Faculty of Health and life sciences

Department of Psychology

Master Thesis 5PS22E, 30 ECTS

Spring 2020

A Transparent Agile Change

- Predicting a Transparent Organizational Change from

Change Recipients’ Beliefs and Trust in Management

Author: Towe Nilsson

Supervisor: Andrejs Ozolins

Examiner: Rickard Carlsson

Abstract

The popularity of agile methodologies is steadily increasing. This study is an intent to

balance the agile change literature with a psychological perspective and quantitative measures

of an agile change made within a Swedish organization. Organizational change recipients’

beliefs (discrepancy, appropriateness, valence, efficacy, & principal support) and trust in

management were measured in an online survey to see how well these variables could predict

a successful agile change towards transparency. The results indicate a lack of support for

several previously cited success factors in the agile literature and a need for more quantitative

and research-driven literature. No support could be found for a relationship between

discrepancy, appropriateness, valence, principal support, trust in management, and the

outcome of a successful implementation of transparency. Efficacy was found to be a

significant and robust predictor of the outcome. More research is needed to ensure the

generalizability of the results.

Keywords: organizational change, change recipients’ beliefs, trust in management,

agile adoption, Scaled Agile Framework (SAFe), Scrum, transparency

Acknowledgments

This thesis could never have been made without the support and interest from the right

persons. First, I want to thank all key persons within the studied organization who believed in

and contributed to this research. I want to especially highlight my two main points of contact

in the organization, who contributed with many great ideas and critical discussions. For seeing

the value of this study for their change initiative as well as for an increased understanding of

agile change initiatives in general. Thank you to all who participated and showed interest in

the study, for lending me your time, effort, and enthusiasm. This thesis is the result of your

support, interest, and well-aimed questions. Your reflections have not only produced the

results but also continuously challenged me to think critically about my research. Although

this thesis can only reflect a small part of your contribution, the value continues within your

organization as well as in my improved understanding of what it means to be a part of a large

agile change initiative. It has been a true adventure to be a part of your change journey and I

have learned more than I ever could have hoped for.

Thank you to my ever so supportive supervisor and program coordinator at Linnaeus

University, Andrejs Ozolins. Your encouraging words, timely and constructive feedback gave

me the energy I needed, when I needed it the most. Throughout the process, you have

encouraged me to feel proud of my work and saw the value I could not always see myself.

Your kind words made me blush more than once.

Finally, I want to thank my family and friends for hearing my thoughts and

proofreading my text, clearing out some confusion and mistakes (missing the “r” in Scrum is

not a mistake you want to publish…), and nicely telling me when my thoughts were not really

in print. And not least, thank you for supporting me throughout my studies, knowing when to

distract me, when to give me space and when to lend me your energy. I am lucky to have you.

With all these contributions this work is much better than I could have made on my

own, but of course, any potential confusion and mistakes that remain are on me. Some of

them I have already had the opportunity to learn from, others I hope to reflect upon in future

discussions.

Authors Note

This study was preregistered at Open Science Framework, including planned

hypothesis, measured variables and demographics, planned sample and stopping rule for data

collection, planned data analyses, alpha-level and rules of data exclusion (e.g.,, treatment of

outliers & influential cases). See registration at https://osf.io/8ntw7

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A Transparent Agile Change: Predicting a Transparent Organizational Change from

Change Recipients’ Beliefs and Trust in Management

The continuous pressure to produce more, better, and faster than your competitors is

vital for the survival of an organization. Especially in the software business where the pace is

accelerating. Agile methodologies grew as a reaction to be able to faster deliver value to

customers with less risk of being outrun by your competitors without having had the

opportunity to release the product you are working on. The popularity of agile methodologies

is steadily increasing (CollabNet VersionOne, 2019). From traditionally being used in

software development, the methods are today considered useful in other areas of product

development as well (Long & Starr, 2008). They are adopted in a variety of sectors such as

education, communications, marketing, sales (Oprins et al., 2019), manufacturing, training,

research (Totten, 2017), hardware development, and business (Scaled Agile, 2019a). With this

increase in usage, there is also an increased need for empirical research to better understand

these changes and thus better guide practitioners. Even if claimed to have many benefits (e.g.,,

Scaled Agile, 2019a; Schwaber & Sutherland, 2017), agile methods are not widely

researched, with the increased popularity mostly driven by consultants and practitioners

(Tripp, 2012). The success factors and challenges of adopting agile methodologies have been

widely referenced, often in case studies describing success stories or more qualitative studies.

Although important, a balance is needed to gain more profound knowledge and a need for

more academic research can be noted in the field (e.g., Dikert et al., 2016). The trend can be

further problematized in large-scale agile frameworks, intending to implement agile on a

larger scale in organizations, where the research is considered scarce (Dingsøyr et al., 2019;

Turetken et al., 2017). According to a literature review by Dikert et al. (2016), 90 % of the

literature on large-scale agile changes is based on experience reports. Due to this, some

caution is appropriate when drawing conclusions from the agile literature. An interest in a

more psychological perspective can also be noted in the field (e.g., So & Scholl, 2009).

Researchers or practitioners are often from an engineering background, several of which

reference traditional psychological theories (e.g.,, Pedrycz et al., 2011; Tripp et al., 2016).

Findings by Gandomani and Nafchi (2016) indicate several of the most important challenges

are of a social, people-oriented nature, rather than a technological. Barroca et al. (2019) have

also found that the success of larger agile transitions is rather dependent on more

psychological factors such as commitment to change, communication, engaging people,

management support, team autonomy, and training.

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One aspect of agility that is often referenced (e.g., Kalenda et al., 2018; Scaled Agile,

2019b; Schwaber & Sutherland, 2017) is transparency. This is both considered a prominent

part of commonly used agile methodologies and frameworks (Scaled Agile, 2019b; Schwaber

& Sutherland, 2017), as well as a found challenge in agile transitions (e.g., Kalenda et al.,

2018; Barroca et al. 2019). Transparency was also one of the biggest reported benefits among

respondents who had been working according to SAFe, a framework for scaling agile

methodologies in an organization, for several years (Laanti & Kettunen; 2019). This research

is an intent to follow an agile change within an organization, mainly focusing on

understanding the potential predictors of the intended outcome of increased transparency, and

by this enable a deepened understanding of changes specifically targeted at transparency.

