Eindhoven University of Technology MASTER Error management at ...

Eindhoven University of Technology

MASTER

Error management at National Oilwell Varco's shop floortransforming errors into a facilitator of innovation

Krijnen, H.A.M.

Award date:2015

Link to publication

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https://research.tue.nl/en/studentTheses/eed09aba-1852-4f30-9d7c-e33e2e94c172

Etten-Leur, October 2015

by

H.A.M. (Henk) Krijnen

Student identity number 0819176

In partial fulfilment of the requirements for the degree of

Master of Science

in Innovation Management

Supervisors:

Dr. S. Rispens, TU/e, HPM

Dr.ir. P.A.M. Kleingeld, TU/e, HPM

Company supervisor:

Ing. D.C.G. Verbeek, Quality Assurance Manager, National Oilwell Varco Inc.

Error management at National Oilwell

Varco’s shop floor:

Transforming errors into a facilitator of

innovation

TUE. School of Industrial Engineering

Series Master Theses Innovation Management

Subject headings: error management, mistakes, shop-floor, learning, innovation.

III

"If you make a mistake and do not correct it, this is called a mistake." –

Confucius (551 B.C. - 479 B.C.)

V

Preface

Dear reader, a little more than three years ago, I made the transition from my

completed study Mechanical Engineering at the University of Applied Sciences in Breda, to

the master program Innovation Management at the University of Technology in Eindhoven.

What appeared to become a long and challenging journey, has reached its end ostensibly fast.

And by writing the final words of this report, it seems that the end of a wonderful time is

approaching me even faster.

Although 99 percent of the time it was a privilege to study at an University with such

a pleasing atmosphere, it is self-evident that also I did not enjoy every single moment. During

my time in Eindhoven I have had my setbacks. However, by making use of the opportunities

these setbacks provided to learn and to improve myself, I am convinced, I made even greater

advances. This process of personal ‘innovation’ via setbacks does not only apply to

individuals like me, also organizations can learn and innovate in response to things that have

gone wrong. By experiencing this process by myself, my interest was piqued, when about a

year ago, I heard about Error Management and how this could contribute to innovation. And

soon after this moment it would become the theme of the thesis you have in front of you.

I sincerely thank everybody who has helped me during my thesis project at both a

professional and personal level; supervisors, friends, family, and colleagues. In particular, I

would like to thank my supervisor dr. Sonja Rispens for her guidance, support and advice.

The same goes for my second supervisor dr.ir. Ad Kleingeld: thank you for your insights,

suggestions and feedback. Much gratitude goes to Dees Verbeek for his hospitality, help and

the opportunity he gave me to perform this research project within an interesting and

supportive organization. Besides Dees, I would like to thank all my colleagues at National

Oilwell Varco for their helpfulness, valuable insights and all the enjoyable moments. It is too

risky to sum up their names without forgetting anyone. I also would like to say thanks to

Manon Krijnen for her critical and refreshing view, and to my fellow student Alex Paauw for

sharing experiences and knowledge with each other. Last but certainly not least, I will thank

my ‘sparring partner’ Anton Krijnen for his encouragement, advice and input for this project.

Henk Krijnen

Management Summary

In this research report an answer is given on the question how NOV Etten-Leur can

ensure that errors, which internally arise during the manufacturing of (sub) products, are

managed in such a way that they can optimally profit from the positive error consequences

(e.g. learning and innovation) as well as reducing the negative ones (e.g. loss of time and

faulty products). In this research project, the effect of both organizational cultural aspects and

current business practices on dealing with error at NOV Etten-Leur are investigated. As result

of the investigation of these effects, areas for improvements are provided.

The results of this study can be used to enhance learning from errors (i.e. the

prevention of errors in the future, and thus increased quality), innovation through errors (i.e.

improvements of existing processes, procedures, and products), and the reduction or even

avoidance of potential negative errors consequences. In this way, NOV Etten-Leur is likely to

gain higher customer satisfaction (i.e. minimized risk, increased uptime and improved

performance in drilling operations), a lower scrap rate, and less rework and future errors.

This study revealed that shop floor employees feel low error strain, perceive almost

no error reporting costs, coordinate error handling, and communicate about errors. Within

NOV Etten-Leur, all these facets of error management contribute to innovation among

employees and learning from errors by employees. Areas in which NOV Etten-Leur can

improve are:

Consistent error reporting at an organizational level through the official procedure

of making NCRs.

Analyzing errors on an organizational level.

Providing feedback about errors.

Defining the “true” root cause of errors.

To improve, multiple actions can be taken by the management of NOV Etten-Leur.

An overview of these actions is provided in Figure 1. First, to stimulate correct (i.e. through

the official procedure of making NCRs) and consistent error reporting it is recommended to

improve the process of making NCRs in terms of ease of use and time consumption. In

addition, guidance and training can be provided in making NCRs. If more correct NCRs are

made, within NOV they will be better able to identify both the biggest mistakes and certain

patterns in mistakes (e.g. every time the same: product, material, tool, person etc. involved).

By doing so, more effective actions can be taken. Nevertheless, it is recommended to actually

VII

thoroughly analyze the reported NCRs consistently. When both NCRs are made and errors

are analyzed more consistently, the opportunity arises to provide more structural feedback to

shop floor employees (e.g. what were the consequences, what is being done with it, and what

is the final result of the actions taken. This will enhance the conceived usefulness of making a

NCR, and is therefore recommended. If it does not stop by only providing feedback both

individually and in groups, but when management is able to engage, challenge, and

encourage shop floor employees in jointly analyzing errors and thinking about improvements,

they are likely to intrinsically motivate shop floor employees (Amabile, 1998) to come up

with improvements and thorough analyses of errors by themselves. As a side effect,

employees will gain experience in root cause analyses, which ensures that the ‘true’ root

cause is identified more often. In addition, training could be provided to teach employees

how to define the ‘true’ root cause.

Figure 1: Visualization of the Recommendations for NOV Etten-Leur

VIII

Table of Contents

Preface....................................................................................................................................... V

Management Summary ............................................................................................................ VI

List of Figures ........................................................................................................................... X

List of Tables ........................................................................................................................... XI

1. Introduction ........................................................................................................................ 1

1.1 Research Context......................................................................................................... 2

1.2 General Research Topic: Error Management .............................................................. 2

1.3 Company Description: National Oilwell Varco .......................................................... 4

1.4 Delimitation: Type of Errors ....................................................................................... 5

1.5 Problem Statement ...................................................................................................... 6

1.6 Research Questions ..................................................................................................... 8

1.7 Report Structure .......................................................................................................... 9

2. Literature Review: Theoretical Background and Hypotheses ......................................... 10

2.1 Structure of the Literature Review ............................................................................ 11

2.2 Error Detection .......................................................................................................... 11

2.3 Error Reporting ......................................................................................................... 13

2.4 Error Handling........................................................................................................... 15

2.4.1 Learning from Errors ......................................................................................... 16

2.4.2 Innovation through Errors .................................................................................. 17

2.5 Error Management Culture Strength ......................................................................... 18

2.6 Research Model ......................................................................................................... 20

3. Research Methodology .................................................................................................... 22

3.1 Questionnaire ............................................................................................................ 23

3.1.1 Sample................................................................................................................ 23

3.1.2 Distribution Procedure ....................................................................................... 24

3.1.3 Measurements .................................................................................................... 25

IX

3.1.4 Questionnaire Design ......................................................................................... 29

3.1.5 Method for Data Analysis .................................................................................. 31

3.2 Interviews .................................................................................................................. 31

3.2.1 Procedure ........................................................................................................... 31

4. Results .............................................................................................................................. 32

4.1 Results Quantitative Study: Questionnaire ............................................................... 33

4.1.1 Examination of Data .......................................................................................... 33

4.1.2 Descriptive Statistics .......................................................................................... 34

4.1.3 Results of the Hierarchical Regression Analysis ............................................... 37

4.1.4 Additional Analyses: Differences between Groups ........................................... 42

4.2 Results Qualitative Study: Interviews ....................................................................... 45

4.2.1 Error Detection................................................................................................... 45

4.2.2 Error Reporting .................................................................................................. 46

4.2.3 Error Handling ................................................................................................... 47

5. Discussion ........................................................................................................................ 48

5.1 General Discussion .................................................................................................... 49

5.2 Theoretical Implications ............................................................................................ 51

5.3 Practical Implications ................................................................................................ 52

5.4 Limitations and Future Research............................................................................... 54

References ................................................................................................................................ 56

Appendix A: Organization Chart NOV Etten-Leur ................................................................. 60

Appendix B: Error Handling Procedures ................................................................................. 61

Appendix C: Interview Guide .................................................................................................. 63

X

List of Figures

Figure 1: Visualization of the Recommendations for NOV Etten-Leur ................................ VII

Figure 2: Error Management Culture and its Potential Effects (Van Dyck et al., 2005). .......... 3

Figure 3: The Lewinian Experiential Learning Model (Kolb, 1984) Linked to the Common

Practices of an EMC and their Effects as Defined by Van Dyck, et al. (2005) ......................... 4

Figure 4: Porter’s Value Chain (1985)....................................................................................... 6

Figure 5: Three Phases that Errors Must Pass Through in order to become a Source of

Learning, Innovation and Damage Control. ............................................................................ 11

Figure 6: Antecedents of Error Detection (Zhao and Olivera, 2006) ...................................... 12

Figure 7: Research Model ........................................................................................................ 21

Figure 8: Research Model with all Significant Relations ........................................................ 43

Figure 9: Cause and Effect Diagram of Issues with regard to Error Management in which

NOV can improve themselves ................................................................................................. 50

Figure 10: Visualization of the Recommendations for NOV Etten-Leur ................................ 53

Figure 11: Organizational Chart NOV Etten-Leur at Management Level .............................. 60

Figure 12: A Systematic Process Overview of how NOV Etten-Leur is Coping with Product

Non-Conformities (Errors)....................................................................................................... 61

Figure 13: A Systematic Process Overview of how NOV Etten-Leur is Coping with

Corrective Actions (CAR) and Preventive Actions (PAR) ...................................................... 62

file:///D:/Onderwijs/Master%20IM/Afstudeerproject%20NOV/7.%20Master%20Thesis/Krijnen,%20H.A.M.,%202015,%20Error%20management%20at%20National%20Oilwell%20Varco’s%20shop%20floor%20-%20transforming%20errors%20in%20a%20facilitator%20of%20innovation.docx%23_Toc431230576
















XI

List of Tables

Table 1: Participating Departments in the Questionnaire ........................................................ 24

Table 2: Error Orientation Measures for Individuals (source: Rybowiak, et al. (1999)) ......... 26

Table 3: Measures of Perceived Error Reporting Costs and Innovation among Individuals ... 27

Table 4: Skewness and Kurtosis Test for Normality ............................................................... 34

Table 5: Factor Analysis to Demonstrate that Learning From Errors by ................................ 35

Table 6: Means, Standard Deviations, and Correlations.......................................................... 36

Table 7: Hierarchical Regression Analysis of ......................................................................... 37

Table 8: Hierarchical Regression Analysis of Employees’ ..................................................... 38

Table 9: Hierarchical Regression Analysis of Error Reporting ............................................... 39

Table 10: Hierarchical Regression Analysis of Perceived Error ............................................. 39

Table 11: Hierarchical Regression Analysis of Employees’ Error .......................................... 40

Table 12: Hierarchical Regression Analysis of Coordinated and Effective ............................ 41

Table 13: Hierarchical Regression Analysis of Analyzing ...................................................... 41

Table 14: Hierarchical Regression Analysis of Communication ............................................. 42

Table 15: ANOVA between Different Types of Contract ....................................................... 44

Table 16: ANOVA between Employees and Management ..................................................... 45

file:///D:/Onderwijs/Master%20IM/Afstudeerproject%20NOV/7.%20Master%20Thesis/Master%20thesis%20concept%20v4.docx%23_Toc430611888



1. Introduction

1.1 Research Context

1.2 General Research Topic: Error Management

1.3 Company Description: National Oilwell Varco

1.4 Delimitation: Type of Errors

1.5 Problem Statement

1.6 Research Questions

1.7 Report Structure

1. Introduction

2

1.1 Research Context

This research has taken place in the context of the analysis, (re)design and

management of the operational processes of innovation. Innovation is defined as ‘the act or

process of introducing new ideas, devices, or methods’ (Merriam Webster, 2015). Nowadays,

innovation is an important topic in science. Innovation can arise in different ways, including

through errors (Hammond & Farr, 2011). Errors help people to explore the environment in

which the error occurs, and in addition motivate individuals to gain a better understanding of

this environment (Dormann and Frese, 1994). In this way, errors may facilitate innovation

through the opportunities an error creates for improvements of existing processes, procedures

or products (Hammond and Farr, 2011; Keeth and Frese, 2011). In this research, the focus is

on how businesses can make sure that they deal with errors in such a way that they become

more innovative. In order to combine science with business practice, this study has been

carried out at National Oilwell Varco (NOV) Etten-Leur.

1.2 General Research Topic: Error Management

‘To err is human’ is a common saying in global society. Errors have been defined as

“unintended deviations from plans, goals, or adequate feedback processing as well as an

incorrect action that result from lack of knowledge” (Van Dyck, et al., 2005, p. 1229).

Because errors are assumed to be inseparable from human action (e.g. Frese, 1991; Reason,

1997), every organization is confronted with errors. However, as stated by Van Dyck, et al.

(2005), the way in which an organization deals with errors can make them besides suffering

from the negative error consequences (e.g. loss of time, faulty products, accidents) also

profiting from the positive ones (e.g. learning, innovation).

