lewin, who pioneered change management research, simultaneously pioneered the concept of RtC in the 1940’s. The concept itself has been subject to controversy from that moment till recent years (Burnes & Cooke, 2013; Dent & Goldberg, 1999; Ford et al., 2008; Lewin, 1951; Piderit, 2000). In its original meaning, RtC describes the interplay of “roles, attitudes, behaviours, norms” of all actors in a system (Dent & Goldberg, 1999, p. 31). As such, RtC is the outcome of this interplay, in contrast to being inherent to the human nature. Next to the possibility that RtC resides in a person, it is possible that the source of this resistance is due to other factors within the system (Dent & Goldberg, 1999; Kotter, 1995). It is this view on RtC that is adopted by the model and theories considered in this research (Ford et al., 2008; Kotter, 1995, 2012).
Ford, Ford and D’Amelio explore RtC as being caused by the interaction between the change agent and the change recipient and how change agents contribute to RtC. They categorize their findings in three themes: “resistance as change agent sensemaking”, “change agent contributions to resistance”, and “resistance as a resource” (Ford et al., 2008, pp. 363– 368). Within the change agent sensemaking theme, the authors discuss how RtC can be induced by the change agent by expecting and anticipating on resistance. Furthermore, they discuss how RtC is assigned to be the cause for failed change efforts, despite not being proven to be (Ford et al., 2008). The second theme is about change agents’ contributions to RtC. Within this theme, the authors indicate change agents contribute to change through: breaking contracts, both physical and psychological, whilst subsequently breaking trust, which leads to warped perceptions of organizational justice and induced change cynicism (Bommer, Rich, & Rubin, 2005; Ford et al., 2008; Rafferty, Jimmieson, & Armenakis, 2013). Second, change agents contribute to RtC through breakdowns in communication. Within communication breakdowns, three types are distinguished: failure to legitimize change, misrepresentation of the change effort (e.g. overestimating benefits) and a lack of calling to action (i.e. goalsetting, performance reviews, follow-ups). Third, change agents contribute to RtC through a phenomenon called resisting resistance, where change agents ignore change recipients’ input to the change effort (Ford et al., 2008). The third theme handles how resistance is a possible resource for the change effort. As such, resistance is an asset to a change effort. The authors discriminate between three values: existence value, engagement value and strengthening value (Ford et al., 2008).
In line with the characteristics of change efforts in a DDC, as stated by Davenport and Westerman (2018), this research considers the theme of how change agents contribute to RtC. Unclear visions and end goals directly impact the legitimization aspect of the communication section (Ford et al., 2008; Kotter, 1995; Lewis, Schmisseur, Stephens, & Weir, 2006). In turn, this increases RtC (Ford et al., 2008). The third RP is therefore:
RP3: RtC is increased due to the characteristics of the change effort in a DDC. 2.3 Big Data, Data Analytics and the Data-Driven Context
One of the most commonly known definitions of big data include the three V’s: volume, velocity and variety (Gandomi & Haider, 2015). Gartner defines big data as: “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation” (Gartner, n.d.). Furthermore, IBM has coined a fourth V as veracity, which
points to the reliability of data. It encompasses the dimension of gaining knowledge from unreliable sources. In turn, to gain data-driven insights and to make data-driven decisions, these data assets must be analysed. Big data analytics refers to the tools and methods used to gain these insights (Gandomi & Haider, 2015). In line with this, businesses leverage big data and data analytics and the resulting applications to create value (Hartmann, Zaki, Feldmann, & Neely, 2016; Teece, 2010). Subsequently, the DDC refers to the environment in which these businesses operate, which are proposed to be characterized by the agility of the digital era, which results in: 1). unclear end goals and visions; 2). rapidly changing markets; and 3). transformation decisions based on hype (Davenport & Westerman, 2018). Furthermore as a result of this agility, Davenport and Bean from NewVantage Partners (2018) state that almost 80% of executives fear disruption due to data-driven competitors:
Executives perceive growing threats from data-driven, highly agile competitors, including the big Tech Giants – Amazon, Google, Applen and Facebook – as well as those competitors within their own industry, who are demonstrating the ability to compete on data and analytics, especially those who have forged data cultures which give them agility and speed. (p. 7)
In turn, a change effort in a DDC refers to a change effort in which a company implements methods, tools and/or techniques to facilitate in data-driven insights and decision-making processes.