To increase understanding of how to successfully implement agile methodologies and

transparency, this study is an intent to bridge the gap of the practitioners-driven and

qualitative focus in agile literature and shortage of quantitative studies. This will be done by

applying well-established psychological literature and theory of organizational change to

explore how well this theoretical foundation can be applied in agile change initiatives.

Specifically, the study intends to measure how change beliefs and trust in management can be

related to a successful implementation of transparency in a company going through an agile

transition.

The thesis is arranged in the following sections: First, a theoretical background,

including a short introduction to agile methodologies and an overview of research from the

field on transparency, change recipients’ beliefs, and trust in management. This will be

followed by the proposed research question and hypothesis, a methodological part, results,

and lastly a discussion including implications for future research.

Theoretical Background

Scrum, SAFe, and Agile

Agile methodologies started as a reaction in the software development business to

more traditional project methods (such as Waterfall). Compared to traditional linear methods

often focused on releasing a finished end-product, agile methods are created to be more

flexible to better adapt to changed circumstances (Beck et al., 2001). It is an iterative and

incremental way of developing, continuously adding value to a product, combined with a

close relationship with the customer, or end-user, to allow for continuous feedback and

improvements. There are a variety of agile methodologies (e.g., Scrum, Kanban, eXtreme

Programming, Crystal Clear, & DSDM, Björkholm & Brattberg, 2016). Although agile

methodologies are often discussed as a coherent set of working methods in research, they

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include a wide range of practices, not necessarily compatible (Tripp, 2012). The

methodologies are often fragmentized with only parts being used (Pries-Heje & Baskerville,

2017). This research focuses on Scrum, which is the most commonly applied agile method

(e.g., CollabNet VersionOne, 2019) and SAFe, which is a framework for scaling agile within

the organization (Scaled Agile, n.d.). The term “scaling agile” refers to the introduction of

agile methodologies on a bigger scale within an organization. SAFe is the agile scaling

approach reported mostly used in practice (CollabNet VersionOne, 2019).

Transparency

Although there might be differences in conceptualizations of transparency regarding

what is disclosed and level of analysis, the core of defining transparency is similar in

conceptualizations (Palanski et al., 2011), involving availability, openness, and disclosure of

information. This study applies a definition close to that used by Palanski et al. (2011), who

defined transparency in a team as “the sharing of relevant information and explanations within

a team to enable its members to carry out their responsibilities within the team” (p. 203). A

similar definition is used by Laanti et al. (2011), defining visibility and transparency in an

agile context as information sharing and tracking of program/projects.

According to Iivari and Iivari (2011), openness, communication, and honesty are some

of the essential cultural attributes in the agile literature. Transparency is described as part of a

shift to a collaborative culture (Stettina & Hörz, 2015). It is one of four core values in SAFe, a

part of an alignment to work more efficiently in a fast-paced environment where “all work is

visible, debated, resolved and transparent” (Scaled Agile, 2019b, para. 5). It is also described

as a pillar in Scrum (Schwaber & Sutherland, 2017).

Transparency is described as a critical factor during agile transitions (Dikert et al.,

2016; Barroca et al., 2019). It supports the alignment of activities at different organizational

levels (Suomalainen et al., 2015). Transparency has been reported as one of the three biggest

benefits of adopting SAFe (Laanti & Kettunen, 2019). Agile practices have been found to

increase the transparency of projects on a team level as well as the organizational level

(McHugh, Conboy, & Lang 2012). Another benefit of transparency is the teams’ ability to see

the progress of their work (Asnawi et al., 2012). Transparency might be especially beneficial

in a larger agile context to visualize interdependence between multiple teams and the flow of

work items from a general portfolio down to team-level (Horlach et al., 2018).

Despite the found benefits, transparency is also described as one of the challenges in

an agile transformation (e.g., Figalist et al., 2019; Kalenda et al., 2018; Suomalainen et al.,

2015). This paradox was also found in research by Laanti et al. (2011), describing how

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transparency, along with requirements management, was “perceived as among the most

beneficial, yet also among the most challenging, areas of agile development throughout the

survey population” (p.277). Although not academically approved, organizational

representatives from Healthwise (Long & Starr, 2008), describe how transparency was

considered one of the main reasons for implementing agile methods in the organization, yet

several developers were lost due to their perceived misfit with the transparent environment.

Schnitter and Mackert (2011) found that several of the team members’ arguments proposed

against Scrum were related to transparency, such as “Scrum forces me to report every failure

and impediment. This puts me under pressure” (p.212). The resistance was found to be

reduced by explaining that transparency was needed for the teams to be able to work more

independently. The importance of having the right tool to facilitate transparency and

visualization has been found in several studies in previous research (e.g., Conboy, 2009;

Kalenda et al., 2018).

Change Recipients’ Beliefs

Although it is important to measure performance and financial aspects, theorists (e.g.,

Oreg et al., 2013) argue psychological factors and employees need further consideration for

the success of organizational changes. One theory with an employee-centric approach is

organizational change recipients’ beliefs (OCRB, Armenakis & Harris, 2009) intending to

understand what factors determine if a change recipient will be motivated to support a change.

OCRB focuses on five different beliefs of interest for change motivation and buy-in among

employees (Armenakis & Harris, 2009). Mainly, discrepancy, appropriateness, efficacy,

principal support, and valence. Discrepancy is related to whether there is a perceived need for

a change, while the change is considered appropriate if it is thought to address the

discrepancy. Efficacy concerns employees’ perception of themselves to have the capability of

implementing the change. Principal support concerns the perceived change support from

managers, change agents, and peers. Lastly, valence is the perceived personal value of a

change.

Change recipients’ beliefs have been related to essential variables during

organizational change, such as change resistance and support for change (Abdel-Ghany, 2014;

Armenakis et al., 2007). Structural equational modeling has suggested change recipients’

beliefs mediate the relationship between employee readiness factors and behavior of either

supporting or resisting the change (Abdel-Ghany, 2014). However, indications of a reversed

causality have been found by Hameed et al. (2019) with results indicating that change

recipients’ beliefs positively effect employees’ readiness for change. OCRB have also been

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used to describe change readiness prior to organizational change (Doyle et al., 2009; Hameed

et al., 2019).

