Dr Nick Bontis - Performitiv - www.NickBontis.comChief Data
Scientist, Performitiv
3M National Teaching Fellow
• boost productivity and efficiency
• speed up innovation & creativity
• achieve industry leading competitiveness
• cope with information bombardment
• leverage time saving practices
provide global expertise on national intellectual capital
• Microsoft called him to reorient their staff towards knowledge
era training
• Accenture sought his insight on guiding teams to become more
efficient and effective
• London Drugs trusted him to entertain and enlighten 1000 of its
major suppliers and commercial partners
• Royal Bank hired him to navigate through its corporate
transformation
• TELUS selected him for a national multi-city tour to attract new
clients
• US Navy contracted him to train senior officers on causal
models
• HRPA featured him for their executive seminars on human capital
measurement and employee
engagement assessment
Professor Nick Bontis is recognized internationally as a leading
strategy and management expert. He has delivered keynote
presentations on every continent for leading organizations in both
the private and the public sector. His dynamic, high-energy
presentations provide concrete recommendations for improving
individual, team and organizational effectiveness leaving audiences
with the tools, inspiration and motivation to accelerate management
performance. His customized programs are a mix of practical
managerial tools, rigorous academic research, strategic consulting,
entertaining humour and a blast of youthful exuberance.
ACCELERATING MANAGEMENT PERFORMANCE
Dr.
He has the credibility, universal appeal, and know-how to make sure
your event is an unforgettable success!
[email protected]
www.NickBontis.com
With his unique combination of substance and sizzle, Dr. Nick
Bontis is guaranteed to ignite, entertain and educate audiences,
empowering them with both the tools and the inspiration to perform
at an accelerated level of management performance.
Tom Stewart, former editor of Harvard Business Review and Fortune
Magazine, states that “he is not only a pioneer, but one of the
world’s real experts as well.” His dynamic delivery and concrete
advice will leave your audience enlightened, inspired and ready for
action. His expertise has been tapped by several Fortune 500
companies and even the United Nations who hand picked him for a
high profile initiative. His ground-breaking doctoral dissertation
is the #1 selling thesis in Canada, in all fields of study. As an
award-winning tenured professor of strategy, he has won over a
dozen teaching awards and several research awards. Maclean’s
Magazine has identified him as one of McMaster University’s most
popular business professors for six years in a row! He is also a 3M
National Teaching Fellow, an exclusive honor only bestowed upon the
top university professors in the country!
NickBontis
ACNielsen Ast Living Federation Automotive Ind Assn AXA Insurance
Bank of Canada Bank of Montreal BC HR Mgmt Assn Century 21 CGI CIBC
City of Beijing Can Revenue Agency Coast Hotels Conference Board
CUMIS Dofasco Drake International Electro Federation Environment
Canada Great West Life Grocery Innovations Hartford Insurance
Health Canada House of Commons HR Professional Assn IBM Global
Services ING Bank Internet World Jamaican Government Japanese Works
Inst Kelsey’s Restaurants KM World KPMG Laurentian Bank L3 Wescam
Mackenzie Financial Manulife Marsh & McLennan Ont Real Estate
Assn Ont Hospital Assn Ont LT Care Assn Ont Min of Labour OpenText
Petro de Venezuela Project World RCMP Rogers Communications Sandia
Nat Labs SaskTel Sears Spherion Statistics Canada Sun Microsystems
TD Bank Telus Tim Horton’s Uniglobe Travel
What do audiences have to say?
Argentina • Australia • Canada • Dominican Rep • Denmark •
Finland
Greece • Italy • Jamaica • Japan • Jordan • Mexico • Netherlands •
New Zealand
Slovenia • South Africa • Sweden • Taiwan • Tunisia • UK • USA •
Venezuela
We selected Dr. Bontis again to present on our main platform stage
as a keynote speaker in front of over 6,500 financial
professionals. His presentation was beyond outstanding! Just like
last time, he delivered a highlight performance that resulted in a
standing ovation.
Million Dollar Round Table
He was the funniest, yet most insightful business speaker I have
ever heard!
Young Presidents Organization
Do NOT design a conference program without considering him as your
main event keynote speaker. He is, defacto, the reason why any one
should attend an event he speaks at!
ING Bank
Nick’s reputation as a world expert in his field is indisputable.
However, the real magic occurs when he steps in front of a crowd.
His charisma acts like a magnet and captures everyone’s
attention.
United Nations
Not a single person left the room without a vision and a
commitment!
Uniglobe Travel
Nick Bontis is a brilliant, provocative thinker who understands the
deep changes underway in our society. His presentations are
perceptive and persuasive, and always done with great gusto and
humour.
Government of Ontario
Bontis’ talent for forcing the audience to think differently was of
great benefit – a completely new view on how organizations can be
left behind should they decide not to change.
London Drugs
speaker. Bravo!
Great West Life – London Life
He delivers true wisdom. I felt motivated to carry the message to
almost anyone who would listen.
The Strategy Institute
Best speaker we have ever seen, anywhere, period! Century 21
You leave his sessions not only feeling energized but having also
learned so much!
Bank of Montreal
Speaking Topics (brand new for 2020 – all available Virtually as
well) TRANSFORMING YOUR LEADERSHIP & PRODUCTIVITY FOR PEAK
PERFORMANCE Are you an ambitious individual that feels she has too
much on her plate but still wants to blast through every target and
then some? Dr. Nick Bontis has leveraged two decades worth of
research on personal productivity that will shrink your
ever-growing “to do” list to the most important activities that are
necessary for you to delight your customers, impress your team
members, and solidify your value to your organization. Never worry
again about not having enough time to do this or that. The time has
finally come to transform your personal productivity for peak
performance.