It is commonly known that errors can lead to multiple negative consequences. A

normal strategy for avoiding these negative consequences is error prevention. Error

prevention means that errors are eliminated before they occur (Keeth & Frese, 2011). Besides

that errors never can be avoided in its entirety (Frese, 1991; Reason, 1997), an error

prevention approach minimizes the opportunity to learn from errors and reduces the change

to benefit from the potential long-term positive consequences of errors (Van Dyck, et al.,

2005). In the last few decades, the awareness arose that the concept of error prevention was

not comprehensive in the way it was dealing with errors. As result, another particular form of

dealing with errors arose, namely: “error management”.

Error management (EM) focuses on increasing the potentially positive consequences

of an error (e.g. learning, innovation) and reducing the negative ones (e.g. loss of time, faulty

Error management at National Oilwell Varco’s shop floor

3

products) (Frese, 1991). The EM approach assumes that human errors are inevitable, and

therefore aims at the actions that can be taken if an error has occurred; it attempts to

effectively handle and minimize their negative impact, but also tries to enhance the positive

consequences. In this way it distinguishes itself from error prevention, which only focuses on

avoiding the negative error consequences by avoiding the error all together (Frese, 1991).

An organizational error management culture (EMC) includes shared beliefs, norms,

and common practices regarding the management of errors in a organization (O'Reilly and

Chatman, 1996; Van Dyck, et al., 2005). According to Van Dyck, et al. (2005), an

organizational error management culture includes common practices, such as: (1)

communicating about errors, (2) sharing error knowledge, (3) helping in error situations, (4)

quick error detection and damage control, (5) analyzing errors, (6) coordination error

handling, and (7) effective error handling. These facets of an EMC are suggested by Van

Dyck, et al. (2005) to reduce negative error consequences (via control of these

consequences), and at the same time, as shown in Figure 2, promote among other things the

following positive error consequences: learning (e.g. the prevention of mistakes in the

future), innovation (e.g. development of new ideas), and initiative (e.g. put developments in

motion). The investigated positive effect of an EMC on firm performance is assumed to be

mediated by these affected error consequences. However, these mediators are theoretically

described, but have not been empirically tested in the study of Van Dyck, et al. (2005).

The model of Van Dyck, et al. (2005) (see Figure 2) exhibits similarities with the

Lewinian Experiential Learning Model (see the inner circle of Figure 3) as created by Kolb

(1984). As stated by Kolb (1984) there cannot be learning or real understanding of a situation

or concept (and eventually innovation) without an experience. Therefore, the model starts

with a concrete experience (e.g. error detection) followed by a reflection stage, in which data

Error Management Culture:

Common Practices

Communication about errors

Sharing error Knowledge

Helping in error situations

Quick error detection and

damage control

Analyzing errors

Coordination error handling

Effective error handling

Mediators

Reduced and contained negative error consequences

Learning

Secondary error prevention

Innovativeness

Exploration, experimentation,

and initiative

Improved quality of products, services, and work procedures

Firm Performance

Firm goal achievement

Firm survivability

Return on Assets (ROA)

Note: Dashed lines indicate that these mediators are theoretically described but have not been

operationalized in the study of Van Dyck et al. (2005).

Figure 2: Error Management Culture and its Potential Effects (Van Dyck et al., 2005).

1. Introduction

4

is collected and analyzed. Subsequently the reflection leads to modifications (e.g. innovation

of existing processes, procedures or products), which will be implemented and tested in the

final phase. As just mentioned and illustrated in Figure 3, the common practices of an EMC

and their potential consequences (Van Dyck, et al., 2005), can be linked to the Kolb’s (1984)

model. Partly based on Kolb’s (1984) model, for this study, the common practices of an EMC

are divided over three phases: (1) error detection, (2) error reporting, and (3) error handling.

The error reporting phase is added, and has no direct link with Kolb’s (1984) model. As

stated by Zhao and Olivera (2006), individuals can keep errors (i.e. experiences) to

themselves, just as the Lewinian Experiential Learning Model can be completed by one

single individual (Kolb, 1984). In contrast, an EMC, as described by Van Dyck, et al. (2005),

is more focused on the organizational level, wherein error reporting is required to benefit as

an organization. Therefore the error reporting phase is added. From now on, when be spoken

about dealing with errors, these three phases of dealing with errors are meant.

1.3 Company Description: National Oilwell Varco

One organization that, like every other organization, is confronted with errors is the

facility of NOV based in Etten-Leur (Noord-Brabant, Netherlands). NOV Etten-Leur is part

of the American multinational corporation NOV. NOV is, with 64.000 employees, a

Figure 3: The Lewinian Experiential Learning Model (Kolb, 1984) Linked to the Common

Practices of an EMC and their Effects as Defined by Van Dyck, et al. (2005)


5

worldwide leader in providing major mechanical components for land and offshore drilling

rigs, complete land drilling and well servicing rigs, tubular inspection and internal tubular

coatings, drilling string equipment, extensive lifting and handling equipment, and a broad

offering of downhole drilling motors, bits and tools.

With 442 people employed (April 2015), NOV Etten-Leur specifically focuses on the

development and manufacturing of the so-called handling tools. These tools are used for

suspending, moving and rotating tubulars in and around the well center and on the drill floor.

NOV Etten-Leur supplies its products to drilling contractors. Commissioned by oil- and gas

companies, these contractors take care of the extraction of oil and gas. Due to innovation,

increased quality and safety provided to their customers, NOV’s mission is to minimize risk,

increase uptime and improve performance in drilling operations around the globe (Rovig,

2015).

As will be explained in the literature review; a proper way of dealing with errors (i.e.

EM) leads to learning (i.e. the prevention of errors, and thus increased quality in the future),

innovation (i.e. improvements of existing processes, procedures and products), and a

reduction or even avoidance of the potential negative error consequences (Keeth and Frese,

2011). It is plausible that, via its potential consequences, EM can be helpful to achieve

NOV’s mission to minimize risk, increase uptime and improve performance in drilling

operations around the globe. And thereby could lead to higher customer satisfaction, a lower

scrap rate, and a decrease in rework and future errors.

1.4 Delimitation: Type of Errors

Because of the variety in activities (both primary and supporting) and the

corresponding divergent functions and tasks within NOV Etten-Leur, there is a wide diversity

in errors. However, by using Porter’s Value Chain (1985) as framework (see Figure 4), this

research focusses on errors that are made during the primary activity: operations (i.e. the

process that converts inputs (raw materials, labor and energy) into outputs (goods and/or

services)). More specifically, this study focusses on product related errors that (1) arise

during the manufacturing of (sub) products within NOV, and (2) which are detected, reported

or handled inside NOV Etten-Leur.

The latter delimitations are meant to indicate that in this study, the focus is only on

NOV’s internal environment, and not on NOV’s micro environment (i.e. customers,

suppliers, etc.). This distinction is important because, in general, NOV Etten-Leur exploits

1. Introduction

6

Figure 4: Porter’s Value Chain (1985)

multiple activities related to dealing with product related errors. For example, if a customer

detects and subsequently reports that an error has occurred in the manufacturing of a product,

normally, this error needs to be rectified and analyzed (i.e. error handling) by NOV.

However, this task can better be placed among the primary activity: service (i.e. activities that

keep products/services working effectively for the buyer after it is sold and delivered (Porter,

1985)). As another example, NOV Etten-Leur is confronted with errors in purchased

products, outsourced products, and foundry that are made by one of the suppliers, and which

are detected, reported and handled together with the supplier. In turn, these tasks can better be

gathered under the support activity: procurement (i.e. the acquisition of goods, services or

works from an outside external source (Porter, 1985)).

Within NOV, there is some overlap amongst the different activities with regard to

dealing with errors that arise during the manufacturing of (sub) products. And although all

error related areas are interesting for investigation, to avoid that the focus of the research

topic becomes too broad (i.e. too many stakeholders, perspectives, etc.); this study is only

focused on product related errors that are made by NOV’s own shop floor in the

manufacturing of (sub) products, and which internally go through all three phases of dealing

with errors (i.e. error detection, error reporting, and error handling).

1.5 Problem Statement

Because NOV Etten-Leur tries to deal with errors properly, it appears they are aware

that EM can positively contribute in achieving innovation, quality and safety. For example,

for the purpose of the coordination of error handling and to effectively deal with errors, NOV

Etten-Leur maintains procedures (based on API and ISO norms) to identify the controls and

Firm Infrastructure

Human Resource Management

Technology

Procurement

Inbound

Logistics

Operations

Outbound

Logistics

Marketing

and Sales

Service

Sup

po

rt A

ctiv

itie

s

Primary Activities


7

related responsibilities and authorities for addressing non-conforming products that result

from an error (see Appendix B). In addition, recently, they have introduced a new software

system (PIMS) in order to collect data about errors, to analyze this data, and to take action

based on these analyses. Moreover, NOV has appointed seven quality inspectors, who are

responsible for error detection, error registration (i.e. sharing error knowledge), and the

coordination of error handling. Together, all these actions are expected to result in a more

efficient and effective way of dealing with errors, and thereby contribute to NOV’s focal

points: quality, innovation and product-safety.

However, initial conversations with 15 managers from different departments and

some manufacturing employees suggested that there are issues concerning the way errors are

dealt with. At first sight, these issues seem to be related to the common practices of an EMC

(e.g. communication about errors, analyzing errors, sharing error knowledge, etc.). For

example, it was indicated that frequent errors keep returning and that rarely the true root

cause of an error is defined. In addition, it is questionable to what extent employees realize

when an error occurs, and whether or not they decided to report an error. An anecdote

illustrates that errors are not always reported; one time in the past, at the bottom of a storage

box only wrong machined pins were found. Apparently, in order to conceal the mistake that

was made, these pins were hidden at the bottom of the box, under correctly machined pins.

In conclusion, NOV Etten-Leur both tries to increase the potential positive

consequences of an error and to reduce the negative ones. However, there are issues

concerning the way product related errors are dealt with. As is apparent from initial

conversations, the issues are related to the common practices of an EMC (e.g. communication

about errors, analyzing errors, sharing error knowledge, etc.). It seems that, despite NOV’s

efforts, the common practices of an EMC are not fully applied within their organization. As a

result, the current way of dealing with errors within NOV Etten-Leur will not optimally

contribute to achieving innovation, a higher quality standard and improved product safety.

This because most of the time, it appears that errors are just solved (single-loop learning), and

the actual cause of an error is not addressed (double-loop learning). Therefore, this research is

conducted to provide an answer on the following problem statement:

How can NOV Etten-Leur ensure that errors, which internally arise during the

manufacturing of (sub) products, are managed in such a way that NOV Etten-Leur can

optimally profit from the potential positive error consequences as well as reducing the

negative ones?

1. Introduction

8

By finding the answer to this problem statement, NOV Etten-Leur obtains insight into

what actions can be taken to ensure that they are more able to reduce the negative error

consequences and increase the potential positive ones (Van Dyck, et al., 2005). In doing so,

NOV Etten-Leur is likely to gain higher customer satisfaction (i.e. minimized risk, increased

uptime and improved performance in drilling operations), a lower scrap rate, and less rework

and future errors. Although, probably, the results of this research cannot be generalized to

every manufacturing company, theories and models with regard to the general research topic

are tested and developed.

1.6 Research Questions

To deal with the problem statement and to accomplish the goal of this research,

multiple research questions are answered. Based on the model, shown in Figure 3, the

research questions are divided over three phases: (a) error detection, (b) error reporting, and

(c) error handling. To eventually profit from the potential positive error consequences as well

as reducing the negative ones, these three phases must be completed (Kolb, 1984; Van Dyck,

et al., 2005). In this research project, the focus lies on two aspects within NOV that

potentially influence the efficiency and effectiveness of the completion of these three phases:

(1) cultural aspects and (2) business practices adopted by NOV (i.e. methods and procedures).

First, organization-culture aspects within NOV Etten-Leur are considered to be

important. This because these cultural aspects determine if it is possible to, for example,

unencumbered report errors, learn from mistakes, and to jointly avoid making errors in the

future (Cannon and Edmondson, 2001). In addition, it entails whether or not every single

member within the organization is willing to overcome difficulties associated with errors (e.g.

is willing to: communicate about errors, analyze errors, and quickly detect errors and control

error damage); and to what extent errors are accepted as part of everyday life (e.g. error

aversion and error awareness). No matter what management demands or how good the error

procedures/processes are; if there is not an error management culture within every layer of

the organization, it is likely that organizations will not be able to optimally profit from the

potential positive error consequences as well as reducing the negative ones (Van Dyck, et al.,

2005). If, for example, employees are afraid to report an error, it is unlikely that

organizational learning will take place, i.e.: the error will not be treated because employees

do not report it. Therefore is it necessary to answer the following question:


9

RQ1: How are organizational cultural aspects related to (a) error detection, (b) error

reporting, and (c) error handling?

Besides the cultural aspects, NOV maintains procedures and methods (e.g. the error

handling procedure that is shown in Appendix B) that are used to deal with errors and that

can hinder or stimulate the error detection, error reporting and error handling. Initial

conversations indicated that there are issues considering these business practices that hinder

the way an error is dealt with. Therefore, the following research questions need to be

answered:

RQ2: How are current business practices (i.e. the use of specific methods and

procedures) related to (a) error detection, (b) error reporting, and (c) error

handling?

After analyzing the effect of both cultural aspects and business practices on the way

errors are managed, the following research questions is answered:

RQ3: How can NOV Etten-Leur promote their EMC and improve the methods and

procedures they are using for (a) error detection, (b) error reporting, and

(c) error handling?