Within this research, big data and data analytics refers to the use of a Customer Data Platform (CDP) or a Data Management Platform (DMP). A CDP is a system that processes structured and unstructured data from multiple channels. These data can be fed in batches and through a continuous stream. The CDP functions as a centralized node in the marketing data environment and it builds a 360-degree view of the customer. Furthermore, the CDP segments customer groups by characteristics and can show trends for these segments. The CDP’s output can be integrated into other systems (Earley, 2018). The differences between a DMP and a CDP are outside the scope of this research, but regarding big data and data analytics characteristics, the DMP shares commonalities with the CDP. These commonalities refer to: multichannel data feeds that are structured and unstructured, reporting and analytics functionalities, and its output can be integrated into other systems (Elmeleegy et al., 2013). As such, the implementation of a CDP or DMP is the constraint with which each company has to comply to be considered a change effort in a DDC and thus as a right case for this research.
3. Research Design & Case Description
First, this section explains the research design. Second, examples of within- and cross-case analyses are given, after which the cases are described through the use of two case-study overviews regarding company agility, change effort structure and perception of RtC.
The goal of this research is to explore the characteristics of change efforts in a DDC and test the applicability of current change and RtC models and theories in a DDC. As argued in the introduction section of this research, this research considers where a change effort in a DDC fits in the change type dichotomy of Weick and Quinn (1999). Assigning the right type of change is important to understand the nature of the change, for different types of change require different theories and models. Furthermore, Kotter’s Accelerate model (2012) is considered, for it is his most recent iteration on his eight-steps model (Kotter, 1995), which is among one of the most popular change models (Appelbaum et al., 2012; Hughes, 2016). where this sequentiality is a critique to his previous model (Appelbaum et al., 2012; Hughes, 2016; Kotter, 2012). For RtC, the paper of Ford, Ford and D’Amelio (2008) is considered, because the theory aligns itself with Lewin’s definition of RtC. In this definition, RtC is the interplay of “roles, attitudes, behaviours, norms” of all actors in a system (Dent & Goldberg, 1999, p. 31). As such, RtC is the outcome of this interplay, in contrast to being inherent to the human nature. Next to the possibility that RtC resides in a person, it is stated that the source of resistance is due to other factors within the system (Dent & Goldberg, 1999; Kotter, 1995).
This research has been designed as a qualitative holistic multiple-case study, because there are no logical subunits and the model and the considered theories have an holistic nature (Yin, 1994). A multiple case study design allows this research to seek out similarities and differences between cases, which culminates in new insights. Furthermore, the advantage of a multiple case studies approach is that results from this approach are considered more
robust and reliable, because conclusions about similarities and differences can be drawn (Baxter & Jack, 2008). The units of analysis consist of six operating companies (OpCo), owned by a holding company (HoCo), operating autonomously in the energy sector. These OpCos are spread throughout Europe, one from the Netherlands, one from Belgium, one from the Czech Republic and three from Germany. Access to the units of analysis has been provided through the internship of the researcher at the HoCo. For scope, this research has considered the marketing function of each OpCo, which were implementing a CDP and/or a DMP at the time of data collection, where CDPs and DMPs are big data and data analytics tools (Earley, 2018; Elmeleegy et al., 2013). Data was collected through semi-structured interviews, which fits the exploratory nature of this research (Saunders, Lewis, & Thornhill, 2012). Each case study consisted of two interviews: one with a change agent, coded as interviewee xC, for Changer; and one with a change recipient, coded as interviewee xB, for Business. These chosen roles allowed this research to analyse the change and RtC
characteristics as an interplay of both parties, which fits the model and theories this research considered. An interview protocol (appendix II) and a checklist (appendix I) have been constructed, where the checklist consists of the characteristics deducted from the model and theories. The interviews (n=12) took 45 minutes and the sampling method for these interviews was purposive typical case, to sketch a typical profile (Saunders et al., 2012) of the change effort in a DDC. Due to the international nature of this research, interviews were conducted through digital means and recorded. Furthermore, some of the interviews were conducted in Dutch, while others were conducted in English. For validity reasons, the interviews are kept as close to the source language as possible, which means they were only translated for quoting purposes. During transcription, data was anonymized. Subsequently, interviews were coded (appendix III) within NVivo 12 by QSR International (Saldaña, 2015). Initial codes and themes were deductively derived from the checklist made prior to the interview. Data was analysed in a directed qualitative content analysis approach, which is suitable to build upon existing research (Hsieh & Shannon, 2005; Zhang & Wildemuth, 2016). Additional codes and themes were made to make up for inductive insights. To keep up with train of thoughts, linkages and outliers during analysis, memos were written down. Next to the within-case analysis, a cross-case analysis was conducted to compare similarities and difference between cases, which improves generalizability (Eisenhardt, 1989). Tables were made in Excel.