People buy-in has also been found to be an essential prerequisite in organizations

going through large-scale agile transitions and is in a qualitative study by Gandomani and

Nafchi (2016) described as a prerequisite for agile transformation. Change beliefs have been

found to be higher among those going through a lean change compared to control groups who

did not (Mazur et al., 2011). Principal support and efficacy have previously been found to

predict technology acceptance (Brown, 2009), which might be closely related to the outcome

measured in this study as transparent tool usage. Change efficacy also predicted personal

initiative of adopting the new technological system.

Although not scientifically approved, to the knowledge of the author, awareness of the

importance of the beliefs in OCRB can be found in the Agile practitioner community. The

organization Mountain goat, a prominent actor in the Agile practitioners’ community, has

developed a model with similar variables as OCRB. The ADAPT model refers to the

importance to create Awareness and Desire when implementing agile methods. To develop

the right Ability, Promote successes of the implementation and to Transfer the implications of

agile to other parts of the organization (Cohn, 2010). Where awareness can be related to

perceived discrepancy, desire to valence, ability to efficacy, and promoting success to

principal support and appropriateness.

Discrepancy and Appropriateness

The importance of a perceived need for a change, discrepancy, can be seen in several

classic theories of organizational change, such as the eight-step model by Kotter (2012) and

the unfreeze-change-refreeze model by Kurt Lewin (see Burnes, 2020 for an analysis and

review of the model and its origin). Support can also be found in several studies (e.g.,

Armenakis & Harris, 2002; Abdel-Ghany, 2014; Hameed et al., 2019). Gandomani and

Nafchi (2016) found pre-startup assessment to be one of seven prerequisites before an

organization starts a large-scale agile transition. One challenge noted in the literature of agile

change initiatives is top-down steered changes which might make it harder to understand

reasons for initiating a change (Dikert et al., 2016).

The importance of a proper diagnosis prior to change has been raised in research

dating back to 1965 (Armenakis et al., 2007) and can be considered a way to assess needed

change interventions. Careful planning and deliberation were found to be related to less

uncertainty among change recipients (Rafferty & Griffin, 2006). Perceived weaknesses in a

proposed change have been found to be a reason for resisting change (Lunenburg, 2010). In a

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report of readiness for implementation of a national framework for early childhood care in

Ireland, a relationship was found between knowledge of the change and appropriateness

(Doyle et al., 2009).

Although not directly measured, findings from the agile research literature indicate the

importance of appropriateness in this context as well. No clearly defined goal from the

management of an agile transition might risk a feeling among employees that agile

methodologies can be replaced (Dikert et al., 2016). Too strict, “by-the-book”

implementations, without adjusting for specific needs, have been problematized as a challenge

in agile transitions. Teams who adjusted practices to their environment did better than those

who did not, especially when implementing agile at a larger scale (Dikert et al., 2016). Dikert

et al. (2016) found that practices might need to be adjusted to different contexts. Starting with

a pilot before implementing the bigger change was found to demonstrate the suitableness of

the agile change and increase acceptance. The importance of appropriateness can potentially

also be related to the found positive influence of success stories (Dikert et al., 2016).

Valence

The importance of valence for buy-in among change recipients is based upon Vroom’s

VIE theory of motivation (Armenakis et al., 2007), lifting the importance of the perceived

attractiveness of an outcome. This can be of both an intrinsic and extrinsic nature (Armenakis

et al., 2007). Autonomy is proposed as intrinsic motivation (Van den Broeck et al., 2016) and

has also been found to be related to increases in job satisfaction in an agile context (Tripp et

al., 2016). Perceived and expected costs and benefits of a change have been related to attitude

toward job changes (van Dam, 2005; Giangreco & Peccei, 2005), resisting change (Giangreco

& Peccei, 2005; Lunenburg, 2010), and cognitive resistance (Oreg, 2006). A lack of a clear

understanding of the benefits of Agile transitions has been found to be related to skepticism

towards agile (Dikert et al., 2016). As discussed above, benefits of transparency have been

reported in previous research, as well as its challenges and drawbacks. Further, since

transparency could have the potential to negatively affect employees (e.g., information about

performance), it is not necessarily perceived as valuable. It can thus be proposed that valance

of transparency could be perceived as either high or low in an agile change.

Efficacy and Principal Support

Change efficacy concerns employees’ perception of themselves to have the capability

of implementing change (Armenakis et al., 2007). The theoretical foundation of this variable

dates back to Bandura’s (1997) theory of self-efficacy, where one’s perceived capability is

proposed to be context-specific, in this case measured in a change context. This variable

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might also be related to change support since support from managers and role modeling are

considered important to improve self-efficacy (Stajkovic & Luthans, 1998). A relationship

between self-efficacy and perceived support has been found in agile contexts as well (Seger,

Hazzan, & Bar-Nahor, 2008). However, the relationship was only found on the managerial

level and not on a junior level. Chreim (2006) found that employees consider existing

capabilities before embracing a change. This is aligned with change-related self-efficacy as a

found predictor for a positive and proactive organizational change response (Ahmad, 2019).

Self-efficacy has also been found to mediate the relationship between transformational

leadership and reactions to change (Bayraktar & Jímenez, 2020).

One study explicitly measuring self-efficacy in an agile context was made by Seger et

al. (2008) which found self-efficacy to be related to an agile orientation among managers but

not among juniors. However, self-efficacy was found to be related to change motivation

among both juniors and managers. Although change efficacy has not been extensively

addressed in agile research, the importance of related factors such as prior knowledge and

management support have been found to be key success factors in large-scale agile transitions

in a study by Kalenda et al. (2018). Further, a study by Sundborg (2019), found self-efficacy

and principal support to be mediators in the relationship between knowledge and affective

commitment. The found importance of knowledge in agile transitions might thus rather have

an indirect effect on the outcome of a change, through the perceived efficacy. The importance

of knowledge, training, and coaching has been found by several researchers in agile

transitions (Asnawi et al., 2012; Dikert et al., 2016; Mohan, 2018). The importance of training

has also been found in a study by Gandomani and Nafchi (2016), where the absence of proper

training was perceived to cause several problems in the transition, low confidence being one

of them.