1.7 Report Structure

In the following chapter the results of an extensive literature review can be found.

Based on this literature review, hypotheses were drawn up that should help to give an answer

on the research questions. To test these hypotheses and to examine the effect of both cultural

aspects and business practices on error detection, error reporting, and error handling, a

quantitative study has been prepared in chapter 3. In the same chapter, in addition to the

questionnaire, semi-structured interviews have been prepared. The goal of these semi-

structured interviews is to validate the findings of the quantitative study, and to explore and

create a deeper understanding about the positive and negative aspects of NOV’s culture,

methods and procedures. The results of both quantitative- and qualitative study are shown in

chapter 4. And finally, in chapter 5 the results are discussed and a conclusion a given.

2. Literature Review:

Theoretical Background

and Hypotheses

2.1 Structure of the Literature Review

2.2 Error Detection

2.3 Error Reporting

2.4 Error Handling

2.5 Error Management Culture Strength

2.6 Research Model

In this second chapter, an overview of the most prominent literature on error

management is given. This literature review put forward different theories about concepts

that possibly have an effect innovation via a proper way of dealing with errors. These theories

have resulted in a research model, which includes multiple hypotheses that were examined

during this research.


11

2.1 Structure of the Literature Review

As will be explained further on in this literature review; an EMC increases learning

(i.e. the prevention of errors, and thus increased quality in the future) and innovation (i.e.

improvements of existing processes, procedures and products), and reduces or even avoids

the potential negative error consequences (Keeth and Frese, 2011). As already mentioned in

the introduction, according to Van Dyck, et al. (2005), an organizational EMC includes

common practices such as: (1) communicating about errors, (2) sharing error knowledge, (3)

helping in error situations, (4) quick error detection and damage control, (5) analyzing errors,

(6) coordination error handling, and (7) effective error handling. The common practices of an

EMC that increases the potential positive error consequences (e.g. innovation and learning),

(Van Dyck, et al., 2005) can be linked to Kolb’s (1984) Lewinian Experiential Learning

Model (see Figure 5). As stated in the introduction, partly based on Kolb’s (1984) model, the

common practices of an EMC are divided over three phases: (1) error detection, (2) error

reporting, and (3) error handling (see Figure 5). These three phases will be used as guidance

for this literature review, which ultimately shows how errors could lead to learning and

innovation.

2.2 Error Detection

A common practice of an EMC, other than for example, communicating about errors

and analyzing errors, is quick error detection and damage control (Van Dyck, et al., 2005).

Keeth and Frese (2011) argue that quick error detection is essential in order to reduce the

negative consequences; eventually, only errors that are detected can be handled. Error

detection is defined as the individuals’ realization that an error has occurred, whether or not

Outcomes

Innovation

Learning

Avoiding

negative error

consequences

Phase 1:

Error detection

• Quick error detection

Phase 2:

Error reporting

• Communicating about errors (i.e.: reporting an error

Phase 3:

Error handling

• Coordinated and effective error handling

• Analyzing errors

• Communication about errors (i.e.: helping in error situations and sharing error knowledge)

Figure 5: Three Phases that Errors Must Pass Through in order to become a Source of

Learning, Innovation and Damage Control.

2. Literature review

12

they understand its cause and nature (Zapf and Reason, 1994). Amongst other, this factor is

the one that leads to the improvement of product quality, service quality, and work

procedures (Van Dyck, et al., 2005). Keeth and Frese (2011) give as example a calculation

error. They claim that if a calculation error is detected quickly and immediately reported to

the responsible department, it is less likely that the erroneous calculation already is made

again and not yet an erroneous decision is made based on this primary calculation error. So,

the sooner an error is detected, the better the consequences can be handled.

Error detection can take place in three different ways (Zhao and Olivera, 2006). First,

action-based detection means that errors are caught while they occur. Mostly this happens

with the aid of aspects of the errors action itself (e.g. visual or auditory signals). For example,

an parking sensor that makes a sound when the car driver comes too close to an object.

Second, outcome-based detection means that errors are noted when the actual results are not

in line with the intended ones (i.e. based on some aspect of the consequences of the erroneous

action). For example, a doctor may realize that illness of a patient was diagnosed incorrectly

because the illness symptoms do not disappear. And lastly, detection by external limiting

functions implies that the external environment provides a signal that an error was made (i.e.

constraints in the outside world prevent further action). For example, at the time the guests

invited for a meeting do not appear, the person who incorrectly scheduled the meeting will

discover the error.

Based on empirical evidence from prior research, Zhao and Olivera (2006) relate

several factors to the error detection modes (see Figure 6). These factors are: the visibility of

error (i.e. “how noticeable or observable occurrence(s) or consequence(s) of an error is (are)

to an individual” (p. 1017)), error anticipation (i.e. “a general and realistic expectation that

errors will happen” (p. 1017)), and understanding the organizational goals (i.e. the

individuals’ understanding how their actions relate to the objectives they want tot achieve).

Figure 6: Antecedents of Error Detection (Zhao and Olivera, 2006)

Contextual

Individual

Antecedents

Visibility of error

Error anticipation

Understanding

organizational goals

Error Detection Mode

Action based

Outcome based

Limiting function


13

In line with the assumed importance of error anticipation (Zhao and Olivera, 2006),

Keeth and Frese (2011) argue that organizations that narrowly focus on error avoidance, are

less likely to quickly detect errors (i.e. they are so convinced about their error prevention

capabilities, that they don’t expect any error to happen). On the other hand, employees within

an organization that are aware that they make mistakes (even if they try to avoid it) are more

likely to detect errors and respond quickly to them (Keeth and Frese, 2011). In addition, error

anticipation prepares organizations for the handling of errors (Van Dyck, et al., 2005).

Therefore, a positive relationship between the error anticipation within NOV and error

handling is expected.

H1: Employees’ error anticipation is positively related to error handling by

employees, i.e.: (a) coordinated and effective error handling, (b) analyzing errors,

and (c) communicating about errors.

Because this this study focuses on product related errors that are made by NOV’s own

shop floor in the manufacturing of (sub) products, and which internally go through all three

phases of dealing with errors (i.e. error detection, error reporting, and error handling), only

the action-based and outcome-based error detection modes as seen from the individual level,

are relevant (see Figure 6). Therefore, only the error detection antecedent ‘error anticipation’

is included in this study.

2.3 Error Reporting

In order to optimally profit from the potential positive error consequences as well as

reducing the negative ones, after an error is detected, there is need for damage control,

analysis of the error, and coordinated and effective error handling (Van Dyck, et al., 2005).

However, before these actions can be carried out, an error needs to be shared and

communicated (i.e. reported) by the individual that detects the error (Van Dyck, et al., 2005).

Six types of behavioral responses are specified in the literature (Zhao and Olivera, 2006): (1)

reporting as it is, (2) rationalized reporting (i.e. with a reconstructed story), (3) blaming

someone else, (4) covering up (i.e. hiding the error without attempting to fix it), (5) handling

on one’s own (i.e. not reporting the error but taking actions to fix it), and (6) ignoring the

error. An EMC is clearly an advocate of the first type of reporting behavior because then, the

best damage control and coordinating and effective error handling can takes place (Van

Dyck, et al., 2005). Because covering up an error (i.e. a negative reporting behavior) hinders


14

the communication about-, and the correction and analysis of errors (i.e. error handling), it is

expected that there is a positive relationship between the error reporting behavior (i.e. on the

one hand reporting as it is or rationalized reporting of an error, and on the other hand

covering up, handling on one’s own or ignoring an error) within NOV and error handling.

H2: Employees’ error reporting behavior is positively related to error handling by

employees, i.e.: (a) coordinated and effective error handling, (b) analyzing errors,

and (c) communicating about errors by employees.

Zhao and Olivera (2006) state that, based on a cost-benefit evaluation, an individual

decides whether or not and how to report an error. This cost-benefit evaluation implies that

error reporting involves evaluating costs and benefits (Uribe, et al., 2002). There are several

reasons (i.e. costs) why an individual may not want to report an error. For example, an

exploratory investigation in a medical environment (Uribe, et al., 2002) identified the main

obstacles of physicians and nurses to report an error. The physicians’ top six barriers were:

(1) not knowing the usefulness of reporting, (2) extra work involved in reporting, (3) time

involved in documenting an error, (4) lack of knowledge of what shoud be reported, (5) lack

of information on how to report an error, and (6) thinking that reporting has little contribution

for improvement of quality of care. On the other hand, the top six barriers for nurses were:

(1) time involved in documenting an error, (2) not being able to report anonymously, (3)

extra work involved in reporting, (4) hesitancy regarding “telling” on somebody else, (5)

thinking that it is unnecessary to report the error because it had no negative outcome, and (6)

the fear of lawsuits. Zhao and Olivera (2006) also categorised several reasons why an

individual, who detects an error, may not want to report it: material costs (e.g. monetary

penalties, suspension, or job loss), potential damage to personal image (i.e. harming the

perceptions about an individual’s competences and professionalism), effort costs (e.g. scarce

time), economic costs (e.g. product recalls, workgroup penalties), and reputation costs (of the

whole organization). It is expected that within NOV, these error reporting costs negatively

influences error reporting behavior.

H3: Error reporting costs perceived by employees are negatively related to

employees’ error reporting behavior.

Besides the reporting costs mentioned above, emotions, such as fear, shame,

embarrassment, and guilt (Rybowiak, et al., 1999), can directly influence whether or not an


15

individual reports an error (Zhao and Olivera, 2006). An error can result in strong negative

emotions (Rybowiak, et al., 1999). As example, pilots from the Italian Air Force indicate that

errors frequently result in high emotional stress (Catino and Patriotta, 2013). Strong

emotional responses like these can directly influence decision making and judgment (Zhao

and Olivera, 2006). Because error reporting behavior implies the decision wheter or not to

report an error, it is expected that wihtin NOV, error strain (i.e. negative emotions resulted

from errors) negatively influences error reporting behavior.

H4: Perceived error strain by employees is negatively related to the employees’ error

reporting behavior.

More often, through their effect on individual’s cognitions about the costs of

reporting, it’s expected that negative emotions indirectly affect the error reporting behavior

of individuals (Zhao and Olivera, 2006). Zhao and Olivera (2006) based their reasoning on

Forgas’s (1995) affect infusion model. This model states that behaviors are affected by

emotions through cognitions in cases where the judgement or decision is of high personal

relavance (Forgas, 1995). Generally speaking, it is expected that the perceived error reporting

costs weigh more heavily for an individual who feels a strong, negative emotions when

decting an error (Zhao and Olivera, 2006). In this scenario, individuals consider their emotion

as relevant information for the cost evaluation (Forgas, 1995; Zhao and Olivera, 2006).

Therefore it is expected that within NOV, error strain indirectly, negatively influences error

reporting behavior through the positive effect on error reporting costs.

H5: Perceived error strain by employees is positively related to error reporting costs

perceived by employees.

2.4 Error Handling

After an error is detected and reported, one can start with error handling. According

Van Dyck, et al. (2005), handling errors include: error correction (i.e. coordinated and

effective error handling), analyzing errors (i.e. attempt to learn and innovate), and

communication about errors (i.e. sharing error knowledge and helping in error situations). In

this way, errors can lead to learning, innovation, and the avoidance of potential negative error

consequences (Keeth and Frese, 2011). In turn, via these three error consequences, the

common practices of an EMC may positively influence firm performance (Keeth and Frese,


16

2011; Van Dyck, et al., 2005). In the following sections the learning and innovation processes

will be discussed in more detail.

2.4.1 Learning from Errors

Sitkin (1992) states that mistakes may be more useful to learn from than successes,

because failures raise the awareness of risk and a motivation for change that otherwise would

not exist. Mittelstaedt (2004) agrees with this; from comprehensive organization crisis

observations, he concluded that making failures is important to success. Consistent with

Sitkin (1992), Mittelstaedt (2004) states that companies will operate from an unrealistic an

uniformed perspective if they don’t have errors. From this point of view, mistakes, while not

exactly welcome, are certainly useful. Even in the world of training methods, the unique

learing potential of errors didn’t stay unnoticed (e.g. Keith and Frese, 2008). In a meta-

analysis, Keith and Frese (2008) analyzed 24 studies (overall N=2,183) on Error Management

Training (EMT). EMT is a training method that explicitly encourages trainees to make

mistakes. This is done by providing a minimum of guidance (i.e. no step-by-step instructions)

on a difficult, individual task. In this way EMT is a training method that involves active

explorations, i.e. it encourage learners to use errors to try something different and to think

ahead. Herein, errors serve as means of positive feedback. The results of the meta-analysis

suggested that EMT leads to better training outcomes than alternative training methods such

as error-avoidant training. It has revealed that errors can be effective means for the promotion

of learning (Keith and Frese, 2008).

However, in contrast to errors in training methods, the appearance of errors in daily

life are often not planned. Therefore, on can imagine that specific conditions (i.e. cultural

aspects an business practices) need to be met in order to stimulate the learning-from-errors

process. Several common practices of an EMC (e.g. communicating about errors, coordinated

and effective error handling, and analyzing errors) are assumed to enhance the learning

process, and in this way stimulate innovation and enhances firm performances (Keeth and

Frese, 2011; Van Dyck, et al., 2005). Though, this mediating relationship was not tested

empirically in the study of Van Dyck, et al. (2005), it is expected that within NOV, these

error handling practices positively influences learning.

H6: Error handling by employees, i.e.: (a) coordinated and effective error handling,

(b) analyzing errors, and (c) communicating about errors, is positively related to

learning from errors by employees.


