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8-15-2012

An investigation of employee engagement andbusiness outcomes at an engineering services firmPreetinder Singh Gill

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An Investigation of Employee Engagement and Business Outcomes at an Engineering

Services Firm

by

Preetinder Singh Gill

Dissertation

Submitted to the College of Technology

Eastern Michigan University

in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Technology

Concentration Engineering Management

Dissertation Committee:

John Dugger III, Ph.D., Chair

Pamela Becker, Ed.D.

Robert Chapman, Ph.D.

Huei Lee, Ph.D.

August 15, 2012

Ypsilanti, Michigan

ii

Dedication

I dedicate this work to my family. I thank my beautiful and loving wife, Navneet,

for her encouragement, which supplemented my determination and constantly pushed me

towards excellence. I also thank our wonderful daughter, Sehaj, and charming son, Adit,

who are my strength. Their exuberance and gestures of support made this project an

enjoyable experience.

iii

Acknowledgement

I thank Dr. John Dugger III, who served as my research adviser and as the

chairperson of my dissertation committee. His insightful advice and valuable critique has

been indispensable. I greatly appreciate the time he spent editing and reflecting on my

work throughout the doctoral program.

I thank Dr. Pamela Becker, Dr Robert Chapman, and Dr. Huei Lee for serving on

my dissertation committee and providing me with valuable feedback. I thank the faculty

and staff of the College of Technology, the College of Business, and the Graduate School

for their much appreciated assistance. I am grateful to the engineering service firm and its

employees for awarding me with the opportunity to conduct this study.

iv

Abstract

This research study investigated the constructs and dimensions of employee

engagement and their relationship with business outcome data from 2009 to 2011 at a

mid-sized engineering services firm and two of its business units. Employee engagement

ratios, as defined by Crabtree (2004), for all entities studied were below that associated

with world-class organizations. Survey items related to salary and compensation received

low scores from both engaged and disengaged employees. Kruskal-Wallis’s median

comparison tests revealed that many survey items varied over the period of three years

for each entity studied. A factor analysis of companywide survey data yielded 5

dimensions of employee engagement, which was consistent with existing literature of

Cummings and Worley (2008), Richman (2006), Shaw (2005), and Robinson et al.

(2004). Structural equation modeling revealed that the dimension of communication has

a causal relationship with the dimension of management effectiveness, which, in turn, has

causal relationships with dimensions of 1) salary and compensation, 2) opportunity for

development and recognition, and 3) alignment with the organization. A single variable

linear regression analysis between average ratings of every survey item and every

selected business outcome showed that a significant relationship existed between several

pairs of variables for each entity studied. However, many of these pairs were found to be

inversely proportional. This finding, subject to limitations and assumptions of this study,

is inconsistent with the findings of Harter et al. (2009) and Buckingham and Coffman

(1999).

The study uncovered many areas of improvement and elucidated several solutions

aimed at enhancing employee engagement at the engineering services firm. New possible

v

relationships between employee engagement and business outcomes are also presented.

These possible relationships in turn serve as an impetus for future research. Additional

research work is needed to understand intrinsic and extrinsic factors that influence and

promote employee engagement at services firms. Structural equation modeling

procedures could be used to develop a better understanding of how these factors

influence employee engagement and business outcomes.

vi

Table of Contents

CHAPTER 1: INTRODUCTION ........................................................................................1

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

Importance of the Research Study ................................................................................3

Problem Statement ........................................................................................................7

Objective/Purpose of the Research Study .....................................................................7

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

Hypotheses ....................................................................................................................9

Delimitations/Limitations ...........................................................................................10

Assumptions ................................................................................................................11

Definition of Terms.....................................................................................................12

Summary .....................................................................................................................14

CHAPTER 2: BACKGROUND AND REVIEW OF LITERATURE ..............................15

Introduction .................................................................................................................15

Employee Engagement ...............................................................................................15

What Is Employee Engagement? .........................................................................16

Relevance of Employee Engagement Dimensions ..............................................23

Employee Engagement: A Global Panorama ......................................................24

Employee Engagement Surveys .................................................................................25

Professional Services Firms ........................................................................................26

Engineering Services Firms ........................................................................................27

Business Outcomes .....................................................................................................29

Role of Economic Performance Indicators .................................................................31

vii

Summary .....................................................................................................................31

CHAPTER 3: RESEARCH METHODOLOGY ...............................................................33

Introduction .................................................................................................................33

Study Type and Study Design.....................................................................................33

Correlation Research ............................................................................................35

Survey Research...................................................................................................35

Developmental Research .....................................................................................36

Study Population and Sampling ..................................................................................37

External Validity ..................................................................................................37

Internal Validity ...................................................................................................38

Instrumentation Design ...............................................................................................41

Instrumentation Validity .............................................................................................42

Classical Factor Analysis .....................................................................................42

Instrumentation Readability ........................................................................................45

Scale Reliability ..........................................................................................................46

Data Collection ...........................................................................................................47

Data Analysis ..............................................................................................................49

Structural Equation Modeling ..............................................................................49

Safety, Confidentiality, and Anonymity of Human Subjects .....................................53

Summary .....................................................................................................................54

CHAPTER 4: RESULTS ...................................................................................................55

Introduction .................................................................................................................55

Characteristics of Survey Data ....................................................................................55

viii

Employee Engagement Ratios ....................................................................................59

Hypothesis Testing......................................................................................................64

Hypothesis 1.........................................................................................................65

Hypothesis 2.........................................................................................................67

Business unit-transportation ..........................................................................67

Business unit-water .......................................................................................67

Results of Structural Equation Modeling .............................................................68

Hypothesis 3.........................................................................................................73

Hypothesis 4.........................................................................................................74

Business unit-transportation ..........................................................................74

Business unit-water .......................................................................................74

Summary .....................................................................................................................77

CHAPTER 5: CONCLUSIONS, IMPLICATIONS, AND RECOMMENDATIONS ......79

Introduction .................................................................................................................79

Conclusions .................................................................................................................79

Employee Engagement Ratios .............................................................................80

Practical implications ....................................................................................82

Hypotheses 1 and 2 ..............................................................................................82

Practical implications ....................................................................................83

Theoretical contributions ..............................................................................85

Business Outcomes ..............................................................................................86

Hypotheses 3 and 4 ..............................................................................................87

Practical implications ....................................................................................88

ix

Theoretical contributions ..............................................................................89

Recommendations for Future Studies .........................................................................90

Summary .....................................................................................................................92

REFERENCES ..................................................................................................................93

Appendices .......................................................................................................................116

Appendix A ...............................................................................................................117

Appendix B ...............................................................................................................123

Appendix C ...............................................................................................................124

Appendix D ...............................................................................................................125

Appendix E ...............................................................................................................128

Appendix F................................................................................................................129

Appendix G ...............................................................................................................130

Appendix H ...............................................................................................................131

Appendix I ................................................................................................................132

Appendix J ................................................................................................................133

Appendix K ...............................................................................................................134

Appendix L ...............................................................................................................135

x

List of Tables

Table 1: Response rate over three-year period ................................................................. 40

Table 2: Number of actual responses vs. min. number of responses needed .................... 41

Table 3: Cronbach’s alphas over the three-year study period ........................................... 47

Table 4: Number of complete responses by year and entity ............................................. 52

Table 5: Descriptive analysis of survey questions ............................................................ 57

Table 6: Descriptive analysis of survey questions ............................................................ 57

Table 7: Descriptive analysis of survey questions ............................................................ 58

Table 8: Descriptive analysis of survey questions ............................................................ 58

Table 9: Descriptive analysis of survey questions ............................................................ 59

Table 10: Comparison of average survey ratings for engaged vs. disengaged

employees by year and entity ............................................................................................ 62

Table 11: p Values associated with one-way ANOVA & and Kruskal-Wallis

median comparison tests ................................................................................................... 66

Table 12: Data analysis based on structural equation modeling procedure for the

engineering service firm over the three-year period with bootstrap sampling rates

of 100, 200, and 300 ......................................................................................................... 70

Table 13: Data analysis based on structural equation modeling procedure for the

business unit-transportation over the three-year period with bootstrap sampling rates

of 100, 200, and 300 ......................................................................................................... 71

xi

Table 14: Data analysis based on structural equation modeling procedure for the

business unit-water over the three-year period with bootstrap sampling rates of

100, 200, and 300 .............................................................................................................. 72

Table 15: Survey questions and business outcomes for the engineering services firm .... 75

Table 16: Survey questions and business outcomes for business unit-transportation ...... 76

Table 17: Survey questions and business outcomes for business unit-water .................... 76

List of Figures

Figure 1: Research study focus ........................................................................................... 7

Figure 2: Data used in research study ............................................................................... 48

Figure 3: Emp engagement ratio based on all 47 survey questions by year and

entity ................................................................................................................................. 60

Figure 4: Emp engagement ratio based on Gallup Q12 survey questions by year

and entity ........................................................................................................................... 61

Figure 5: Structural equation model for the five dimensions of employee engagement .. 69

CHAPTER 1: INTRODUCTION

Introduction

A key component of any organization is its employees. The employees are critical

to the viability and the competitiveness of the organization. Engaged employees are

invaluable assets to an organization. High levels of employee engagement in domestic

and global firms promote retention of talent, foster customer loyalty, and improve

organizational performance and stakeholder value (Lockwood, 2007).

An organization is a dynamic social entity composed of individuals with specific

responsibilities, who share ambitions. It is characterized by specific objectives, a

“deliberate structure, coordinated activities” and a relevance “to the external

environment” (Daft, 2010, p.11). The interactive interplay of relationships in an

organization manifests into unique attributes and characteristics that can be measured

psychometrically.

Employers can reap rich returns from investments in their employee base.

Maskell, Baggaley, and Grasso (2011) presented investment in people as one of the key

practices associated with lean accounting. They also argued that successful lean

organizations make employee training, involvement, and empowerment utmost priorities.

Xu & Thomas (2011) maintained that organizations aspire to have engaged employees

and spend considerable resources to measure and improve employee engagement. Gill &

Kustron (2011) demonstrated that formalized professional certifications are positively

correlated with enhanced innovation. Furthermore, Brás and Rodrigues (2007) elucidated

that expenditure associated with employee development could be translated into

quantifiable organizational assets.

2

Assessing workforce attitude is a business imperative. Havill (2010) maintained

that knowing the level of engagement within the enterprise can be the foundation for

organizational change and ongoing success. Van Rooy et al. (2011) argued that

measuring employee engagement should not be foregone even in an economic downturn.

Campbell and Katona (1953) surmised that survey instruments could be used to

assess the psychometric attributes and characteristics of an organization. Furthermore,

Krosnick (1999) asserted that collecting survey data helps researchers gain useful insights

into the basics of social interaction and cognition. Leedy and Ormrod (2010) maintained

that survey research could be used to acquire information about a group of people.

Maskell, Baggaley, and Grasso (2011) argued that employee surveys could be used to

effectively assess dimensions such as employee empowerment and potency of an

organization’s management capabilities. Many companies use surveys to assess employee

sentiment and opinions. One such survey instrument has been used since 1998 at a mid-

sized Midwestern engineering services firm. This engineering services firm and two of its

business units were the focus of this study. A version of the survey instrument, modified

for the purposes of maintaining anonymity and confidentiality, used to measure employee

engagement at the engineering services firm is shown in Appendix A.

This research study 1) explored employee engagement and 2) investigated the

relationship between employee engagement, as defined by the employee survey

instrument, and selected business outcomes at the engineering services firm and two of its

business units over a period of three years. The cross-sectional aspect of the study

involved the analysis of data from the two business units of the engineering services firm.

The longitudinal aspect of the study involved the analysis of aggregated annual data over

3

a period of three years. The results of the study established a quantifiable relationship

between employee engagement, as defined by the employee survey instrument, and

selected business outcomes. The study also helped in identifying the aspects of employee

engagement, which varied temporally and with each business unit. The existence of a

quantifiable relationship between employee engagement and business outcomes provides

support for investing resources in promoting employee engagement in an effort to

instigate financial success.

Importance of the Research Study

Existing studies present positive relations between different facets of employee

engagement and business outcomes. Mathew, Ogbonna, and Harris (2011) demonstrated

that satisfaction and productivity at work and the quality of work lead to firm profitability

and growth in software companies, while the quality of work contributes to

organizational innovation. Brown and Lam (2008) reported that a significant positive

relationship exists between employee job satisfaction and customer satisfaction. Snipes et

al. (2005) supported the idea that employee job satisfaction is a relevant factor in service

quality improvement. These studies, however, are not specific to engineering services

firms.

Morse and Babcock (2010) contended that properly oriented and well-trained

employees lead to the creation of an efficient and effective work force. They also

reported a positive correlation between employees’ perception of an organization’s

human resource policies and practices and the customer’s perceptions of quality and

service. Pfau and Kay (2002) found evidence that superior human capital practices or

human resource development activities are a leading indicator of financial performance

4

and that better human capital practices provide higher returns to shareholders. Activities

related to human resource development “play an important role in improving firms’

financial performance” (Bassi & McMurrer, 2008, p. 864).

Gallup Inc. (2010) proposed that a high ratio between the number of engaged

employees and the number of disengaged employees ensures superior financial

performance. It was purported that world-class organizations have an employee

engagement ratio of 9.57. Wagner and Harter (2006) presented specific examples where

management leveraged employee engagement to successfully meet business challenges.

Furthermore, Harter et al. (2009) and Buckingham and Coffman (1999) demonstrated

that employee engagement and business outcomes share a directly proportional

relationship. However, these results were based on a meta-analysis of data from several

organizations and associated business units rather than business units of a single

engineering services firm over consecutive time periods.

Researchers have employed a wide variety of constructs to describe employee

engagement in an organization. For example, Sanchez-Burks (2005) used the socio-

religious construct of Protestant Relational Ideology to describe organizational behavior

in American firms. Markos and Sridevi (2010) discussed the complexity and diversity

associated with adequately describing employee engagement. They argued in favor of

understanding various dimensions associated with employee engagement. Meduna (2009)

identified multiple themes associated with employees that could be instrumental to

organizational success. Avolio et al. (2004) acknowledged the existence of a variety of

constructs associated with employees’ attributes. Additionally, “(the) meaning of

employee engagement is ambiguous among both academic researchers and among

5

practitioners” (Macey & Schneider, 2008, p. 3). They also emphasized the need to better

understand of the effects of employee engagement in terms of business outcomes. It can,

therefore, be argued that companies that use employee surveys should verify the

dimensions of employee engagement they are measuring. This is important because

employer-driven actions aimed at improving employee engagement without employee

buy-in could prove ineffective, if not completely futile.

Wang and Spitzer (2005) underscored that the field of human resource

development has faced challenges in measuring the impact of both human capital and the

investments associated with its development. Although the quality of the human capital is

an important predictor of an organization’s business results, organizations do not have

systems to “reflect this importance, meaning that organizations require a separate system

for measuring and managing their human capital and its development” (Bassi &

McMurrer, 2005, p. 194). Shaw (2005) argued that effective initiatives associated with

enhancing employee engagement should be driven by clear, specific, and measurable

objectives guided by an organizational vision statement. Any strategy associated with

improving employee engagement should begin with a thorough comprehension of

employee engagement. This in turn requires understanding the impact of employee

engagement on business outcomes.

Ongoing research efforts tend to indicate that employee engagement is directly

proportional to business outcomes. Research by Truss et al. (2006), McGee and Rennie

(2011), Gatenby et al. (2008), Harter et al. (2009), Corporate Leadership Council (2004),

and Cohen (2006) illustrate relationships among employee attitudes, the way people are

managed, and business performance. However, none of these studies focused specifically

6

on employee engagement and business outcomes at a Midwestern U.S. engineering

service firm in a temporal fashion.

Evanschitzky et al. (2011) reported a positive and direct relationship between

employee satisfaction and customer satisfaction. However, the authors emphasized the

need to analyze longitudinal data over multiple time periods to draw strong causal links.

Previously, Allen and Grisaffe (2001) also acknowledged that little empirical research

had examined the linkage between employee attitudes and organizational outputs. Harter,

Schmidt, and Hayes (2002) wrote that business unit level employee satisfaction and

engagement have positive correlations with the business unit outcomes of customer

satisfaction, productivity, profit, employee retention, and employee safety. They also

underscored the need for longitudinal studies, focusing on comprehensively and reliably

understanding the relationship between employee engagement and business outcomes.

Furthermore, Endres and Mancheno-Smoak (2008) highlighted the need to study

employee engagement in a temporal fashion. Cooil et al. (2009) also underscored the

need to study employee engagement and business outcomes over multiple time periods

while trying to generalize the relationship between the two in other fields.

There is a need to further study and better understand the relationship between

employee engagement and business outcomes, more specifically, in a longitudinal

fashion. This is depicted in Figure 1. Existing studies underscore the need to study a large

array of organizations and industries to generalize the relationship between employee

engagement and business outcomes. Furthermore, minimal research has focused on

studying the relationship between employee engagement and business outcomes in the

engineering services field.

7

Figure 1. Research study focus.

Problem Statement

Levels of employee engagement at engineering services firms have not been

adequately studied in a temporal fashion. Furthermore, the relationship between

employee engagement and business outcomes in engineering service firms and their

business units, especially along a timeline, is not clear.