Supportive management is described as an essential part of the Lean leadership

proposed by SAFe, described as role models, and actively driving the change towards large-

scale agile adoption (Scaled Agile, 2020). Change support has also been found to be

important in research on agile transitions. Korhonen (2013) propose that winning the active

participation and support from all members of the organization is needed to reach the benefits

of an agile change. Although general support (Korhonen, 2013), as well as teamwork support

(Kalenda et al., 2018), has been referred to, the importance of management support has been

put forward as a key success factor in agile changes (e.g., Barroca et al., 2019; Kalenda et al.,

2018). A literature review of large-scale agile transformations by Dikert et al. (2016) found

management support to be considered a success factor in 38 % of the reviewed cases. Further,

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resistance on management levels was reported as a challenge in 10 % of the literature

reviewed (Dikert et al., 2016). Gandomani and Nafchi (2016) found that participants

perceived that low commitment among management could create challenges in an agile

transformation, in both their own management role and responsibilities, as well as the roles of

others. Perceived level of support was found to be related to agile orientation (Seger et al.,

2008).

Trust

This research is built on the theoretical foundation and definition of trust proposed by

David, Mayor, and Schoolman (1995), defining trust as:

the willingness of a party to be vulnerable to the actions of another party based on the

expectation that the other will perform a particular action important to the trustor,

irrespective of the ability to monitor or control that other party (p.712).

A similar definition has been proposed by Rousseau et al. (1998), defining trust as a

‘‘psychological state comprising the intention to accept vulnerability based upon positive

expectations of the intentions or behavior of another.’’ (p. 395).

Several findings indicate trust-building is related to less resistance to change

(Bruckman, 2008; Oreg, 2006). Oreg (2006) found a relation to cognitive, affective, as well as

behavioral resistance and action against change initiatives. Trust in management has also been

positively related to commitment to change (Meyer & Hamilton, 2013). Apart from this, trust

in supervisors has been found to mediate the relationship between openness to change and

predictors such as managerial communication, and participations (Ertük, 2008).

According to Iivari and Iivari (2011), the agile literature is highly people-oriented,

where trust is one of the essential cultural attributes. Lack of trust between management and

engineers has been found to be a reason for reimplementing a waterfall model (Murphy &

Donnellan, 2009). In the agile literature, trust is often described from the perspective of teams

needing trust from managers to operate in an agile way (e.g., Sidky, 2007; van Manen & van

Vliet, 2014; Tessem & Maurer, 2007). It is also described of importance for teams to develop

trust from customers (e.g., Bang, 2007). The importance of trust within agile teams has also

been addressed (McHugh et al., 2012; van Manen & van Vliet, 2014), where agile practices

such as daily stand-ups and sprint planning are proposed to enhance trust in the team

(McHugh et al., 2012).

In a study by Gregory et al. (2016), trust was identified as one of the challenges in

agile methods. Transparency is described in the SAFe framework as “an enabler of trust”

(Scaled Agile, 2019b, para. 13) and has been found to improve trust and collaboration in

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qualitative research (Stettina & Hörz, 2015). In a qualitative study by McHugh et al. (2012),

agile practices and transparency are described as related to increases in trust. However, to the

extent of the author’s knowledge, the importance of team members' trust in management

during an agile transition has not been addressed in the literature. McHugh et al. (2012)

propose future research to investigate other aspects of trust. Further, when increasing

transparency and availability, of for example performance-related information, trust might be

even more important. This since trust in management was found to be potentially critical for

employee’s change commitment during conditions that might be perceived as a risk (Meyer &

Hamilton, 2013). Although perhaps not the intention, performance-related information can be

argued to have the potential to be used against the benefits of an employee. The need for trust

in management acting in the best interest of the employee should thus potentially be even

more important due to the potentially higher vulnerability. One indication of the proposed

relationship between vulnerability and transparency can be found in a qualitative interview

study by Schnitter and Mackert (2011), where interviewees raised perceptions of having to

demonstrate their flaws due to the increased transparency. Examples of this could be noted in

the following two statements against Scrum: “My manager might get a wrong impression of

my work in case someone reports how slow my work progresses” and “Scrum forces me to

report to the team every failure and impediment. This puts me under pressure” (p. 212). Most

of the resistance was found to be removed when the necessity of transparency was pointed out

to the team members as an enabler for the team to work more independently (Schnitter &

Mackert, 2011). A similar finding was demonstrated by Mohan (2018) who found

transparency to be related to perceptions of having to show weaknesses and the importance of

assurance from managers that the information would be used for improvements rather than

demonstrating weaknesses. This challenge was more apparent in the initial phase of the

transformation.

Research Question and Hypothesis

As has been discussed above, this study made it possible to study transparency more

closely, one of the found challenging aspects of agile methodologies (e.g., Kalenda et al.,

2018). This study thus has the potential to enable valuable insights and a deepened

understanding of changes specifically targeted at transparency. The above described

theoretical foundation describes how change beliefs and trust in management have been

related to important variables for change success. And although the agile change literature

relies to a large extent on case studies and more qualitative research (e.g., Gandomani and

Nafchi, 2016; McHugh et al., 2012; Schnitter and Macker, 2011; Stettina & Hörz, 2015),

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indications of the importance of the described variables can be found in agile change literature

as well. This study aims to contribute to a quantitative foundation and research-driven

approach for potential relationships between the proposed predictors and the outcome of an

agile change initiative. In this study, the measured outcome consists of a successful

implementation of transparency, through a transparent tool usage. In this case a transparent

tool usage refers to the use of the tool for transparency purposes, as this was the proposed

goal of the studied change initiative. The following research question and hypothesis are

proposed:

RQ1: Can beliefs about an organizational change and trust in management predict

successful implementation of transparency within an IT-department of a Swedish

organization?

H1: Change recipients’ beliefs and trust in management can predict successful

implementation of transparency.

Methodology

Organization

The study was conducted primarily in the IT-department of a larger heavy industry

organization in the southern part of Sweden. The organization was currently going through a

change of working methods towards agile methodologies when the study was conducted. The

change was driven by the implementation of a new tool for development (Azure DevOps

Services), aimed to be used for increased transparency of work within and between teams.

The tool also aimed to make the work of the teams more transparent on managerial levels

(e.g., project & program level), as well as for stakeholders outside the department.

Participants

A survey was sent out to all registered users of the tool. 220 of these were estimated to

be potential participants. In total, 149 responses were received. 118 of the respondents

indicated they were using the tool. However, a final sample of 110 participants remained

since 8 of the respondents indicated they had been in the company for less than half a year, or

less than a year without specifying how much less. All respondents who indicated they had

been in the organization for more than half a year remained in the sample. Since a tenure

below half a year would indicate respondents might not have been in the company for enough

time to have experienced the entire change, the responses were considered less reliable.