First, quotes in the within-case analysis were evaluated upon fit (good-, semi-good-, no fit) with the proposition or characteristic. Next, combining quotes within-case gave an evaluation of the fit of the case (resulting in differing percentages of interview- and case fit). Afterwards, evaluated cases were compared to seek out similarities and differences in the cross-case analysis (Eisenhardt, 1989; Miles & Huberman, 1994). In addition to quotes, extant literature was used to build support. This increases internal validity, which is important with theory building (Eisenhardt, 1989). To ensure reliability, a case study protocol was developed to provide transparency, while a case study database was maintained to ensure replicability (Gibbert & Ruigrok, 2010).
The RPs’ analysis methods were different, due to the differing nature of the propositions. For RP1, quotes were collected and evaluated on the position of the
interviewees and cases regarding six distinct characteristics (example of three characteristics in table 1, column 1) uncovered during the early phases of the research (data-collection, transcription, coding). Subsequently, these characteristics are defined after the work of Weick and Quinn (1999), being specific to one of the two change types (table 1, column 2), as stated by the same authors. If the results of the cross-case analysis indicated one type of change on one characteristic, while indicating the other type of change on another characteristic, this would suggest RP1 to be true. Table 1 displays an example of the within-case analysis of three characteristics of the change typology for RP1. Regarding RP2, analysis was done in an equivalent way, but due to bigger within- and cross-case differences, percentages of fit of interviews and cases with change model characteristics were calculated and included. In the cross-case analysis, cases were only considered to have a fit with the characteristic, if the statement of one interviewee of the respective case indicated a good fit and the statement of the other interviewee of the respective case indicated at least a semi-good fit. Table 2 displays an example of the within-case analysis for RP2, where the colours coincide with the level of fit.
Table 1: Example of within-case analysis of case 6, RP1, regarding three characteristics.
Table 2: Example of within-case analysis of case 1, RP2, regarding three characteristics.
In contrast to the former RPs, RP3 is a single statement without sub characteristics. Therefore, the stance and the position of the firm was collected with the support of quotes. Table 3 displays an example of the analysis method used for RP3.
Table 3: Example of within-case analysis of case 4, RP3.
Table 4 displays the general overview of each case-study. Analysis uncovered that almost all interviewees are unanimous in stating that their companies are adopting the agile way of working. Each analysed company is currently in the process of becoming data-driven, where starting dates differ from 2012 up till 2016. There are no end dates set for the change efforts. The urgency to change is clear. On a department level visions are in place, while this is less apparent at the company-wide level. Visions are not translated into overarching operational goals. Regarding planning structures: short-term goals are set, mid-term goals slightly less, while long-term goals are mostly not set. These goals are regularly adjusted.
Table 4: Case-study overview: agile and change effort structure
Regarding RtC characteristics in change efforts in a DDC, table 5 displays the perception of the RtC phenomenon per interviewee. Findings suggest that people within the analysed companies are mostly motivated to change, meaning RtC is not a critical issue. However, RtC does exist and can be sourced to one of three categories: RtC due to fear caused by uncertainty, RtC as a part of human nature and RtC due to a lack of resources. RtC is handled in multiple ways, but clear, regular communication is the most quoted.
Table 5: Case-study overview: perception of RtC
With the research design explained, the research methods described, and an overview of the case-studies provided, this section is concluded. The next section covers the
description of the characteristics of change efforts in a DDC, followed by the cross-case analysis for each RP.
4. Cross-Case Analysis & Discussion
This section closes the loop. The first subsection presents a general description of the characteristics of a change effort in a DDC. In the remaining subsections a cross-case analysis per RP is conducted to address the RP through the use of the collected data. Table 6 provides a graphical representation per RP for each individual case and in a cross-case manner.
Table 6: Results per RP per case and cross-case.
Furthermore, additional literature provides support to the findings and the combination of extant literature the findings of this research culminates in the generation of future research proposition. These future research propositions are combined in a conceptual model in section five.
4.1 Characteristics of change efforts in a DDC
As stated in the introduction of this research, change efforts in a DDC are different from traditional change efforts due to the agility of the digital era, which results in: 1). unclear end goals and visions; 2). rapidly changing markets; and 3).transformation decisions based on hype (Davenport & Westerman, 2018). Next to describing the characteristics of a change effort in a DDC, this section tests whether this is true.
All the analysed companies are in the process of becoming data-driven and all of them stated that there is no end date set when the change effort should be completed. Even more, the change effort to become data-driven is an ongoing process according to all of them. As interviewee 2C (hereinafter 2C) stated: “I think that the data-driven environment, outside
the company, is running that fast that there will be an ongoing process also in [the] change effort, that we need to stay competitive in terms of new competitors for example, so I think the environment is that you always need at least some change. I think it's an ongoing project, because it's not like that you say "okay we want to have a change in our mindset" or
something, because all the things that happen in the data environment happen really fast. And there are new companies, new startups that really can make a game changer very quick, therefore you always need a willingness to change the thing that is state of the art for you.”