Objective/Purpose of the Research Study

This research study determined and analyzed employee engagement ratios

associated with a Midwestern mid-sized engineering services firm and two of its business

units over a period of three years. Dimensions of employee engagement were verified,

and relationships between these dimensions were examined. Variations in the levels of

employee engagement, as defined by an employee survey, were studied for the

engineering services firm and two of its business units over a period of three years.

Additionally, the study established quantitative relationships between employee

engagement, as defined by the employee survey, and selected business outcomes of the

engineering services firm and two of its business units over the same time period. The

first business unit is involved in water-related projects, while the second business unit is

Business Outcomes

Employee Engagement

Additional research needed in this area

Engineering Services Firm

8

involved in transportation-related projects. The three-year period encompasses 2009,

2010, and 2011.

The employee engagement has been measured at the engineering services firm

and its business unit with help of a question survey instrument. The survey instrument

has been used at the engineering services firm since 1998. Details associated with the

survey instrument are presented in Instrumentation Design section of Chapter 3.

The study attempted to enhance generalizability of the existing findings by

performing the study at the engineering services provider over three consecutive years.

Furthermore, this study also compared and contrasted the relationship between employee

engagement and selected business outcomes at two business units of the engineering

service provider over the same time period.

Direct benefits of this study include 1) validation of the dimensions that describe

employee engagement in an engineering services firm, 2) quantification and verification

of the relationship between employee engagement and business outcomes in the

engineering services field, 3) better understanding of the relationship over successive

time periods, and 4) creation of a basis for informed decision-making by organizational

policymakers with regard to investment in employees.

Research Questions

The following research questions were addressed by this research study:

1) Which survey items, related to employee engagement of the engineering

services firm, vary over a period of three years - 2009, 2010, and 2011?

9

2) Which survey items, related to employee engagement of the two business units

of the engineering services firm, vary over a period of three years - 2009, 2010, and

2011?

3) What relationship, if any, exists between employee engagement, as defined by

the employee survey, and selected business outcomes of the engineering services firm

over a period of three years - 2009, 2010, and 2011?

4) What relationship, if any, exists between employee engagement, as defined by

the employee survey, and selected business outcomes of the two business units of the

engineering services firm over a period of three years - 2009, 2010, and 2011?

Hypotheses

The hypotheses associated with this study were tested at a 0.05 significance level.

The hypotheses to be tested were:

1) There is no significant variability in employee engagement, as defined by the

employee survey questions, of the engineering services firm over the years 2009, 2010,

and 2011.

2) There is no significant variability in employee engagement, as defined by the

employee survey questions, for the two business units of the engineering services firm

over the years 2009, 2010, and 2011.

3) There is no significant relationship between any item related to employee

engagement, as defined by the employee survey, and any business outcome of the

engineering services firm over the years 2009, 2010, and 2011.

10

4) There is no significant relationship between any item related to employee

engagement, as defined by the employee survey, and any business outcome of the two

business units of the engineering services firm over the years 2009, 2010, and 2011.

Delimitations/Limitations

The delimitations imposed on this research study include:

Delimitation: Data were collected from one large Midwesternf engineering

services firm.

Delimitation: Data collected for the study represent a time period of three years:

2009, 2010, and 2011.

Delimitation: Data associated with business units represent the two largest and

oldest business units of the engineering services firm.

Delimitation: In the year 2011, the survey instrument changed slightly. Keywords

in two survey questions were replaced by synonyms, two questions were added, and one

survey question was dropped. These three questions were not included in this research

study in order to ensure that the internal validity is not compromised.

Delimitation: For the purposes of this study, the following annualized business

outcomes from the engineering services firm, business unit-transportation and business

unit-water, were used: net revenue, net profit, net profit to net revenue ratio, utilization

rate, employee growth rate, and employee turnover rate.

Delimitation: Dimensions of employee engagement were defined in terms of the

survey questions, with the highest loading values from the factor analysis of the survey

data from the engineering services firm. It is plausible that the loading values of survey

items that constituted employee engagement dimensions might be unique to this study.

11

Hence, formative models were used for structural equation modeling (Henseler, Ringle,

& Sinkovics, 2009).

Limitations of this research study include:

Limitation: The data associated with employee engagement are limited to the data

collected by a preexisting survey instrument administered to all employees of engineering

services firm.

Limitation: The data associated with business outcomes are limited by

performance indicators tracked by the engineering services firm.

Limitation: The employee participation in the survey was voluntary. Hence,

response rates vary with year.

Limitation: The potential sample sizes varied with year and business entity.

Assumptions

It was assumed that the employee engagement data and business outcome data

collected by the engineering services firm are representative of the business units and

their population for any given year. Furthermore, it was assumed that no bias exists in the

process of data collection and reporting on the behalf of the engineering services firm.

The survey delivery and subsequent response collection methods were assumed to be

effective, accurate, and duress- and bias-free. Additionally, the dimensions of the

employee engagement are assumed to be same for the engineering services firm and two

of its business units.

Employee responses to the employee survey were assumed to be honest,

unambiguous and bias-free. The business outcome data provided by the engineering

services firm were assumed to be collected accurately and reported precisely. For the

12

purposes of this study, firms with 300 to 1000 employees were considered mid-sized

businesses.

Levinson (2011) and Ryan (2011) acknowledged the Statgraphics Centurion

software package for its capabilities in performing advanced statistical analyses. Fink,

Lausen, Seidel, and Ultsch (2010), Malhotra (2010), and Vinzi, Chin, Henseler, and

Wang (2010) recommended the use SmartPLS for performing analysis related to

structural equation modeling procedure. Furthermore, a search performed on Google

Scholar on July 25, 2012, for keywords Statgraphics Centurion returned citations from

54 scholarly studies. Similarly, a search performed on Google Scholar on July 25, 2012,

for keywords Smart PLS returned citations from 946 scholarly studies. Hence it is

assumed that algorithms and outputs from Statgraphics Centurion XV® version 15.2.14

and SmartPLS© 2.0 M3 provide accurate analyses of the data associated with study.

Furthermore, the capability and accuracy of this software package are assumed to be

adequate for this research study.

Definition of Terms

Business outcome. Business outcome identifies the desired business results that

keep the business viable. Typical categories of business outcomes include cost,

profitability, quality/effectiveness, output, and time/efficiency (Stone, 2008).

Business unit. A business unit is an organizational unit that shares common

business drivers, goals, information needs, and processes and is treated as separate entity

(Heise, 2002).

Employee engagement. Employee engagement can be defined as “as an

individual’s degree of positive or negative emotional attachment to their organization,

13

their job and their colleagues” (Scarlett, 2007). Employee engagement pertains to “the

degree to which employees perform their role in a positive and proactive manner.”

Employee “engagement can be summed up by how positively the employee: thinks about

the organization; feels about the organization; is proactive in relation to achieving

organizational goals for customers, colleagues and other stakeholders.” Thinking, feeling,

and doing are the three aspects of employee engagement (Cook, 2008, p. 3-4).

Employee engagement ratio. Employee engagement ratio is defined as the ratio

of the number of employees who are engaged to the number of employee who are not

engaged (Gallup, Inc, 2010).

Employee engagement type. Engaged employees include employees whose

average rounded rating on the employee survey is ≥ 4. Disengaged employees include

employees whose average rounded rating on the employee survey is ≤ 3 (Taylor, 2009;

Crabtree, 2004).

Employee growth rate. Employee growth rate is the percent change in the

average number of employees over a specified period of time (Welbourne et. al., 1998;

Blazey, 2009).

Employee survey. The employee survey for the purposes of this study refers to a

set of multi-questions that were presented to the employees of the engineering firm over

the years 2009, 2010, and 2011.

Engineering services firm. The engineering services firm is an engineering and

technical consulting services provider from the Midwestern United States.

Employee turnover rate. Employee turnover rate is the ratio of the number of

employees leaving an organization over a specified period to average number of

14

employees in the organization during the same period expressed as a percentage

(Armstrong, 2009; Burkholder et al., 2007).

Utilization rate. The utilization rate is the ratio of hours spent on billable projects

to the total number of hours worked expressed as a percentage (Wintner, 2010).

Summary

This chapter includes the objective and importance of the research study. The

research study attempted to enhance the understanding of employee engagement. The

study also sought to investigate the relationship between employee engagement, as

defined by the employee survey instrument, and selected business outcomes. The

hypotheses and research questions associated with the problem statement are presented.

Definitions of important term, delimitations, limitations, and assumptions associated with

this research study are also presented in this chapter.

15

CHAPTER 2: BACKGROUND AND REVIEW OF LITERATURE

Introduction

This chapter begins by defining key terms and summarizing the results of research

related to employee engagement. Various employee engagement dimensions are

consolidated and classified into five groups. A global account of employee engagement is

also presented.

Business outcome indicators, used in this research study, are described in the

penultimate part of this chapter. This is followed by a discussion about professional

services firms. The unique aspects of engineering services firms are highlighted. A

summary of the topics discussed is presented at the end of this chapter.

Employee Engagement

In order to stay competitive, organizations today need to be both proactive and

reactive. Schumpeter (1942) asserted that innovation ensures economic success.

Organizations need to pursue relentless problem-solving. They also need to commit to

continual innovation. Morse and Babcock (2010) point out numerous ways in which

individuals in an organization are instrumental in endeavors like creativity, problem

solving, invention, innovation, and subsequent deployment and implementation.

As we press on into the 21st century and grapple with the modalities of the

knowledge economy, the need for education cannot be overemphasized. An educated

workforce could be treated as a natural resource that provides impetus to wealth creation

(Morgan, 2008). Workers in wealthier nations do tend to be more educated. In fact,

“[t]oday's workers are better educated and more literate that ever before. … they have

access to unprecedented learning opportunities through community colleges, universities,

16

TV, and the Internet. Moreover, our culture places a premium on creativity and individual

expression” (Dennis, 2007, p. 102).

It would be very prudent for an organization to leverage the education and

experience of its employees to achieve better business outcomes. Moreover, forming an

organization rooted in continuous improvement is contingent upon the presence of

engaged employees, “especially those on the front lines, where the real work gets done”

(Dennis, 2007, p. 102). Thus, it can be argued that engaged employees can successfully

identify and fix the innumerable smaller problems that are continually encountered,

which, in turn, could lead to much bigger issues. Babbitt (2010) cited the Gulf of Mexico

Deep Horizon oil spill and the Three-Mile Island nuclear plant leakage as examples

where the cumulative effects of smaller problems led to major disasters.

What is Employee Engagement?

Academic interest in employee engagement has been rising over the last decade.

A keyword search on ProQuest LLC.’s ABI/IFORM on January 3, 2012, showed that 45

articles/papers in scholarly journals contained the term Employee Engagement in their

citation and abstract between 1/1/2000 and 12/31/2005; 118 articles/papers in scholarly

journals contained term the Employee Engagement in their citation and abstract between

1/1/2006 and 12/31/2008; and 195 articles/papers in scholarly journals contained term the

Employee Engagement in their citation and abstract between 1/1/2009 and 12/31/2011. A

keyword search on Thomson Reuters’s Web of Science database on January 3, 2012, also

showed a similar trend. The efforts concentrated in these articles/papers could be

categorized as 1) attempts to define employee engagement and 2) understanding cause

and/or effects of employee engagement.

17

Consistent with research works cited in Chapter 1, Gatenby et al. (2008) pointed

out that there seems to be fair amount of confusion surrounding the definition of

employee engagement. Nevertheless Kahn (1990), in his much cited paper, described an

employee’s “personal engagement” as “the harnessing of organization members’ selves

to their work roles; in engagement, people employ and express themselves physically,

cognitively, and emotionally during role performances” (p. 694). Physical engagement

pertains to actual labor expended by an employee to accomplish his/her tasks and duties.

Cognitive engagement relates to employees’ perception and knowledge of thet work

environment. Emotional engagement concerns employees’ feelings and attitudes about

the employer and working conditions. Kahn (1990) postulated that role engagement has

two critical components: attention and absorption. Rothbard (2001) elucidated that

attention refers to “cognitive availability and the amount of time one spends thinking

about a role” while absorption “means being engrossed in a role and refers to the

intensity of one’s focus on a role” (p. 656). Additionally, while attention could be

described as an “invisible, material resource that a person can allocate,” absorption

“implies intrinsic motivation in a role” (Rothbard, 2001, p. 657)

Woodruffe (2006) contended that employees’ engagement is contingent upon

satisfaction of their needs by their employers. He categorized these needs as

compensation package, job satisfaction, and employability potential. Furthermore, he

highlighted that vertical and horizontal communication is a key facet of employee

engagement.

Employee engagement was defined as a “heightened emotional connection that an

employee feels for his or her organization, that influences him or her to exert greater

18

discretionary effort to his or her work” (Gibbons, 2006, p. 4). Soldati (2007) analyzed

Gibbons’s work to conclude that eight drivers of employee engagement included

• Trust and integrity – how well managers communicate and walk the talk?

• Nature of the job –Is it mentally stimulating day-to-day?

• Line of sight between employee performance and company performance –

Does the employee understand how his work contributes to the company's

performance?

• Career Growth opportunities –Are there future opportunities for growth?

• Pride about the company – How much self-esteem does the employee feel

by being associated with the company?

• Coworkers/team members – How do they significantly influence one's

level of engagement?

• Employee development – Is the company making an effort to develop the

employee's skills?

• Relationship with one's manager – Does the employee value his or her

relationship with his or her manager? (Soldati, 2007, para. 7)

Herzberg (1959) postulated that motivation and hygiene factors adequately

explain employee attitudes and motivation. Lack of hygiene factors such as salary, work

conditions, and job security lead to employee dissatisfaction. Motivation factors such as

recognition, responsibility, growth, and achievement result in better job performance.

Subsequently, Scarlett (2007) categorized employee engagement as engagement drivers

and disengagement drivers. The engagement drivers are composed of dimensions such as

advancement, freedom, job assignment, personal growth, recognition, and work

19

objectives. Dimensions such as benefits, communication, company pride, compensation,

fellow associates, manager and work conditions, and objectives constitute disengagement

drivers. It can be argued that measurements of the levels of these drivers could provide a

reliable way to quantify the level of employee engagement.

Heintzman and Marson (2005) proposed that employee engagement is composed

of two distinct yet related components: employee commitment and employee satisfaction.

Peters (2007) explained that employee satisfaction is related to “the level of contentment

or happiness a person assigns to attributes of their job/position, their organization, and the

general or overall way they feel about their employment” (p. 1). Additionally, employee

commitment involves the pride people “feel for their organization [and] the degree to

which they intend to remain with the organization, desire to serve or to perform at high

levels, positively recommend their organization to others and, strive to improve the

organization's results” (p. 2).

Nink and Welte (2011) classified the 12 questions that Buckingham and Coffman

(1999) used to measure employee engagement in a hierarchal order. They classified

Questions 1 and 2 as basic needs. The next level – management support – consisted of

Questions 4 through 6. Teamwork forms the next level and it consisted of Questions 7

through 10. Questions 11 and 12 formed the top level called growth. The list of 12

questions – Gallup’s Q12 – is presented in Appendix B.

Baumruk (2004) referred to engagement as the energy or the passion that

employees harbor for their jobs and their employer, which result in emotional and

intellectually commitment to their organization. He proposed three primary behaviors [or

constructs] that could be used to measure employee engagement:

20

1) Say. They are passionate advocates for the workplace, consistently speaking

positively about the company to co-workers, potential employees, and customers.

2) Stay. These employees have an intense desire to be a member of the

organization, despite opportunities to work elsewhere.

3) Strive. They routinely go above and beyond, exerting extra effort to produce

extraordinary service and results for customers and colleagues. (Baumruk, 2004,

p. 49)

Richman (2006) described engagement as an impetus for an employee to employ

his/her discretionary efforts, experience, and energy, which engender generate creative

solutions that, in turn, directly benefit the employers without any explicit assurance of

personal gain. Richman (2006) also proposed a three-tier model for defining employee

engagement. The first tier was called threshold factors and included constructs related to

compensation, benefits, and safety. The second tier was called enablers and included

constructs related to skills development and rewards based on contribution. The third tier

was called commitment drivers and included constructs pertaining to communication, job

satisfaction/flexibility, diversity, inclusion, management effectiveness, work-life support,

and career advancement. Engaged employees “ [are] action-oriented and know how to

take intelligent risks…[believe] they have a stake in the company …[exert] extraordinary

effort to … make and keep the company successful, while embracing the company's

culture” (Richman 2006, p. 38). Furthermore, employee engagement has a “lot to do with

how an employee feels about the work experience, how he or she is treated. Engagement

ultimately comes down to people's desire and willingness to give discretionary effort” to

their jobs (Frank et al., 2004, p. 16).

21

The Corporate Leadership Council (2004) postulated that employee engagement

could be classified as 1) rational – extent to which their employees believe that employers

are aligned with their personal interests and 2) emotional – extent to which employees

value, enjoy, and believe in their employers. Employee engagement materializes as

“discretionary effort” and “intent to stay”…. “Focal points” associated with employee

engagement are “day-to-day work, team, direct manager, organization” (Corporate

Leadership Council, 2004, p. 3).

Richman (2006) and Shaw (2005) pointed out that engaged employees have high

degrees of involvement and attachment to their employers and/or organizations.