According to power guidelines by Cohen (1992) and Field (2009), the sample size was

enough for the study to have an acceptable power, 80 % power, to find relationships of

medium effect size with the proposed analysis and variables.

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The respondents consisted of 77 males and 28 females, whereas five preferred not to

answer. The mean age was 40.8 years (Confidence Intervals, CI: 38.8, 42.8; SD: 10.0) and

had a range from 23 to 62 years. The respondents had on average been within the company

for 5.2 years (CI: 4.0, 6.5; SD 6.2). Years within the company ranged from 8 months to 25

years.

Measurements

All variables were measured with questionnaires in an online survey. Where possible,

previously developed, psychometrically valid, and reliable scales were used.

Transparency - Successful Implementation of Agile Methodologies

The study aimed to understand the predictors of a successful implementation of agile

methodologies. Success is operationalized to consist of an increase in transparency, measured

by a transparent use of the tool being implemented to drive the agile processes in the

organization. Since no scale could be found to measure the proposed outcome variable, a

scale was developed to measure a transparent tool usage. The scale was developed to be

aligned with operationalizations made by representatives of the organization of the goals of

the initiated change towards transparency, as well as the agile framework used (SAFe) and the

tool being implemented (Azure DevOps Services). Since transparency according to the SAFe

framework consists of visibility of information between several work roles and organizational

levels (Scaled Agile, 2019b), items were framed on a more general level to include different

perspectives and make it possible to include different roles in the sample. Inspiration could

also be found in other researchers’ definitions of transparency as visualizing (Laanti et al.

2011), giving alignment, keeping people updated and seeing the progress (Suomalainen et al.,

2015) as well as the amount of information shared as well as its thoroughness (Palanski et al.,

2011).

Items were measured on a 5-point Likert scale ranging from strongly disagree to

strongly agree. The items concerned whether the agile tool was used in a way that increased

transparency, e.g., if the tool was used to easily identify dependencies between teams (see

items in Appendix A). The scale used a design and phrasing of items similar to what has been

applied in a scale by Hameed et al. (2019) to measure employees’ readiness for change. The

items were tested in a pilot survey with representatives from the organization to ensure they

were properly understood, and minor adjustments were made in the descriptions to adjust for

feedback from the pilot.

The scale initially had six items. However, one of the scale items was excluded due to

low correlations with three other items of the scale (r = .11, r = .19, r = .24). An initial factor

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analysis had been planned but since the sample excluding low correlating items was deemed

most appropriate size was too small to conduct a reliable factor analysis (see

recommendations for at least 300 cases, Tabachnick, Fidell, & Ullman, 2007), excluding low

correlating items was deemed most appropriate considering low correlations might indicate

the item does not measure the same underlying factor as the other items.

Change Recipients’ Beliefs

Change recipients’ beliefs were measured with a psychometrically sound, 24-item

scale developed by Armenakis et al. (2007). The items are measured on a 7-point Likert scale

ranging from strongly disagree to strongly agree. Change beliefs were categorized into the

following five subscales (with example items referenced within parenthesis): Discrepancy

(e.g., “We need to change the way we do some things in this organization”), appropriateness

(e.g., “This organizational change will prove to be best for our situation”), efficacy (e.g., “I

have the capability to implement the change that is initiated”), principal support (e.g., “The

top leaders support this change”), and valence (e.g., “This change will benefit me”). All sub-

scales had an acceptable coefficient alpha above .80 in the validation studies of the scale

(Armenakis et al., 2007).

Trust in Management

To measure trust in management, a scale developed by Mayer and Gavin (2005) was

used. The full 10-item scale was not used since it was argued by the researchers to be

multidimensional. Instead, the more generic, five-item solution proposed by the researchers

was used. The five-item solution is an updated version of the original scale by Mayer and

Davis (1999), with one additional item not included in the original scale. An example item

from the scale is “I really wish I had a good way to keep an eye on _____”. The updated

version had acceptable reliability with coefficient alphas above .70 (Mayer & Gavin, 2005).

The items were measured on a 7-point Likert scale, ranging from strongly disagree to strongly

agree. Alterations were made in the scale introduction to target the manager with whom you

had the closest relationship. This, since it is likely the closest manager which is more

practically involved and taking part in the increase of transparency of an individual

employees’ work. Higher management is less likely to be invested in the work of individuals

but rather focusing on a more strategic level and overall results. Further, Dirks and Ferrin

(2002) have found immediate leaders, for example supervisors, to be particularly important

for trust. Similar alterations to target specific levels of management have been made in other

cases where trust in management has been studied (e.g., Mayer & Davis, 1999). The scale

included two reversed items which were transformed before conducting any analyses.

16

Procedure

No random sampling could be made since all participants in the organization were

needed for maximum power of the study. After an introductory email by one of the change

agents, all registered users of the tool received an email invitation to participate in the study.

The invitation contained information regarding the purpose of the study, who would take part

in the results, and a link to a web-based survey. This information was also included in an

introductory letter in the web-based survey to ensure that participants received the right

information to make an informed decision on whether to participate in the study or not. Where

possible, teams were approached with an additional physical presentation. Although this

might introduce bias in the responses it was deemed necessary due to the high risk of low

power if not enough responses could be collected. The teams were introduced the same

information as in the initial email invite to reduce potential bias. All respondents filled in the

same web-based survey. Two additional email reminders were sent out before closing the data

collection.

Data Analysis

Descriptive analyses were made, presenting the mean scores from the different scales

along with confidence intervals. The reliability of all scales was tested using Cronbach’s

alpha. Since the sample was too small to conduct a reliable factor analysis, reliability of the

transparency scale was conducted including an assessment of correlations between the items

of the scale. To analyze the proposed research question, a multiple regression analysis was

performed, using a forced entry method. Tests and analyses of assumptions of multiple

regression were made according to criteria presented by Field (2009), complete results of

these tests can be found in the Appendix B. As proposed by Field (2009), a final regression

model was presented, only containing predictors that contribute substantially to the prediction

of the outcome. As the last step, the final model was controlled for demographic variables

(age, gender, & tenure), using a hierarchical regression analysis. Demographics were included

in the first block and found predictors in the second, controlling the significance of R2 change

in the second model. Comparisons of parameters and significance of individual predictors

were made.

Ethical Considerations

Conducting a study within an organization deserves some specific considerations.