ACCELERATING COLLABORATION & COMMUNICATION AT HYPER-SPEED Is
your team just spinning its wheels, but you know deep-down it can
do more with less? Dr. Nick Bontis has harnessed the best practices
of knowledge sharing by consulting with some of the world’s leading
organizations. High performance teams are supposed to harvest the
synergy embedded in all of their members within and across
departments to create value above and beyond what is expected. Help
your team solidify its reputation as the smartest group around by
accelerating collaboration and communication at hyper-speed.
LEADERSHIFTING AND STRATEGIZING TOWARDS INNOVATION & GROWTH The
markets are turbulent, the geopolitical economy is unstable, your
competition is frothing at the mouth, and you are holding it all
together and executing the strategic plan. As the senior leader in
your organization, you know you can’t do it alone. How do you
harvest the full intellectual capital potential of your
organization? Dr. Nick Bontis is an award-winning professor of
strategy and the most-cited author on the planet in his field. Let
him show you a clear path. Don’t let unforeseen disruptive
technology make you and your organization obsolete.
NickBontisMedia
@NickBontis
NickBontis
Human Capital Depletion: loss of knowledge (turnover rates) Human
Capital Development: knowledge increase (learning &
development)
Human Capital Investment: knowledge acquisition (recruitment &
hires) Human Capital Valuation: knowledge assets (headcount &
compensation)
Intermediate Outputs: knowledge transfer behaviours Economic
Results: outcomes (business performance)
Human Capital Outcomes
Allstate Insurance Company
Blue Cross Blue Shield of Illinois / Texas
Blue Cross Blue Shield of North Carolina
CNA Commercial Insurance
HCV
HCV
HCV Sample Banks Insurance -
HCV
HCV
+ 0.36 (p < 0.01)
+ 0.32 (p < 0.01)
+ 0.26 (p < 0.01)
+ 0.43 (p < 0.01)
Job
Satisfaction
Training &
Development
Pay
Satisfaction
Supervisor
Satisfaction
Job
Insecurity
Employee
Employee
Satisfaction
Training &
Development
Human
Capital
Relational
Capital
0.358
Employee
Satisfaction
Training &
Development
Human
Capital
Relational
Capital
0.285
0.214
Optimal Temporal Impact
Optimal Temporal Impact
Self-paced web-based
Instructor led
Case 7c: Demographic review
Causal Model: Next Steps
• QUICK WIN: develop a causal map for your organization derived on
already existing survey and human capital data (e.g., annual
employee surveys, human capital metrics)
• Larger sample across many more firms / industry groups / nations
(for bench-marking).
• Longitudinal nature of impacts (i.e., time lag effects of
constructs)
• Alternative financial capital measures
• Culture integration, post-merger migration
• Intermediating effects (i.e., fear, trust, empowerment, health,
work-life balance, compensation alignment)
Book Website and Reviews:
address book
www.NickBontis.com Thank You
CPHR caught up with key- note speaker Dr. Nick Bontis to learn more
about him and his message. The
award-winning professor of strategy at McMaster University is a
leading expert on intellectual capital and its impact on business
performance. The critical message in his keynote presentation
focuses on collaboration as an essential precursor to
organizational innovation.
CPHR: How can HR facilitate collaboration? NB: HR really needs to
spend more time devoting resources to all four pro- cesses in the
SECI model as opposed to just one. SECI is a model of how organ-
izational knowledge is created and it stands for socialization,
externalization, combination and internalization.
Interview
Socialization is the first process. Technology stops us from doing
the simple things when we socialize, such as looking into someone’s
eyes. Really, the only people that are socializing in the company
are the smokers outside. It’s very important for HR to re-empha-
size socialization opportunities within the organization. It
becomes too easy to not put a face to someone’s e-mail request and
ignore it. When we speak to our colleagues in person, we use many
varying degrees of emotion and communication cues that are not
avail- able in electronic media.
The second step, externalization, means we have to automate
processes in HR so that they remain in organiza- tional memory.
This is a problem I see more in smaller organizations where one or
two people are doing all the
HR functions and they don’t have the technological infrastructure
like an HRIS or PeopleSoft or their equivalent available to them. A
huge amount of the organization’s knowledge is resi- dent in that
HR person’s brain and the risk is that when she leaves, that know-
ledge is gone. So what we have to con- centrate on is getting HR
professionals to codify what they know.
The next process is combination. This is where knowledge starts
com- ing together from disparate parts of the organization. There
is room for improvement here because HR some- times doesn’t get
called into meet- ings they should be in. Let’s use the development
of the company intranet as an example. The intranet is typically
the domain of the IT folks. What they might do is bring in someone
from
Fe at
ur e
Saskatchewan • #HRSKMagazine 11
finance to talk about the expendi- ture and someone from compliance
to talk about privacy, but they rarely bring in someone from HR to
discuss the culture of the organization, what motivates people to
share information with one another, and how this may impact the
incentive and compensa- tion structures of the firm. That’s partly
because HR has never expressed itself as having any IT competence,
per se. If you know that technological initia- tives for knowledge
and document
sharing are going on in the organiz- ation, you need to put up your
hand and say HR needs to be a part of this conversation.