The trigger of change is external and due to the agility of the digital era, which consists of: 1). the adoption of data-driven tools and techniques by competitors and the resulting threat of competition, which forces the company to invest in new technologies. 2C: “Because when we
are not doing it, at the end we will be eaten by another competitor because they're doing that better: they have lower costs, they have more satisfied customers, they have more customers and that is why we need the data to make our business more successful.”; 2). changing
customer demands, due to digital channels, 6B: “So, when we actually found out that
customers who are using the principal search online [but] shop offline, we understood for the first time that we have to focus on the digital media and our presence in the digital media in order to be successful in the offline sales.”; and 3). The overall agility of the evolution of the
digital landscape, 2B: “Nothing evolves that quick like the digital, for example there are
always so many new impulses there, and then an impulse gets a hype and a hype gets a trend and, for example we have to cope with all these aspects, let it be payment, or let it be mobile things, or let it be complete home automatization.” All of the analysed companies stated that
the effort in becoming data-driven consists of multiple smaller change efforts, which not only are conducted subsequently, but also simultaneously within multiple departments, as
described by 1C: “While in the digital sector [effort], it is more diffused, more diverse, more
here a project and there a project, it's not a whole program.” Almost all analysed companies
have an urgency to change in place, while not all of them have company-wide vision regarding data in place, as perceived by some of the interviewees. Still, every interviewee perceives a vision regarding data to be in place for the department. These visions are not translated into overarching operational goals, because of rapidly changing external environments, which is in line with the statements of Davenport and Westerman (2018). Subsequently, while short-term goals are in place, mid-term are less so and short-term the least. As 3C stated: “Whenever you’re working with things about systems, data and digital,
you can’t look too far ahead, because these things are so subjected to changes. Looking further ahead than two years is just not smart. You can give directions, sure, but you shouldn’t operationalize it.” As previously mentioned, the need to change is clear in a DDC.
In line with that thought, people are inherently motivated to change, they have an open mindset towards the change, they are motivated by proof of success of other change efforts in a DDC and by seeing the value and benefits of the desired outcomes. Overall, this is due to the data-driven environment, and comes from negative and positive sources. 4C perfectly stated the threats and the opportunities specific to the DDC: “Every employee in the sector
knows that we’re coming from an old industry and that we have to change to survive.” and “We’re active in E-Mobility, smart home, blockchain, which are all technologies that are discussed in high-tech companies. Nowadays, we, as an old industry, are active in that field. That stimulates people.” When asked how the motivation is being kept alive throughout the
change effort, there is some overlap with how the employees were motivated in the first place. On top of that, the use of communication and the sharing of success stories of change efforts is being named. For the people who have had a negative reaction to change, it is reported this is due to: fear due to uncertainty, RtC as a part of human nature and RtC due to a lack of resources. Handling negative reactions to change is mostly done by clear
communication, which is open and transparent, by showing the benefits of the outcome of the change effort and making sure there are enough resources, participation and involvement. When asked whether RtC can be of value to the change effort, multiple interviewees stated that input from change recipients altered the change efforts for the better.
Overall, the notion that the agility of the digital era causes change efforts in a DDC to be different from traditional change approaches, as stated by Davenport and Westerman (2018), is found to be true. First, visions are in place department-wide, while only most of the interviewees agreed on company-wide visions. However, as stated above, overarching operational goals are not in place, which is due to uncertainty regarding future applications. Thus, the notion that the agility of the digital era results in unclear end goals and visions is partly found to be true: visions are in place, however not operationalized. Second, it is noted that data-enhanced systems and techniques change and evolve rapidly and thus, the threat of competition is a constant through the adoption of these systems and techniques by
competitors. As a result, the notion that markets change rapidly due to the agility of the digital era is found to be true. However, no evidence is found regarding the third notion, where it is stated that the agility of the digital era results in transformation decisions being based on hype.
4.2 RP1: types of change
This section handles the first RP: change efforts in a DDC exhibit both characteristics of planned, episodic change and emergent, continuous change. Regarding the type of change, as it is described in the works of Weick and Quinn (1999), change in a DDC is not exclusive to either one of the types. Rather, it exhibits a mixture of both types. Table 7 displays the translation of the characteristics of the change effort in a DDC to the two main types of change, together with illustrative quotes from the interviewees.
Table 7: Data-driven characteristics translated to the characteristics of the change types.