Employee involvement “seeks to increase members’ input into decisions that affect the

organization performance and employee well-being.” Four key elements associated with

employee involvement include “power…, information…, knowledge and skills…

[and]…rewards” (Cummings & Worley, 2008, p. 351). Furthermore, Shaw (2005)

maintained that employee involvement could entail physical involvement as well as

emotional involvement. Emotional involvement is related to knowledge and perception of

an organizational vision and mission. Physical involvement relates to organizational

design.

Robinson et al. (2004) argued that the definition of employee engagement

overlaps with well established constructs such as organizational commitment and

organizational citizenship behavior. However, they pointed out that employee

engagement is unique since it involves a two-way interaction. On the one hand,

employers have to expend efforts to engage employees; on the other hand, employees

have to select the level of engagement to offer to their employers. Robinson et al. (2004)

22

offered ten dimensions that describe an employee who feels valued and involved. This, in

turn, enhances employee engagement. In order of increasing importance, the dimensions

are job satisfaction, friendliness at work, co-operation, health and safety, pay and

benefits, equal opportunities and fair treatment, communication, performance and

appraisal, role of immediate management, and training, development, and career.

Shaw (2005) acknowledged that there are myriad of dimensions or constructs

associated with employee engagement because the concept of employee engagement

emanates from interactions of unique individuals under diverse work conditions. This

necessitates the need for focusing on only a few key dimensions or constructs. Saks

(2006) categorized employee engagement into closely related constructs – job

engagement and organizational engagement. Furthermore, as argued by Saks (2006),

employee engagement has been defined and measured in many different ways.

Many dimensions associated with employee engagement have been described in

the preceding discussion. However, despite these differences, there seems to be a

commonality in the dimensions/constructs associated with employee engagement.

Synthesis of the information accrued in the literature review process suggested that

dimensions associated with employee engagement fall into one of the following five

categories:

1) alignment with the organization,

2) management effectiveness,

3) salary and compensation,

4) communication, and

5) opportunity for development and recognition.

23

Hence, an engaged employee is someone who is well compensated and has

his/her interests aligned with the organization. He/she also seeks opportunities for

development and recognition. Furthermore, an engaged employee believes in the

management’s effectiveness and expect open and clear communication with all levels of

the organization.

Relevance of Employee Engagement Dimensions

Employees could prove more effective if their interests and activities align with

organizational goals. Gagnon and Michael (2003) maintained that successful

implementation of strategic organizational initiatives require employee alignment.

Furthermore, Speculand (2006) surmised that a vast majority of organizational strategies

fail because of employee ignorance and/or misperception. Alignment ensures that

employees clearly comprehend what they can contribute to the organization (Loch,

2008). Additionally, Vlcek (1987) contended that “high turnover [is] due in part to

insufficient personnel standards and ambitious individuals who sometimes contradict

company goals” (p. 71).

Macky and Boxall (2008) maintained that managers play a critical role in

developing systemic linkages which, in turn, engender high employee involvement.

Successful business endeavors require trust that is “built on respect and timely

communication, as well as energized leaders and strategic responses that eliminate

actions based on panic” (Goodridge, 2009, p. 28). Goodridge added that “the keys to

getting through the tough times are respect and timely communication” (2009, p. 30).

Ahmed et al. (2010) found that communication assists in “crafting healthier relations

between … [managers] and their employees and the entire organization ultimately

24

benefits from this relationship” (p. 107). Communication “affects coordination of effort,

job instruction, performance feedback, group effort, motivation of employees, [and]

interpersonal relations …” which espouse vibrant organizational climate (Khetarpal,

2010).

Job-related training assists an employee in becoming adept in his/her realm of

responsibility. Recognition, on the other hand, provides an employee with motivation to

perform his/her duties well. A recognized employee not only gains respect from his/her

peers but also provides them an impetus to excel in their respective tasks. Axtell et al.

(2000), Kinsey (2009), Sand et al. (2011), Mahal (2009), and Beattie and Smith (2010)

underscore that employee development through training, encouragement for efforts, and

recognition of achievements contribute to value creation for an organization.

Employee Engagement: A Global Panorama

Flade (2003) reported the proportions of engaged workers for eleven countries:

USA – 27%, Canada – 24%, Germany – 12%, Japan – 9%, Great Britain – 19%, Chile –

25%, France – 12%, Israel – 20%, Australia – 18%, New Zealand – 23%, and Singapore

– 6%. Gallup Inc. (2005) reported that 12% of Chinese workers and 12% of Thai workers

were engaged. Crabtree (2011) reported that the ratio of engaged to actively disengaged

workers in Western Europe is 0.81:1, which is relatively high by global standards,

although it falls well below Canada and the U.S., where the ratio is 1.44:1. Crabtree

(2011) also reported that the ratio of engaged to actively disengaged workers in Australia

and New Zealand almost is 1:1. Alternatively, about 20% of the workforce in Western

Europe, Australia, and New Zealand were found to be engaged. In India about 10% of the

work force were engaged (Crabtree, 2011).

25

It can therefore be seen that employee engagement varies from nation to nation.

The data presented above showed that a vast majority of employees across the global are

not engaged. Additionally, nations are a long way from achieving the over 90% employee

engagement described as ideal by Gallup Inc. (2010). It could be argued that employees

who are not actively engaged are not realizing their full potential. Crabtree (2004)

estimated that actively disengaged employees cost the U.S. economy a loss of about $300

billion per year.

Employee Engagement Surveys

Macey et al. (2009) maintained that employee engagement surveys are principally

used to measure and benchmark engagement levels in an organization. They also asserted

that various engagement enhancement methodologies employ surveys to access work

environment conditions that might affect engagement. Subsequent analyses of survey

data provide valuable insights to employee engagement, which, in turn, help in devising

treatments to ameliorate identified issues. Employee engagement surveys are “best suited

for measuring employee engagement feelings or state [of] engagement” (Gruman & Saks,

2011, p. 127). In fact several researchers, as reported by Arkin (2011), Bart (2011), Klie

(2007), and Wiley (2011), have used surveys to develop meaningful conclusions and

actionable plans related to employee engagement.

Organizations conduct surveys to address both the actual challenges in the short

term and potential issues over the long term. Wiley (2010) classified the reasons for

conducting surveys as:

1. To identify warning signs of trouble within the organization

2. To evaluate the effectiveness of specific programs, policies, and initiatives

26

3. To gauge the organization's status or strength as an employer of choice among

its workforce

4. To predict and drive organizational outcomes, including customer satisfaction

and business performance. (p. 8)

Effective employee surveys are tools that could be used to make the workforce

congruous with organizational goals. Church et al. (2001) proposed a seven-step process

to efficiently design and use surveys: “pooling resources, … designing and developing,

… communicating objectives,…administering and improving, … analyzing and

interpreting, … transferring and action planning” (p. 18)

Professional Services Firms

Baschab and Piot (2005) maintained that professional services require specialized

knowledge and skill usually of an intellectual nature. Professional services often require a

license, certification, or registration. Additionally, Baschab and Piot (2005) define

professional service as:

work rendered by an independent contractor who has a professed knowledge of

some department of learning or science used by its practical application to the

affairs of others or in the practice of an art founded on it, including but not limited

to accounting and auditing, court reporters, X-ray technicians, legal, medical,

nursing, education, engineering, actuarial, architecture, veterinarians, and

research. The knowledge is founded upon prolonged and specialized intellectual

training which enables a particular service to be rendered. The word professional

implies professed attainments in special knowledge as distinguished from mere

skills. (p. 6)

27

Ulrich (1998) and Burr and Girardi (2002) held that intellectual capital grounded

in employee competence and commitment has gained importance in the knowledge

economy. Importance of this intellectual capital is paramount for professional services

firms. Professional services firms are knowledge-intensive organizations that provide

services in the form of specialized guidance, based on their expertise for specific tasks,

while being in direct contact with the customers or their clients (Lowendahl, 2005).

Furthermore, services provided and the processes involved are customized or

adapted to individual customers’ needs (Maister, 1993). Professional services firms are

especially active in the fields of accounting, architecture, engineering, management

consultancy, and legal services (Chang & Birkett, 2004). Young (2005) maintained that

the professional services industry is one of the largest and most diverse sectors of the

modern economy. Additionally, the “defining characteristic of a professional services

firm is the skill set it offers to its clients. The expertise is what clients seek and pay for”

(Young, 2005, p. 2).

Engineering Services Firms

Professional services firms that primarily provide engineering solutions to

customer issues could be categorized as engineering service firms. The American Council

of Engineering Companies (2012) described its members as firms engaged in wide array

of engineering based tasks. The engineering solutions provided by an engineering

services firm could be further categorized into product-based services or process-based

services. The product-based services are concerned with assisting customers in creating

and/or optimizing value-rich tangibles that can be sold in the marketplace. The process-

based services are concerned with a series of interrelated and interdependent activities

28

that lead to the creation of a product. Thus, engineering services specialists design,

construct, install, commission, equip, and test specialized products and processes.

Employees of engineering services firm possess fairly advanced engineering and

technical skills based on their experience and/or education. According to the U.S. Bureau

of Labor Statistics (2009), about 30 percent of engineering-related jobs were in the

professional, scientific, and technical services industries, primarily in architectural,

engineering, and related services. DiNatale (2008) suggested that consulting engineers

possess technical and analytical expertise and project management skills including good

writing and communication skills. Furthermore, consulting engineers must place greater

emphasis on developing and maintaining good client relationships. Professionals

employed by consulting services firms might be asked to provide designs, give advice,

solve problems, or provide expert testimony (Morse & Babcock, 2010). Rubenstein

(2009) emphasized that these professionals are concerned with innovation,

commissioning, deployment, application, operation, and management of technological

components and services.

Engineering services firms could serve industrial sectors such as manufacturing,

environmental sustainability, transportation, infrastructure, healthcare, energy, natural

resources and construction. They may operate across the wide range of sectors or

specialize in a few of them. Engineering services is a “very broad sector spanning a

whole range of sub-sectors …The average cost of [an engineering services] project… is

[about] $300 million but some super projects can be $1billion or much higher ” (Clark &

Colling 2005, p. 181).

29

Business Outcomes

Traditionally, businesses were considered properties of the shareholder. However,

in today’s knowledge economy, businesses are akin to wealth-creating communities in

which both the shareholders and employees act as stakeholders (Handy, 2002).

Businesses are organizations that work to achieve specific objectives aimed at providing

specific outcomes to their stakeholders while ensuring economic longevity. The

objectives, in turn, are formed by implicitly or explicitly defined organizational vision

and mission. Expected business outcomes must be defined for all areas on which

organizational survival depends. Drucker (1952) defined eight key business output areas:

market share, innovation, productivity and quality, physical and financial resources,

manager performance and development, worker performance and attitude, profitability,

and social responsibility.

Buckingham and Coffman (1999) listed profitability, productivity, employee

turnover, and customer satisfaction/loyalty as aggregate business performance measures.

In addition to these measures, Harter et al. (2009) included safety incidents, absenteeism,

shrinkage associated with employee theft, and quality defects as indices of business

performance. Parmenter (2010) maintained that key business outcomes included

customer satisfaction, net profit before tax, profitability, employee satisfaction, and return

on capital employed. It must be noted that business outcomes are indicators of a

business’s economic viability. Carton and Hofer (2008) held that an organization’s

performance is a measure of the change in its financial state. The performance could be

measured in terms of business outcomes that “result from management decisions and the

30

execution of those decisions by members of the organization” (Carton & Hofer, 2008, p.

3).

Business outcomes selected as key business performance indicators depend on

goals the business is trying to achieve. Dragana et al. (2011) asserted that appropriately

selecting which business outcomes to serve as performance indicators is of great

importance to businesses. Carton and Hofer (2008) maintained that the business

outcomes used to represent performance must be in accordance with the organizational

circumstances under consideration. Parmenter (2010) proposed that key performance

indicators represent a set of “measures focusing on those aspects of organizational

performance that are the most critical for the current and future success of the

organization” (p. 4). Richardson (2010) noted that key performance indicators could be

categorized as “leading [future oriented] or lagging [past oriented] indicators,” “simple or

composite indicators,” and “quantitative or qualitative indicators” (p. 367).

Measurement of business outcomes forms an integral part of assessing the success

of an organization in achieving its goals. The performance indicators serve also as

controls that help to ensure that organizational goals are being met. Gupta (2006)

highlighted that an organization’s performance measurement system should monitor and

enable achievement of organizational goals. A good control system, built on periodic

measurement of business outcomes, must be “effective … efficient … timely…

flexible… understandable … tailored … [able to] highlight deviations … [able to] lead to

corrective actions” (Morse & Babcock, 2010, p. 176-177). Additionally, the goals of

successful organizations must be dictated by the organizational vision and mission

statements. Niven (2006) asserted that components of an effective business performance

31

measurement and control system include organizational mission, core values, vision, and

strategy.

Role of Economic Performance Indicators

There are many reasons for studying and interpreting business outcomes in terms

of organizational economic performance indicators. Some of these include:

1) Get the best return on [investment],

2) Measure [economic success of] products,

3) Judge if the time is right to give the go-ahead to a new capital investment

project, to launch a takeover or to move into new markets,

4) Get a better understanding of how a [business] is performing,

5) Judge the [management’s] economic policies,

6) Obtain a feel for [a new business venture],

7) Compare several [businesses],

8) Make a forecast, or

9) Simply obtain a better understanding of the [overall market place]. (The

Economist, 2011, p. 2)

Summary

The advent of the knowledge age has brought about intense focus on the skills,

talents, knowledge, and experience of modern employees. Employers are trying to

enhance the level of employee engagement in order to become more innovative,

competitive, and sustainable. Surveys are considered effective instruments to measure

employee engagement. The five dimensions associated with employee engagement are

alignment with the organization, management effectiveness, salary and compensation,

32

communication, and opportunity for development and recognition. Engineering services

firms provide specialized services to their customers based on their employees’

professional prowess. Business outcomes vis-à-vis key performance indicators not only

provide information about an organization’s financial health, but also could serve as

controls for achieving organizational goals. The next chapter provides information

relevant to the methodology used in this research.

33

CHAPTER 3: RESEARCH METHODOLGY

Introduction

Research is a systematic investigative process that involves interpretation of

observations, guided by the previously existing body of knowledge, with an aim to

answer specific questions. Research methodology can be defined as the “path along

which research can be directed” (Jonker & Pennink, 2010, p. 40). Furthermore, research

methodology “dictates the particular tools” i.e., mechanisms or strategies used to “collect,

manipulate, or interpret data” that will be utilized in the research (Leedy & Ormrod,

2010, p. 12).

This chapter describes the approach used to address the research questions listed

in Chapter 1. The research design and study type are presented. The study population and

sampling are also described. This is followed by an account of the instrumentation design

along with arguments for instrument validity, readability, and reliability. The data

collection process and subsequent data analysis are presented in detail. Additionally,

aspects of confidentiality and anonymity associated with human subjects are covered in

this chapter. A summary of the topics discussed is presented at the end of this chapter.

Study Type and Study Design

After obtaining the necessary permission from the engineering service firm,

approvals were obtained from the research committee and EMU’s Human Subjects

Review Committee. The approval letter from EMU’s Human Subjects Review

Committee is shown in Appendix J. Employee engagement data were collected using a

multi-question survey instrument. The business outcome data were provided by the

engineering services firm’s human resources and finance/accounting departments. The

34

employee responses for each of the survey questions for each year of the three-year

period—2009, 2010, and 2011—formed the independent variables. The business outcome

indicators for each of the three years formed the dependent variables.

The SAGE glossary of the social and behavioral sciences (2009) illustrated that

quantitative research uses quantitative data that are:

collected based on precise measurement of variables using structured,

standardized, and validated data collection instruments and procedures. Data are

analyzed using descriptive and inferential statistics. The desired product is

research findings that generalize broadly. (p. 429)

“Quantitative techniques are particularly strong at studying large groups of people and

making generalizations from the sample being studied to broader groups beyond that

sample” (Holton & Burnett, 2005, p. 30). This research study primarily involved

collection and interpretation of quantitative data. Furthermore, the design of this study

closely matched the description of qualitative research as presented above.

Glass and Hopkins (2008) maintained that descriptive research involves gathering

data pertinent to an event and then performing statistical analysis in an effort to describe

the event. McNeil and Chapman (2005) and Borg and Gall (1989) argued that descriptive

research helps discover answers to questions like what is, how many, and who. This

research study used descriptive statistics to condense and organize large amounts of data

in an effort to describe the relationship between employee engagement and business

outcomes.

Suter (2011) explained that inferential research “attempts to generalize from a

sample that provides data to a large population of interest via use of statistical

35

maneuvers” (p. 59). Furthermore, inferential questions “relate variables or compare

groups in terms of variables so that inferences can be drawn from the sample to a

population” (Creswell, 2009, p. 152). This study involved the usage of a sample to draw

conclusions about a population. Hence, it can be concluded that this is a quantitative

descriptive and inferential research study. The research design is described in detail

below.

Correlation Research

Upon conclusion of data analysis, this study described the statistical relationship

between employee engagement and business outcomes of a Midwestern engineering

services firm. Additionally, this study examined the extent to which differences in one

characteristic or variable are related to differences in one or more other characteristics or

variables. The research design is categorized as co-relational because data analysis

showed whether “…when one variable increases, another variable … increases or

decreases in a somewhat predictable fashion” (Leedy & Ormrod, 2010, p. 183). It must,

however, be noted that existence of a strong correlation between dependent and

independent variables might, or might not, indicate causality.