The final step is internalization. HR plays a significant role in
the dichot- omy of the learning and unlearning that goes on in an
organization. During the annual strategic planning process, HR
should be articulating the things that didn’t work in the previous
fiscal cycle. Very rarely do I come across a firm that has
formalized the idea of
finding out what didn’t work so they don’t do it again.
CPHR: Out of these four processes, which one is the hardest to do
for HR? NB: Socialization – it’s just too much work. If you think
of externalization and combination, we all have these mod- ern
software tools available for us to use such as video conferencing,
docu- ment repositories and voice recogni- tion. Internalization is
easy because we do it naturally – people will always talk and we
constantly internalize their feedback. Socialization, on the other
hand, takes effort because you have to take your bum out of your
seat and engage with someone face-to-face. We have to get back to
the old school way of talking to each other. It would clear up a
lot of issues.
My old boss at CIBC once gave me a great piece of advice. He
advised me to physically get out of my office and go have lunch
with someone different every day. Decades later, I now realize the
ROI of his suggestion.
CPHR: If HR is to be the catalyst for innovation, do we have to
institution- alize more face-to-face time? NB: Absolutely. HR can
manifest this through office furniture and design, employee events
and annual confer- ences, common kitchen and meeting areas – all
three of which got shut down in many organizations in the last few
years because of budget cuts. But it’s those three things that
impact social- ization and are critical to the develop- ment of
rapport among each other.
Generally speaking, our HR depart- ments have not invested heavily
in the social fabric of our organizations and HR’s role is to get
out the needle and start weaving that fabric together.
CPHR: Your research mentions the con- cept of unlearning. What is
it exactly and why is it critical? NB: When I was growing up, in
school we learned that the one thing on earth you could see from
space was the Great Wall of China. When a Chinese astro- naut
finally got to go up into space, he looked down at earth, and
couldn’t see it. Why? It turns out it’s not true.
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Dr. Nick Bontis (www.NickBontis.com, @NickBontis,
[email protected])
is a dynamic keynote speaker and leading management expert. With
humour and passion, he empowers individuals, teams and
organizations to build their brainpower for high performance and
sustainable competitive advantage. His dynamic delivery and
concrete advice will leave your audience enlightened, inspired and
ready for action.
Nick Bontis • Graduated: HBA’92 and PhD’99 from
Ivey Business School at Western University
• Musical instrument: euphonium, played in the Wind Symphony at
Western U
• First job: By-law enforcement clerk at the City of
Scarborough
• Best boss and why: John Vivash, CEO of CIBC Securities; he had
killer com- petitive instincts
• Courses taught at McMaster: strat- egy for undergrads, knowledge
management for MBAs, digital trans- formation for EMBAs, advanced
sta- tistics for PhDs
• Teaching awards: 12, plus the 3M National Teaching fellow for top
pro- fessor in Canada
• Total research citations: 29,000+ • Recent book:
Information
Bombardment, eBook available on Amazon
• Consulting clients: United Nations, Accenture, RBC, US Navy,
Microsoft, Health Canada
• Largest keynote ever: MDRT, audi- ence of 6500 in
Indianapolis
• Favourite song: La virgen de la macarena
• Favourite food: meat lovers’ pizza • Favourite sport: lives,
breathes, eats
soccer 24/7 • Favourite vacation: Santorini, Greece • Favourite
car: Tesla Model X • Favourite HR person: Dr. Jac Fitz-enz •
Current smartphone: Samsung
Galaxy S9 • Source of inspiration: 3 children –
Charlie 15, Dino 14 and Tia Maria 12 • Most recent thrill: winning
the bid to
host the FIFA World Cup in 2026, he is Vice President of Canada
Soccer
• Best advice: Happy wife, happy life
are there any priority metrics HR should be calculating? NB: I have
spent much of my academic career since 1994 studying human capital
measurement. It is a fascinat- ing field that has grown
exponentially but I would argue that Canadian HR professionals are
woefully behind in terms of global best practices. I have worked
with corporations that are now using causal model techniques that I
have developed to forecast workforce demand into the future and
link them to all sorts of business outcomes. I have assisted
several Canadian firms, especially within the financial servi- ces
sector, who are doing insightful work in linking HR soft measures
(e.g., employee engagement) with HR hard measures (e.g., voluntary
turnover) and business outcomes (e.g., revenue growth) – and have
been doing so for several years.
But, if an HR professional is just start- ing out in human capital
measurement, I would recommend the following three quantitative
metrics which are the most important to start tracking, at least at
the beginning: a) $ Revenue ÷ # FTE, b) % voluntary turnover, and
c) $ Learning & Development ÷ # FTE – these three represent
business outcome, negative input and positive input
respectively.
Plus, be sure to invest in a robust employee survey process that
meas- ures employee engagement, stress and leadership
capability.
CPHR: Do you have one last piece of advice that you would like to
offer HR departments? NB: When it comes to your annual employee
survey, most firms are so cheap, they are now only doing it once
every two years. This is entirely use- less! No one can manage an
organ- ization effectively by diagnosing it so infrequently. My
advice is this, survey one twelfth of your employ- ees every month
– this way you get more frequent diagnoses with less survey
fatigue.
Finally, start leveraging more sophis- ticated analytical
capabilities to find the meaning behind the data. It’s all there if
you know where to look.
Finally, a long-held hypothesis has been invalidated.