The findings of this research suggest that change efforts in a DDC are a mixture of both planned, episodic change and emergent, continuous change. As stated in section 5.1, the trigger to change has an external source, which is due to the agility of the digital era, which consists of: 1). the adoption of data-driven tools and techniques by competitors and the resulting threat of competition, which forces the company to invest in new technologies; 2). changing customer demands, due to digital channels; and 3). The overall agility of the evolution of the digital landscape. These external triggers to change then lead to an intentional change effort where data becomes a part of the strategy of the company. 2B:
“They pointed out why they wanted to do it, what their goals were and why the company as a whole should have a certain perspective on big data or data analytics.” Furthermore, most of
the analysed companies explicitly mentioned they desire a new equilibrium where data is used for being more centric. 1B: “We have to think more and act more
customer-centric and therefore we need the data, or otherwise we are blind.” Those characteristics
coincide with planned, episodic change as mentioned by Weick and Quinn (1999). The interviewees view change as an ongoing process, where micro change efforts are incremental and build on previous changes. 4B: “It evolves continuously… …we continuously build and
tweak upon that.” Furthermore, they stated they see change as being a part of day-to-day
activities, 2C: “I think it's more like an ongoing progress.” While in some of the analysed companies, change is a mindset within the organizational culture, as 4C stated: “Yes, I think
that 99% who work at the company nowadays are motivated to change, they are really actively searching for ways to make the change happen.” To address the first RP, the findings
change and emergent continuous change and is thus a hybrid of both types.
The characteristics of change efforts in a DDC that coincide with planned, episodic change show similarities with a causation approach as described by Sarasvathy (2001), where an effect, or goal is set, and the means, or tools to reach that goal are collected. This is shown by the external environmental trigger of change and the intent of the change. In contrast, characteristics that coincide with emergent, continuous change exhibit similarities with an effectuation approach. In this approach, means are given, while the end goal is uncertain (Sarasvathy, 2001). The almost unanimous omittance of long term goals and overarching operational goals, indicate an effectuation approach, where uncertainty comes from the rapidly changing DDC. In previous research, it is proposed for departments in R&D and those dealing with new product development, which face uncertainty, that they should be ambidextrous. In this ambidexterity, they leverage both causation and effectuation approaches (Berends, Jelinek, Reymen, & Stultiëns, 2014; Brettel, Mauer, Engelen, & Küpper, 2012; Sarasvathy, 2008). Although this research is not regarding R&D or new product
development, the findings suggest the same need for ambidexterity. Future research should analyze the impact of ambidexterity in causation/effectuation processes on change efforts in a DDC in a causal way. This research proposes a positive impact of causation and effectuation ambidexterity on a successful change effort in a DDC.
Figure 1: Conceptual model of future RP1.
Furthermore, characteristics that coincide with emergent, continuous change exhibit a similarity with the agile way of working. In the agile way of working, companies work in carefully planned micro change efforts, where change is incorporated in company culture and where the way of working is centred around adapting to change. Next, agile is an approach which is suitable for uncertain environments. On the other hand, characteristics of planned, intentional change also coincide with the agile way of working, as a vision is an essential capability for the agile way of working, where companies consciously react on an external trigger of change (Cooper & Sommer, 2016; Nerur, Mahapatra, & Mangalaraj, 2005; Rigby, Sutherland, & Takeuchi, 2016; Sharifi & Zhang, 1999; Sherehiy, Karwowski, & Layer, 2007). Concluding, this research states change efforts in a DDC benefit from the agile way of working, for agile in itself is proposed to be a way of working that combines both planned, intentional change and emergent, continuous change. The underlying assumption is that the agile way of working is an ambidextrous approach, switching between effectuation and causation processes. This linkage should be empirically researched in future research.
Figure 2: Conceptual model of future RP2. 4.3 RP2: Kotter’s Accelerate model (2012)
In this section, the second RP is discussed: in order to be aligned with the DDC, characteristics of Kotter’s Accelerate model (2012) require improvement. Regarding the characteristics of the model, only a few can be backtracked to change efforts in a DDC. Table 8 summarizes the characteristics that have a fit and those that have a ‘misfit’ with change efforts in a DDC. Furthermore, the total percentage fit of cases with the model and the total percentage fit of interviews with the model is shown in table 8.
Table 8: Overview of fit of Accelerate characteristics with change efforts in a DDC.