This, since it is possible results will affect decisions made and if anonymity cannot be ensured

might also affect individuals in the organization negatively. It was therefore especially

important to ensure the anonymity of respondents in the organization, as well as present how

17

the results would be used in the organization. It was expressed in both email invitations,

during physical presentations and in the introductory page of the online survey, that no

individual answers would be shared with the organization and that the results would be used

to guide the change in the organization. The study was in all letters and physical presentations

presented as voluntary since it otherwise could have been a risk that the participants perceived

themselves as obliged to participate due to the close relation to the change work in the

organization.

Results

Descriptives

Averages for the predictors ranged from 4.3 to 6.0, measured on a 7-point Likert scale

(see Table 1 below). Valence had the highest standard deviation of 1.0, which could be

mainly attributed to one question in the scale (whether one perceived to get a higher salary

after the change). This item had an average of 2.6, which equals 1.8 standard deviations below

the average of the scale. However, all original scale items were kept since the scale still had

an acceptable reliability (α = .72).

All scales except one, had an acceptable reliability of a Cronbach’s alpha above .7

(criteria applied from Field, 2009). Trust in management had a rather low reliability (α = .50).

Efficacy had the highest correlation with the outcome of a transparent tool usage (r = .41, p <

.001) followed by principal support (r = .37, p < .001). Appropriateness (r = .20, p = .02) and

valence (r = .18, p = .03) both had small sized correlations with a transparent tool usage.

These significant relationships provided initial support for the hypothesized relationships.

However, discrepancy (r = .02, p = .44) and trust (r = .02, p = .43) did not correlate well with

a transparent tool usage. Trust generally correlated weakly with the other predictors

(correlations below r < .05, p = ns), except for principal support (r = .34, p < .001) and

efficacy (r = .16, p = .05).

Table 1

Summary of Descriptives

Change Beliefs*

Mean Std. Deviation CI

Discrepancy 6.0 0.8 [5.9, 6.2]

Appropriateness 5.7 0.9 [5.5, 5.9]

Valence 4.4 1.0 [4.2, 4.6]

Efficacy 5.5 0.8 [5.3, 5.6]

18

Note. *Measured on a 7-point Likert scale. **Measured on a 5-point Likert scale. CI = Confidence Intervals of

Mean.

Can Change Beliefs and Trust in Management Predict Implementation of Transparency?

The initial model, including all hypothesized predictors, significantly predicted a

transparent tool usage (p < .001). The model had two significant predictors, efficacy (b = .27,

β = .39, t = 2.91, p = .004) and principal support (b = .19, β = .27, t = 2.08, p = .04). 95 %

confidence intervals for the b-values of efficacy (CI = 0.09, 0.46) and principal support (CI =

0.01, 0.37) were broad and might thus not be representative of the true population values, but

still significant and not crossing zero. Except for efficacy and principal support, the other

parameters were close to zero and non-significant. Three highly influential cases were found

(Mahalanobis distance, p < .01). Removing these cases reduced the significance level of

principal support (p = .15), as well as reduced the parameters of the predictor to a beta-value

of .20. Further removing the 10 most influential cases (Mahalanobis distance, p < .05)

reduced the beta-value of principal support to .13 (p = .35). A table of initial model

parameters, without influential cases, is found in the appendix (Appendix C). The power of

the initial parameters and significance of principal support was thus attributed to a few

influential cases rather than a reliable predictive power of the variable.

Table 2

Descriptives for Final Multiple Regression Model

Note. R2

adjusted = 0.16 (p < .001). CI = Confidence Intervals for B.

A final regression model was thus presented, excluding non-significant predictors. As

seen in Table 2, efficacy could significantly predict successful implementation of

transparency (p < .001). A small difference between R2 (.168) and adjusted R2 (.16) indicates

Principal support 5.1 0.8 [5.0, 5.3]

Trust in Management* 4.3 0.9 [4.1, 4.4]

Transparency** 3.4 0.6 [3.3, 3.5]

B 95 % CI SE B β t p <

Constant 1.97 [1.30, 2.64] .34 5.81 .001

Efficacy .29 [0.17, 0.41] .06 .41 4.67 .001

19

the model did not have a poor cross-validity and thus generalizes well. The beta-value

indicated a prediction of medium effect size (β = .41). Efficacy remained a significant

predictor (t = 5.63, p < .001) when controlling for the potential effect of age, gender, and

tenure in a hierarchical regression, nor did the control model reduce the beta-value of efficacy

but rather increased it (β = .51). However, the model including only demographics (age,

gender, & tenure) did not predict any significant variance in transparent tool usage (p = .44)

and none of the control variables were significantly correlated with the outcome.

No troublesome violations of assumptions could be found. In general, the assumptions

of multiple regression were found to have been met. Except for a potential heteroscedastic

variance of residuals of principal support and with a few more cases with higher standardized

residuals than would have been expected in a normal distribution. However, the violations

were minor, and the somewhat troublesome variance of principal support did not affect the

final regression model since this variable was not included. Further details of the controls of

assumptions can be found in the appendix (Appendix B).

Discussion

The results indicate one of the proposed predictors, efficacy, had a significant

correlation with a successful implementation of transparency. Thus, partial support can be

found for the proposed hypothesis. Although initial support could be found for the predictive

power of principal support, this relationship decreased below significant levels, as well as

decreased the model parameters to indicate only a potential predictive power of small effect,

when removing highly influential cases. Due to the ambiguity of the predictive power of this

variable, it was not included in the final model. One reason for this can tentatively be related

to the construction of the scale measuring management support as well as support from

change agents and peers, which might increase the heterogeneity of responses. Another

potential reason can be found in qualitative comments in the questionnaire as well as

discussions with the teams, indicating insecurity about management support.

No indication of a relationship could be found between the three other change beliefs

(discrepancy, appropriateness, & valence), trust in management, and a successful

implementation of transparency. A somewhat surprising result considering the proposed

importance of the variables in the agile change literature (e.g., Barroca et al., 2019; Dikert et

al., 2016; Kalenda et al., 2018; Korhonen, 2013; McHugh et al., 2012). However, this study

intended to measure direct relationships to the intended behavioral outcome of the change

among employees, through a transparent use of the tool implemented to increase

transparency. It could thus be argued that the relationships are rather of an indirect nature

20

such as through readiness for a change (as proposed in Hameed et al., 2019) or related to

resistance to change (as in Abdel-Ghany, 2014; Armenakis et al., 2007). This would then

potentially explain the lack of direct relationships between the variables. Another potential

explanation is how well the change initiative has been shared and understood by change

recipients. It might be that the intentions of implementing the tool have not been properly

understood, leading to a possible high perception of change recipients’ beliefs and trust, while

still scoring low on the intended outcome, simply because there is a lack of understanding or

confusion of the intended outcome of the change and the intended use of the tool for

transparency. Indications of this have been found in discussions with the teams, indicating a

confusion of how to use the tool for some of the stated purposes.