The problem with organizations is that some of our senior HR exec-
utives have strong held beliefs that have never been tested. Some
of them are so wedded to their convictions because of some
associated cost – financial, reputational, and emo- tional – they
don’t want to let it go. But at some point HR has to step up and
say this method or theory has been invalidated, it does not work,
let’s unlearn it. We have lots of obsolete knowledge in the HR
world.
CPHR: How do you go about meas- uring what your organization needs
to unlearn? NB: That’s the million dollar question! When I’m asked
this by my consulting clients, I answer in terms of the stra- tegic
planning process. During that process, organizations use templates
for the strategic plan and accompany- ing SWOT – strengths,
weaknesses, opportunities, threats – analysis. They incorporate
budgeting, variance and competitive analysis into that plan. I
recommend making a new supple- mental section of that plan: to list
what we did last year that didn’t work, so we don’t repeat those
same mistakes.
CPHR: You talk about knowledge obsolescence, can you explain what
it is? NB: Knowledge obsolescence is dir- ectly correlated to the
rate of change in an industry. In some industries, software for
example, the rate of obsolescence is huge. In others, such as
construction, the change is not as quick. When there is a fast rate
of obso- lescence, HR must ensure it is adjusting its training
budget to reflect that rate. If you are in a business that is going
to be fundamentally changed by a regulatory requirement, for
example, you need to do some extra training to compensate for the
increase in the knowledge obso- lescence rate and adjust the budget
accordingly.
CPHR: You have published many aca- demic research papers in the
area of HR measurement and benchmarking,
Saskatchewan • #HRSKMagazine 13
Intellectual capital ROI
223
Journal of Intellectual Capital, Vol. 3 No. 3, 2002, pp. 223-247. #
MCB UP Limited, 1469-1930
DOI 10.1108/14691930210435589
Intellectual capital ROI: a causal map of human capital antecedents
and consequents
Nick Bontis DeGroote School of Business, McMaster University,
Hamilton,
Ontario, Canada, and
Keywords Human capital theory, Knowledge management, Staff
turnover, Leadership, Performance
Abstract This report describes the results of a ground-breaking
research study that measured the antecedents and consequents of
effective human capital management. The research sample consisted
of 76 senior executives from 25 companies in the financial services
industry. The results of the study yielded a holistic causal map
that integrated constructs from the fields of intellectual capital,
knowledge management, human resources, organizational behaviour,
information technology and accounting. The integration of both
quantitative and qualitative measures in an overall conceptual
model yielded several research implications. The resulting
structural equation model allows participating organizations and
researchers to gauge the effectiveness of an organization’s human
capital capabilities. This will allow practitioners and researchers
to more efficiently allocate resources with regard to human capital
management. The potential outcomes of the study are limitless,
since a program of consistent re-evaluation can lead to the
establishment of causal relationships between human capital
management and economic and business results.
Introduction Today’s knowledge-based world consists of universal
dynamic change and massive information bombardment. By the year
2010, the codified information base of the world is expected to `
double every 11 hours’’ (Bontis, 1999, p. 435). Information storage
capacities continue to expand enormously. In 1950, IBM’s Rama C
tape contained 4.4 megabytes and they were able to store as many as
50 of these tapes together. At that time, 220 megabytes represented
the frontiers of information storage. Many of today’s standard
desktop computers are being sold with 40 gigabytes of hard disk
space. It is sobering to remember that full motion video in
uncompressed form requires 1 gigabyte per minute and that the 83
minutes of Snow White digitized in full colour amount to 15
terabytes of space. Unfortunately, the conscious mind is only
capable of processing somewhere between 16 and 40 bits of
information (ones and zeros) per second. How do we reconcile this
information bombardment conundrum when it seems that human beings
are the bottle-neck?
The current issue and full text archive of this journal is
available at
http://www.emeraldinsight.com/1469-1930.htm
The authors would like to acknowledge the following organizations
for their financial support: Accenture, Saratoga Institute and the
Institute for Intellectual Capital Research. The authors would also
like to highlight the contribution of Vanessa Yeh, who administered
the data collection phase of this research.
224
In the closing years of the last millennium, senior managers have
come to accept that ` people, not cash, buildings or equipment, are
the critical differentiators of a business enterprise’’ (Fitz-enz,
2000, p. 1). For senior managers to manage the dynamic changes of
turbulent economic environments and filter the massive sources of
information into knowledge (or, better yet, wisdom), an integrated
perspective of human capital management plays a considerable
role.
Often, the anthropomorphization of an organization is a difficult
conceptual leap for senior managers to make. Can we actually
improve the organizational learning capabilities of firms?
Furthermore, can we translate knowledge management practices into
financial gain?
All the issues above have human capital management at their root.
However, the extant literature has yet to integrate the appropriate
fields of the literature necessary to uncover the hidden meaning.
The purpose of this paper is to integrate constructs from the
fields of intellectual capital, knowledge management, human
resources, organizational behaviour, information technology and
accounting in order to uncover a more holistic perspective of
organizational performance.
The five key objectives of this research study are to:
(1) Reconcile the use of both economic and perceptual measures of
human capital management and its antecedents into triangulated
indices that have yet to be measured.
(2) Determine path coefficient relationships between constructs
developed from an overall conceptual model based on the academic
and practitioner literature.
(3) Benchmark the relative standing of participating organizations,
so that client human resources may be reallocated more
effectively.