The findings suggest Kotter’s Accelerate model is partially applicable to explain change efforts in a DDC. Since the dual network structure is a prominent characteristic of Kotter’s (2012) model, this is tested for first. The results indicate there are no clear guiding coalitions in place regarding the total effort of becoming data-driven. For the marketing department, every company has a topic-specific change team in place, while not company-wide. Change initiators do not possess full autonomy to implement change initiatives, 2C:
“When it's not higher than for example 200.000 euro I have the autonomy, but when it's more I have to go to the next level.”. Subsequently the interviewees stated that the relationship
between them and the next level is good. The second autonomy related obstacle is regarding silos, 3C: “Projects like the GDPR and a DMP overarch multiple business units, therefore,
you have to drop by every business unit.” Previous research suggests that while possessing
more autonomy, these projects can be implemented more smoothly by change agents (Walker & Ruekert, 1987). Furthermore, the interviewees are not unanimous in whether the group of change agents is represented from all levels and departments from the organization, which is a requirement of the dual network structure, according to Kotter (2012). Next, it does not appear these groups exist in a network structure next to the hierarchy, but that they are moving towards an agile way of working, which is fused with the hierarchical structure. 1B:
“After that, we got a new boost with this NOW [New Way of Working] program, and now we've got an interesting mix between a classical line function with hierarchies, but mixed with a lot of interdisciplinary groups who work, yeah, together with us.” Because there are no
guiding coalitions as described by Kotter (2012), it is not possible to speak about a dual network structure, a volunteer army or barrier removal by the guiding coalition. The accelerators do appear to work in tandem as opposed to being ticked off sequentially. This is tested by asking whether goals are adjusted after they have been set. 6C: “Oh yeah. It
happens all the time, that they are adjusted.” Ex ante adjustment indicates a break from this
sequentiality and therefore supports the notion that the accelerators work in tandem. The difference between sequential steps and accelerators that work in tandem are stated as being to be at the core of his 2012 model by Kotter, for the sequentiality of the steps is a critique to his previous model (Appelbaum et al., 2012; Hughes, 2016; Kotter, 2012).
A sense of urgency is apparent, but not all analysed companies formed it around a big opportunity. One example of a sense of urgency around a big opportunity, 1B: “There is no
question. We have to. There is a need. It's not: "should we do this, or should we do that", we have to, because if we don't do this, we will lose customers, we are not competitive and other companies will overroll us and we will lose market share... ...It's building value for the customer.” Some interviewees stated a vision being formed around a big opportunity, 5C: “It's a vision. A strategic vision, not a tactical effort I would say… …Yes ofcourse, through customer focus.” Subsequently, almost all analysed companies which had a vision formed
around a big opportunity in place, used this vision to create buy-in. 2C: "So that is how I
communicate why it's so important to getting the data accessible that we use for each decision that we make in a business."
Only one interviewee explicitly stated they used the celebration of reaching milestones in their company to create buy-in. The other interviewees either stated they
celebrated reaching milestones, but not with the goal of creating company-wide buy-in, while the remaining interviewees stated they did not celebrate reaching milestones. After reaching a milestone, all analysed companies used the completion of these milestones to create new milestones and as such reiterate upon completed change efforts to create continuous change and create a change mindset. 6C: “With every new use case or every new tool, new
opportunities arise. So, I don't think you can ever be done with it, really.” Finally, almost all
analysed companies reported instituting the change efforts, which is not surprising, as the change efforts are about the implementation of tools, with which people have to work.
To conclude, looking at Kotter’s Accelerate model (2012), there are characteristics that fit and those that do not fit with the change effort in a DDC. Consequently, to develop a change management model which is applicable to a change effort in a DDC, Accelerate characteristics (Kotter, 2012) that fit with such a change effort should be taken into consideration. These characteristics consist of the notion that goals are adjusted during the change effort, buy-in creation through the communication of a vision formed around a big opportunity, which is born out of an urgency to change. Characteristics of the model that do not fit a DDC, but require improvement are regarding the guiding coalition and subsequent assumptions that require the guiding coalition.
4.4 RP3: RtC
This section handles the third RP: RtC is increased due to the characteristics of the change effort in a DDC. Previous research stated that change efforts in a DDC are perceived to be challenged by RtC (Davenport & Bean, 2018; Harvey Nash & KPMG, 2017). In contrast, this research does not find RtC to be a critical issue within change efforts in a DDC, however, RtC does exist. Interviewees ranged from stating that RtC is on an average level, on par with change efforts in a traditional change effort, to stating RtC is not apparent at all. Where RtC is not apparent, people are motivated to change or even possess a change mindset. When asked about the source of these reactions, some of these are directly or indirectly interpreted as being due to a DDC. The stated sources of RtC either fall in one of three categories: RtC due to fear caused by uncertainty, RtC as a part of human nature and RtC due to a lack of resources. Research has shown that higher levels of uncertainty instil a lack of control over a given situation in employees, which in turn has a negative effect on psychological strain and fear. Uncertainty can be divided into three categories: strategic, structural and job-related (Bordia et al., 2004). The causes of fear in a DDC mentioned by the interviewees are mostly related to job-related uncertainty, as stated by 5C: "Maybe their future is not safe, because in
other digitalizations, jobs are not needed anymore.". Furthermore, previous research found
that quality communication and participation in decision making processes decrease
perceived uncertainty and thus decrease the fear of change (Bordia et al., 2004). This leads to the conclusion that in order to lower the perceived RtC in a DDC, uncertainty should be reduced Uncertainty reduction is done true high-quality communication and participation of change recipients (Bordia et al., 2004). Future research should empirically research this link in a DDC.