17

2.4.2 Innovation through Errors

It is likely that errors help people to explore the environment in which the error

occurs, and in addition motivates individuals to gain a better understanding of this

environment (Dormann and Frese, 1994). In this way, errors may facilitate innovation

through the opportunities an error creates for improvements of existing processes, procedures

or products (Hammond and Farr, 2011; Keeth and Frese, 2011). Hammond and Farr (2011)

even argue that successes may contribute less towards innovation than errors. Where having

success leads to complacency and thereby results in repetition of existing behavior and little

change (Hammond and Farr, 2011), errors could imply a speedy need for a change (Farr and

Ford, 1990) either via identifying an existing problem or an opportunity for future innovation

(Hammond and Farr, 2011).

By means of accepting errors as natural part of work, communicating about them, and

analyzing them, an EMC is considered to encourage individuals to explore and experiment,

and in doing so, increasing the degree of innovativeness (Van Dyck, et al., 2005). In a sample

of German service- and manufacturing firms, Frese, et al. (2010) found support for this

hypothesis which states that firms who perceive errors as a natural part of the developing

process and explicitly use previous errors to improve ideas, more often have innovations.

Vice versa, innovations are naturally uncertain, and as a result errors are likely to occur (Van

Dyck, et al., 2005). Since exploration and experimentation contribute to the degree of

innovation (Keeth and Frese, 2011), it also results in a higher level of errors. It seems that

errors and exploration/experimentation reinforce each other. It is expected that within NOV,

error handling practices positively influences innovativeness.

H7: Error handling by employees, i.e.: (a) coordinated and effective error handling,

(b) analyzing errors, and (c) communicating about errors, is positively related to

innovation among employees.

In the literature also other potential facilitators are mentioned, which may or may not

be part of an EMC (e.g. Amabile, 1996; Patterson, 2002; Zhou and Shalley, 2003). Based on

this variety of theories and narrative reviews of empirical work, the meta-analysis of

Hammond, et al. (2011) identified and examined multiple factors on individual innovation.

First of all, Hammond, et al. (2011) concluded that multiple individual differences seem to

contribute to innovation on an individual level. For example: a creative personality,

individual motivation, and self-efficacy are likely to be positively related to innovative


18

performance. In contrast, education and tenure were not consistently related to individual

innovation. Secondly, they show that several job characteristics held a strong positive

relationship with creativity and innovation. The results show that a high job complexity,

autonomy and clear role expectations contribute to creativity and innovation. Finally, they

indicate that several contextual factors seem to foster innovation through its effect on

psychological conditions and motivation. These factors include: an organizational climate for

creativity and innovation (e.g. participative, open and safe climate), general support, a

positive climate, supervisor support, leader-member exchange quality, and transformational

leadership. Many of these latter contextual factors are part of an EMC (Van Dyck, et al.,

2005).

2.5 Error Management Culture Strength

As mentioned earlier, an organizational error management culture includes shared

beliefs, norms, and common practices regarding the management of errors in a organization

(O'Reilly and Chatman, 1996; Van Dyck, et al., 2005). As explained earlier, an EMC includes

multiple organizational practices, e.g.: (1) communicating about errors, (2) sharing error

knowledge, (3) helping in error situations, (4) quick error detection and damage control, (5)

analyzing errors, (6) coordination error handling, and (7) effective error handling (Van Dyck,

et al., 2005). The different degrees of an error management culture can be placed on a

continuum ranging from high error management to error averse (Gronewold, et al., 2013). An

EMC is qualified as ‘high’ if the latter practices are present within the organization, and is

therefore characterized by amongst others: errors are accepted as part of everyday life as long

they are learned from and not repeated, open discussions about errors, carrying out analysis

of errors and their causes, not punishing for reporting errors, and management being positive

toward the communication about errors (Gronewold, et al., 2013; Van Dyck, et al., 2005).

The strength of a culture, such as an EMC, indicates to what extend a set of norms and

values are shared and beheld throughout an organization (O'Reilly and Chatman, 1996).

Moreover, a culture can be considered strong if these set of norms and values are widely

shared and intensely held throughout the organization (O'Reilly and Chatman, 1996). In other

words, as stated by DelCampo (2006), if there is agreement among member about these

norms and values, there is a strong culture. People in such a setting should produce uniform

behavior (Schneider, et al., 2002). On the other hand, if there is no agreement among

members about these norms and values (i.e. dispersion), on can refer to it as a weak culture

(DelCampo, 2006).


19

Because in a strong EMC, errors are widely accepted as part of everyday life

(Gronewold, et al., 2013; O'Reilly and Chatman, 1996; Van Dyck, et al., 2005), in such an

environment, organizations are better prepared for error handling, i.e.: (a) coordinated and

effective error handling, (b) analyzing errors, and (c) communicating about errors (Van Dyck,

et al., 2005). Hence, based on logical reasoning, it is expected that there is more

organizational support (i.e. a facilitating role of organizations) in these environments for

individuals in the preparation of error handling. On the other hand, one would expect that in a

strong error averse culture, this organizational support is the most consistently negative.

Therefore, it will be hypothezed that the strength of an error management culture influences

the positive relationship between employees’ error anticipation and the error handling

(hypothesis H1), in a way that a strong error management culture strengthens the effect of

employees’ error anticipation on error handling.

H8: The EMC strength within each department moderates the positive relation

between employees’ error anticipation and error handling by employees, i.e.: (a)

coordinated and effective error handling, (b) analyzing errors, and (c)

communicating about errors, in such a way that the positive relation will be stronger

for high ECM strength.

As revealed by the study of Gronewold, et al. (2013), which was conducted in a

professional service environment, members of an organization think that colleagues are less

reluctant to report discovered self-made errors in a high error-management (versus error

averse) culture. In such an environment, psychological safety (i.e. reduced concerns that

people have about someone else’s reaction towards actions, which have the potential for

embarrassment or threat) makes organizational members feel free to talk with others about

(their) mistakes (Edmondson and Nembhard, 2009). For example, if an employee experiences

managerial intolerance of errors, employees could experience high strain (i.e. a high negative

emotion to errors), and thus decide to not report an error (Zhao, 2011). Because in a strong

error management culture errors are widely accepted among employees (Gronewold, et al.,

2013; O'Reilly and Chatman, 1996; Van Dyck, et al., 2005), it is expected that in such an

environment, the negative emotions (i.e. error strain) caused by an error will be relativised

before they are included as information in the evaluation of the error reporting cost.

Therefore, it will be hypothesized that the strength of an error management culture

influences the relationship between error strain and the perceived error reporting costs


20

(hypothesis H5), in a way that a high error management culture strength weakness the effect

of error strain on perceived error reporting costs.


between perceived error strain by employees and error reporting costs perceived by

employees in such a way that the positive relation will be weaker for high ECM

strength.

A strong culture implies that there is behavioral consistency across individuals in an

organization (Sørensen, 2002). In this way a strong corporate culture enhances goal

alignment, an thus clarity about corporate goals and practices (Sørensen, 2002). Hence, it is

expected that a strong error management culture enhances alignment and support inside an

organization in terms of error handling practices. Therefore, it will be hypothezed that the

strength of an error management culture influence the positive relationship between error

handling by employees and both learning and innovation (hypothesis H6 and H7), in a way

that a strong error management culture strengthens the effect of an employees’ error handling

practices on both learning and innovation.


between error handling by employees, i.e.: (a) coordinated and effective error

handling, (b) analyzing errors, and (c) communicating about errors, and learning in

such a way that the positive relation will be stronger for high ECM strength.


between error handling by employees, i.e.: (a) coordinated and effective error

handling, (b) analyzing errors, and (c) communicating about errors, and innovation

in such a way that the positive relation will be stronger for high ECM strength.

2.6 Research Model

The research model in Figure 7 shows the to-be-examined relationships between the

different focal constructs.


21

Fig

ure

7:

Res

earc

h M

od

el

3. Research Methodology

3.1 Questionnaire

3.1.1 Sample

3.1.2 Distribution Procedure

3.1.3 Measurements

3.1.4 Questionnaire Design

3.1.5 Method for Data Analysis

3.2 Interviews

3.2.1 Procedure

To examine the hypotheses, which were drafted in the previous chapter, a quantitative

study is designed. This quantitative research consists out of a questionnaire. In this chapter:

the sample, distribution procedure, measurements items, questionnaire design and method for

data analyses of this questionnaire are discussed. In addition to the questionnaire, semi-

structured interviews are prepared in this chapter, which are used to validate the findings of

the quantitative study, and to explore and create a deeper understanding about the positive

and negative aspects of NOV’s culture, methods and procedures.


23

3.1 Questionnaire

The quantitative, and most elaborate, part of this research project consists out of a

questionnaire, which is conducted amongst employees of NOV Etten-Leur. In this way, the

effect of both NOV’s cultural aspects and business practices on error detection, error

reporting, error handling, and eventually learning and innovativeness amongst employees

were examined. The goal of this survey was to test the defined hypotheses, and thereby

provide sufficient detail and coverage of the research questions. The reasons why there has

been chosen for a questionnaire are mainly its anonymous character and its reach of

participants within a limited timeframe (Blumberg, et al., 2008). In the following paragraphs:

the sample, procedure, measurement scales, questionnaire design and method for statistical

analysis are described.

3.1.1 Sample

At the end of April 2015, 442 people were employed at NOV Etten-Leur. These

employees worked at various departments (see Appendix A). Within NOV Etten-Leur a large

part of these employees have to deal with errors that arise during the manufacturing of (sub-)

products. In other words, they detect, report, and handle both errors made by either NOV’s

own shop floor or one of NOV’s suppliers in the manufacturing of (sub) products. However,

only the employees that are directly involved in the detection, reporting and handling of

errors made by NOV’s own shop floor were asked to participate (see Table 1). Departments

that are not directly confronted with product related errors that arise internally during

manufacturing were left out this study.

So, to address errors made by NOV’s own shop floor, all shop floor employees,

working at five different manufacturing departments (i.e. shop floors), received a

questionnaire. These are the employees who are responsible for the manufacturing of the final

product, and are the people who in particular have to deal with errors that are internally made.

Because the population is relatively small and diverse (e.g. varying in functions), a census

study is chosen over a sampling study (Blumberg, et al., 2008). Therefore, no sample

methods were used. In total 226 employees received a questionnaire of which 94 participated

(41.6% response rate).

All of these respondents were male (100%, N=94). And the major part of the

respondents were between 40 and 54 years old (43.6%, N=41), followed by groups of: 25 to

39 years olds (31.9%, N=30), 55 to 67 years olds (20.2%, N=19) and below 25 years old

3. Methodology

24

(3.2%, N=3). Various functions were present within the sample, i.a.: machinists (35.1%,

N=33), assembles (23.4%, N=22), and welders (11.7%, N=11). A considerable share of the

sample (67%, N=63) was permanently employed, while others (10.6%, N=10) were

temporary employed or (22.3%, N=21) externally hired. Regarding the years of services,

41.5% worked for 1 to 5 years at NOV (N=39), 23.4% 6 to 10 years (N=22), 14.9% 11 to 20

years (N=14), 13.8% more than 20 years (N=13), and 6.4% less than 1 year (N=6). Most

participants were Dutch (81.9%, N=77) versus 18.1% who spoke English (N=17). Finally,

24.5% filled in the questionnaire anonymous (N=23) compared to 75.5% who filled in their

name (N=71)

3.1.2 Distribution Procedure

Besides the earlier mentioned positive aspects of conducting a questionnaire, a

questionnaire has also its disadvantages (e.g. Blumberg, et al., 2008). One weakness is that,

often, people fail to reply to a questionnaire. This shortcoming heavily influences the quantity

of information secured (Blumberg, et al., 2008). Besides the raffle of three €25,- Intratuin gift

cards and three NOV goodie backs amongst respondents, multiple actions with regard to the

distribution of the questionnaire have been taken to positively influence the participant’s

motivation on cooperation.

First, to inform all employees and to reach as much participants as possible within a

limited timeframe, a digital version (i.e. a PDF-file) of the questionnaire was sent by email.

In the accompanying email, the same information was mentioned as in the introduction of the

questionnaire. By using Microsoft Outlook, a follow-up reminder was added, which was set

at one week after sending. In addition, hardcopies of the questionnaire were deposited at three

coffee corners on the shop floor. Hardcopies of the questionnaire could be submitted at four

sealed mailboxes, which were spread over the facility of NOV Etten-Leur. Because of the

Department Number of employees Response (rate)

External Production Cell 26 15 (57.7%)

Small Components Cell 38 13 (34.2%)

Slip Cell 60 27 (45%)

Elevator Cell 50 15 (30%)

Large Part Cell 52 20 (38.5%)

Not known - 4

Total: 226 94 (41.6%)

Table 1: Participating Departments in the Questionnaire


25

limited time shop floor employees spend behind a computer, no online survey was

distributed.

In order to raise the response rate, following the two initial distribution actions, the

questionnaire was personally presented as hardcopy to the respondents (Jack and Clarke,

1998). By personally addressing the importance of the survey and creating somewhat

compassion for the interviewer (Blumberg, et al., 2008) an attempt was made to increase the

participant’s motivation to cooperate. To minimize bias in the results due to different

distribution methods (e.g. the possibility of further clarification in case a survey was

personally presented in comparison with only an email) (Marshall, 2005), the same storyline

as in the email was used during the personal presentation. In total, 139 employees of who it

was known that they had not responded yet were personally approached. The remaining 87

employees submitted the questionnaire by themselves or did not responded because they were

on a holiday, had to deal with long-term illness, or have been missed as result of shiftwork.