(4) Establish a research trajectory that is more advanced and
innovative than anything currently being considered in the fields
of intellectual capital or knowledge management.
(5) Set a base line for trending, norming and forecasting human and
financial capital links.
Literature review The following section briefly describes the
concepts germane to this study, which include: human capital,
structural capital, relational capital, leadership, employee
sentiment, turnover and knowledge management.
Human capital is the profit lever of the knowledge economy. An
organization’s members possess individual tacit knowledge (i.e.
inarticulable skills necessary to perform their functions) (Nelson
and Winter, 1982). In order to illustrate the degree to which tacit
knowledge characterizes the human capital of an organization, it is
useful to conceive the organization as a productive process that
receives tangible and informational inputs from the
Intellectual capital ROI
225
environment, produces tangible and informational outputs that enter
the environment, and is characterized internally by a series of
flows among a network of nodes and ties or links (Bontis,
1999).
Human capital has also been defined on an individual level as the
combination of these four factors: your genetic inheritance; your
education; your experience; and your attitudes about life and
business (Hudson, 1993). Human capital is important, because it is
a source of innovation and strategic renewal, whether it is from
brainstorming in a research lab, day-dreaming at the office,
throwing out old files, re-engineering new processes, improving
personal skills or developing new leads in a sales rep’s little
black book. The essence of human capital is the sheer intelligence
of the organizational member.
Wright et al. (1994), working from a resource-based perspective,
argue that in certain circumstances sustained competitive advantage
can accrue from ` a pool of human capital’’ which is larger than
those groups, such as senior managers and other elites, who are
traditionally identified as determining organizational success or
failure. This is achieved through the human capital adding value,
being unique or rare, imperfectly imitable and not substitutable
with another resource by competing firms. Storey supports this
focus:
This type of resource [human capital] can embody intangible assets
such as unique configurations of complementary skills, and tacit
knowledge, painstakingly accumulated, of customer wants and
internal processes (1995, p. 4).
A firm is not a passive repository of knowledge. Multiple knowledge
nodes of the firm interact and recombine with each other with
varying intensity (the tacit knowledge of the collective in the
form of organizational culture may interact with the explicit
knowledge of the individual or the structural capital of a
database), get converted from one form to the other and mobilize,
recombine and transform the resources of the firm so as to add
value. What results from these re-combinations and conversions is
the new knowledge – as organizational learning and/or
innovation.
Human capital is also a primary component of the intellectual
capital construct (Bontis, 1996, 1998, 1999, 2001a, b, 2002a,b;
Bontis et al., 1999; Edvinsson and Malone, 1997; Edvinsson, 2002;
Stewart, 1997, 2001; Sveiby, 1997; Bontis and Girardi, 2000). The
intellectual capital literature has grown tremendously in the last
decade (see Bontis (2002a, b) and Choo and Bontis, (2002) for
comprehensive edited volumes). Whereas human capital embodies the
knowledge, talent and experience of employees, structural capital
represents the codified knowledge bases that do not exist within
the minds of employees (e.g. databases, filing cabinets,
organizational routines). Furthermore, relational capital
represents the knowledge embedded in the organizational value
chain. That is, the knowledge embedded in the relationships that
the firm has with suppliers, customers and any entity outside the
boundaries of the firm. Although there is general agreement on the
aforementioned description of these three constructs, empirical
research has been minimal (see Bontis (1998) for an exception).
Most importantly, however,
JIC 3,3
226
there is still no clear empirical validation as to which construct
drives organizational performance directly or whether or not a
combination of each is required.
The behaviours exhibited by senior management are an important
variable to consider when examining how an organization leverages
its human capital. Lyles and Schwenk (1992) suggest that the
cognitive maps of top management members closely represent core
aspects of all organizational members. Leaders such as ` boundary
spanners’’ (Michael, 1973) and ` technological gatekeepers’’
(Allen, 1977) have an important role in facilitating value
alignment in support of an organization’s innovative capability.
Managerial leadership acts as a catalyst to fuel learning in firms.
The leader’s support cast is also very important. After all,
although organizational learning requires a champion, it also needs
subordinates and followers (Pedler et al., 1996). Organizations
must emphasize that leaders will have ` learning paths’’, not `
jobs’’ (Wilson et al., 1994).
Edmondson (1996) also argues that leadership is an important
antecedent for human capital development. She claims that it is not
enough for leaders to design appropriate organizational structures
and continue to make well- reasoned decisions; instead,
organizations must be characterized at all levels by a ` leading
attentiveness’’ to changing conditions.
Another important antecedent to human capital development rests
with general employee sentiment. Employee sentiment can be defined
as the inter- relationship between employee satisfaction,
commitment and motivation. Of course, these all relate with an
organization’s overall culture. Organizations that have a culture
that supports and encourages cooperative innovation should attempt
to understand what it is about their culture that gives them a
competitive advantage and develop and nurture those cultural
attributes (Barney, 1986). Culture constitutes the beliefs, values
and attitudes pervasive in the organization and results in a
language, symbols and habits of behaviour and thought. Increasingly
it is recognized as the conscious or unconscious product of the
senior management’s belief (Hall, 1992). Barney discussed the
potential for organizational culture to serve as a source of
sustained competitive advantage. He concluded that ` firms that do
not have the required cultures cannot engage in activities that
will modify their cultures and generate sustained superior
performance, because their modified cultures typically will be
neither rare nor imperfectly imitable’’ (1986, p. 656). Human
capital development, as it relates to culture can be managed, if
the organizational membership is relatively stable. This task
becomes much more difficult when there is mobility in the employee
base. This transient change in an organization’s employee profile –
also called turnover – is a significant challenge when attempting
to leverage human capital.