Another potential reason for a lack of relationships can be the measured outcome.

Although the items intended to measure transparent tool usage on an individual level, it can

be argued that individual use of the tool for transparency is dependent on how others use the

tool and share information. Even if the respondent might be willing to use the tool in the

intended way, this might then not be possible if the right information is not shared in the tool.

However, although it could be argued that others’ tool usage is a potentially confounding

variable, it could also be argued that the predictive power of change beliefs as a measurement

of motivation would be related to the perceived benefits of using the tool for transparency.

This would then be perceived as less if not the right information was available in the tool. The

found relationship between efficacy and transparent tool usage would also contradict this.

With perceived high dependence on other parties to successfully implement the change, it

would potentially be less likely for an individual’s efficacy to predict a transparent use of the

tool since this would then rather be dependent on others’ tool usage. Whether a recipient

perceives themselves as capable of successfully implementing the change would then have

less impact on the outcome of the change.

Lastly, the lack of relationship between trust in management and a transparent tool

usage is not surprising considering the low reliability of the trust scale (α = .50). Even though

guidelines of Cronbach’s alphas below 0.70 have been proposed for psychological constructs

that are more challenging to measure (Breakwell, Smith & Wright, 2012; Field, 2009), low

reliability reduces the chance of finding any relationships even though they might exist

(Caruso, 2000). Since the scale had acceptable psychometric properties when developed

(Mayer & Gavin, 2005), the low reliability might rather be attributed to attributes of this study

or potentially the heterogeneous sample, as will be discussed in Limitations.

21

Efficacy was found to be a significant and robust predictor of successful

implementation of transparency, even when controlling for influential cases and demographic

variables. The relationship can be explained by previous findings of a relationship between

change efficacy and resistance versus support for change (e.g., Abdel-Ghany, 2014), as well

as commitment to change (Sundborg, 2019). A high efficacy would indicate the recipient is

more likely to be supportive of and committed to the change and less resistant, as would be

demonstrated in high usage of the tool in the intended way rather than resisting to be a part of

the implementation. Efficacy has also been found to predict technology acceptance and

personal initiative of adopting new technological systems (Brown, 2009), which would be

aligned with a transparent tool usage. The relationship can also be understood from the found

relationship between self-efficacy and motivation (e.g., Latham, 2012), where a higher usage

could be understood from an increase in motivation due to a higher sense of change-efficacy.

Lastly, a reversed causality might also be likely. It would be logical to conclude that an

already high usage of the tool in the intended transparent way would be followed by a

perception of a high capability to succeed in the change, simply because these respondents

already perceive they are managing the change successfully.

No clear violations of assumptions and only a small difference between R2 and

adjusted R2 support the generalizability of the predictive power of efficacy for the success of

agile change initiatives especially targeted at transparency. However, generalizations of

efficacy as a significant predictor of a successful implementation of transparency should still

be made tentatively. This since the results from this study are based on the specific premises

of the change journey made within one organization. The results found in this study should

thus be followed by replication in other contexts to ensure generalizability to other

organizations going through an agile change.

Limitations

Response Rate

The response rate deserves further reflection. A low response-rate might indicate a

biased sample where respondents potentially more invested in the change (positively or

negatively) might be more prone to participate compared to those less interested in the

change. As previously mentioned, 220 employees were initially estimated to use the tool

actively, while responses could only be collected from 118 active users. However, as became

apparent during team presentations as well as comments in the survey, several of the teams

estimated to be using the tool did not perceive themselves to be active users and thus

restrained the sample frame. One work role was lower represented in the sample than would

22

have been estimated. Agile methodologies aim to include business representatives,

stakeholders, more actively in IT operations and development. However only 13 stakeholders

participated in the survey, and of these 4 perceived themselves to be using the tool, thus also

indicating a restriction in the sample frame. However, difficulties in reaching stakeholders in

the study might not be surprising considering previously found difficulties of involving

customers in agile transitions (e.g., Hoda, Noble, & Marshall, 2011).

Heterogeneous Sample

In the studied organization, employees with different roles used the tool being

implemented. This might affect how the tool is used. It might also affect how the change is

perceived since more managerial roles might have different beliefs about the change. A

heterogeneous sample could thus also be a potential explanation of the lack of found

relationships, as well as the low reliability of the scale for trust in management. Although

somehow problematic, it was considered of interest to measure increases of transparency on

all organizational levels involved in the change since measuring only one organizational level

would not give a fair picture of the success of the change. Transparent use of the tool is

needed on both a managerial, team, and individual level. Due to a possible risk to anonymity,

no questions about specific roles were included but instead on a more general organizational

level. Potential differences in usage were taken into consideration when designing the

measurement of transparency. This was done together with the change agents in the

organization to ensure all roles were considered and only measuring parts of the tools which

were intended to be used by all roles. Further, other studies measuring change beliefs have

included a wide range of organizational roles such as administrative workers, technicians,

nurses, and managers (e.g., Mazur et al., 2011).

The teams consist of consultants and directly hired employees. This might affect

responses and response rates. There could also be a higher risk of social desirability since

consultants’ continued work might to a higher extent be dependent on a positive image

towards the firm. Since this risk might have increased if consultants were not allowed to

answer the survey anonymously, this was not controlled for. However, due to a high rate of

consultants in the organization, it was not considered beneficial to exclude these from the

sample. To increase the response rate in both groups, information was directed at both groups,

and participants were ensured of their anonymity in upcoming presentations of the results. It

can still be argued that consultants might, for example, be less involved in a change compared

to employees. However, this confounder might to some extent be reduced by the fact that

23

many consultants had been in the company for years. Furthermore, only respondents who

indicated they had been in the company for at least half a year were included in the analysis.