Survey Research

Survey research design involves “acquiring information about…groups of

people…by asking them questions and tabulating their answers” (Leedy & Ormrod,

2010, p. 187). This research study assessed dimensions/aspects of employee engagement

by using a survey instrument. Survey respondents provided their ratings on a 5-point

Likert rating scale. The ratings were based on their levels of engagement based on self-

36

observation and self-reporting. These ratings were aggregated and analyzed in this

research study.

Developmental Research

Developmental research helps assess changes in groups of people over an

extended period of time (Heffner, 2004). The developmental research study design could

be categorized as: 1) a cross-sectional study design that “compares people in different

…groups” and 2) a longitudinal study design that “follows a …group over …a period of

time” (Leedy & Ormrod, 2010, p. 108). Comer and Gould (2012) explained that, on the

one hand, a pure longitudinal design is plagued by participant mortality, and on the other

hand, a pure cross-sectional design cannot explain intra-group effects and changes. They

also maintained that developmental research design that amalgamates longitudinal and

cross-sectional designs leverages the underlying advantages while eliminating the

inherent disadvantages. The data analyses associated with this study involved both cross-

sectional and longitudinal aspects. This in turn produced a more comprehensive

understanding of the phenomena being studied. Leedy and Ormrod (2010) maintained

that a combination involving both cross-sectional and longitudinal study designs

constitute a cohort sequential study design. Schaie and Baltes (1975) maintained that the

cohort sequential design provides a valuable insight by highlighting inter- and intra-

individual differences over a period of time. Cohort sequential design, also known as

cross-sequential design (Heffner, 2004), was selected for this study.

The design for this study was an amalgamation of 1) cross-sequential

developmental, 2) observational, 3) correlational, and 4) survey research. The study

design entailed analyzing employee engagement data collected by a survey instrument

37

and the selected business outcome data from two business units of the engineering

services firm over a three-year period: 2009, 2010, and 2011.

Study Population and Sampling

In accordance with U. S. Small Business Administration's Business Size

Standards effective November 5, 2010, the engineering services firm, the subject of this

study, can be termed a medium-sized business. For the purposes of this study, firms with

300 to 1000 employees were considered mid-sized businesses. The population associated

with this study included privately held mid-sized engineering services firms in the United

States. The engineering services firm, the subject of this study, constituted a sample of

this population.

External Validity

The engineering services firm, the subject of this study, is very similar to other

U.S.-based privately held mid-sized engineering services firms in terms of numbers of

employees, number of executives, revenue, areas of expertise, and qualification of

management. This comparison was based on the information available from Dun &

Bradstreet, Inc.’s D & B The Million Dollar Database, Bloomberg L.P.’s Bloomberg

Business Week, and Zoom Information, Inc.’s ZoomInfo.com. The similarity between the

engineering services firm, the subject of this study, and other U.S.-based privately held

mid-sized engineering services firms provides solid evidence for the external validity of

this study. This, in turn, implies that the results of this study can be generalized to all

U.S.-based privately held mid-sized engineering services firms.

38

Internal Validity

Internal validity “of a research study is the extent to which its design and the data

it yields allow[s] the researcher to draw accurate conclusions about … relationships

within the data” (Leedy & Ormrod, 2010, p. 97). A 50-question survey was administered

to the employees of the engineering services firm in the years 2009 and 2010. However,

in 2011 keywords for two questions were slightly modified, two questions were added,

and one question was dropped. Hence, these five questions will not be included in this

research study. The list of 47 questions along with the associated coding used in this

study is shown in Appendix D. This was done to ensure that any inadvertent changes in

the survey instrument, method of delivery, and data collection don’t cause variation in the

data quality. The method of delivery and data collection remained unchanged over the

three-year period of 2009, 2010, and 2011. Additionally, the period chosen for this study

was free of any reorganization/downsizing activities. During this period, the

organizational composition of the engineering services firm remained intact. This ensured

minimal subject mortality since employees working for specific business units remained

the same. Furthermore, there were negligible numbers of employee transfers between the

two business units of the engineering services firm during the three-year period. The data

collection method ensured that individual computer terminals were used to complete the

survey instrument. This mitigated possible instances of copying among respondents. The

business outcome data used in this study were collected and reported by the engineering

services firm according to its policies and procedures. The researcher had no influence on

the process of the business outcome data collection and reporting.

39

The employee base of the engineering services firm included people from diverse

backgrounds with regard to experience, responsibility, and age. Since maintaining

anonymity of the survey participants was paramount, there was no reliable way of

guaranteeing that each element of the employee base will be represented. Hence, a non-

probabilistic convenience sampling approach will be used for this study (Leedy &

Ormrod, 2010). Internal validity issues associated with convenience sampling were

alleviated by a broad selection and widespread participation of employees. The survey

instrument was administered to all employees of the engineering services firm. In order to

ensure that the survey results represent the entire employee base at the engineering

services firm, response rates were examined. Leedy and Ormrod (2010) maintained that

response rates of over 70% are very good. Punch (2003) recommended that survey

researchers should strive for response rates 60% or better. Babbie (2008) found that a

response rate of 50% is considered adequate for “analysis and reporting. A response of

60% is good; a response rate of 70% is very good” (p. 289). Martin (2006) considered a

response rate of more than 60% to be high. Phillips et al. (2001) maintained that a

response rate between 50%-80% is considered high. Baruch and Holtom (2008) found, in

their analysis of over 1600 studies from 17 refereed journals, that the response rate for

surveys sent to individuals in organizations averaged around 50%. Sheehan (2001) found

that the response rate for electronically delivered surveys hovered around 40%. The

response rates for the survey of each year of the three-year period are shown in Table 1. It

can be concluded that the response rates were at an acceptable level. They also indicated

that the research study has minimal non-respondent bias.

40

Table 1

Response rate over three-year period

Year 2009 2010 2011

Response Rate 70.3% 80.3% 78.1%

The minimum number of responses, n, needed for adequate estimates from a

sample is given by

( ) ( )

( )

2/ 2

2

1

:, 1 ,

z p pn

Ewherep population proportion confidence level E confidence interval

α

α

⋅ ⋅ −=

= − = =

If no approximation of p is known, one should use p = 0.5. “This value will …

guarantee an accurate prediction, given the confidence interval and the error of estimate”

(Bluman, 2010, p. 349-350). For each of the three years, the number of survey

respondents is greater than the corresponding n values determined at a 95% confidence

level and confidence interval of 0.05. This is shown in Table 2.

41

Table 2

Number of actual responses vs. min. number of responses needed

Year

2009 2010 2011

Total number of

employees

370 362 365

Minimum number

of responses needed

189 187 187

Actual number of

responses

263 284 262

Instrumentation Design

The primary purpose of the survey instrument was to measure employee concerns

with the aim of improving organizational performance. The survey contained questions

pertaining to demographics and employee engagement. The demographic questions

pertained to employee experience, responsibilities, locations, and business unit

affiliations. Employee engagement was measured in the terms of multiple-choice

questions. The questions on the survey can be categorized as 1) classification questions

and 2) factual questions. The respondents provided their input along a continuum of

strongly disagree to strongly agree. This rating scale on these multiple-choice questions

was akin to a 5-point Likert scale. Bryman and Bell (2007) recommended that the Likert

42

scale could be used effectively for qualifying responses to questions about attitudes. The

words on the questions were kept simple, non-hypothetical, and unambiguous. A version

of the survey instrument, modified for the purposes of maintaining anonymity and

confidentiality, used to measure employee engagement at the engineering services firm is

shown in Appendix A. Employee participation in the survey was voluntary.

Demographics-related questions were mandatory. However, survey respondents were not

mandated to complete every question pertaining to employee attitude.

Instrumentation Validity

The survey instrument included Gallup Inc’s Q12, as shown in Appendix B, along

with other questions. Buckingham and Coffman (1999) first recommended the use of the

Q12 to measure employee engagement. Many researchers, including Avolio, Gardner,

Walumbwa, Luthans, and May (2004), Bassi and McMurrer (2005), Bassi and McMurrer

(2008), Catteeuw (2007), Dickson, Ford, and Upchurch (2006), Endres and Mancheno-

Smoak (2008), Gardner and Schermerhorn (2004), Harter, Schmidt, and Hayes (2002),

Harter, Schmidt, Asplund, Killham, and Agrawal (2010), McLagan (2003), Messner

(2009), Milman (2003), and Phillips (2004), have referenced these questions in their

works related to employee engagement. Such widespread use across many different fields

bears testimony to the construct validity of the Q12. Other questions were developed by a

survey consultant based on the needs expressed by the engineering services firm.

Classical Factor Analysis

Classical factor analysis was used to extract a few significant factors from a large

number of quantitative variables. “Common factor analysis seeks to partition variance of

the observable [measured] variables into common variance and error variance. The

43

relationships between the observable variables and factors are called factor loadings”

(Yang, 2005, p. 184). Classical factor analysis could be categorized as exploratory factor

analysis (EFA) and confirmatory factor analysis (CFA). CFA “is more appropriate for

confirming a predetermined factor structure based on theory or prior research” (Yang,

2005, p. 182).

“Communality is assessed as the squared multiple correlation for the variable

using the factors as predictors” (Yang, 2005, p. 186). Communality is given by

ifactorwithkiableofncorrelatiotheofsquareS

factorspecificaiiablesofnumberkwhere

Shk

ikik

var

,,var:1

22

=

==

= ∑=

The sum of squares of the correlations of each variable with each factor gives the part of

the variance accounted for by the factors. In other words, communality is the percentage

of variance in a given variable explained by all the factors jointly. As Tinsley & Tinsley

(1987) explained:

Given that the objective of factor analysis is to identify latent dimensions

underlying the common variance, it is essential to determine what proportion of

the variance in the matrix is common variance. This is unknown prior to factor

analysis, but many forms of factor extraction require [an] initial estimate of the

proportion of common variance. This estimate is referred to as the communality

estimate. The communality, in turn, is the proportion of the total variance of a

variable that is common variance. (p. 417)

The survey instrument demonstrated a high reliability level. Details of scale

reliability of the survey instrument are presented in the next section. Reliable variables

44

were internally consistent and had low random error. Hence, they led to adequate

communalities in factor analysis (Yang, 2005).

There could be many factor solutions to the correlation matrix. The statistical

technique of rotation assists in determining which factor solution should be chosen.

“Rotation [of factors] clarifies the factor structure by spreading variance across the

factors a bit more equitably. Rotation generally results in a more interpretable solution

and one that is more likely to generalize to other samples from the same population”

(Tinsley & Tinsley, 1987, p. 420-421). Orthogonal rotation tends to generate uncorrelated

factors. Varimax rotation is considered the best orthogonal rotation (Fabrigar et al.,

1999). “This rotation method attempts to maximize the variance of squared loadings on a

factor in order to produce some high loadings and some low loadings for each factor”

(Yang, 2005, p. 192).

Statgraphics Centurion XV® version 15.2.14 was used to perform classical CFA

on the survey data from the years 2009, 2010, and 2011. This was done to confirm

whether the data collected by the survey displayed the five constructs of employee

engagement described in Chapter 2. Estimated communalities were revised until the

proportional change in their sum was less than 1x10-10– (the stopping criterion) or 1000

iterations were reached. Only records where all responses were present were used. A

varimax rotation was applied. An eigenvalue of 1 was used as the criterion to determine

the number of factors to extract. The classical factor analysis, which involves adjusting

diagonal elements using estimates of the communalities, generated five factors with

eigenvalue > 1. Together these five factors accounted for 83.831% of the variance. It

must be noted that a total of 20 factors explained 100% variance. Since the last 15 factors

45

had an eigenvalue < 1 and contributed to less than 17% of variance, they were not

considered significant.

Variable loading from the classical CFA were subjectively interpreted to verify

that the factors identified by the factor analysis aligned with the constructs revealed by

the literature review. Appendix E shows results of the CFA. Appendix E also presents

groupings of questions under each dimension associated with employee engagement.

Appendix F shows percentage of variance associated with each factor. The factors

generated by the CFA were consistent with the five dimensions of the employee

engagement discovered in the literature review. This, in turn, conferred well-reasoned

construct validity to the survey instrument.

Instrumentation Readability

Calderón et al. (2006) maintained that Flesch-Kincaid and Flesch Reading Ease

formulas are most commonly used to assess survey readability. Flesch-Kincaid Grade

Level score rates text on a U.S. school grade level. Flesch Reading Ease score rates text

on a 100-point scale. The higher the score, the easier it is to understand the document.

The Flesch-Kincaid Grade Level score is given by (0.39 x ASL) + (11.8 x ASW) – 15.59.

The Flesch Reading Ease score is given by 206.835 – (1.015 x ASL) – (84.6 x ASW). In

the preceding formulas, ASL denotes the number of words divided by the number of

sentences, and ASW denotes the number of syllables divided by the number of words.

The Flesch Reading Ease score for the survey instrument was 58.71. The Flesch-

Kincaid Grade Level score for the survey instrument was 6.86. Calderón et al. (2006)

held that a Flesch Reading Ease Score of around 60 is only moderately difficult to read.

Furthermore, Wilson (2003) reported that the average “American reads at an eighth- or

46

ninth-grade level” (p. 877). Hence, it could be argued that the readability of the survey

instrument is acceptable.

The survey instrument has been used at the engineering services firm since 1998.

The questions selected for this research study have remained unchanged during this

period. The feedback received by the engineering services firm data collectors confirmed

that the survey instrument can be read easily and in an efficient manner.

Scale Reliability

All employee engagement-related data were collected with the same survey

instrument. The business outcome data were collected and reported by the engineering

services firm in accordance with its policies and procedures. Hence, the data collection

methodology was consistent over the three-year period: 2009, 2010, and 2011.

Cronbach (1951) introduced an index of scale reliability commonly referred to as

Cronbach’s alpha. Cronbach’s alpha can be calculated as:

Σ−

−=

test

i

VV

nn 1

1α where n =

number of questions, Vi = variance of scores on each question and, Vtest = total variance

of overall scores of the survey instrument. Nunnally (1978) recommended a Cronbach’s

alpha value of 0.8 or higher for basic research studies and 0.9 or higher for applied

research studies. Also, Murphy and Davidshofer (1988) recommended a Cronbach’s

alpha value of 0.9 or higher. Cronbach’s alpha values for the survey instrument

associated with this research study are presented in Table 3. Since all values are over

0.90, the scale associated with survey instrument was considered acceptable.

47

Table 3

Cronbach’s alphas over the three-year study period

Year 2009 2010 2011

Cronbach’s alpha 0.96 0.97 0.96

Data Collection

Employee engagement-related data were collected using a multi-question survey

instrument. The survey instrument contained 50 questions related to employee

engagement in the years 2009 and 2010. In the year 2011, the survey instrument

consisted of 51 questions related to employee engagement. Only 47 questions were

identical over the three-year period. Hence, only these 47 questions, related to employee

engagement, were used for this research study. These survey questions along with the

associated coding are presented in Appendix D.

SurveyMonkey® was used to administer the survey instrument. Once the survey

was created on SurveyMonkey®, the link to the survey was emailed to the entire

employee base of the engineering services firm. SurveyMonkey® was set up so that any

personal identifying information about the computer used to fill out the survey was not

recorded. Furthermore, no demographic information, other than that illustrated in

Appendix A, was collected. This ensured complete anonymity for the survey respondents.

The responses were downloaded from SurveyMonkey® in a tabular format. These data

were then imported into Microsoft® Office Excel© format.

SurveyMonkey® has been successfully used at various service organizations to

collect employee related data Collier et al. (2005), Massat et al.(2009), Well et al.(2010),

48

Bui et al.(2011) and Cyton (2011). A search performed on Google Scholar on July 25,

2012, for keyword SurveyMonkey returned over 18, 000 citations from scholarly articles.

It could thus be argued that SurveyMonkey® is a reliable and robust tool for survey data

collection.

The business outcome data were collected by the engineering services firm’s

accounting and finance controlling departments. These data were reported by the

engineering services firm upon the request of the researcher. These data were also

imported into Microsoft® Office Excel© format. All data related to employee

engagement and business outcomes were then imported into Statgraphics Centurion

XV®. Various types of data used for this research study are shown in Figure 2.

Figure 2. Data used in research study

49

Data Analysis

Descriptive analysis of the data sets associated with employee engagement and

business outcomes has been presented. The descriptive analysis involved calculations of

the mean, median, mode, range, standard deviations, standard skewness, and standard

kurtosis values associated with the survey data. Employee engagement ratios were

calculated for the engineering services firm and each of the two business units for each of

the three years. One-way analysis of variance (ANOVA) was performed to:

1) establish whether ratings of each of the 47 survey questions vary by year for

the engineering service firm,

2) establish whether ratings of each of the 47 survey questions vary by year for

each of the two business units of the engineering services firm.

The Kruskal-Wallis median comparison tests were also performed to confirm the

results of the one-way ANOVA.

Structural Equation Modeling

The Structural Equation Modeling (SEM) procedure was employed to illustrate

the linkages, in terms of the Student’s t statistic, between the five dimensions of the

employee engagement. The SEM procedure also demonstrated that confidence level

associated with these linkages seemed to change over the three years for each entity.

Furthermore, the structural model associated with SEM procedure was used to describe

the causal relationships among the latent variables/constructs (Anderson & Gerbing,

1982).