Turnover is the rotation of workers around the labour market;
between firms, jobs and occupations; and between the states of
employment and unemployment (Abassi and Hollman, 2000). This
workforce activity segments into two categories, voluntary and
involuntary. Involuntary turnover refers
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227
to the dismissal of employees, whereas voluntary turnover occurs
when employees resign. While many studies have clustered these two
distinct classifications, this study is aiming to examine voluntary
turnover specifically. Since management cannot control voluntary
turnover, these are fertile grounds for research, and by examining
the implications of this phenomenon, such research asserts the need
to establish preventive measures for minimizing collateral
damage.
Voluntary turnover often results in departing employees migrating
to competing firms, creating an even more critical situation, since
this knowledge can now be used against the organization. Voluntary
turnover has in fact been accelerating over the past decade, as
recent studies have shown that employees on average switch
employers every six years (Kransdorff, 1996). This situation
demands senior management to consider the repercussions of
voluntary turnover, and immediately create contingency plans.
Otherwise, senior management may be caught unprepared, if (or when)
their best performers leave. Recent research supports the notion
that organizations generally do not manage their turnover
effectively, as it relates to knowledge management (see Stovel and
Bontis (2002) in this issue).
Knowledge management behaviours include three primary activities:
knowledge generation – which describes the way employees improvise
and organizations innovate; knowledge integration – which describes
how employees transform their tacit knowledge into explicit
knowledge by codifying their ideas into the systems of the
organization and knowledge sharing – which describes the
socialization process through which employees share knowledge with
one another (Nonaka and Takeuchi, 1995). Ultimately, the goal of
knowledge management is to leverage the intellectual capital that
is currently resident in the organization and to convert that
knowledge into sustainable competitive advantage through increased
business performance.
Conceptual model The purpose of this study is to model and measure
the antecedents and consequents of effective human capital
management. The general quantitative antecedents of human capital
include management’s ability to continue to invest in human
capital, while defending the organization from human capital
depletion (see Figure 1).
Proxies of human capital investment and depletion include turnover
rates and training and development expenditures respectively. The
outcome of human capital valuation is the positive impact human
capital management has on effectiveness, which can be measured
using revenue and profit per employee. The data collection phase of
this study was used to operationalize this model.
Methodology A total of 25 companies in the financial services
industry were targeted for this study (see Table I). These
companies averaged $8.5 billion in revenues with
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over 16,000 employees and spent over $45 million training a
workforce that collected $1 billion in compensation (see Table
II).
Research data were collected in two phases. The objective of the
first phase was to collect all quantitative information from each
company including revenue, profit, number of employees, turnover
and training information, which was secured from the accounting and
HR departments. A survey was administered in the second phase to
collect all the qualitative information. The second survey
consisted of perceptual items based on Likert-type scales that
required respondents to note their level of agreement to certain
items. These items were developed from scales previously published
by the Institute for Intellectual Capital Research. Items for
certain constructs were further edited by a design team, which
consisted of representatives from the Saratoga Institute and
Accenture.
Figure 1. Conceptual model
Table I. Participating companies (25)
ABN AMRO North America Inc. Hartford Financial Services Allstate
Insurance Company Hewitt Associates, LLC AMP Australia
Intermountain Health Care AMP UK International Monetary Fund
Andersen Consulting Merrill Lynch Aon National City Corp. AXA
Client Solutions Northwestern Mutual Life Blue Cross Blue Shield of
Florida Penn National Insurance Blue Cross Blue Shield of
Illinois/Texas PNC Bank Blue Cross Blue Shield of North Carolina
Savings Bank of Utica CNA Commercial Insurance United Health Group
Equitax Zurich US Farmers Insurance Group
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229
The survey sample consisted of 76 respondents from the 25
organizations. The respondents were the most senior executives in
the company (e.g. CEO, CFO and Senior VP HR), who represented the
overall views of the organization (Hambrick and Mason, 1984). A
brief covering letter explained the importance of the research and
options for response (i.e. by fax, mail or e-mail).
Quantitative results The quantitative metrics used in this study
tap into four constructs:
(1) Human capital effectiveness;
(2) Human capital valuation;
(3) Human capital investment;
(4) Human capital depletion.
The hypothesized relationships among these four constructs can be
found in Figure 1.
Descriptive statistics for quantitative metrics In order to compare
the quantitative results of the organizations in this sample with
other companies, each quantitative metric was benchmarked against
the results of the Human Resource Financial Report as published by
the Saratoga Institute. The results of Saratoga’s report encompass
a sample of 753 companies in over 29 industry groups. The metrics
are benchmarked against Saratoga’s overall sample as well as the
means of each specific industry group. Since the study focused on
financial services organizations, results were benchmarked against
Saratoga’s results for banking, insurance (all lines), insurance
(health, property), casual and personal as well as non-bank
financial groups.