Two and a half week after the initial email, and one and a half week after the

automatic follow-up reminder, a second mail was sent to ask again for everyone’s

participation. After four weeks, the questionnaire was closed.

3.1.3 Measurements

3.1.3.1 Focal Constructs

The developed research model (see Figure 7) contains 10 focal constructs, which are

measured with multi-item scales from existing research whenever possible. Multi-item scales

are used because: (1) these are usually more reliable and less sensitive to random

measurement errors than single-item measures, (2) latent variables are usually not easily

measured with a single item, and (3) a single item often cannot distinguish between fine

degrees of a property (Feed, 2013). All constructs were measured by the average of the

measurement items. In addition, because there are both Dutch and English speaking

employees within NOV Etten-Leur, all items were translated from English to Dutch. These

translations were checked by first supervisor Dr. S. Rispens, two not involved colleagues

from NOV, and a friend with excellent working knowledge of human resource management.

To measure seven of the constructs amongst shop floor employees, validated

measurement items were conducted from the Error Orientation Questionnaire of Rybowiak,

et al. (1999). The Error Orientation Questionnaire is developed and validated for individuals

(Rybowiak, et al., 1999). By using these measurement scales (see Table 2), the following

constructs were measured: (1) error anticipation, (2) error strain, (3) error reporting

3. Methodology

26

Construct/Items Cronbach’s α

Error anticipation .55

In carrying out my task, the likelihood of errors is high.

Whenever I start some piece of work, I am aware that mistakes occur.

Most of the time I am not astonished about my mistakes because I expected them.

I anticipate mistakes happening from my work.

I expect that something will go wrong from time to time.

Error strain .75

I find it stressful when I err.

I am often afraid of making mistakes.

I feel embarrassed when I make an error.

If I make a mistake at work, I 'lose my cool' and become angry.

While working I am concerned that I could do something wrong.

Error reporting behavior (reversed) .70

Why mention a mistake when it isn't obvious?

It is disadvantageous to make one's mistake public.

I do not find it useful to discuss my mistakes.

It can be useful to cover up mistakes.

I would rather keep my mistakes to myself.

Employees, who admit to their errors, make a big mistake.

Coordinated and effective error handling .74

When I have made a mistake, I know immediately how to correct it.

When I do something wrong at work, I correct it immediately.

If it is at all possible to correct a mistake, then I usually know how to go about it.

I don't let go of the goal, although I may make mistakes.*

Analyzing errors .89

After I have made a mistake, I think about how it came about.

I often think: 'How could I have prevented this?'

If something goes wrong at work, I think it over carefully.

After a mistake has happened, I think long and hard about how to correct it.

When a mistake occurs, I analyze it thoroughly.

Communicating about errors .76

When I make a mistake at work, I tell others about it in order that they do not make

the same mistake.

If I cannot rectify an error by myself, I turn to my colleagues.

If I cannot manage to correct a mistake, I can rely on others.

When I have done something wrong, I ask others, how I should do it better.

Learning from errors .90

Mistakes assist me to improve my work.

Mistakes provide useful information for me to carry out my work.

My mistakes help me to improve my work.

My mistakes have helped me to improve my work.

* Item excluded after reliability analysis

Table 2: Error Orientation Measures for Individuals

(source: Rybowiak, et al. (1999))


27

Table 3: Measures of Perceived Error Reporting Costs and Innovation among

Individuals

behavior (reversed), (4) coordinated and effective error handling, (5) analyzing errors, (6)

communicating about errors, and (7) learning from errors. The operationalization of each of

these constructs was measured on an ordinal measurement level. For this purpose, a 5 point

Likert-scale ranging from ‘Not at all’ to ‘Totally’ was used.

All constructs, except error anticipation, scored a sufficient Cronbach’s α (>.60)

(Hair, et al., 2010). However, due to the lack of alternatives, error anticipation is not

excluded. In addition, coordinated and effective error handling scored initially a weak

Cronbach’s α of .60. The item “I don't let go of the goal, although I may make mistakes” did

not correlate very well with the scale overall (.19), and was therefore dropped. This resulted

in an increased Cronbach’s α of .74.

No validate measurement scale was found for assessing reporting costs. Therefore, a

new scale was developed (see Table 3), which is based on three out of five categories that are

identified by Zhao and Olivera (2006), of reasons why and individual, who detects an error,

may not want to report it. These three categories are: material costs to themselves (e.g.

monetary penalties, suspension, or job loss), potential damage to personal image (i.e. harming

the perceptions about an individual’s competences and professionalism), and effort costs (e.g.

Construct/Items Cronbach’s α

Error reporting costs .80

The personal mistakes that I report have a negative effect on my annual performance

bonus.

If I report a mistake, the risk increases that I will lose my job.

I have concerns about job security that makes it difficult to confirm a mistake.

Because of the reaction of coworkers I am reluctant in disclosing mistakes.

By reporting a mistake, I reveal my shortcomings.

Colleagues blame me if I report a mistake.

Reporting a mistake is too complicated.

It takes too much time if I have to report a mistake.

Reporting a mistake is quite a hassle.

Innovation .81

In your job, how often do you…

… make suggestions to improve current products?

… produce ideas to improve work practices?

… acquire new knowledge?

… actively contribute to the development of new products?

… optimize the organization of work?

3. Methodology

28

scarce time). The response scale ranged from 1 ‘Not at all’ to 5 ‘Totally’. Cronbach’s α for

error reporting costs was .80.

When the tasks of an employee are completely focused on innovation (e.g. R&D

workers), often, objective measures of innovative outputs can be found (e.g. number of

patents) (De Jong and Den Hartog, 2008). However, in current scenario, these objective

measures for the innovative output of employees are not that clear. Therefore, to measure

innovation, a self-rated, 5-item innovative output scale was used (see Table 3) that is based

on the measurement scale of De Jong and Den Hartog (2008). The items varied from 1

‘Never’ to 5 ‘Always’. Originally, Axtell, et al. (2000) developed this scale to measure the

extent in which machine operators on a shop floor put forward suggestions for innovations

and ideas for change of various aspects of work, and the effort they put in the implementation

of these ideas. De Jong and Den Hartog (2008) adapted these scale in a way that it asks for

the frequency of employees’ suggestions and implementation efforts related to new products

and services, work practices and knowledge. Because primarily NOV Etten-Leur is a product

supplier, for this study, the items were adjusted so that they only focus on products, and not

on services anymore.

Consistent with prior research on climate strength (Schneider, et al., 2002; Sora, et al.,

2013), EMC strength was measured as the degree of agreement on error management culture

among employees within a specific department. After a factor analysis, Van Dyck, et al.

(2005) concluded that an EMC is considered as an aggregate incorporation of the constructs:

(1) coordinated and effective error handling, (2) analyzing errors, (3) communicating about

errors, and (4) learning from errors. Indeed, a reliability analyses revealed that together the

16 measurement items of the latter constructs have a Cronbach’s α of .86, and thus can be

used as a reliable measurement scale for EMC. Following the examples of Schneider, et al

(2002) and Sora, et al. (2013), the strength of this EMC was measured by using the Average

Deviation Index (ADM(J)). “The Average Deviation for an item (ADM(j)) involves determining

the extent to which each item rating differs from the mean item rating, summing the absolute

values of these deviations, and dividing the sum by the number of deviations” (Burke and

Dunlap, 2002, p. 160). The average of the Average Deviations (ADM(j)) from each of the 16

items (i.e. the Average Deviation Index (ADM(J))), is used to operationalize EMC strength

within each department (i.e. agreement on error management culture). The index was

multiplied with -1, so that a higher score for the Average Deviation Index correlates with a

smaller amount of variability in the responses is (i.e. higher agreement), and hence also a

higher EMC strength within a department. The ADM(J) values ranged from -1.021 to -.792.


29

3.1.3.2 Control Variables

Although organizational commitment (i.e. emotional attachment to-, identification

with-, and involvement in the organization) was not a focal construct in this study, because of

its effect on employees’ behavior, it was included as a control variable. Previous research

shows that organizational commitment both influence (1) in-role behaviors, including

performance, absence, lateness, and turnover, and (2) non-role behaviors, which includes

behaviors that are not formally rewarded or sanctioned by the organization, such as

creativeness or innovativeness (Mathieu and Zajac, 1990).

For the measurement of commitment to the organization, four items were adopted

from Meyer, et al. (1993): (1) “I do not feel a strong sense of ‘belonging’ to my organization”

(reversed), (2) “This organization has a great deal of personal meaning to me”, (3) “I do not

feel like ‘part of the family’ at my organization” (reversed), and (4) “I would be very happy

to spend the rest of my career with this organization”. These items were measured on a 5

point Likert-scale ranging from ‘Not at all’ to ‘Totally’. The Cronbach’s α of commitment to

the organization was respectively .61. The construct was measured by the average of the four

items.

3.1.3.3 Demographic variables

Additionally to the control variables, for the classification of the respondents,

demographic information was asked. Respondents were asked for their: (1) gender, (2) age (<

25 years; 25-39 years; 40-54 years; 55-67 years; and > 67 years), (3) department, (4) current

function, (5) years of service (< 1 year; 1-5 years; 6-10 years; 11-20 years; and > 20 years),

(6) contract type (permanent; temporary; or externally hired), and (7) working hours per

week. In order to avoid that people lose their feeling of anonymity, the number of years were

clustered on forehand for the variables age and years of service.

3.1.4 Questionnaire Design

Because it was decided to distribute the questionnaire both as PDF-file, by email, and

as a hardcopy, the questionnaire was designed using Microsoft Word, and no online survey

software (e.g. Google Forms or Surveymonkey) was used. The questionnaire included a

cover page, introduction, and four sections with questions.

The cover page contained the title: ‘Dealing with non-conformities at NOV Etten-

Leur’, and the subtitle: ‘As part of a graduation research in collaboration with TU/Eindhoven’

of the questionnaire. This subtitle was added on the basis of recommendations from NOV

employees, which stated that the employees’ willingness to participate is bigger for external-

3. Methodology

30

than internal surveys. For the same reason, the front page displayed the TU/e logo. Another

underlying argument for including the logo was that the logo enhances the credibility

(Oppenheim, 1992) and makes the questionnaire stand out from the ‘crowd’, which normally

displays a NOV-logo.

The introduction started with the sentence: ‘once in a while, everyone makes a

mistake’. There has been chosen for this sentence because of its accessible nature and almost

trivializing character, which is expected to lead to more openness and willingness to talk

about a sensitive topic such as errors. Subsequently, to further enhance the willingness to

participate, the purpose of the research was explained (Blumberg, et al., 2008). However, this

has been done in a disguised way to influence the answers, given by the employees, as little

as possible (Blumberg, et al., 2008). In addition, it was mentioned that respondents had a

chance to win one of the three Intratuin €25 gift cards or one of the three NOV goodie backs.

Thereafter, it was explained where the bigger part of the questions went about, and illustrated

with an example, what the questions look like. Then, in order to decrease the fear of

consequences of participation, the anonymity of participation was emphasized (Blumberg, et

al., 2008). Hereafter, the respondents were informed that it would take about 8 minutes to

complete the questionnaire. Multiple pre-test were conducted to validate this quantity of time.

The introduction was closed with a word of thanks and the contact information of both Dr. S.

Rispens (for credibility) and myself.

The main part of the survey consisted out of four sections, each with their own

instructions. The first three sections comprise 72 multiple-choice questions, which are

divided over several clusters. Most of these clusters with questions were extracted from

validated measurement scales, which are explained in the previous sections. The first section

started with questions about the respondents’ job (e.g. work pressure, organizational

commitment and job satisfaction). These questions were asked first, because they: do not ask

for sensitive and ego-involving information, are relatively simple to answer in comparison

with the other questions (Blumberg, et al., 2008), and show interest in the participant’s job.

The second and third section includes target questions that consecutively address the

constructs of investigation about EM (e.g. error strain and error communication) and job

outcomes (e.g. innovation). Lastly, because asking classification questions (e.g. demographic

information) can be perceived as threatening (Quinn, 1995); these questions are asked in the

fourth and final section. Finally, the questionnaire was closed with: a word of thanks, the

opportunity to write down comments and/or questions, hand in instructions, and the

possibility to fill in an email address make a chance on the gifts.


31

3.1.5 Method for Data Analysis

To analyze the data retrieved from the questionnaires, multiple statistical methods can

be used. The technique that is used during this research is hierarchical regression analysis

(HRA). There is chosen for this variant of multiple regression analysis because HRA

investigate if an additional independent variable can be associated with some predictive

capacity of a dependent variable above and beyond other variables (Field, 2009). To

investigate this, first, the variables that are controlled for during testing the variable of real

interest are included in the model. Accordingly, the primary variables of interest are included.

Eventually, the regression output tells if these variables add something extra in the prediction

of the dependent variable. Every single hypothesis is tested in this way.

3.2 Interviews

In addition to the questionnaire, semi-structured interviews are prepared. The goal of

these semi-structured interviews is to validate the findings of the quantitative study, and to

explore and create a deeper understanding about the positive and negative aspects of NOV’s

culture, methods and procedures. The choice is made to use semi-structured interviews in this

situation, because this method of collecting qualitative data is particular useful to evoke,

detect and identify the issues that are relevant for understanding and exploring the situation

(Blumberg, et al., 2008). In addition, a semi-structured interview, unlike for example surveys,

allows the interviewee to follow his or her own thoughts to frame a story, which can lead to

information that the interviewer has not thought about beforehand (Blumberg, et al., 2008).