SEM models consist of observed variables (also called manifest or measured, MV

for short) and unobserved variables (also called underlying or latent, LV for short)

50

that can be independent (exogenous) or dependent (endogenous)\ in nature. LVs

are hypothetical constructs that cannot be directly measured, and in SEM are

typically represented by multiple MVs that serve as indicators of the underlying

constructs. The SEM model is an a priori hypothesis about a pattern of linear

relationships among a set of observed and unobserved variables. (Shah &

Goldstein, 2006, p. 149)

For this research study, the variance based partial least square (PLS SEM)

technique was used to explore the relationship between public health and technological

innovation. “PLS is primarily intended for causal-predictive analysis in situations of high

complexity but low theoretical information” (Jöreskog & Wold, 1982, p. 270).

PLS path models are formally defined by two sets of linear equations: the inner

model and the outer model. The inner model specifies the relationships between

unobserved or latent variables, whereas the outer model specifies the relationships

between a latent variable and its observed or manifest variables … The PLS

algorithm is essentially a sequence of regressions in terms of weight vectors.

(Henseler, Ringle, & Sinkovics, 2009, p. 284)

The individual path coefficients of the PLS structural model can be interpreted as

standardized β coefficients (slopes) of ordinary least squares regressions. Parameter

estimates are obtained by minimizing the residual variances of dependent variables. In

order to determine the confidence intervals of the path coefficients and draw statistical

inference, a re-sampling bootstrapping technique, involving random drawing of cases

with replacement from the original sample, can be used to calculate a Student’s t statistic

(Tenenhaus et al., 2005).

51

The nonparametric bootstrap procedure can be used in PLS path modeling to

provide confidence intervals for all parameter estimates, building the basis for

statistical inference … The PLS results for all bootstrap samples provide the mean

value and standard error for each path model coefficient. This information permits

a Student’s t-test to be performed for the significance of path model relationships.

(Henseler, Ringle, & Sinkovics, 2009, p. 305-306)

Chin (1998) proposed the Student’s t statistic which is given by: )(wse

wt = where

w is the original PLS estimate of a certain path coefficient, and se (w) is its boot

strapping standard error.

For the purposes of this research study, the five dimensions of employee

engagement were defined in terms of survey questions with highest loading values from

the factors analysis. The classical factor analysis employed in this study is described

earlier in Chapter 3. Henseler, Ringle, and Sinkovics (2009) asserted that a formative

measurement model “is adequate when a construct is defined as a combination of its

indicators” (p. 289). Furthermore, the PLS bootstrap path modeling algorithm allows for

the computation of cause effect relationship models that employ both reflective and

formative measurement models (Diamantopoulos & Winklhofer, 2001). Green and Ryans

(1990), Johansson and Yip (1994), Birkinshaw, Morrison, and Hull (1995), Venaik,

Midgley, and Devinney (2005), Julien and Ramangalahy (2003), and Nijssen and

Douglas (2008) maintained that the PLS could be used for data with any type of

distribution and in cases with large or small sample sizes. Hence, the PLS SEM formative

models were determined to be adequate for data associated with this research study.

52

The five dimensions of employee engagement served as the latent variables in the

PLS SEM diagrams. Various survey questions with the highest loading values served as

the endogenous and exogenous variables relative to places in the PLS SEM diagrams.

The results of the PLS SEM are presented in Chapter 4. Student’s t statistic was

employed to test whether path coefficients between different latent variables were

statistically significant. Henseler, Ringle, & Sinkovics (2009) proposed that degrees of

freedom are given by: m+n-2 where m is the number of PLS estimates for w in the

original sample, n is the number of bootstrap estimates. Table 4 presents the number of

completed responses by entity and year. The minimum number of responses was found to

be 45. This number of responses served as a conservative estimate of degrees of freedom

for calculation of acceptable t values. For data samples with degrees of freedom ≥ 45,

statistical significance is demonstrated at 95%, two-sided, confidence intervals if the t

values ≥ 2.02.

Table 4

Number of complete responses by year and entity

Year Entity Code 2009 2010 2011 Engineering services firm esf 242 255 229 Business Unit – Transportation esf_bu_tr 64 64 65 Business Unit – Water esf_bu_wa 45 52 49

Linear regression analyses were performed. Annual average ratings of each

survey question served as independent variables. The annual average business outcome

data served as dependent variables. The R-Squared statistic was calculated for every pair

of dependent and independent variables for each business unit and the engineering

services firm overall. The R-Squared statistic indicated the amount of variability

53

explained by the model. Linear regression equations were constructed for annual average

ratings of each of the 47 survey questions and annual business outcome data. These

regression equations served as predictive models to estimate changes in the dependent

variables associated with changes in the independent variables. A figure in Appendix G

summarizes the data analyses with respect to the research questions and the respective

hypotheses. The statistical significance level for the study will be a p value of ≤ 0.05.

Safety, Confidentiality, and Anonymity of Human Subjects

Safety, confidentiality, and anonymity of human subjects were paramount, and

every effort was made to ensure these. A permission letter had been obtained from the

engineering service firm to use their data pertaining to 1) business outcomes and 2)

employee surveys. The letter was signed by the engineering services firms Chief Human

Resources Officer. A copy of the permission letter is shown in Appendix C. A version of

the survey instrument, modified for the purposes of maintaining anonymity and

confidentiality, used to measure employee engagement at the engineering services firm is

shown in Appendix A.

The individual records from human subjects were anonymous. Identifying

information related to the engineering services firm, their business units, and their

employees, including but not limited to names, places of work, and locations of offices

has been kept anonymous and confidential and was removed from the data sets received

by the researcher. Additionally, any identifying information about the engineering

services firm will be kept confidential and was not be included in this research study.

The use of information associated with the human subjects was in accordance

with the guidelines of EMU’s Office of Research and Development. Furthermore, the

54

data use and analysis began pursuant to formal approval by EMU’s Human Subjects

Review Committee on the Request for Human Subject Approval form.

Summary

The study involved cross-sequential developmental, correlational, and survey

research. The research design vis-à-vis population, sample, business outcome data, and

survey instrument ensure external, internal, and construct validities. The levels of

readability and reliability associated with the survey instrument used in the study are

acceptable. Methods and processes associated with data collection and subsequent

analysis are discussed in detail. Safety, confidentiality, and anonymity of human subjects

were maintained.

55

CHAPTER 4: RESULTS

Introduction

This chapter presents the results of the data analyses described in Chapter 3.

Descriptive data analysis was performed to establish the characteristics of the survey

data. Employee engagement ratios are tabulated for each year and each entity. Each

hypothesis is tested, and evidence supporting rejection of all four null hypotheses is

presented. Results of the one-way ANOVA and the Kruskal-Wallis median comparison

tests are presented in an effort to illustrate consistency in the ratings of survey questions

over the period of three years. Statistically significant regression equations representing

items related to employee engagement, as defined by the employee survey, and business

outcomes have been presented. Additionally, findings of the data analysis associated with

the survey questions based on structural equation modeling procedure have been

illustrated in this chapter. A summary of the results is presented at the end of this chapter.

Characteristics of Survey Data

Descriptive analysis of the survey question was performed. Results of the

descriptive analyses are presented in Tables 5 through 9. The mean values of the survey

questions ranged from 2.83 to 4.14. The median values of the survey question ranged

from 3 to 4. The Pearson product moment correlation coefficients ranged from 0.86 to

0.11. The median value for the Pearson product moment correlation coefficients was 0.33

and the mode value was 0.39. Only two pairs of survey questions shared Pearson product

moment correlation coefficient greater than 0.85. 1) AS6 (I have a regular at least

monthly one on one with my manager) and C9 (I have a regular at least monthly one on

one meeting with my supervisor) shared a Pearson product moment correlation

56

coefficients of 0.86. PBC4 (I am paid fairly) and PBC6 (My total compensation is

competitive) shared a Pearson product moment correlation coefficients of 0.85. The

description of these questions is quite similar. Hence, it not surprising that they are

correlated. However, it can be inferred that the most survey questions did not show a

strong correlation. The complete Pearson product moment correlation matrix for the

survey questions is presented in Appendix H. It must be noted that all correlation

coefficients had p-values below 0.05. The p-value tests the statistical significance of the

estimated correlations. Hence, it can be inferred that all survey questions exhibited

statistically significant non-zero correlations at the 95.0% confidence level.

Of particular interest here were the standardized skewness and standardized

kurtosis values. These values determine whether the samples come from a normal

distribution. Values of these statistics outside the range of -2 to +2 indicate significant

departures from normality. Either the standardized skewness values or standardized

kurtosis values or both the values for all survey questions were outside this range. Tutz

(2011) and Stephens (2004) explained that survey data typically exhibit a multinomial

distribution. Notwithstanding this fact, Chapman (2012), Siegel (2011), Asp (2006), and

Poser and Bloesch (2002) suggested that survey data have been typically treated as

normally distributed in many widely accepted survey-based studies. The survey data in

this study was treated as normally distributed. However, statistical test not dependent on

the assumption of normality were used to confirm the results.

57

Table 5

Descriptive analysis of survey questions

AS1 AS2 AS3 AS4 AS5 AS6 AS7 AS8 AS9 AS10 Count 807 807 807 806 805 807 808 808 806 804 Mean 3.14 3.7 3.51 3.59 3.52 2.94 3.8 3.25 3.19 3.96 Std Err 0.03 0.03 0.03 0.03 0.03 0.04 0.03 0.04 0.03 0.03 Median 3 4 4 4 4 3 4 3 3 4 Mode 3 4 4 4 4 2 4 4 3 4 Std Dev 0.91 0.75 0.8 0.77 0.86 1.17 0.89 1.06 0.84 0.83 Min 1 1 1 1 1 1 1 1 1 1 Max 5 5 5 5 5 5 5 5 5 5 Range 4 4 4 4 4 4 4 4 4 4 Std. skew. -4.46 -6.30 -5.15 -6.52 -5.49 0.44 -10.10 -4.02 -3.37 -9.94 Std. kurt. -0.73 6.65 1.67 5.73 3.39 -6.02 4.81 -3.16 2.80 7.68

Table 6

Descriptive analysis of survey questions

AS11 C1 C2a C2c C2d C4 C5 C6 C7 C8 Count 805 807 807 804 805 808 809 808 808 806 Mean 3.58 3.45 3.63 3.91 3.05 3.23 3.57 3.62 3.78 3.87 Std Err 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 Median 4 4 4 4 3 3 4 4 4 4 Mode 4 4 4 4 4 3 4 4 4 4 Std Dev 0.9 0.95 0.9 0.9 0.94 0.86 0.91 0.91 0.87 0.9 Min 1 1 1 1 1 1 1 1 1 1 Max 5 5 5 5 5 5 5 5 5 5 Range 4 4 4 4 4 4 4 4 4 4 Std. skew. -8.42 -8.92 -10.61 -12.89 -2.18 -4.60 -10.37 -11.02 -12.43 -11.19 Std. kurt. 3.68 -0.73 2.66 8.50 -5.03 1.09 3.38 4.17 7.97 6.17

58

Table 7

Descriptive analysis of survey questions

C9 C10b C11a C11b C12 PBC1 PBC2a PBC2b PBC2c PBC3 Count 806 800 808 808 809 809 809 809 805 807 Mean 3 3.33 3.65 3.7 3.76 3.76 4.11 3.94 4.12 4.14 Std Err 0.04 0.03 0.04 0.03 0.03 0.03 0.02 0.03 0.03 0.03 Median 3 4 4 4 4 4 4 4 4 4 Mode 4 4 4 4 4 4 4 4 4 4 Std Dev 1.23 0.95 1.02 0.93 0.93 0.93 0.64 0.82 0.71 0.83 Min 1 1 1 1 1 1 1 1 1 1 Max 5 5 5 5 5 5 5 5 5 5 Range 4 4 4 4 4 4 4 4 4 4 Std. skew. -0.07 -4.69 -10.12 -10.34 -7.51 -12.44 -10.79 -13.18 -15.52 -15.72 Std. kurt. -6.62 -2.98 1.74 4.34 2.09 5.27 16.21 10.71 23.04 15.07

Table 8

Descriptive analysis of survey questions

PBC4 PBC5 PBC6 PBC7 PBC8 Q1 Q2 Q3 Q4 Q5 Count 808 808 807 809 805 808 807 801 805 808 Mean 3.51 3.09 3.49 3.33 3.65 4.04 3.84 3.69 3.25 3.96 Std Err 0.04 0.04 0.03 0.04 0.03 0.03 0.03 0.03 0.04 0.03 Median 4 3 4 4 4 4 4 4 3 4 Mode 4 4 4 4 4 4 4 4 4 4 Std Dev 1.01 1.12 0.95 1.03 0.87 0.81 0.94 0.97 1.2 0.86 Min 1 1 1 1 1 1 1 1 1 1 Max 5 5 5 5 5 5 5 5 5 5 Range 4 4 4 4 4 4 4 4 4 4 Std. skew. -9.82 -3.40 -9.97 -7.07 -12.18 -14.63 -12.40 -9.06 -3.01 -11.06 Std. kurt. 0.64 -5.17 1.50 -1.59 7.43 12.69 5.18 0.08 -5.68 7.33

59

Table 9

Descriptive analysis of survey questions

Q6 Q7 Q8 Q9 Q10 Q11 Q12 Count 809 807 808 806 807 805 806 Mean 3.66 3.68 3.55 4.11 2.83 3.46 3.83 Std Err 0.04 0.04 0.03 0.03 0.03 0.04 0.03 Median 4 4 4 4 3 4 4 Mode 4 4 4 4 3 4 4 Std Dev 1 1 0.96 0.77 0.98 1.09 0.93 Min 1 1 1 1 1 1 1 Max 5 5 5 5 5 5 5 Range 4 4 4 4 4 4 4 Std. skew. -8.36 -10.45 -8.00 -12.39 1.23 -7.03 -11.95 Std. kurt. 0.14 2.43 1.02 11.42 -2.93 -2.93 6.24

Employee Engagement Ratios

Annualized employee engagement ratios for each entity are presented in Figure 3.

Survey responses with no entries were excluded from calculations used for determining

employee engagement ratios. It was found the business unit-water had the highest

employee engagement ratios among the three entities. The business unit-transportation

had the lowest employee engagement ratios among the three entities. It was also found

that the employee engagement ratios were the highest for the year 2010 for the

engineering services firm and business unit-transportation. The year 2011 exhibited the

lowest employee engagement ratios for all entities. In case of the business unit-water

employee engagement ratios displayed an almost straight-line decline. However, its

employee engagement ratios fared generally better than the other two entities.

World-class organizations have an employee engagement ratio of 9.57, whereas

average organizations have an employee engagement ratio of 1.83 (Gallup Inc., 2010).

However, these limits were established by Gallup Inc. based on analysis of the Q12 survey

questions from several organizations. Employee engagement ratios were calculated for all

60

entities based on questions in the survey instrument, which were derived from Gallup

Inc.’s Q12 survey questions. These ratios are shown in Figure 4. It could be inferred that

employee engagement ratios for all entities studied, calculated from Q12 survey questions,

were above the level associated with an average organization in the years 2009 and 2010.

Employee engagement ratios for all entities were the highest in the year 2010. In the year

2011, the employee engagement ratios for the engineering services firm and business

unit-transportation fell below the level associated with an average organization.

Additionally, the employee engagement ratios, calculated from Q12 survey questions, for

business unit-water showed least amount of year-to-year variation. Its employee

engagement ratios, unlike other two entities, stayed above the employee engagement ratio

associated with an average organization over the three-year period. However, the

employee engagement ratios for all three entities were lower than work class

organizations for all three entities.

Figure 3. Employee engagement ratio based on all 47 survey questions by year and entity

61

Figure 4. Employee engagement ratio based on Gallup Q12 survey questions by year and entity

Average ratings for all survey questions, from engaged and disengaged

employees, are presented in Table 10. The average ratings for all survey questions were

ranked from highest to lowest for every year and every entity studies. The top 12 (≈ 25%

of the total number of questions) survey questions in terms of average ratings are

highlighted in green. The bottom 12 (≈25% of the total number of questions) survey

questions in terms of average ratings are highlighted in red.

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Table 10 Comparison of average survey ratings for engaged vs. disengaged employees by year and entity

63

It was found that survey questions related to the employee engagement dimension of

salary and compensation PBC5 (I feel secure from layoffs) and PBC 7 (The Engineering

Services Firm's compensation program encourages me to work efficiently effectively)

received low ratings at all three entities for most years from both engaged and disengaged

employees. Survey questions related to the employee engagement dimensions of

communication, opportunity for development and recognition, and management

effectiveness C2d (Communications are good between offices), C4 (The mission of the

company is updated using employee input), C9 (I have a regular at least monthly one on

one meeting with my supervisor), AS6 (I have a regular at least monthly one on one with

my manager), AS8 (I have opportunities for advancement at The Engineering Services

Firm), and AS9 (Concerns expressed during previous employee surveys are being

addressed) also received low average rating compared to other questions. Furthermore,

Q10 (I have a best friend at work) received low ratings for all but one year.

Survey questions related to prevalence of respect among employee base, PBC2a

(Employees are treated with respect by co workers), PBC2b (Employees are treated with

respect by management), and PBC2c (Employees are treated with respect by supervisor)

consistently received high ratings. PBC3 (There exists flexibility for personal tie/family

needs) received high ratings across the board. Q1 (I know what is expected of me at

work), Q9 (My co workers are committed to doing quality work), and AS10 (I intend to

remain at The Engineering Services Firm employee for the next two years) also received

high ratings from both engaged and disengaged employees, for most years, at all three

entities.