Table II. Descriptive statistics
Full-time regular employees 13,149 Part-time regular employees 676
Regular employees 13,795 Contingency employees 1,820 Total
headcount 16,353 Total full-time equivalents 21,006
Headcount: executive 3.2% Headcount: supervisor 12.7% Headcount:
professional 41.7% Headcount: administrative 42.3%
Average age: executive 48 years Average age: supervisor 42 years
Average age: professional 38 years Average age: administrative 38
years
Tenure: executive 15 years Tenure: supervisor 11 years Tenure:
professional 8 years Tenure: administrative 7 years
Total compensation cost $998,173,818 Average year of incorporation
1902 Total workforce trained 12,823 Total training cost
$45,582,889
Revenues $8,534,652,304 Operating expenses $7,510,438,534 Net
profit after tax $659,560,770 Return on assets 4.86%
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Human capital effectiveness Human capital effectiveness is the
dependent component of the conceptual model. In other words, the
other antecedent constructs are used to predict it. The construct
comprises four measures: revenue factor, expense factor, income
factor, and human capital ROI. The revenue factor metric is a basic
measure of human capital effectiveness and is the aggregate result
of all the drivers of human capital management that influence
employee behaviour. Revenue factor is calculated by taking the
total revenue and dividing it by the total headcount of the
organization. Although the Saratoga Institute argues that FTE
(full-time equivalents) should be used in this calculation instead
of headcount, a significant number of respondents did not provide
the FTE value, so the headcount measure was used instead.
Typically, the headcount value is lower than the FTE measure, so we
should expect an overestimate compared to the Saratoga sample (see
Figure 2 for benchmark of this sample versus Saratoga Institute
database).
The results show that the sample had an average revenue factor of
over $600,000 per employee, which was significantly higher than any
of the Saratoga benchmark values, as expected. The expense factor
metric is calculated by taking the total operating expenses and
dividing it by the total headcount of the organization. Once again,
the Saratoga Institute argues that FTE (full-time equivalents)
should be used in this calculation instead of headcount. The sample
had an average expense factor of over $526,000 per employee, which
was significantly higher than any of the Saratoga benchmark values,
as expected. Income factor is calculated by taking the total
operating income and dividing it by the total headcount of the
organization. The sample had an average income factor of over
$36,000 per employee, which was significantly lower than most of
the Saratoga benchmark values. Human capital ROI calculates the
return on investment on a company’s employees (HC ROI = (revenue –
(expenses – compensation))/compensation). This is equivalent to
calculating the value added of investing in the organization’s
human assets. The numerator in this metric is profit-adjusted for
the cost of people (the Saratoga measure also includes benefit
costs). The results show that the organizations in this study had a
human capital ROI of 2.70, which was significantly higher than the
Saratoga sample. The 2.7 value means that, for every $1.00 spent on
employees, the organization realizes $2.70 in return.
Human capital valuation Human capital valuation is the mediating
construct that predicts human capital effectiveness. Compensation
figures are used to act as proxies for the value of human capital
in organizations. The construct comprises five measures:
compensation revenue factor, compensation expense factor,
compensation factor, executive compensation factor, and supervisory
compensation factor. The compensation revenue factor metric
describes how much was paid to employees as a percentage of sales.
Over time, this measure shows if your organization is obtaining
more or less return on every dollar it invests in its people. The
results
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232
show that organizations in the sample spent over 13 percent of
their revenues on compensation, which was in line with the Saratoga
sample (see Figure 3). The compensation expense factor metric
describes how much was paid to employees as a percentage of overall
operating expenses. This measure shows the compensation cost
structure of an organization. The results show that organizations
in the sample spent over 15 percent of their costs on compensation,
which was in line with the Saratoga sample. The compensation factor
metric measures the average compensation paid to each employee in
the organization. This measure is typically used by HR departments
to determine the relative standing of salary levels within an
industry. The results show that organizations in the sample had a
compensation factor of over $54,000, which was higher than the
Saratoga sample. The executive compensation factor metric describes
how much was paid on average to executives. Executives were defined
as individuals at the VP level or higher. The results show that
executives from the organizations in the sample were paid an
average of $290,000 per annum, which was significantly higher than
the Saratoga sample. The supervisory compensation factor metric
describes how much was paid on average to supervisors. Supervisors
were defined as individuals at the management and director level
with supervisory roles that were not VPs. The results show that
supervisors from the organizations in the sample were paid an
average of $71,000 per annum, which was in line with the Saratoga
sample.
Human capital investment Human capital investment is hypothesized
to have a positive influence on human capital management.
Organizations invest in human capital primarily through training
and development expenditures. The construct comprises three
measures: development rate, training investment, and training cost.
The development rate describes how well an organization provides
access to training programs for its workforce. As the workforce
talent pool becomes more shallow, organizations are forced to
design and provide training programs that increase the level of
overall intellectual capital from within. The results show that
organizations in the sample had a development rate of 82 percent.
which was higher than the Saratoga sample (see Figure 4). The
training investment metric identifies the average dollar amount
spent on training for each employee. whether they were trained or
not. This measure is typically used to compare against industry
competitors. The results show that organizations in the sample
spent an average $1,693 per employee on training, which is
significantly higher than the Saratoga sample. The training cost
factor measures the average dollar amount spent on training for
each employee that was trained. This measure is typically higher
than the training investment metric. The results show that
organizations in the sample spent $2,083 per employee trained,
which is significantly higher than the Saratoga sample.
Human capital depletion Human capital depletion is hypothesized to
have a negative influence on human capital management.