For the interviews, the five managers of the different departments were asked to

participate. In addition, 6 shop-floor employees were selected.

3.2.1 Procedure

During the semi-structured interviews, an interview guide was used to ensure that all

necessary areas were covered and all the questions were asked in a similar, if not identical,

way in all interviews (Blumberg, et al., 2008). This does not mean that no additional

questions were asked (i.e. follow-up questions or probing questions), or questions were

modified in order to make them relevant for each respondent. Because some participants may

not wish to reveal their true feelings, confidentiality of the answers was emphasized at the

beginning of the interviews. To ensure the explorative character of the interview (Blumberg,

et al., 2008), the guide was not too specific. The interview guide is shown in Appendix C.

4. Results

4.1 Results Quantitative Study: Questionnaire

4.1.1 Examination of Data

4.1.2 Descriptive Statistics

4.1.3 Results of the Hierarchical Regression Analysis

4.1.4 Additional analyses: Differences between Groups

4.2 Results Quantitative Study: Interviews

4.2.1 Interviews

In this chapter the results of the quantitative data analyses are described.

Subsequently, the main findings out of the interviews are highlighted.


33

4.1 Results Quantitative Study: Questionnaire

4.1.1 Examination of Data

To clean the data to a format most suitable for multivariate analysis (Hair, et al.,

2010), the missing data was analyzed and set of data was checked for outliers. Subsequently,

the data was tested for compliance with the normality assumption.

4.1.1.1 Missing Data

To determine the extent of missing data, frequency checks were performed. 78 cases

(i.e. 83%) had non-missing values, ten cases had one missing value, four cases had two

missing values, one case had three missing values, and one case had five missing values. On

the other hand, there were three variables with respectively four, three, and two missing

values. All the other variables had no more than one missing values. Considering the extent

of missing data, it was not necessary to delete individual cases and/or variables (Hair, et al.,

2010). A non-significant Little's MCAR test revealed that the missing values were missing

completely at random (χ2(1026) = 973.0, p = .88), so no bias was introduced in the results

(Hair, et al., 2010). All missing values were single measurement items, and multiple items

together form the constructs in a reliable way (see the reliability analyses in section 3.1.3).

Therefore, instead of using any imputation method, the mean of the remaining items was used

for the construct.

4.1.1.2 Outliers

First, to detect univariate outliers, the data was searched for unique or extreme

observations. This was done by running descriptive statistics and by calculating the standard

scores (i.e. z-value: 𝑧𝑖 =𝑥𝑖−�̅�

𝑠𝑑) for each data point. Some standard scores exceeded the

threshold value of 3.0 (Hair, et al., 2010). However, there were no truly distinctive

observations that needed to be designated as outlier. Second, the probability of the

Mahalonobis distance (D2) scores showed that there were no multivariate outliers. So, there

were no cases with an unusual combination of values (Hair, et al., 2010).

4.1.1.3 Assumption testing

To complete the data examination, the normality assumption was tested, which

underlies the statistical bases for multivariate analysis (Hair, et al., 2010). To examine

normality, both a graphical analyses and statistical tests (i.e. skewness and kurtosis) were

done. The statistical test supported (see Table 4) the observed non-normality in the

4. Results

34

Table 4: Skewness and Kurtosis Test for Normality

Item Skewness z-skewness

Kurtosis z-Kurtosis

Focal Constructs

Anticipation -0.044 -0.174 -0.031 -0.061

Error strain 0.874 3.459 * 0.382 0.756

Error Reporting Costs 1.308 5.177 * 1.538 3.044 *

Error Reporting Behavior 1.849 7.319 * 3.651 7.226 *

Coordinated and Effective

Error Handling -1.158 -4.583 * 0.997 1.973 *

Analyzing Errors -1.367 -5.411 * 2.945 5.828 *

Communication about Errors -0.601 -2.379 * -0.403 -0.798

Learning from Errors -0.595 -2.355 * -0.443 -0.877

Innovation -0.181 -0.716 0.266 0.526

EMC strength 0.097 0.384 -1.407 -2.785 *

Control Variables

Commitment -0.235 -0.930 -0.202 -0.400

*Calculated z value exceeds the specified critical value of ±1.96, which corresponds to a .05 error level

(Hair, et al., 2010)

histograms and normal Q-Q plots. Based on the ±1.96 critical value as presented by Hair, et

al. (2010), it seems that seven constructs have an unbalanced distribution (i.e. are skewed)

and five constructs deviate in their peakedness or flatness from a normal distribution (i.e. are

leptokurtic or platykurtic).

However, it has been decided to not transform the data, and to run the hierarchical

regression analyses with the current set of untransformed data. This because, amongst others,

as stated by Glass, et al. (1972) “the payoff of normalizing transformations in terms of more

valid probability statements is low, and they are seldom considered to be worth the effort” (p.

241). In addition, there is a risk of analyzing a statistical model with ‘wrongly’ transformed

data of which the consequences could be worse than the consequences of using the

untransformed scores (Field, 2009). And as final argument, because transformation leads to a

change of the original construct, which has obvious implications for interpreting the results

(Grayson, 2004), it would bring a lot of unnecessary complexity into this research.

4.1.2 Descriptive Statistics

Table 6 shows the means and standard deviations of-, and correlations between all

control- and focal and variables. In total, there are 15 significant correlation coefficients that

represent a medium size of effect (r = ±.3), and three that represent a large significant effect

(r = ±.5) (Field, 2009). However, this not necessarily has to mean that there is causality

(Field, 2009).


35

In comparison with prior research of Van Dyck, et al. (2005), NOV scores relatively

high. In the study of Van Dyck, et al. (2005), companies out of different industries (i.e.:

production and construction (N=19), business services (N=16), finances and insurances

(N=10), and trade (N=20)) scored an average on error management culture (M = 3.22, SD =

.27) that was lower than NOV’s (M = 4.14, SD = .11). However, in contrast with this

research, that includes a self-rated questionnaire, which is conducted among shop floor

employees, Van Dyck, et al. (2005) used statements that were asked to the managers of the

participating companies and which applied to the people in their organization in general. So,

based on this comparison, no conclusions can be drawn.

The control variable organizational commitment only appears to significantly

correlate with the variables innovation among employees and employees’ error reporting

behavior, i.e.: there is a medium effect on both the variables innovation among employees (r

= .34, p < .01) and employees’ error reporting behavior (r = .26, p < .05). However, to be

consequent during the analyses, the control variable will be used in testing all the hypotheses

of the research model.

A noteworthy result that is shown in the correlation matrix, is that there is a

substantial correlation between learning from errors by employees and innovation among

employees (r = .37, p < .01). Besides, the measurement items of both constructs show some

similarities (see and Table 3). Therefore, a factor analysis was conducted to check whether

they are really distinguishable from one another. Assuming that the constructs learning from

errors by employees and innovation among employees would correlate somewhat, there is

chosen to use oblique rotation (Conway and Huffcutt, 2003). De results in Table 5 show that

there is a clear distinction between the two constructs.

Table 5: Factor Analysis to Demonstrate that Learning From Errors by

Employees and Innovation among Employees are two distinguishable constructs

Measurement item Component 1 Component 2

Innovation 1 .205 .822





Learning from errors 1 .890 .338




Notes: Extraction Method: Principal Component Analysis

.Rotation Method: Direct Oblimin

4. Results

36

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37

4.1.3 Results of the Hierarchical Regression Analysis

To test hypotheses H1 – H11, multiple HRA were carried out. An overview of al

significant results is provided in Figure 8. In the first step of these analyses, only the control

variable organizational commitment was entered. Accordingly, in the second step, the

independent variable of real interest was included. In some analyses, a third step was added

to test for potential moderation effects (hypotheses H8 – H11). To examine these potential

moderation effects, a moderator was added to the second step and an interaction effect was

included in the third step. To create this interaction effect, after they were centered to avoid

multi-collinearity between the interaction term and its main terms (Field, 2009), the

independent- and moderator variable were multiplied.

The first hypothesis H1 stated that employees’ error anticipation is positively related

to error handling by employees, i.e.: coordinated and effective error handling (hypothesis

H1a), analyzing errors (hypothesis H1b), and communicating about errors (hypothesis H1c).

The results of the HRA are shown in Table 7. It appears that employees’ error anticipation

has no significant effect on: coordinated and effective error handling, analyzing errors, and

communicating about errors. Hence, hypothesis H1 is rejected in its entirety. In addition, to

test for the hypothesized moderation effect of the within department error management

culture strength (hypothesis H8), an additional step was added to the latter HRA.

Table 7: Hierarchical Regression Analysis of

Employees’ Error Anticipation on Error Handling by Employees

Coordinated and

effective error

handling

Analyzing errors Communication

about errors

Step 1

Organizational commitment .14

.16

.18 †

Control

R2 .020

.026

.032 †

variable

Step 2


.16

.18 †

Focal

Error anticipation -.04

.15

.06

variable

EMC strength -.05

.06

.10

∆ R2 .003

.024

.013

R2 .023

.050

.045

Step 3


.17

.20 †

Moderator

Error anticipation -.04

.15

.06

effect

EMC strength -.05

.05

.10

Error anticipation * EMC

strength .00

-.04

-.11

∆ R2 .000

.002

.011

R2 .023 .052 .057

Note: N=94; standardized coefficients are reported; **p<.01; *p<.05; †p<.10

4. Results

38

Table 8: Hierarchical Regression Analysis of Employees’

Error Reporting Behavior on Error Handling by Employees

Coordinated and

effective error

handling

Analyzing errors Communication

about errors

Step 1


.16

.18 †

Control

R2 .020

.026

.032 †

variable

Step 2


.13

.12

Focal

Error reporting behavior .20 †

.13

.22 *

variable

∆ R2 .039

†

.016

.044 *

R2 .058

†

.042

.076 *


In hypothesis H8 it was expected that the error management culture strength within a

department moderates the positive relation between employees’ error anticipation and (a)

coordinated and effective error handling, (b) analyzing errors, and (c) communicating about

errors, in such a way that the positive relation will be stronger for high ECM strength. The

interaction term, which is presented in Table 7, shows that the moderator error management

culture strength has no significant effect on any of these relations. So, there is no support

found for hypothesis H8.

In contrary to hypothesis H1, partial support was found for hypothesis H2. A positive

relation was expected between employees’ error reporting behavior and coordinated and

effective error handling (hypothesis H2a), analyzing errors (hypothesis H2b), and

communicating about errors (hypothesis H2c). As reported in Table 8, weak support was

found for the positive, hypothesized effect of employees’ error reporting behavior on

coordinated and effective error handling (β = .20, p < .10). In contrary, no significant effect

of employees’ error reporting behavior on analyzing errors was found. Finally, in line with

the expectations, employees’ error reporting behavior seems to have a positive effect on

communicating about errors (β = .22, p < .05).

On its turn, it was hypothesized that employees’ error reporting behavior is both

negatively influenced by error reporting costs perceived by employees (hypothesis H3) and

perceived error strain by employees (hypothesis H4). The output of the HRA as shown in

Table 9, indicates that error reporting costs perceived by employees has a strong negative

influence on employees’ error reporting behavior (β = -.41, p < .01). And thereby, support

was found for hypothesis H3. In contrast, the output of the HRA as shown in Table 10, does

not indicate any significant effect of perceived error strain by employees on employees’ error

reporting behavior. So, no support was found for hypothesis H4.


39

Table 9: Hierarchical Regression Analysis of Error Reporting

Costs Perceived by Employees on Employees’ Error Reporting Behavior

Error reporting behavior

Step 1

Organizational commitment .26 *

Control

R2 .069

*

variable

Step 2


Focal

Error reporting costs -.41 **

variable

∆ R2 .168

**

R2 .237

**


Table 10: Hierarchical Regression Analysis of Perceived Error

Strain by Employees on Employees’ Error Reporting Behavior


Step 1


Control

R2 .069

*

variable

Step 2


Focal

Error strain -.15

variable

∆ R2 .023

R2 .092

*


Although error strain has no significant effect on employees’ error reporting behavior

(hypothesis H4), as shown in Table 11, it does have on error reporting costs perceived by

employees (β = .33, p < .01). This significant positive effect provides support for hypothesis

H5, which stated that perceived error strain by employees is positively related to error

reporting costs perceived by employees. An interaction term was added to the third step of

the HRA (see Table 11) to test for hypothesis H9. Hypothesis H9 stated that the error

management culture strength within a department moderates the positive relation between

perceived error strain by employees and error reporting costs in such a way that the positive

relation will be weaker for high ECM strength. However, no significant effect of the

interaction term was found during the analysis, and thus, hypothesis H9 is rejected.

Coordinated and effective error handling, analyzing errors, and communicating about

errors by employees were expected to be positively related to learning from errors by

employees (hypothesis H6) and innovation among employees (hypothesis H7). To test these

hypotheses, six HRA were conducted of which the results are shown in Table 12

4. Results

40

(independent variable: coordinated and effective error handling), Table 13 (independent

variable: analyzing errors), and Table 14 (independent variable: communicating about errors).

The HRA output in Table 12 shows that coordinated and effective error handling did

not influence learning from errors by employees, and thus that there is no support for the

hypothesis H6a. Coordinated and effective error handling appears to have a positive effect on

innovation among employees (β = .24, p < .05). Thus, hypothesis H7a is supported.

Table 13 shows the effect of analyzing errors on learning from errors by employees

(hypothesis H6b) and innovation among employees (hypothesis H7b). A positive effect of

analyzing errors on learning from errors by employees was found (β = .20, p < .10), which

provides weak support for hypothesis H6b. In addition, the significant effect of analyzing

errors on innovation among employees (β = .24, p < .05) provides support for hypothesis H7b.