Future Research

As previously discussed, one aspect worth further investigating is potential indirect

relationships between the proposed predictors and successful change implementation. It could

be that no direct relationship can be found to transparent tool usage due to a rather indirect

relationship through, for example, resistance or support for a change. It could also be of

interest to test relationships with other potential transparency outcomes of a change and not

only a transparent tool usage. This could, for example, be employees’ willingness to share

information and more attitudinal measures as these might give other indications. It would be

interesting to look further into potential relationships, or lack of relationships, between change

support and outcome of an agile change. This since management support has been one of the

more frequently reported key factors for success in the agile literature (e.g., Barroca et al.,

2019; Dikert et al., 2016; Kalenda et al., 2018).

It would have been interesting to analyze transparency on a team level (as has been

done by Palanski et al., 2011). This since how the tool is used might differ between teams

rather than within teams. Unfortunately, this was not possible since some of the teams were

too small to ensure the anonymity of the respondents. However, as has been previously

mentioned a transparent use of the tool might also differ based on differing work roles, thus

making a team aggregation less applicable in this context. And although differences are likely

to exist, all teams had the same agents driving the change and the same tool and agile

framework being implemented since one of the reasons for the proposed change was to

improve collaborations between teams. This would thus potentially increase the similarities

between teams.

Lastly, more research is needed to further study the generalizability of change-efficacy

as a significant predictor of a successful implementation of transparency. No change journey

is like another and this change was mainly driven through the implementation of a tool to

increase the transparency of one’s work. However, similar relationships are not necessarily

found in other contexts and this research thus needs to be followed by other studies to further

investigate the generalizability and predictive power of efficacy in agile change initiatives.

Practical Implications

The results from this study might be used for more practical implications within

organizations going through agile changes. Efficacy was in this study found to be

significantly related to a successful implementation of transparency. This might therefore be

24

used to guide practitioners in their agile change initiatives to target efficacy in their

interventions. It could also be interpreted as support for the previously found importance of

related factors such as training, coaching and knowledge during agile changes (Asnawi et al.,

2012; Dikert et al., 2016; Mohan, 2018). However, this study also indicates that some caution

might be needed before applying results from previous research to develop a change strategy.

This since there is a need for more quantitative support to further investigate the

generalizability of success factors described in more qualitative research.

Conclusion

This study indicates a lack of quantitative support for several of the previously cited

success factors in the agile change literature. A need for more quantitative and research-

driven literature can therefore be noted to balance and further investigate quantitative support

for previously reported themes and variables in qualitative literature and case studies in the

agile literature. No support could be found for a relationship between discrepancy,

appropriateness, valence, principal support, trust in management, and successful

implementation of transparency. However, the results indicate efficacy is a significant and

robust predictor of a successful implementation of transparency. However, more research is

still needed to ensure the generalizability of efficacy as a predictor of the outcome of an agile

change initiative. Hence, this study could only find partial support for the applicability of

psychological change theories to predict the outcome of an agile change.

25

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Appendix A – Transparency Scale

I use Azure DevOps Services to easily...

TA1 ...see WHAT needs to be done

TA2 ...see WHEN things need to be done

TA3 ...see the CURRENT status of things

TA4* ...find information of HOW things are solved

TA5 ...see WHOM I can contact when I need more information

TA6 ...see how my work RELATES to the work of others

Note. *Excluded due to low correlations with TA 1 (r = 0.11), TA3 (r = 0.19) and TA6 (r = 0.24). Reliability of

initial 6-item scale: α = 0.72. Reliability of 5-item scale: α = 0.71. Items were measured on a 5-point Likert scale

ranging from strongly disagree to strongly agree.

33

Appendix B - Tests of Assumptions, Multiple Regression

In general, the assumptions of a multiple regression can be argued to have been met.

With a potential heteroscedastic variance of residuals in principal support and with a few

more cases with higher standardized residuals than would have been expected in a normal

distribution. However, the violations were minor, and the somewhat troublesome variance of

principal support did not affect the final regression model.

The assumption of an unbounded outcome variable seems to have been met since the

outcome variable varied from 1.8-5 (on a scale from 1-5). There was variance in the variables.

No multicollinearity could be found with predictors highly correlated with one another (r >

.9). No VIF value was over 10 (initial model: average VIF = 1.95), no tolerance value was

below 0.2 and no predictors were loading highly on the same small eigenvalues.

Although impossible to test all external variables, significant predictors were checked

for correlation with potentially influencing demographics (age, tenure, & gender), especially

since a skewness could be found in the demographic variables. No correlations were found.

Visually controlling for homoscedasticity, no clear violations were found in partial plots or

residual plots, except for a potentially heteroscedastic variance of residuals in principal

support, forming a weak s-shape. This could potentially be attributed to the nature of the scale

measuring support from both managers (top & immediate) and peers, which do not

necessarily have to be perceived in the same way. However, this predictor had other reasons

to not be included in the final analysis. Except for potentially principal support, no violations

of linearity could be seen by inspecting partial plots.

With a Durbin-Watson value close to 2 (2.05, initial model, 2.13 final model) the

assumption of independent errors can be argued to have been met. Residuals seemed to be

normally distributed around a mean of zero. Normal Probability Plot of residuals looked OK

and followed the line quite well. A histogram of residuals seemed normally distributed. Less

than 5 % of the cases had a standardized residual of more than +/- 2 in the initial model.

However, 3 cases had a standardized residual of more than +/- 2.5, which is more than the

expected 1 % (= 1.1 cases). However, all had a standardized residual within +/- 3 and one of

the three cases was just above the threshold (2.55). In the final model, 6.4 % of the cases (7

cases) had a standardized residual of more than +/- 2. However, only one case (< 1 %) had a

standardized residual of more than +/- 2.5.

34

Appendix C – Initial Regression Model

Table

Descriptives of Initial Multiple Regression Model

B 95 % CI SE B β t p

Constant 2.23 [1.17, 3.28] .53 4.19 .000

Valence .04 [-.12, .21] .08 .07 .51 .61

Appropriateness -.06 [-.31, .19] .13 -.09 -.5 .62

Efficacy .33 [.09, .56] .12 .41 2.76 .01

Discrepancy -.08 [-.26, .09] .09 -.11 -.94 .35

Principal support .10 [-.11, .31] .11 .13 .94 .35

Trust in Management -.08 [-.21, .06] .07 -.11 -1.08 .28

Note. Excluding the 10 most influential cases (Mahalanobis distance, p < .05) since these had a high influence on

model parameters and significance levels. CI = Confidence Intervals for B.