Figure 3: Conceptual model of future RP3.
RtC as a part of human nature is a popular argument amongst the interviewees. As 2C d:
“I think it's in the genes of humans [to fear change] and in some cases it's more and in other cases it's less.”, and 5B: “They're always scared of change.” However, various papers have
stated that this is an oversimplification (Dent & Goldberg, 1999; Ford et al., 2008; Piderit, 2000). As such, this research takes the side of Lewin’s original meaning of RtC, meaning an interplay interplay of “roles, attitudes, behaviours, norms” of all actors in a system (Dent & Goldberg, 1999, p. 31). However, RtC as a part of human nature is not directly related to the characteristics of a change effort in a DDC.
Resources are being referred to as budget, need for prioritization and personnel, as described by the following quotes: 3C: “Because it is still unclear whether there'll be budget
for it and if it actually is going to happen.”, 1B: “They are really motivated to do the change, in most of the cases, but on the other hand we have a lot of other things to do. So, I think the question is not so much on motivation, it's more on prioritization.”, and 6B: “Sometimes it's more difficult, especially if we're facing a lack of capacities [personnel].” Regarding RtC, the
category of resources is specific to the DDC, as it is a lack of human resources with an expertise in data (Dahlander & Wallin, 2018) and budget allocation processes which are not up to speed with the rapidly changing DDC. Prioritization is a problem which results from the constraints of the other resources. On the one hand, more personnel and budget can decrease this problem, but it is also dependent on the speed and development of the external data-driven environment. An example of budget allocation constraints by 5C: “On the other
hand, there are some structures within the company, which are very old fashioned. So we have IT services, where, if you need some new software we have to make a proposal and there is a standard process and this could take a long time, for instance a year or half a year, so that's not suitable.” The situation where resources are constrained is a situation where
effectuation processes, as discussed in section 5.2, are best suited for (Sarasvathy, 2001). However, uncertain environments increases RtC (Bordia et al., 2004), which can be managed through working in an agile way (Rigby et al., 2016), where a mix of causation and
effectuation approaches, as described by Sarasvathy (2001) , are leveraged. Thus, this research proposes that to decrease RtC in a DDC, an agile way of working should be employed which, due to being ambidextrous in nature, reduces RtC.
Figure 4: Conceptual model of RP4.
Overall, the findings oppose the third RP, due to RtC not being perceived as a critical issue within change efforts in a DDC. When asked about sources for RtC, only fear due to uncertainty and RtC due to a lack of resources are linked to change efforts in a DDC. The reason why RtC is not considered to be a critical issue is, according to the interviewees, due to employees having a change mindset or a clear sense of the need of change. This research proposes that having a change mindset is correlated with companies’ adoption of the agile way of working (Cooper & Sommer, 2016; Nerur et al., 2005; Rigby et al., 2016; Sherehiy et al., 2007). This correlation should be empirically analysed in future research.
Figure 5: Conceptual model of future RP5. 5. Future Research
Combining the propositions for future research uncovered during the analysis phase results in a combined conceptual model for future research as displayed in figure 6.
This research’s results support the notion that change efforts in a DDC differ from traditional change efforts due to the agility of the digital era. First, visions are in place, however not operationalized. Second, it is noted that data-enhanced systems and techniques change and evolve rapidly and thus, the threat of competition is a constant through adoption of these systems and techniques by competitors. As a result, markets change rapidly due to the agility of the digital era. The support for the notion that change efforts in a DDC differ from traditional change efforts validates the need for future research in a DDC.
Next to the aforementioned calls for future research, the proposed conceptual model also serves as a framework for improved change management models. As such, these new models need to address reducing uncertainty inherent to a DDC. Second, although RtC is not claimed to be more apparent when compared to traditional change efforts, further reduction of RtC increases the chance for successful change efforts in a DDC. Therefore, new change management models need to address reducing RtC in a DDC. Third, regarding the
characteristics as proposed by Kotter (2012), such a model should emphasize forming an urgency to change, and a holistic vision, around a big opportunity, which is used to create buy-in. Fourth, reiteration and institution of reaching milestones and micro change efforts (Kotter, 2012) is inherent to change efforts in a DDC. Subsequently future research should emphasize change as an ongoing process, which should coincide with company vision and strategy. Fifth, next to analysing the impact of effectuation and causation ambidexterity, and the agile way of working, if proven true, this should be included in new change management models as well.