The positive, hypothesized effect of communicating about errors on learning from

errors by employees (hypothesis H6c) and innovation among employees (hypothesis H7c) are

supported by the two HRA that are shown in Table 14. Both learning from errors by

employees (β = .33, p < .01) and innovation among employees (β = .26, p < .05) are

positively influenced by communicating about errors.

Hypothesis H10 stated that the error management culture strength within a department

moderates the positive relation between error handling by employees, i.e.: (a) coordinated

Table 11: Hierarchical Regression Analysis of Employees’ Error

Reporting Behavior on Error Reporting Costs Perceived by Employees

Error reporting costs

Step 1

Organizational commitment -.11

Control

R2 .013

variable

Step 2


Focal

Error strain .33 **

variable

EMC strength .02

∆ R2 .106

**

R2 .119

*

Step 3


Moderator

Error strain .33 **

effect

EMC strength .02

Error strain*EMC strength -.01

∆ R2 .000

R2 .119

*



41

Table 12: Hierarchical Regression Analysis of Coordinated and Effective

Error Handling by Employees on Learning and Innovation among Employees

Learning from errors Innovation

Step 1


.34 **

Control

R2 .004

.117 **

variable

Step 2


.31 **

Focal

variable

Coordinated and effective error

handling .11

.24 *

EMC strength .10

.19 †

∆ R2 .020

.088 *

R2 .024

.204 **

Step 3


.32 **

Moderator

effect


handling .12

.26 *

EMC strength .09

.18 †


handling * EMC strength .10

.13

∆ R2 .009

.016

R2 .033 .220

**


Table 13: Hierarchical Regression Analysis of Analyzing

Errors by Employees on Learning and Innovation among Employees


Step 1


.34 **

Control

R2 .004

.117 **

variable

Step 2


.30 **

Focal

Analyzing errors .20 †

.24 *

variable

EMC strength .08

.17

†

∆ R2 .047

.088 *

R2 .051

.204 **

Step 3


.29 **

Moderator

Analyzing errors .21 †

.25 *

effect

EMC strength .08

.16 †

Analyzing errors * EMC

strength .04

.03

∆ R2 .001

.001

R2 .052

.205

**


4. Results

42

Table 14: Hierarchical Regression Analysis of Communication

about Errors by Employees on Learning and Innovation among Employees


Step 1


.34 **

Control

R2 .004

.117 **

variable

Step 2


.29 **

Focal

Communication about errors .33 **

.26 *

variable

EMC strength .06

.15

∆ R2 .111

** .094

**

R2 .115

*

.210 **

Step 3


.32 **

Moderator

Communication about errors .32 **

.25 *

effect

EMC strength .07

.16 †

Communication about errors *

EMC strength -.12

-.15

∆ R2 .015

.021

R2 .130

* .231

**


and effective error handling, (b) analyzing errors, and (c) communicating about errors, and

learning from errors by employees in such a way that the positive relation will be stronger for

high ECM strength. As is reported under step 3, in table: 12, 13, and 14, none of the

interaction variables appears to have a significant effect on the dependent variable: learning

from errors by employees. This means that hypothesis H10 must be rejected.

The same goes for hypothesis H11 (i.e.: also no support was found for this hypothesis).

Hypothesis H11 stated that the error management culture strength within a department

moderates the positive relation between error handling by employees, i.e.: (a) coordinated

and effective error handling, (b) analyzing errors, and (c) communicating about errors, and

innovation among employees in such a way that the positive relation will be stronger for high

ECM strength. However, as shown in table: 12, 13, and 14, also the dependent variable

innovation among employees is not significantly influenced by the interaction variables that

are included in the third step.

4.1.4 Additional Analyses: Differences between Groups

No hypotheses were made for potential differences between demographic groups on

the focal variables, and in addition, no classification variables were included as control

variable. The latter is done because: (1) the absence of a theoretical foundation (2) lack of


43

Fig

ure

8:

Res

earc

h M

od

el w

ith

all

Sig

nif

ican

t R

elati

on

s

4. Results

44

correlation with the variables of interest, or (3) they are significant related to the control

variable organizational commitment (e.g. the dummy variable: years of service less than one

(r = -.48, p < .01)). However, it could be interesting for both NOV as science to see if there

are significant differences between groups. Therefore, as additional analyses in this study,

multiple ANOVA’s are carried out to test for potential differences.

No significant differences on the focal variables were found between (1) departments,

(2) age categories, (3) functions, and (4) working hours per week. There is a significant

difference on coordinated and effective error handling (F(1,92) = 5.219, p < .05) between

years of service (less than one year (N = 6) or more than one year (N = 89)). However,

because of the small sample (N < 15) of employees that work less than one year at NOV, the

null hypothesis could be rejected incorrectly (Field, 2009). As shown in Table 15, there are

some significant differences on the focal variables between contract types (NOV contract or

externally hired).

What already has been pointed out in section 4.1.2, is that NOV scored higher on

EMC (M = 4.14, SD = .11) than the 65 companies Van Dyck, et al. (2005) have examined (M

= 3.22, SD = .27). However, in contrast with this research that includes a self-rated

questionnaire conducted amongst shop floor employees, Van Dyck, et al. (2005) used

statements that were asked to the managers of the participating companies and which applied

to the people in their organization in general. In addition to this research, the same

questionnaire that Van Dyck, et al. (2005) used was spread amongst 16 managers on the shop

floor, of which eight responded. The data retrieved out of these eight questionnaires were not

used in previous analyses. However, the data were used to measure potential perceptual

Table 15: ANOVA between Different Types of Contract

Type of Contract

Externally hired

(N=21)

NOV contract

(N=73)

Anticipation

2.72 (.74) 2.78 (.68)

F(1,92) = .092

Error strain

2.05 (.87) 1.89 (.73)

F(1,92) = .690


1.52 (.56)

1.59 (.61) F(1,92) = .270


4.37 (.69) 4.59 (.53)

F(1,92) = 2.417

Coordinated and Effective Error

Handling

4.17 (.70)

4.51 (.57)

F(1,92) = 4.945 *

Analyzing Errors

3.94 (.82)

4.29 (.65)

F(1,92) = 4.030 *

Communication about Errors

3.73 (.95)

4.15 (.74)

F(1,92) = 4.619 *

Learning from Errors

3.52 (1.01) 3.97 (.87)

F(1,92) = 4.041 †

Innovation

2.46 (.82) 3.31 (.66)

F(1,92) = 24.592 **

Note: means and standard deviations are reported for each group; **p<.01; *p<.05; †p<.10


45

Table 16: ANOVA between Employees and Management

Employees

(N=94)

Management

(N=8)

Anticipation

2.76 (.69) 3.09 (.76)

F(1,99) = 1.383

Error strain

1.93 (.76) 2.03 (.56)

F(1,99) = .117


1.58 (.60)

1.98 (.50) F(1,99) = 3.102 †


4.54 (.58) 4.02 (.55)

F(1,99) = 5.214 *

Coordinated and Effective Error

Handling

4.43 (.62)

3.52 (.96)

F(1,99) = 13.050 **

Analyzing Errors

4.21 (.70)

3.43 (1.02)

F(1,99) = 7.566 **

Communication about Errors

4.05 (.81)

3.82 (.84) F(1,99) = .535

Learning from Errors

3.87 (.92) 3.32 (1.04)

F(1,99) = 2.246

Innovation

3.12 (.78) 2.83 (.48)

F(1,99) = .948

Note: means and standard deviations are reported for each group; **p<.01; *p<.05; †p<.10

distances (i.e. are they in agreement) between employees and management. As shown in

Table 16, there are significant differences between employees (individual perspective) and

management (organizational perspective) on (1) error reporting costs, (2) error reporting

behavior, (3) coordinated and effective error handling, and (4) analyzing errors. The

interviews show what the potential causes of these differences are.

4.2 Results Qualitative Study: Interviews

By interviewing six shop floor employees and five managers, the findings of the

quantitative study are validated and a deeper understanding is created about specific cultural

aspects and business practices, which affect EM within NOV Etten-Leur. In the following

sections the main findings are highlighted.

4.2.1 Error Detection

The interviewed shop floor employees indicated that they are aware errors can arise

during their work. However, they stated that not every task they perform includes mistakes.

These results are in line with the outcome of the questionnaire, where error anticipation is

rated with a mean of 2.76 (SD = .69), which suggests that the expectation an error will

happen is neither high nor low. To determine if a product meets the specifications, a

workmanship standard is used. As turned out of the interviews, in general, this workmanship

standard provides clarity to the employees with regard to the quality of products that NOV

wants to deliver (i.e. it improves the understanding of organizational goals among shop floor

employees).

4. Results

46

4.2.2 Error Reporting

In line with the result of the questionnaire (error strain (M = 1.93, SD = .76)), most

interviewees stated that in general people react quite normal towards a mistake and do not

feel a lot of error strain. However, every now and then, the reactions of coworkers are fierce.

Within NOV, error reporting is done using so-called non-conformity reports (NCRs). Based

on a NCR, it is decided what to do with the error (see Appendix B - Figure 12). Shop floor

employees stated that they are not reluctant to report an error, which corresponds to the

outcome of the questionnaire, where error reporting behavior is rated by employees with a

mean of 4.54 (SD = .58). In addition, just as the results of the questionnaire showed with

regard to error reporting costs (M = 1.58, SD = .60), the interviewed shop floor employees

indicated they do not perceive high costs of reporting an error that hinder their reporting

behavior. This is caused by the fact that most of the time a shop floor employee only reports

an error informally to a quality inspector without making a NCR. At their turn, these quality

inspectors, whose job it is to fill in the NCRs (i.e. reporting at an organizational level), do

indicate that reporting an error takes too much time and is not user friendly enough. As a

result, to save some time and to reduce the big pile of non-conformities that need to be

reported to make a rework order, these employees put multiple non-conformities under one

NCR (i.e. incorrect reporting). In addition, regularly, non-conformities are only fixed and not

reported at all by means of an NCR (i.e. inconsistent reporting). In these situations, as

indicated by management, a quick solution is chosen above a sustainable one, which can take

many months to implement. It could be therefore that management rated the error reporting

behavior within their organization significantly lower than the shop floor employees (M =

4.02, SD = .55).

It became clear that shop floor employees do not always see the need of making a

NCR. Especially for mistakes which are ‘small’ and ‘accepted’ (e.g. breaking of a drill bit),

filling in a NCR is skipped. Partially this is the result of the fact that for the non-conformities

they reported, some employees miss (on time) feedback and have the feeling nothing is being

done with their notification. Finally, as stated by a machinist: ‘sometimes it’s unclear what

they expect you to fill in’. The employees who have to process the completed NRC’s are

noticing this because of the incomplete, incorrect or unclear NCRs they are confronted with

every day.


47

4.2.3 Error Handling

In addition to the questionnaire, the interviews did not reveal any new information

about coordinated and effective error handling (M = 4.43, SD = .62), and communicating

about errors (M = 4.05, SD = .81) by shop floor employees. The gist of the interviews

suggests that in general, fixing non-conformities on time and in a proper way is not a big

problem. In contrast, about the analysis of errors, the opinions within NOV are more divided.

While most shop floor employees, in line with the questionnaire (M = 4.21, SD = .70),

indicate that they analyze errors carefully, management stated that people often take the easy

way out and do not really define a correct root cause. This corresponds to the significant

lower score that management give for analyzing errors within their organization (M = 3.43,

SD = 1.02) than employees give to themselves. As stated by one manager: ‘defining not the

actual root cause results in incorrect actions’. According to some interviewees, not defining

the “true” root cause of an error is due to lack of time, lack of experience and the difficulty of

defining a right root cause. Besides, there are differences in views of how deep a root cause

analysis should go. Where some respondents stated that a lot of mistakes are just part of the

job, and therefore a root cause analysis is redundant in these situations, other ones are

convinced that there is a reason behind every mistake, and that even, for example, if the

reason for this mistake is that someone has had a lack of sleep, it must be found out.

For errors which frequently occur (e.g. every time the same product, material, tool,

person etc.) or which have a major impact, after they are analyzed at an organizational level,

corrective actions and preventive actions are defined (see Appendix B - Figure 12). However,

because NCRs are not always filled in consistently and in a correct way, people have

difficulties with the extraction of major- and frequently made errors out of the NCR-database.

As a result, the analysis at an organizational level of these errors is hindered, and thus no

effective actions can be taken. This also corresponds to the significant lower score that

management gives for analyzing errors (M = 3.43, SD = 1.02) and coordinated and effective

error handling (M = 3.52, SD = .96) within their organization than employees give to

themselves. As a result of the difficulties with regard to the extraction of errors out of the

NCR-database, management indicates that they are not able to provide structural feedback to

the shop floor employees. While, as mentioned before in section 4.2.2, this feedback is

missed by shop floor employees.

5. Discussion

5.1 General Discussion

5.2 Theoretical Implications

5.3 Practical Implications

5.4 Limitations and Future Research

In this final chapter, an answer is given on the problem statement and the

corresponding research questions. In addition, theoretical and practical implications are

provided. Finally, potential limitations of this research are indicated and suggestions for

future research are given.


49

5.1 General Discussion

The main goal of this research was to examine how NOV Etten-Leur can ensure that

errors, which internally arise during the manufacturing of (sub) products, are managed in

such a way that NOV Etten-Leur can optimally profit from the potential positive error

consequences as well as reducing the negative ones. A quantitative study and semi-structured

interviews were conducted to (1) examine multiple hypotheses about organizational cultural

aspects (RQ1) and business practices (RQ2) that might influence dealing with errors, and to

(2) determine how NOV can promote their EMC and improve their business practices (RQ3).