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Hypotheses Testing

The four null hypotheses presented in Chapter 1 were tested based on the data

analysis methods presented in Chapter 3 per approval of the dissertation committee. The

results of the hypotheses testing are presented in the subsequent sections. As mentioned

previously, the statistical significance level for the study was a p value of ≤ 0.05.

One-way ANOVA was used to test the impact of a single categorical independent

variable on a dependent variable. The impact was determined using the F-test at the ≥

95% confidence level (i.e., p ≤ 0.05). The ANOVA is subject to the assumptions that

samples 1) obtained must be normally or approximately normally distributed, 2) were

independent and, 3) variances of the populations were equal. It was found that the

interaction between the year and the business unit was insignificant for every survey

question. The p values associated with the interaction of these terms can be found in

Appendix L. For non-normal or ordinal data, the Kruskal-Wallis median test could be

employed. The Kruskal-Wallis test evaluates whether the medians of a dependent

variable are the same across all levels of independent variable. Unlike the standard

analysis of variance the Kruskal-Wallis test compares level medians instead of means. In

order to perform this test data from each year was ranked from smallest to largest. The

average rank was then computed for the data from each year. A p value equal to or less

than 0.05 indicated significant difference amongst the medians at the 95.0% confidence

level. These methods were used to test Hypothesis 1 and Hypothesis 2. Note: A

description of survey questions along with the associated coding can found in Appendix

D.

65

Hypothesis 1

The results of the one-way ANOVA and the Kruskal-Wallis median comparison

tests showed that ratings for survey questions Q1, Q2, Q11, C2d, C4, C6, C8, C9, AS1,

AS2, AS3, AS4, AS5, AS6, and AS9 were statistically different for the engineering

services firm over the three years. The p values for the both the ANOVA and Kruskal-

Wallis median comparison tests are presented in Table 11. Multiple range test based on

Fisher’s least significant difference (LSD) procedure was used to identify survey

questions whose mean ratings were significantly different with respect to business unit. It

was found that business unit-water had significantly higher average ratings for survey

questions Q1, C7, C11a, C11b, AS1 and AS2 when compared to business unit-

transportation.

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Table 11

p Values associated with one way ANOVA & K-W median comparison tests

67

Since there were many survey questions whose ratings were statistically different,

the null hypothesis that there is no significant variability in employee engagement, as

defined by the employee survey questions, of the engineering services firm over the years

2009, 2010, and 2011 was rejected at a 0.05 significance level.

Hypothesis 2

Business unit-transportation. The results of the one-way ANOVA and the

Kruskal-Wallis median comparison tests showed that ratings for survey questions Q11,

C4, C6, C11b, AS1, AS2, AS4, AS5, and AS6 were statistically different over the three-

year period.

Business unit-water. The results of the one-way ANOVA and the Kruskal-Wallis

median comparison tests showed that ratings for survey questions C6, C8, AS1, and AS5

were statistically different over the three-year period.

The p values for the both the ANOVA and Kruskal-Wallis median comparison

tests are presented in Table 11. Since there were many survey questions whose ratings

were statistically different, the null hypothesis that there is no significant variability in

employee engagement, as defined by the employee survey questions, for the two business

units of engineering services firm over the years 2009, 2010, and 2011 was rejected at a

0.05 significance level. The engineering services firm ratings for 15 survey questions had

statistically significant differences. In the case of the business unit-transportation, ratings

for 9 survey questions had statistically significant differences. In the case of the business

unit-water, ratings for 4 survey questions had statistically significant differences. Small

variation in the ratings could explain the relatively little change in employee engagement

ratios for the business unit-water from 2010 to 2011.

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The survey questions, which varied by year for each entity, were reviewed in

terms of their relative position in the factor loadings table presented in Appendix E, and

generated by the classical factor analysis described in Chapter 3. The ratings of survey

questions, which varied by year from the engineering services firm, were related to either

opportunity for development and recognition, alignment with the organization,

management effectiveness, or the communication dimension of employee engagement.

The survey questions from business unit-transportation were related to either opportunity

for development and recognition, alignment with the organization, management

effectiveness, or the communication dimension of employee engagement. The survey

questions from business unit-water were related to either alignment with the organization,

management effectiveness, or communication dimension of employee engagement.

However, the ratings of survey questions associated with salary and compensation did not

vary over the three years for any entity.

Results of Structural Equation Modeling

A structural equation model was created to test whether communication had a

causal impact on effective management. Additionally, the structural equation modeling

procedure also tested whether the effective management, in turn, had causal effects on

opportunities for recognition and development, salary and compensation, and alignment

with the organization. This model is associated in Figure 5. These causal paths between

the five latent variables were then evaluated by using the partial least squares bootstrap

algorithm. The bootstrap algorithm involves a re-sampling procedure for obtaining

“empirical standard errors to enable inferential hypothesis testing in partial least squares

framework” (Bovaird et al., 2007, p. 1; Sharma & Kim, 2012). The sampling was

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performed at three different rates: 100, 200, and 300. The results for each year and each

sample rate in the case of the engineering services firm are presented in Table 12. The

results for each year and each sample rate in the case of the business unit-transportation

are presented in Table 13. The results for each year and each sample rate in the case of

the business unit-water are presented in Table 14. The t values were greater than 2.04 for

all paths. This provided evidence that the causal model shown in Figure 5 could be

considered accurate. A detailed path model showing manifest variables and latent

variables (endogenous and exogenous) is presented in Appendix K.

It must be noted that the structural equation modeling procedure could be used to

create a reliable causal model for various dimensions of employee engagement. This

would include an expansive and iterative analysis based on factor loadings of individual

survey questions with regard to specific employee engagement dimensions. Such further

evaluation based on structural equation modeling is beyond the scope of this research

study. However, such an analysis could be part of future research efforts.

Figure 5. Structural equation model for the five dimensions of employee engagement

(Note: Results shown here are for the engineering services firm for the year 2009 with

100 bootstrap samples where β is path coefficient; t is Student’s t statistic.)

Alignment with organization

Salary & compensation

Opportunity for development &

recognition

Management effectiveness

Communication β=0.627 t=8.537

β=0.797 t=23.370

β=0.659 t=10.000

β=0.668 t=10.050

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Table 12 Data analysis based on structural equation modeling procedure for the engineering service firm over the three-year period with bootstrap sampling rates of 100, 200, and 300

Entity Year

Bootstrap Sample

Rate Path Sample Mean

Std dev

Std err t value

Engineering Services

Firm 2009 100

Comm -> Mgt_eff 0.681 0.074 0.074 8.537 Mgt_eff -> Align_org 0.706 0.066 0.066 10.004 Mgt_eff -> Op_dev_rec 0.834 0.034 0.034 23.370 Mgt_eff -> Sal_comp 0.708 0.066 0.066 10.054

Engineering Services

Firm 2009 200

Comm -> Mgt_eff 0.686 0.072 0.072 8.683 Mgt_eff -> Align_org 0.705 0.063 0.063 10.508 Mgt_eff -> Op_dev_rec 0.836 0.035 0.035 22.706 Mgt_eff -> Sal_comp 0.718 0.065 0.065 10.324

Engineering Services

Firm 2009 300

Comm -> Mgt_eff 0.688 0.068 0.068 9.225 Mgt_eff -> Align_org 0.701 0.067 0.067 9.822 Mgt_eff -> Op_dev_rec 0.843 0.033 0.033 24.047 Mgt_eff -> Sal_comp 0.706 0.073 0.073 9.164

Engineering Services

Firm 2010 100

Comm -> Mgt_eff 0.645 0.083 0.083 7.045 Mgt_eff -> Align_org 0.586 0.091 0.091 5.820 Mgt_eff -> Op_dev_rec 0.835 0.032 0.032 24.649 Mgt_eff -> Sal_comp 0.746 0.066 0.066 10.593

Engineering Services

Firm 2010 200

Comm -> Mgt_eff 0.650 0.085 0.085 6.877 Mgt_eff -> Align_org 0.585 0.082 0.082 6.476 Mgt_eff -> Op_dev_rec 0.837 0.035 0.035 22.320 Mgt_eff -> Sal_comp 0.735 0.067 0.067 10.443

Engineering Services

Firm 2010 300

Comm -> Mgt_eff 0.645 0.079 0.079 7.389 Mgt_eff -> Align_org 0.593 0.082 0.082 6.495 Mgt_eff -> Op_dev_rec 0.839 0.033 0.033 23.728 Mgt_eff -> Sal_comp 0.738 0.061 0.061 11.480

Engineering Services

Firm 2011 100

Comm -> Mgt_eff 0.718 0.068 0.068 9.815 Mgt_eff -> Align_org 0.610 0.073 0.073 7.855 Mgt_eff -> Op_dev_rec 0.847 0.031 0.031 26.365 Mgt_eff -> Sal_comp 0.750 0.053 0.053 13.502

Engineering Services

Firm 2011 200

Comm -> Mgt_eff 0.718 0.058 0.058 11.504 Mgt_eff -> Align_org 0.624 0.081 0.081 7.124 Mgt_eff -> Op_dev_rec 0.849 0.033 0.033 24.923 Mgt_eff -> Sal_comp 0.747 0.052 0.052 13.622

Engineering Services

Firm 2011 300

Comm -> Mgt_eff 0.723 0.064 0.064 10.523 Mgt_eff -> Align_org 0.634 0.072 0.072 8.058 Mgt_eff -> Op_dev_rec 0.853 0.035 0.035 23.235 Mgt_eff -> Sal_comp 0.755 0.055 0.055 12.916

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Table 13 Data analysis based on structural equation modeling procedure for the business unit-transportation over the three-year period with bootstrap sampling rates of 100, 200, and 300

Entity Year

Bootstrap Sample

Rate Path Sample Mean Std dev Std err t value

Business Unit -

Transportation 2009 100

Comm -> Mgt_eff 0.760 0.072 0.072 10.037 Mgt_eff -> Align_org 0.712 0.064 0.064 10.536 Mgt_eff -> Op_dev_rec 0.885 0.025 0.025 34.284 Mgt_eff -> Sal_comp 0.702 0.070 0.070 9.860

Business Unit -

Transportation 2009 200

Comm -> Mgt_eff 0.745 0.080 0.080 9.054 Mgt_eff -> Align_org 0.698 0.067 0.067 10.066 Mgt_eff -> Op_dev_rec 0.887 0.023 0.023 37.113 Mgt_eff -> Sal_comp 0.721 0.056 0.056 12.313

Business Unit -

Transportation 2009 300

Comm -> Mgt_eff 0.758 0.066 0.066 10.975 Mgt_eff -> Align_org 0.696 0.065 0.065 10.372 Mgt_eff -> Op_dev_rec 0.891 0.022 0.022 38.727 Mgt_eff -> Sal_comp 0.714 0.061 0.061 11.193

Business Unit -

Transportation 2010 100

Comm -> Mgt_eff 0.733 0.057 0.057 12.180 Mgt_eff -> Align_org 0.607 0.084 0.084 6.742 Mgt_eff -> Op_dev_rec 0.904 0.021 0.021 42.561 Mgt_eff -> Sal_comp 0.813 0.046 0.046 17.227

Business Unit -

Transportation 2010 200

Comm -> Mgt_eff 0.717 0.065 0.065 10.625 Mgt_eff -> Align_org 0.602 0.076 0.076 7.419 Mgt_eff -> Op_dev_rec 0.897 0.024 0.024 36.803 Mgt_eff -> Sal_comp 0.811 0.048 0.048 16.499

Business Unit -

Transportation 2010 300

Comm -> Mgt_eff 0.725 0.062 0.062 11.132 Mgt_eff -> Align_org 0.604 0.068 0.068 8.283 Mgt_eff -> Op_dev_rec 0.899 0.024 0.024 36.588 Mgt_eff -> Sal_comp 0.811 0.046 0.046 17.144

Business Unit -

Transportation 2011 100

Comm -> Mgt_eff 0.778 0.050 0.050 15.279 Mgt_eff -> Align_org 0.751 0.064 0.064 11.465 Mgt_eff -> Op_dev_rec 0.899 0.025 0.025 35.256 Mgt_eff -> Sal_comp 0.807 0.048 0.048 16.306

Business Unit -

Transportation 2011 200

Comm -> Mgt_eff 0.788 0.048 0.048 15.810 Mgt_eff -> Align_org 0.748 0.055 0.055 13.132 Mgt_eff -> Op_dev_rec 0.899 0.022 0.022 39.245 Mgt_eff -> Sal_comp 0.805 0.042 0.042 18.870

Business Unit -

Transportation 2011 300

Comm -> Mgt_eff 0.792 0.045 0.045 16.824 Mgt_eff -> Align_org 0.755 0.053 0.053 13.825 Mgt_eff -> Op_dev_rec 0.901 0.022 0.022 39.498 Mgt_eff -> Sal_comp 0.807 0.044 0.044 17.998

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Table 14 Data analysis based on structural equation modeling procedure for the business unit-water over the three-year period with bootstrap sampling rates of 100, 200, and 300

Entity Year

Bootstrap Sample

Rate Path Sample Mean Std dev Std err t value

Business Unit - Water

2009 100

Comm -> Mgt_eff 0.764 0.043 0.043 17.112 Mgt_eff -> Align_org 0.797 0.038 0.038 20.360 Mgt_eff -> Op_dev_rec 0.941 0.014 0.014 65.414 Mgt_eff -> Sal_comp 0.837 0.035 0.035 23.033

Business Unit - Water

2009 200

Comm -> Mgt_eff 0.772 0.042 0.042 17.467 Mgt_eff -> Align_org 0.798 0.039 0.039 19.984 Mgt_eff -> Op_dev_rec 0.941 0.014 0.014 65.178 Mgt_eff -> Sal_comp 0.831 0.037 0.037 21.783

Business Unit - Water

2009 300

Comm -> Mgt_eff 0.775 0.040 0.040 18.676 Mgt_eff -> Align_org 0.799 0.037 0.037 21.186 Mgt_eff -> Op_dev_rec 0.942 0.013 0.013 69.466 Mgt_eff -> Sal_comp 0.828 0.035 0.035 23.280

Business Unit - Water

2010 100

Comm -> Mgt_eff 0.800 0.040 0.040 18.928 Mgt_eff -> Align_org 0.664 0.066 0.066 9.366 Mgt_eff -> Op_dev_rec 0.897 0.025 0.025 35.497 Mgt_eff -> Sal_comp 0.815 0.031 0.031 25.990

Business Unit - Water

2010 200

Comm -> Mgt_eff 0.804 0.043 0.043 17.837 Mgt_eff -> Align_org 0.669 0.066 0.066 9.311 Mgt_eff -> Op_dev_rec 0.896 0.024 0.024 36.320 Mgt_eff -> Sal_comp 0.819 0.033 0.033 24.549

Business Unit - Water

2010 300

Comm -> Mgt_eff 0.800 0.043 0.043 17.926 Mgt_eff -> Align_org 0.670 0.067 0.067 9.193 Mgt_eff -> Op_dev_rec 0.896 0.024 0.024 37.103 Mgt_eff -> Sal_comp 0.821 0.030 0.030 26.593

Business Unit - Water

2011 100

Comm -> Mgt_eff 0.748 0.055 0.055 12.837 Mgt_eff -> Align_org 0.662 0.075 0.075 7.908 Mgt_eff -> Op_dev_rec 0.879 0.037 0.037 23.355 Mgt_eff -> Sal_comp 0.769 0.042 0.042 17.350

Business Unit - Water

2011 200

Comm -> Mgt_eff 0.749 0.054 0.054 13.175 Mgt_eff -> Align_org 0.658 0.073 0.073 8.193 Mgt_eff -> Op_dev_rec 0.883 0.034 0.034 25.328 Mgt_eff -> Sal_comp 0.769 0.043 0.043 16.977

Business Unit - Water

2011 300

Comm -> Mgt_eff 0.744 0.052 0.052 13.737 Mgt_eff -> Align_org 0.653 0.071 0.071 8.407 Mgt_eff -> Op_dev_rec 0.884 0.032 0.032 27.009 Mgt_eff -> Sal_comp 0.773 0.044 0.044 16.738

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The t-values associated with each path/linkage changed for each year in case of

the engineering services firm, the business unit-transportation and the business unit-

water. These variations demonstrate an inherent variability in the relationship between

different survey questions. The variations in t-values are shown in Appendix I. The

structural equation modeling path model associated with survey questions with high

factor loading values are shown in Appendix K.

Hypothesis 3

Linear regression analyses were performed for each question on the survey

instrument and each business outcome considered in this study. For pairs where the p

values were equal to or less than 0.05, linear regression equations were formed. In these

cases the p values, the R-Square statistic values, type of relational proportionality, and

regression equations are reported in Table 15. It was found that many survey questions

shared a significant relationship with business outcomes. The relationships were

inversely proportional for some pairs and directly proportional for other pairs. Employee

growth rate and ratings associated with Q5 (My supervisor or someone at work cares for

me as a person) had a slope of 3.531. This indicated a strong positive relation between

the two variables. Furthermore, ratings associated with none of the survey questions that

exhibited a significant relationship with a business outcome displayed a statistically

significant variation (ANOVA and Kruskal-Wallis median comparison tests) by year.