6. Conclusion
This section closes the loop. Regarding the research question, current change management models and theories on RtC are partially applicable to change efforts in a DDC. Furthermore, this research paves the way for future research with future research propositions and a proposed framework for improved change management models that are applicable to a DDC. In order to answer the research question, three RPs are adressed. Evidence is found that suggests RP1 and RP2 are true, indicating that current change management models are not up to par to describe change efforts in a DDC. No evidence is found regarding RP3, which indicates that RtC is not increased due to the DDC. Table 9 displays an overview of the RQ, the underlying assumption of the DDC and the RPs, next to the respective findings.
Table 9: Research question, propositions and findings.
First, change efforts display characteristics from both planned, episodic change and emergent, continuous change. Therefore, it is a hybrid of both classifications. This is due to characteristics of change efforts in a DDC. This research proposes that causation and effectuation ambidexterity has a positive impact on successful change efforts in a DDC. Second, most of the characteristics of Kotter’s accelerate model are not adequate to describe change efforts in a DDC. This is due to change efforts in a DDC not having a dual operating network in place. Instead, companies who aspire to become data-driven fuse the agile way of working with their existing structures, where their fused structure is a hybrid of both. Third, RtC is not as apparent as stated by managerial reports in change efforts in a DDC. However, further analysis of causes of RtC, where RtC is apparent, suggests that some of these causes are specific to change efforts in a DDC. Future research propositions are stated to further analyse these causes, their antecedents and possible methods to tackle these causes, all in a DDC. These causes consist of RtC due to: 1). fear; 2). uncertainty; and 3). a lack of resources. To tackle RtC in a DDC, a change agent should minimize uncertainty by making sure change recipients participate in decision making processes, high-quality communication. Next, RtC is decreased by switching between causation and effectuation processes regarding resources and goals through the employment of an agile way of working. Furthermore, when change recipients display a mindset in which change is normal, RtC should decrease. This is proposed to be correlated with an agile way of working.
This research contributes to academia through developing an understanding of the characteristics of the DDC and the subsequent characteristics of change efforts in a DDC. Furthermore, a conceptual model is developed in which future research propositions are combined regarding change efforts in a DDC. Furthermore, this research made an effort in answering the calls for future research on how companies develop and transition toward data-driven business models (Engelbrecht et al., 2016; Günther et al., 2017; Sorescu, 2017).
Practical contributions of this research consist of an awareness in the aforementioned characteristics, an insight into how Kotter’s model (2012) applies to change efforts in a DDC in its current state and how companies can decrease RtC further.
7. Limitations
In this research, the OpCos which are researched fall under a single HoCo. Therefore, although the analysed companies are autonomous, the results are biased, because it is
unavoidable that the analysed companies inherit characteristics of the mother company. This impacts the generalizability of this research.
Regarding the research design, there are some points of improvement for further studies. Because of the opportunity to conduct an international research, and the English language as a second tongue for many of the participants, it is possible errors due to translation from both sides of the communication exist. Furthermore, due to geographical distances, time- and budget constraints, it is not possible to do all the interviews face-to-face, therefore they are conducted through digital means, which decreases the validity of this research.
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9. Appendix
Appendix I: Interview checklist General:
Structure company: agile, hierarchic Currently in DD change effort Start DD change effort End DD change effort
Characteristics data-driven change effort Characteristics “normal” change effort
Difference normal & data-driven transformation? Impact of change effort on day to day work Length of company stay
Length of company role
Part of the change effort since the beginning? Ever in change effort before?
Vision:
Trigger of change; big opportunity
Communication: omnichannel, frequencyJustification: emotional argumentation/rational argumentation Structure: short/mid/long term goals
Reaching a goal: communication, celebration Adjustment of goals
Convincing of feasibility of change: belief affective/cognitive benefits Motivating others to change
Keeping motivation alive
Reaction to change:
Reaction to change: positive/negative Location of resistance, source
Handling reactions: resistance as a resource
Value of resistance (existence, engagement, strengthening Expectation of reactions
Overall growth in change motivation in company
à
buy-in creation Justification of decisions in change process: transparent, consistent)Agent-Recipient relationship:
Level representation of guiding coalition Department representation of guiding coalition TMT: support, participation, top down, bottom up Interaction A-R: 2-way, frequency
Appendix II: Interview protocol
Standard text after turning on the voice recorder: As a reminder for myself I will now check
if my voice recorder is turned on!
Thank you for contributing, this interview will be recorded and transcribed. If you want to, you can receive a copy of the transcript of this interview. Contact information will be anonymized. This interview will be used for academic purposes and to strengthen [holding company] and its partners. You will not be assessed in terms of performance or maturity. The