The results of the quantitative study show that all three error handling practices, i.e.: (1)

coordinated and effective error handling, (2) analyzing errors, and (3) communicating about

errors by employees, are positively related to innovation among employees. In addition, (1)

analyzing errors and (2) communicating about errors by employees are positively related to

learning from errors by employees. The data show that all three error handling practices are

rated as high among shop floor employees. However, as management indicated in the

quantitative study and interviews, shop floor employees do not always define the “true” root

cause of an error due to lack of time, lack of experience and the difficulty of defining the

right root cause. In addition, as the quantitative study among management shows and as is

explained by the interviews, there are some problems with regard to analyzing errors on a

higher, organizational level. These problems appear to be partly caused by incorrect and

inconsequent registering and sharing errors in a way that the whole organization can benefit

from them (i.e. reporting errors at an organizational level). Because of this incorrect and

inconsistent error reporting at an organizational level, errors are only fixed and employees are

not able to identify both the biggest mistakes and certain patterns in mistakes (e.g. every time

the same: product, material, tool, person etc. involved). And thereby no effective actions for

improvement can be taken and the provision of feedback to shop floor employees is hindered.

In contrast to error reporting on an organizational level, according the quantitative data

and interviews, it appears that shop floor employees report their errors consistently at a lower

level within their organization. This reporting behavior seems to be positively correlated with

(1) coordinated and effective error handling, and (2) communicating about errors by

employees, and thus indirectly influence innovation among- and learning by employees.

Employees’ error reporting behavior on its turn, seems to be negatively related to the

perceived error reporting costs by employees. In addition, indirectly via perceived error

reporting costs, employees’ error reporting behavior is negatively related to perceived error

5. Discussion

50

strain by employees. Both perceived error reporting costs and error strain are perceived as

low by employees and in this way both contribute to the high error reporting behavior of

individuals. As revealed by the interviews and the quantitative study among management, it

are the error reporting costs (e.g. too time consuming and complex) and a lack of motivation

(‘nothing is being done with it’) that hinder consistent en correct error reporting on an

organizational level.

In conclusion, except for analyzing errors, the actual situation with regard to error

management by NOV’s shop floor employees appears to be good. As indicated by the

respondents of the questionnaire and interviews: they feel low error strain, there are low

reporting costs, they coordinate error handling, and communicate errors. And thereby

innovation among employees and learning from errors by employees is positively influenced.

However, as displayed in Figure 9, the questionnaire conducted among management and the

interviews show that there is room for improvement with regard to:

Correct and consistent error reporting at an organizational level.

Analyzing errors on an organizational level.

Providing feedback about errors.

Defining the “true” root cause of errors.

Figure 9: Cause and Effect Diagram of Issues with regard to Error Management in

which NOV can improve themselves


51

5.2 Theoretical Implications

The main theoretical implication of this research is that error handling by employees

(i.e.: coordinated and effective error handling, analyzing errors, and communicating about

errors) appears to have a significant effect on innovation among individuals. Where previous

research of Keeth and Frese (2011) provides evidence for the positive relation between an

organizational EMC culture and the amount of innovations a company produces, this research

implies that proper error handling by employees on shop floors positively contributes to

innovative behavior of employees.

Another result of this study provides support for the argumentation of Zhao and

Olivera (2006), which states that high error reporting costs negatively influence the decisions

of individuals whether or not to report an error. In addition, this research suggest that, as

stated by Forgas (1995) and Zhao and Olivera (2006), that individuals do consider their

emotion as relevant information for the cost evaluation. As the result shows, a positive

relation exist between perceived error strain by employees and the perceived error reporting

costs.

A remarkable outcome of this study is that no significant effect was found between

employees’ error reporting behavior and analyzing errors by employees. The fact that this

research implies there is no effect, may be a resultant of the specific measures used. In this

study, the constructs were formulated on an individual level. When errors are covered up, it

not necessarily means that individuals do not think about how it came or not analyze it by

themselves, which may explain the insignificant effect. However, covering up an error may

hinder the analysis on the organizational level because of incomplete information that is

available to other employees within the organization. Therefore, the assumption that

reporting an error as it is contributes to error handling (including analyzing errors) (Van

Dyck, et al., 2005) can not be rejected. This study, which is conducted within only one

organization, provides a too weak basis to do this.

As indicated in earlier research, in strong cultures people should perform uniform

behavior (Schneider, et al., 2002), and thus errors are widely accepted in a strong EMC

(Gronewold, et al., 2013; O'Reilly and Chatman, 1996; Van Dyck, et al., 2005). However, as

the results of this research show, EMC strength did not moderate the hypothesized

relationships between: (1) employees’ error anticipation and error handling by employees, (2)

perceived error strain by employees and error reporting costs perceived by employees, and

(3) error handling by employees and both learning and innovation. Although no significant

5. Discussion

52

result was found, the potential moderating effect of EMC strength should not be dismissed as

unimportant. The fact that this research does not provide support for the hypothesized effects

may be the result of the small sample size (i.e. low power), and relatively small variation (M

= 4.14, SD = .11) between the five different manufacturing departments of NOV with regard

to the EMC strength. So, a potential effect was hard to discover in the current sample. If this

research was carried out at multiple companies instead of multiple departments within one

company, there would probably be more variation in EMC strength. In addition, the fact that

only EMC strength is included as moderator in this study and the level of the EMC is not

taken into account, may also be the reason that no effect was found. The current way the

moderator is operationalized does not indicate whether the culture is strong error averse or

strong pro-error (i.e. EM). This was done because EMC is an aggregate incorporation of

variables, including some independent variables, and thereby too much correlation between

the moderator and independent variable could arise. Following the example of Schneider et

al. (2002), the mean culture rating, culture strength and an interaction term of both variables

could be included in future research to determine any potential effect.

This study shows there is no negative effect of perceived error strain by employees on

employees’ error reporting behavior. It was expected that the high emotional stress that can

be caused by an error (Catino and Patriotta, 2013), just as other strong emotional responses,

directly influence decision making and judgment (Zhao and Olivera, 2006). However, high

emotional stress as result of an error was observed by Catino and Patriotta (2013) among

pilots from the Italian Air Force. It might be due to the distinction between an error which

may put lives at risk, and an error which mainly results in material damage, that within NOV

there does not arise such a high emotional response that can influence decision making and

judgement. Therefore, the potential direct effect (in contrast to the indirect effect via

perceived error reporting costs) of perceived error strain on error reporting behavior might

not be important for this research area, but could be for others.

5.3 Practical Implications

Besides theoretical implications, this study also entails guidelines for the management

of NOV Etten-Leur for dealing with errors, and thus reducing the negative error

consequences and optimally profiting from the potential positive ones. Note that the results

are only generalizable for companies similar to NOV Etten-Leur, and the organizational

activity operations.


53

As the results show, by enhancing: (1) coordinated and effective error handling, (2)

analyzing errors, and (3) communicating about errors among shop floor employees,

management can stimulate innovation by them. According the results, to improve coordinated

and effective error handling, and communicating about errors by individuals, management

can enhance error reporting behavior via lowering the costs of reporting an error and reducing

the error strain an employee perceives. This can be done by creating an EMC in which: errors

are accepted as part of everyday life as long they are learned from and not repeated, there is

open discusions about errors, analysis of errors and their causes are made, there are no

punishments for reporting errors, and management is positive toward the communication

about errors (Gronewold, et al., 2013; Van Dyck, et al., 2005).

However, as this study revealed, for NOV Etten-Leur these actions (with the exception

of analyzing errors on a satisfactory level) do not have the main priority because shop floor

employees do feel low error strain and error reporting costs, report errors, coordinate error

handling, and communicate errors. Instead, as explained in section 5.1 and displayed in

Figure 9, NOV Etten-Leur can improve themselves on: (1) correct and consistent error

reporting at an organizational level, (2) analyzing errors on an organizational level, (3)

providing feedback about errors to shop floor employees, and (4) root cause analyses

Figure 10: Visualization of the Recommendations for NOV Etten-Leur

5. Discussion

54

conducted by shop floor employees. In Figure 10 a visualization of the recommendations for

NOV Etten-Leur is provided.

First, correct and consistent error reporting at an organizational level (i.e. filling in a

NCR) is hindered by (1) the current method, which is perceived as too time consuming and

too complex, and (2) a low motivation to make an NCR, which is caused by the feeling that

nothing is done with notifications. Therefore it is recommended to improve the current

reporting method in terms of ease of use and time consumption, and providing

guidance/training in using this method. In addition, by providing feedback both individually

and in groups about reported errors to shop floor employees (e.g. what were the

consequences, what is being done with it, and what is the final result of the actions taken),

employees are more likely to see the benefits associated with reporting an NCR and therefore

will experience making a NCR as less useless.

As a direct effect of a more consistent flow of correct NCRs, within NOV they will be

better able to identify both the biggest mistakes and certain patterns in mistakes (e.g. every

time the same: product, material, tool, person etc. involved). By doing so, more effective

actions can be taken. Nevertheless, it is recommended to actually thoroughly analyze the

reported NCRs consistently.

When both NCRs are made and errors are analyzed more consistently, the opportunity

arises to provide more structural feedback to shop floor employees. As mentioned before, this

will enhance the conceived usefulness of making a NCR, and is therefore recommended. If it

does not stop by only providing feedback both individually and in groups, but when

management is able to engage, challenge, and encourage shop floor employees in jointly

analyzing errors and thinking about improvements, they are likely to intrinsically motivate

shop floor employees (Amabile, 1998) to come up with improvements and thorough analyses

of errors by themselves. As a side effect, employees will gain experience in root cause

analyses, which ensures that the ‘true’ root cause is identified more often. In addition,

training could be provided to teach employees how to define the ‘true’ root cause.

5.4 Limitations and Future Research

The first limitation of this study is the relatively small sample size of 94 employees. A

sample size this big may raise concerns about the power of this study. Besides, this study is

conducted within only one specific organization and in addition is focused on one

organizational activity (operations). This may cause limitations with regard to the

generalizability of the results to other organizations and organizational activities. A third


55

limitation is that this study primarily is based on a self-rated questionnaire. Because of this,

the validity of the collected data depends on the objectivity and reflection capabilities with

which the respondents evaluate themselves. As stated by Blumberg, et al. (2008) surveys

responses must be accepted for what there are, i.e.: statements by individuals that reflect

varying degrees of the truth. Finally, because the Cronbach’s α of error anticipation (.55) is

actually too low (<.60), the current measurement items do not form a valid measurement

scale (Hair, et al., 2010). It may be therefor that no effect was found for error anticipation on

the three error handling practices.

Future research could focus on error management within other organizational

activities than operations, for example procurement or service. Companies like NOV are not

only confronted with errors that internally arise during the manufacturing of (sub) products.

They are also confronted with product related errors that are caused by suppliers. In addition

organizations are confronted with errors that are reported by their customers. Just as an error

that arise internally and which is internally dealt with, in all likelihood these errors have

potential positive error consequences. Therefore, it could be interesting to investigate how

businesses can assure that these errors, or feedback about errors are managed in such a way

that organizations can optimally profit from the potential positive error consequences as well

as reducing the negative ones. Another interesting topic of research could be the motivational

mechanisms of reporting an error. Where in this report the focus is on barriers to employees

to report an error and the negative effect of these barriers on error reporting behavior, the

interviews show that a lack of feedback demotivates employees. Therefore it might be

interesting to examine what motivational mechanisms stimulate error reporting behavior

(e.g.: make benefits/necessity clear, providing feedback etc.).

References

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Appendix

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Appendix A: Organization Chart NOV Etten-Leur

Fig

ure

11:

Org

an

izati

on

al

Ch

art

NO

V E

tten

-Leu

r at

Man

agem

ent

Level


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Appendix B: Error Handling Procedures

Fig

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12:

A S

yst

ema

tic

Pro

ces

s O

ver

vie

w o

f h

ow

NO

V E

tten

-Leu

r is

Cop

ing w

ith

Pro

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ct N

on

-Con

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itie

s (E

rro

rs)

Appendix

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Fig

ure

13:

A S

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ema

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Pro

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s O

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w o

f h

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(CA

R)

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reven

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Act

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s (P

AR

)


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Appendix C: Interview Guide

Opening

[Tell the respondent that you are happy with the chance to interview him/her. Explain

that you are interested in the effect of NOV’s culture and business practices (i.e. methods and

procedures) on error detection, error reporting and error handling. You would like to know

the positive and negative aspects of these culture and business practices. Tell him/her that it

probably will take 20 minutes to complete the interview. In addition, make clear that the

interview is strictly confidential, and quotes are only published with permission of the

respondent.]

Introductory questions

Can you tell me something about your job; please describe to me what a typical day is

like and whatever else is important.

Can you tell me something about product related errors within NOV; please describe to

me how you are confronted to these errors.

To be treated aspects

Error detection: Can you tell me something about the way errors are detected? [For

example, are people expecting them and are there procedures?]

Error reporting (1): Can you tell me something about the environment within NOV

regarding product related errors and the reporting of it? [Is there, for example, an open

culture?]

Error reporting (2): Can you tell me something about any tools that are used in

reporting errors? [For example the error registration system]

Error handling: Can you tell me something about the way employees are oriented

towards errors; how do they deal with them?

Conclusion

[Provide a summary of the most relevant/interesting comments and thank the respondent for

his/her participation.]

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