Ratings for survey questions AS10, AS7, and Q4 shared significant relationships with

two or more business outcomes. This in turn highlighted the relative importance of these

survey questions with regard to business outcomes at the engineering service firm. AS10

is related to I intend to remain at the Engineering Service Firm for the next two years

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survey question. AS7 is related to I am happy with my job survey question. Q4 is related

to In the last seven days, I have received recognition or praise for doing good work

survey question. Based on the preceding discussion, the hypothesis that there is no

significant relationship between any item related to employee engagement, as defined by

the employee survey and any business outcome of the engineering services firm over the

years 2009, 2010, and 2011 was rejected.

Hypothesis 4

Business unit-transportation. Linear regression analyses were performed for

each question on the survey instrument and each business outcome considered in this

study. For pairs where the p values equal to or less than 0.05, linear regression equations

were formed. In these cases the p values, the R-Squared statistic values, type of relational

proportionality, and regression equation are reported in Table 16. It was found that many

survey questions shared a significant relationship with business outcomes. The

relationships were inversely proportional for all but one pair. Ratings for survey

questions AS2 (The tone of my appraisal conference was comfortable), PBC7 (The

Engineering Services Firm's compensation program encourages me to work efficiently

effectively), and Q9 (My co workers are committed to doing quality work) shared

significant relationships with two or more business outcomes. This in turn highlighted

relative importance of these survey questions with regards to business outcomes at the

business unit-transportation.

Business unit-water. Linear regression analyses were performed for each

question on the survey instrument and each business outcome considered in this study.

For pairs where the p values equal to or less than 0.05, linear regression equations were

75

formed. In these cases the p values, the R-Squared statistic values, type of relational

proportionality, and regression equation are reported in Table 17. It was found that many

survey questions shared a significant relationship with business outcomes. The

relationships were inversely proportional for some pairs and directly proportional for

other pairs. Ratings of no survey questions shared significant relationship with two or

more business outcomes.

Based on the preceding discussion, the hypothesis that there is no significant

relationship between any item related to employee engagement, as defined by the

employee survey, and any business outcome of the two business units of the engineering

services firm over the years 2009, 2010, and 2011 was rejected.

Table 15 Survey questions and business outcomes for the engineering services firm

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Table 16

Survey questions and business outcomes for business unit-transportation

Table 17

Survey questions and business outcomes for business unit-water

Data analyses associated with Hypotheses 3 and 4 showed that slopes between

ratings associated with the many survey questions and business outcomes were < 1.

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Furthermore, multiple pairs between survey questions and business outcomes exhibited

slopes < 0. Inverse proportionality between ratings of multiple survey questions and

business outcomes was observed. In other words, it could be inferred that under specific

circumstances, akin to one part of this study, improvement in employee engagement does

not necessarily indicate improvement in business outcomes.

Summary

It was found that the survey data were not normally distributed. The results from

Kruskal-Wallis median comparison tests showed that there was significant variability for

selected items on the instrument in the case of the engineering services firm, business

unit-transportation, and business unit-water over the years 2009, 2010, and 2011. These

questions were associated with four of the five dimensions of employee engagement that

were identified by the factor analysis of company-wide data. These factor descriptions

are 1) opportunity for development and recognition, 2) alignment with the organization,

3) management effectiveness, and 4) communication. Furthermore, ratings from any

survey questions associated with the employee engagement dimension of salary and

compensation did not change for any entity studied over the three-year period.

Employee engagement ratios were calculated based on questions in the survey

instrument, which were derived from Gallup Inc.’s Q12 survey questions in order to

compare the entities studied to other organizations. It was found that employee

engagement ratios for all three entities were above the levels associated with an average

organization in the years 2009 and 2010. The employee engagement ratios were the

highest in 2010. In 2011, the employee engagement ratios for the engineering services

firm and business unit-transportation fell below the levels associated with an average

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organization. The employee engagement ratios for the business unit – water stayed above

the levels associated with an average organization for all three years. The employee

engagement ratios for the three entities were lower than work class organizations for all

three entities for all three years.

Employee engagement ratios were also calculated on the basis of all 47 survey

questions. It was found that employee engagement ratios were the lowest in 2011. It was

also found that employee engagement ratios for the business unit-water were higher than

employee engagement ratios for the business unit-transportation over the three-year

period.

The structured equation modeling procedure revealed a plausible model that

provided relationship values between the five dimensions associated with employee

engagement. These results also showed that the t values associated with paths between

the different dimensions of employee engagement varied by year.

Last, the results from the linear regression analysis showed that there are

significant relationships between multiple items related to employee engagement, as

defined by the employee survey, and business outcomes of the engineering services firm,

business unit-transportation and business unit-water over the years 2009, 2010, and 2011.

However, these relationships were not limited to direct proportionality. In multiple cases,

the relationships were inversely proportional.

79

CHAPTER 5: CONCLUSIONS, IMPLICATIONS, AND RECOMMENDATIONS

Introduction

This chapter has four objectives. The first objective is to present conclusions

based on the results of data analyses subject to limitations and assumptions associated

with hypotheses testing. The second objective is to present practical implications of the

research study based on a summary of results from the data analyses. The third objective

is to describe the theoretical contributions of the research study based on an

amalgamation of conclusions and practical implications. The fourth objective is to

present recommendations for future research work in light of conclusions and limitations

associated with this study. The chapter concludes with a summary of the entire research

study.

Conclusions

This section begins with the results, conclusions, and practical implications

regarding the employee engagement ratios for the engineering services firm, business

unit-transportation and business unit-water. This section then presents the conclusions

drawn from the testing associated with each research hypothesis. Practical implications,

which can help the engineering service firm in improving its employee engagement, are

also delineated in this section. Theoretical contributions, which can be generalized

regarding midsized engineering services firms, are consociated with the conclusions

drawn from hypotheses testing. Conclusion and practical implications associated with the

analysis of employee engagement ratios are also presented.

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Employee Engagement Ratios

The employee engagement ratio is defined as the ratio of the number of

employees who are engaged to the number of employee who are not engaged (Gallup

Inc., 2010). Employees whose average rounded survey ratings were 4 or above are

deemed to be engaged, while employees whose average rounded survey ratings are 3 or

below are deemed to be disengaged (Taylor, 2009; Crabtree, 2004). The employee

engagement ratio for an average organization has been determined to be 1.83 (Gallup

Inc., 2010) based on the Q12 survey questions. An analysis of Q12 survey question ratings,

from the survey instrument associated with this study, revealed that employee

engagement ratios in 2009 were above 1.83. The year 2010 had the highest employee

engagement ratios for all three entities over the three-year period. The employee

engagement ratios for every entity were at their lowest values in 2011. Furthermore, the

employee engagement ratios for the engineering services firm and the business unit-

transportation were below 1.83 in 2011. The employee engagement ratios for business

unit-water also trended downward. However, its employee engagement ratios stayed

above 1.83 over the three-year period. For all entities, the employee engagement ratios

associated with Q12 survey questions were far below the employee engagement ratio

(9.57) associated with a world-class organization (Gallup Inc., 2010). The levels of

employee engagement ratios, based on employee self-report, show that the management

at the engineering services firm should consider actions to improve overall employee

engagement.

Survey questions related to the employee engagement dimensions of

communication, opportunity for development and recognition, and management

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effectiveness received low ratings on the average. Additionally, when comparing 2009,

2010, and 2011, it was found that overall employee engagement ratios for all entities

were at their lowest in 2011. Dugger (2011) reported that the engineering service firm

underwent a substantial organizational change in 2011. Even though the job

responsibilities and business units, studied for the purposes of this research, remained

unchanged, the reporting structure was realigned. In order to maintain confidentiality,

minimal details are reported. Nevertheless, the reporting structure realignment involved

changes to 1) information flow processes and 2) specific task ownership throughout the

organization. It could be argued that the reporting structure impacts management

effectiveness and communication dimensions of employee engagement. Furthermore,

Beugré (1998) noted that it is not the changes per se that lead to employee perceptions of

unfairness and lower levels of employee engagement, but the way in which employees

are treated (or perceived to be treated) during implementation of specific changes. This

could explain the downward shift in the employee engagement ratios for all entities

included in this research study in the year 2011.

The average survey question ratings were sorted, from high to low, for engaged

and disengaged employees at all three entities over the three-year period. The ranking

showed that employees at the engineering services firm are generally not satisfied with

their salary and compensation. The results of this analysis revealed that mutual respect

between employees at all levels of the organization and accommodation for family needs

were at high levels. Furthermore, most employees also indicated that they intended to

stay employed at the engineering services firm over the next two years in response to

survey question AS10. It could be surmised that despite the perceived low compensation,

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employees are willing to stay because of high mutual respect and the existence of

flexibility for family priorities. However, it was found that the Pearson correlation

statistic between the ratings associated with these survey questions was less than 0.40.

This value is relatively low. Additionally, survey question AS10 (I intend to remain The

Engineering Services Firm employee for the next two years) has an inversely proportional

relationship with the employee turnover rate at the engineering services firm and the

business unit-water. In other words, higher average ratings for this question coexisted

with higher actual turnover. This is counterintuitive.

Practical implications. Baker (2011) held that organizational change

management has a direct effect on employee engagement. In other words, effective

change management assisted by open communication could ensure elevated employee

engagement and performance. Baker (2011) also found that extensively planned and

well-executed changes have a meaningful effect on employees’ personal and professional

lives.

Intra-organization benchmarking could prove useful in devising ways to improve

employee engagement. For example, management at the engineering services firm could

examine how business unit-transportation and business unit-water responses differ in

terms of the five dimensions of employee engagement. The results could then serve as a

blue print for improving employee engagement throughout the organization.

Hypotheses 1 and 2

Average ratings associated with survey question AS1 (Overall, I like The

Engineering Services Firm appraisal system) changed over the three years for all entities.

The average ratings of this question ranked low for engaged employees at all entities.

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Additionally, the average ratings of this question trended downward for disengaged

employees for all entities studied between 2009 and 2011. Based on this analysis, it can

be concluded that most employees perceived that the job appraisal system needs

improvement. However, it was also found that the average ratings for the survey question

C8 (I can tell my supervisor what I think without retribution) ranked high for engaged

employees at all entities. These findings provided evidence that even though engaged

employees did not like the job appraisal system, they felt that they could openly

communicate with their supervisors.

Ratings associated with survey question C6 (Upper management attempts to

effectively communicate the plan to all employees) trended downward over the three-year

period for engaged employees for all three entities. It appears, based on this analysis, that

engaged employees perceived that upper management’s communication regarding the

direction of the company changed for the worse between 2010 and 2011. This may be

attributed to the new organizational structure that was implemented between these two

surveys.

A structural equation modeling procedure also provided evidence that the

employee engagement dimension of communication had a causal impact on the employee

engagement dimension of effective management. Furthermore, this analysis found that

the employee engagement dimension of effective management, in turn, had causal effects

on employee engagement dimensions of opportunities for recognition and development,

salary and compensation, and alignment with the organization.

Practical implications. Xenitelis (2009) recommended that senior management

could improve employee engagement by enhancing the quality of vertical

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communication. Some examples include holding employee and leadership forums,

performing site visits, and maintaining as much personal contact as possible. Redman

(2011) advocated the use of an employee engagement measurement system uniquely

adapted for a service organization. Redman (2011) also proposed specialized training

programs specific to a service organization’s culture to improve leadership capability in

order to improve employee engagement.

Williams (2008) held that providing employees with deserved rewards and

recognition and involving them in organizational decisions could help boost employee

engagement. Smith (2010) maintained that in order to improve employee engagement,

management should demonstrate that it cares about the employees’ well being,

demonstrate genuine interest in their development, and recognize their achievements. The

Healthcare Financial Management Association (2011) reported that open workspace

environments and work-from-home programs led to better employee engagement and

retention and a drop in employee misconduct in the service industry. Saks and Gruman

(2011) contended that employee performance management should involve an evaluation

of employee engagement. The authors also proposed that management can enhance

employee engagement in service settings by attending to 1) psychological

meaningfulness, which refers to “one's belief regarding how meaningful it is to bring

oneself to a role performance”; 2) psychological safety, which involves “one's perception

of how safe it is to bring oneself to a role performance without fear of damage to self-

image, status or career”; and 3) psychological availability, which pertains to “one's

perception of how available one is to bring oneself into a role” (p. 126). Hence, employee

engagement could be improved if employees are matched with jobs responsibilities that

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provide them with psychological meaningfulness, psychological safety, and

psychological availability. This in turn could be achieved by selecting the right

employees at the hiring stage or by reassigning current employees based on their skills

and experience.

Theoretical contributions. The three-year analysis revealed that survey

questions changed over the three-year period for all three entities. This demonstrated that

employee engagement itself changed over the three years. Leaderman (2009) and

Sanborn, Malhotra, and Atchison (2011) argued that employee engagement can be

influenced by factors external to an organization. They hypothesized that global recession

could be blamed for the lowering of employee engagement. Additionally, Sanborn,

Malhotra, and Atchison (2011) found that employee engagement, globally, has been

fluctuating since 2008 but trending downwards since 2010. The trend they observed was

similar to what was found in this study. On the other hand, Kontakos and Stepp (2007)

asserted that internal “[o]rganizational changes (including cost-cutting, structural change

and role reduction) can quickly and easily lead to perceptions of unfairness and a

decrease in employee engagement” (p. 21). Herbert (2009) held that organizational

management has a direct and most significant effect on employee engagement. Saks

(2005) and Kahn (1990) also presented multiple intrinsic antecedents associated with

employee engagement. It is likely that changes in employee engagement at the

engineering service firm could be attributed to both intrinsic and extrinsic factors.

It must be conceded that the structural equation model presented in Chapter 4

considered only factors intrinsic to the engineering services firm to build a causal model.

Nevertheless the Student’s t statistics calculated for the various causal paths also varied

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by year and entity. Furthermore, structural equation modeling procedure also provided

evidence that improvement in employee engagement dimensions of communication and

management effectiveness preceded the improvement in the other three dimensions of

employee engagement. This is consistent with findings of Saks and Gruman (2011),

Redman (2011), Smith (2010), Xenitelis (2009), and Williams (2008). In summary,

effective organizational management coupled with robust horizontal and vertical

communication could result in better business outcomes. This in turn can result in higher

salary and compensation for employees and better opportunities for development and

recognition. Better management practices could also ensure closer alignment of the

employees with organizational mission and vision.

Business Outcomes

Business unit-water had the highest profit/revenue ratio among the three entities

studied over the three-year period. Both the water and transportation business units

exhibited higher profit/revenue than the overall engineering services firm over the three-

year period. It was found that profit/revenue ratio was the lowest for all three entities

studied in the year 2010. Similar trends were also observed for employee growth rate.

The employee turnover rate was the highest in the year 2010 for the entities

studied. Business unit-transportation had the lowest employee turnover rate among the

three entities studied in 2009 and 2010. However, in 2011, business unit-water had the

lowest employee turnover rate among the three entities studied. Furthermore, engineering

services firm had the highest employee turnover rates among the three entities studied

over the three-year period.

87

It was found that the average utilization rate of the engineering services firm was

below the average utilization rate of the business unit-transportation and business unit-

water for all three years. The utilization rate of the two business units was very similar

over the three-year period. However, the utilization rate was the lowest for all three

entities studied in the year 2010.

An analysis of all of the factors that could influence the business outcomes for

midsize engineering service firms is beyond the scope of this study. Nevertheless, the

relationship these outcomes share with employee engagement is discussed in the

following sections. Practical implications and theoretical contributions have also been

presented.

Hypotheses 3 and 4

It was found that employee engagement, as measured by the survey questions,

shared significant relationships with selected business outcomes. However, contrary to

findings presented by researchers associated with Gallup Inc. vis-à-vis Harter et al.

(2010), Harter et al. (2009), Harter, Schmidt, and Hayes (2002) and Buckingham and

Coffman (1999), some relationships were inversely proportional. A possible explanation

for this divergence could be that these studies did not consider the products/services

produced by organizations in their data analyses. Both employee engagement and better

business outcomes could be affected by the products/services produced by an

organization. Better products/services could mean better business outcomes including

better revenues and higher profits. It can be argued that organizations with better business

outcomes would have more resources to spend on improvement of their employees,

which, in turn, could improve the employee engagement. Furthermore, it is plausible that

88

better products/services are byproducts of engaged employees who are more productive,

well rewarded, well recognized, and well managed. This is represents a Series of

symbiotic relations. Sullivan (2012) held that new and better products are associated with

less employee turnover, open communication, opportunities for recognition and

development, effective management and better business outcomes. Works by Lieberman

and Dhawan (2005), Dennis (2007) and Lieberman, and Lau and Williams (1990)

demonstrated that Japanese automobile manufacturers had products with higher customer

demand. They argued that better products shared a symbiotic relation with better firm-

level productivity, more effective management, employees aligned with organizational

vision and better business outcomes.

The Corporate Executive Board (2009) reported that innovative and in-demand

products from Google Inc. influenced employee engagement at the company. It was also

reported that engaged employees in turn enhanced and improved the products. Akshay

(2012) hypothesized that products from Apple, Inc., and Research In Motion, Ltd., had an

impact on the level of employee engagement in those companies. The product/service

produced by an organization is a factor that should be considered in future studies.

Practical implications. In many of the survey questions and business outcome

pairs where the relationship was directly proportional, the slope was less than 1. This

indicated that the rate of increase of business outcome values was lower than the rate of

increase in the survey question ratings. A possible explanation for these findings could be

that factors extrinsic to the organization affect both employee engagement and business

outcomes at various organizations differently. Employee engagement and business

performance are not isolated from the world in which they exist. As the individual

89

circumstances of each employee changes, the level of engagement may also vary. Beatty

(2009) surmised that employees do not necessarily have a constant impact on business

outcomes. One way of minimizing the effect of variation in levels of employee

engagement is to be extra diligent at the hiring stage. Beatty (2009) opined that making

the right employee selection at the hiring stage is critical to having a stable, engaged

workforce. Thus, hiring workers who can engage themselves to a specific work

environment could ensure higher levels of employee engagement for a prolonged period.

Employee engagement exists along a continuum ranging from very engaged to

very disengaged. The degree of engagement along this continuum can be influenced by

various factors to varying extents. Some of these factors could include psychological and

health conditions, financial and retirement planning, personal development, and work-

family balance. Besides improving the hiring process, employers could try to mitigate the

effects of these factors by developing specific programs in consultation with their

employees. Some examples include setting up of work exercise groups, offering financial

planning classes, and assisting employees with professional certifications.

Theoretical contributions. This study provides evidence that there might be

correlation between employee engagement, as defined by the employee survey

instrument, and selected and business outcomes. However, the correlation displayed

direct proportionality in some cases and inverse proportionality in other cases.

Furthermore, a causal relationship cannot be justified by a simple significant correlation.

A possible explanation for a lack of direct proportionality between employee engagement

and business outcomes could be that as employees become less burdened by workload,

they begin feel happier at work. Leidich (2011) asserted that employee happiness is

90

linked to heart, minds, and the work environment. In other words, less burdened

employees are happier at work. Leidich (2011) maintained that happier employees also

tend to be more engaged. Schwartz (2010) and Ashkenas (2012) emphasized that a less

burdensome workplace that gives employees time to think freely helps foster innovation.

Schwartz (2010) also surmised that employees working for employers who don’t

overburden them tend to have an engaged workforce. Thus, a more conducive work

environment could yield more engaged employees.

Recommendations for Future Studies

Future research at the engineering services firm could further investigate the

paradoxical relationship that exists between survey question AS10 (I intend to remain at

The Engineering Services Firm employee for the next two years) and the turnover rate.

Factors that influence the desire to stay and the decision to leave could be addressed by

future studies. More broadly, additional work is needed to understand intrinsic and

extrinsic factors that influence and promote employee engagement at a services firm.

Qualitative studies at the engineering service firm could focus on understanding the

process by which these intrinsic and extrinsic factors influence employee engagement.

Subsequently, the survey instrument could be updated to collect employee responses with

regard to identified extrinsic factors in order to quantitatively understand them and their

effects.

It was found that all entities studied at the engineering services firm had low

employee engagement ratios when compared to world-class organizations. Further

research is needed to understand factors that increase or decrease the levels of employee

engagement dimensions with regard to business outcomes, especially for service firms

91

with low employee engagement ratios. Such studies are necessitated by the dearth of

existing research that focuses on firms with low employee engagement. Furthermore, the

relationship between employee engagement and business outcomes must be studied and

contrasted between firms with high and low levels of employee engagement. For

example, future studies could compare and contrast employee engagement at Apple, Inc.,

and Research In Motion, Ltd. Additionally, future studies could focus on understanding

and comparing employee engagement and its relationship to business outcomes at

companies that are well established and successful, such as recipients of the Malcolm

Baldrige National Quality award, to companies that are not. Other studies could involve

tracking and analyzing data from organizations included in the best corporations (Fortune

500®) and the best employers (100 Best Companies to Work For ®) lists published

annually by the Fortune TM magazine. These causal comparative studies should be

supplemented with more extensive data from multiple time periods and more business

units. It would be ideal to match and study individual productivity outcomes with

individual survey responses. However, the success of this approach is contingent upon

the services firm’s willingness and ability to collect and report those data.

Last, measurement instruments that measure and assist in quantification of

discretionary efforts made by the employees must be developed. Structural equation

modeling procedure could be used further understand the causal relationships between

dimensions of employee engagement, discretionary efforts made by the employees, and

business outcomes. The structural equation modeling procedure could also be used to

understand ancillary factors that influence employee engagement, such as workplace

settings, work schedules, and opportunities for personal development.

92

Summary

This research study investigated the self-reported employee engagement ratings

and selected business outcomes at a mid-sized engineering services firm. It was found

that the level of employee engagement at this firm was below that of world-class

organizations. Ratings of various survey questions used to measure employee

engagement changed over the three-year period. A causal relationship seems to exist

between the five dimensions of employee engagement. It was found that the employee

engagement dimension of communication had a causal impact on effective management.

Effective management, in turn, had causal effects on employee engagement dimensions

of 1) opportunities for development and recognition, 2) salary and compensation, and 3)

alignment with the organization. However, the relationship between individual employee

engagement items, as described in the survey instrument, and selected business outcomes

is not directly proportional in all cases.

93

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Appendices

117

Appendix A

A version of the survey instrument, modified for the purposes of maintaining

anonymity and confidentiality, used to measure employee engagement at the engineering

services firm.

Instructions and Purpose

PURPOSE:

This survey is being conducted to assist the company in identifying employee concerns

and improving organizational performance.

INSTRUCTIONS:

Please complete all of the questions in each of the following sections. In doing so, please

provide only one response for each scaled item. You will see that each section of

questions also provides space for comments. Your frank comments are encouraged.

ANONYMITY:

In making comments, please do not reveal your identity. If you do inadvertently reveal

your identity, the researcher compiling the results guarantees your anonymity. To further

protect your anonymity, the survey settings ensure that your IP address will NOT be

stored in the survey results. Neither the researcher nor The Engineering Services Firm

will be able to identify the computer at which the survey is completed.

DEADLINE FOR SUBMISSION:

Please complete and electronically submit this survey no later than MMM DD, YYYY.

TO BEGIN:

To begin this survey, click on the NEXT button.

118

Demographics

1. 1. I supervise (conduct the appraisal for) one or more employees.

2. The number of full years I have worked with the company. (This represents

your years of service with The Engineering Services Firm only. If you joined the

firm through a merger, please count only the years with The Engineering Services

Firm -less than one full year would be zero).

3. My age category is:

4. My office is located in:

5. I work within the following business unit:

Group and Business Unit Demographic Information

1. Within the XXXX business unit, I work within the following group:

Group and Business Unit Demographic Information

1. Within the XXXX business unit, I work within the following group:

Group and Business Unit Demographic Information

1. Within the XXXX business unit, I work within the following group:

Group and Business Unit Demographic Information

1. Within the XXXX business unit, I work within the following group:

Group and Business Unit Demographic Information

1. Within the XXXX business unit, I work within the following group:

Group and Business Unit Demographic Information

1. Within the XXXX business unit, I work within the following group:

Group and Business Unit Demographic Information

1. Within the XXXX business unit, I work within the following group:

119

Survey Questions

1. General Questions

1. I know what is expected of me at work.

2. I have the materials and equipment I need to do my work right.

3. At work, I have the opportunity to do what I do best every day.

4. In the last seven days, I have received recognition or praise for doing good

work.

5. My supervisor or someone at work cares for me as a person.

6. Someone at work encourages my development.

7. At work, my opinions seem to count.

8. The mission/purpose of the company makes me feel my job is important.

9. My co-workers are committed to doing quality work.

10. I have a best friend at work.

11. In the last six months, someone has talked with me about my progress.

12. During the last year, I have had opportunities to learn and grow.

Comments about General Questions

2. Communications

1. Communications in general are good at The Engineering Services Firm.

2a. Communications are good within my office.

2b. Communications are good within my department.

2c. Communications are good within my team.

2d. Communications are good between offices.

120

3. The company leadership has clearly identified the strategic plan for the

company.

4. The mission of the company is updated using employee input.

5. Upper management is providing a clear company vision for the future.

6. Upper management attempts to effectively communicate the plan to all

employees.

7. I understand my team's goals.

8. I can tell my supervisor what I think without retribution.

9. I have a regular (at least monthly) one-on-one meeting with my supervisor.

10a. I know what the firm is doing to market the team/firm.

10b. I know what the firm is doing to achieve long-term goals.

11a. My team's (group) meetings are held frequently enough.

11b. My team's (group) meetings are conducted effectively.

12. I feel that my supervisor values my input regarding project budget and scope

preparation.

Comments about Communications

3. Pay, Benefits, and Conditions

1. I have good equipment/tools to work with.

2a. Employees are treated with respect by co-workers.

2b. Employees are treated with respect by management.

2c. Employees are treated with respect by supervisor.

3. There exists flexibility for personal time/family needs.

4. I am paid fairly.

121

5. I feel secure from layoffs.

6. My total compensation is competitive.

7. The Engineering Services Firm's compensation program encourages me to

work efficiently/effectively.

8. My benefits are competitive.

Comments about Pay, Benefits, and Conditions

4. My Appraisal Process and Level of Satisfaction

1. Overall, I like the The Engineering Services Firm appraisal system.

2. The tone of my appraisal conference was comfortable.

3. The feedback I received was specific.

4. The feedback I received was appropriate.

5. My supervisor has supported my development plan.

6. I have a regular (at least monthly) one-on-one with my manager.

7. I am happy with my job.

8. I have opportunities for advancement at The Engineering Services Firm.

9. Concerns expressed during previous employee surveys are being addressed.

10. I intend to remain an The Engineering Services Firm employee for the next

two years.

11. During the past 12 months, The Engineering Services Firm has made positive

progress.

Comments about My Appraisal Process and Level of Satisfaction

Open-Ended Questions

In responding to each of the following questions, you are encouraged to be specific.

122

1. If I could change one thing in the next twelve months at work, it would be:

2. In relation to work, the thing I would most like clarified is:

3. Why doesn't someone do something about:

4. My contributions toward making The Engineering Services Firm a better place

to work include:

5. In the past 12-18 months, the best thing to happen at The Engineering Services

Firm was:

6. In the past 12-18 months, the worst thing to happen at The Engineering

Services Firm was:

7. What is the most important thing that The Engineering Services Firm could do

in order to become a Best Place to Work for it's staff?

8. How do you assess our current morale at The Engineering Services Firm and to

what do you attribute this?

Thank You

Thank you for completing this survey. Any questions should be directed to Dr. Abc Xyz.

He may be contacted at

xxxx@aaa.aaa.

To submit this survey, please click on the SUBMIT button below.

123

Appendix B

Gallup Inc,’s Q12

1. I know what is expected of me at work.

2. I have the materials and equipment I need to do my work right.

3. At work, I have the opportunity to do what I do best every day.

4. In the last seven days, I have received recognition or praise for doing good work.

5. My supervisor or someone at work cares for me as a person.

6. Someone at work encourages my development.

7. At work, my opinions seem to count.

8. The mission/purpose of the company makes me feel my job is important.

9. My co-workers are committed to doing quality work.

10. I have a best friend at work.

11. In the last six months, someone has talked with me about my progress.

12. During the last year, I have had opportunities to learn and grow.

124

Appendix C

Permission letter from the engineering services firm

125

Appendix D

List of 47 questions and the associated coding used for this study

1. AS1: Overall, I like The Engineering Services Firm appraisal system

2. AS10: I intend to remain an The Engineering Services Firm employee for the

next two years

3. AS11: During the past 12 months, The Engineering Services Firm has made

positive progress

4. AS2: The tone of my appraisal conference was comfortable

5. AS3: The feedback I received was specific

6. AS4: The feedback I received was appropriate

7. AS5: My supervisor has supported my development plan

8. AS6: I have a regular at least monthly one on one with my manager

9. AS7: I am happy with my job

10. AS8: I have opportunities for advancement at The Engineering Services Firm

11. AS9: Concerns expressed during previous employee surveys are being addressed

12. C1: Communications in general are good at The Engineering Services Firm

13. C10b: I know what the firm is doing to achieve long term goals

14. C11a: My team's group meetings are held frequently enough

15. C11b: My team's group meetings are conducted effectively

16. C12: I feel that my supervisor values my input regarding project budget and scope

preparation

17. C2a: Communications are good within my office

18. C2c: Communications are good within my team

126

19. C2d: Communications are good between offices

20. C4: The mission of the company is updated using employee input

21. C5: Upper management is providing a clear company vision for the future

22. C6: Upper management attempts to effectively communicate the plan to all

employees

23. C7: I understand my team's goals

24. C8: I can tell my supervisor what I think without retribution

25. C9: I have a regular at least monthly one on one meeting with my supervisor

26. PBC1: I have good equipment/tools to work with

27. PBC2a: Employees are treated with respect by co workers

28. PBC2b: Employees are treated with respect by management

29. PBC2c: Employees are treated with respect by supervisor

30. PBC3: There exists flexibility for personal tie family needs

31. PBC4: I am paid fairly

32. PBC5: I feel secure from layoffs

33. PBC6: My total compensation is competitive

34. PBC7: The Engineering Services Firm's compensation program encourages me to

work efficiently effectively

35. PBC8: My benefits are competitive

36. Q1: I know what is expected of me at work

37. Q10: I have a best friend at work

38. Q11: In the last six months, someone has talked with me about my progress

39. Q12: During the last year, I have had opportunities to learn and grow

127

40. Q2: I have the materials and equipment I need to do my work right

41. Q3: At work, I have the opportunity to do what I do best every day

42. Q4: In the last seven days, I have received recognition or praise for doing good

work

43. Q5: My supervisor or someone at work cares for me as a person

44. Q6: Someone at work encourages my development

45. Q7: At work, my opinions seem to count

46. Q8: The mission/purpose of the company makes me feel my job is important

47. Q9: My co workers are committed to doing quality work

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Appendix E

Results of classical factor analysis of companywide survey data

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Appendix F

Percentage of variance associated with each factor calculated from the classical

factor analysis

130

Appendix G

Data analysis procedures uses for each research question and hypothesis

Q3: What relationship, if any, exists between employee engagement, as

defined by the employee survey, and business outcomes of the engineering services firm over a period of three

years - 2009, 2010 and, 2011?

Q4: What relationship, if any, exists between employee engagement, as

defined by the employee survey, and business outcomes of the two business units of the engineering services firm over a period of three years - 2009,

2010 and, 2011?

H3: There is no significant relationship between any item related to employee engagement, as defined

by the employee survey, and any business outcome of the engineering services firm over the years - 2009,

2010 and, 2011.

H4: There is no significant relationship between any employee

engagement dimension, as defined by the employee survey, and any business outcome of the two business units of the engineering services firm over a

period of three years - 2009, 2010 and, 2011.

• Linear regression analyses to generate the R2 statistic for annual average score* of each of the 47 survey questions and annual business outcome data for the engineering service firm - Each Q average (X09, X10, X11) vs. Each Business Outcome (Y09, Y10, Y11)

• Regression equations (Y = a*X+b ) for annual average score* of each of the 47 survey questions (X) and annual business outcome data (Y) for the engineering service firm - Each Q average (X09, X10, X11) vs. Each Business Outcome (Y09, Y10, Y11)

• Use regression equations to construct predictive models

Q1: Which survey items, related to employee engagement of the

engineering services firm, vary over a period of three years - 2009, 2010 and,

2011?

H1: There is no significant variability in employee engagement, as defined by the employee survey questions, of the engineering services firm over the

years - 2009, 2010 and, 2011.

One way ANOVA to compare scores of each of the 47 survey questions* vs. year for the engineering service

firmUsage of PLS-SEM to understand linkages

between dimensions of employee engagements over the three years

Q2: Which survey items, related to employee engagement of the two business units of the engineering

services firm, vary over a period of three years - 2009, 2010 and, 2011?

H2: There is no significant variability in employee engagement, as defined

by the employee survey questions, for the two business units of engineering services firm over the years - 2009,

2010 and, 2011.

One way ANOVA to compare scores of each of the 47 survey questions** vs. year for each of the two business units of the engineering services firmUsage of PLS-SEM to understand linkages

between dimensions of employee engagements over the three years for both

business units

Statistical Methods Research Questions Hypotheses

• Linear regression analyses to generate the R2 statistic for annual average score* of each of the 47 survey questions and annual business outcome data for each of the two business units of the engineering services firm - Each Q average (X09BUi, X10BUi, X11BUi) vs. Each Business Outcome (Y08BUi, Y09BUi, Y10BUi)

• Regression equations (YBUi = a*XBUi+b ) for annual average score* of each of the 47 survey questions (XBUi) and annual business outcome dataBUi (Y) for each of the two business units of the engineering services firm - Each Q average (X08BUi, X09BUi, X10BUi) vs. Each Business Outcome (Y08BUi, Y09BUi, Y10BUi)

• Use regression equations to construct predictive models

* Employee engagegemt ratios will also be determinded for each year.** Employee engagegemt ratios will also be determinded for each year and each business unit.

131

Appendix H

132

Appendix I

Student’s t statistics from SEM analysis are shown below.

133

Appendix J

Human subjects approval letter is shown below.

134

Appendix K

A detailed causal model verified by structural modeling analysis procedure is

shown below. Blue circles represent the employee engagement dimensions (latent

constructs) and yellow rectangle represent survey items with highest loading for

companywide factor analysis (manifest variables).

135

Appendix L

p Values associated with interaction of year and business unit.