Organizations suffer from human capital depletion primarily through
turnover, as intellectual capital walks out of the
Intellectual capital ROI
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Intellectual capital ROI
235
door. The construct comprises three measures: voluntary turnover,
involuntary turnover, and total separation rate. The voluntary
turnover rate describes the percentage of individuals that leave an
organization by choice. This measure has a significant negative
impact on human capital management, since it demonstrates an
employee vote for leaving an organization due to potentially better
circumstances elsewhere. The results show that organizations in the
sample had a voluntary turnover rate of 13 percent, which was in
line with the Saratoga sample (see Figure 5). The involuntary
turnover rate describes the percentage of individuals who were
terminated without choice. This measure describes individuals that
were dismissed, laid off, disabled or died. The reasons for this
rate may include poor hiring practices but typically reflect
economic conditions. The results show that organizations in the
sample had an involuntary turnover rate of 4 percent, which was
lower than the Saratoga sample. The total separation rate describes
the percentage of individuals who were terminated without choice as
well as the individuals who left of their own accord. This measure
is a combination of the two previous metrics and represents the
whole rate of human capital depletion regardless of reason. The
results show that organizations in the sample had a total
separation rate of 17 percent, which was lower than the Saratoga
sample.
Correlations among quantitative measures Pearson’s correlations
were calculated using all the available quantitative measures in
the sample (see Table III). The results show that, for human
capital effectiveness, revenue factor was positively and
significantly correlated with the average tenure of supervisors and
administrative staff. This shows that, as employees develop years
of experience in an organization, more revenue can be generated
from each individual at those levels. Interestingly, the same was
not true (i.e. statistically significant) for professionals and
executives.
Expense factor was also positively correlated with average tenure
for all levels of employees except professionals. Most interesting
was that income factor was positively correlated with the average
tenure of supervisors only. This suggests that the experience of
supervisors clearly plays the most critical role in generating
operating income per individual.
The only statistically significant relationship in this category
was between compensation factor and headcount percentage breakdown
of executives. In other words, as the total number of executives in
an organization increases, so does the average salary per employee.
This is an intuitive hypothesis.
The training cost per trained employee is negatively related to the
average age of executives and the average age of professionals.
This means that as executives and professionals get older, less
money is spent on training them. The training cost per trained
employee is negatively related to the average tenure of
professionals. This means that, as professionals spend more time
with an organization, their training expenditure is less. As the
amount of time spent in an organization increases for
administrative staff, the voluntary turnover rate increases.
Interestingly, this correlation is not statistically significant
for other levels such as professionals, supervisors and
executives.
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Figure 5. Voluntary turnover, involuntary turnover and total
separation rate
Intellectual capital ROI
Qualitative results
The perceptual survey instrument used in this study described 15
latent constructs as follows:
(1) employee satisfaction;
(2) employee motivation;
(3) human capital;
(4) management leadership;
(5) knowledge sharing;
(6) employee commitment;
(7) value alignment;
(8) structural capital;
(9) process execution;
(10) knowledge integration;
(13) relational capital;
Human capital effectiveness Revenue factor Average tenure at
company of supervisors 0.696** Revenue factor Average tenure at
company of
administrative staff 0.670** Expense factor Average tenure at
company of executives 0.632** Expense factor Average tenure at
company of supervisors 0.624** Expense factor Average tenure at
company of
administrative staff 0.647** Income factor Average tenure at
company of supervisors 0.640**
Human capital valuation Compensation factor Headcount percentage
breakdown of
executives 0.686**
Human capital investment Training cost per trained employee Average
age of executives –0.847** Training cost per trained employee
Average age of professionals –0.942** Training cost per trained
employee Average tenure of professionals –0.895**
Human capital depletion Voluntary turnover Average tenure at
company of
administrative staff –0.705**
238
These constructs were selected based on a review of the
intellectual capital, organizational learning and knowledge
management literatures. The items from these constructs were based
on established scales, as published by the Institute for
Intellectual Capital Research. Each construct and item was reviewed
by a team of representatives from the Saratoga Institute and
Accenture for clarity, conciseness and face validity.
Areas of concerns Each of the 76 respondents was asked to select
only three of the 15 constructs as areas of most concern or
challenge. The results show that the three most common areas of
concern with regard to human capital management as selected by the
respondents, are: management leadership, business performance, and
the retention of key people. These three constructs play an
important role in the conceptual model that follows, since they
were assigned as endogenous constructs.
Perceptual means: lowest and highest A total of 82 items were
measured in the perceptual survey with a potential range of
responses from 1 (strongly disagree) to 7 (strongly agree). The
lowest ten items consisted primarily of issues relating to process
and technology. Three of these items belong to the structural
capital construct, three others belong to the knowledge integration
construct, while another two belong to the process execution
construct. The highest ten items generally describe employee
capabilities and competencies. Four of these top ten belong to the
human capital construct.
The data seem to illustrate that, while the respondents work for
organizations that have adequate human capital resources – ` Our
employees generally have the intelligence and aptitude to
succeed’’, their structural capital does not leverage the talent to
its fullest – ` Information systems include employee
knowledge’’.
Item statistics The perceptual items went through a rigorous
psychometric evaluation. The statistical results of this study were
based on the methodological recommendations made by Bontis (1998).
First, a ` Cronbach’s alpha’’ test was used to evaluate the
reliability of the measures, as suggested by Nunnally (1978).
Churchill (1979) suggests that this calculation should be the first
measure one uses to assess the quality of the instrument. Since a
rigorous psychometric evaluation of the instrument had already been
conducted in previous studies, this test was used to confirm those
results. Cronbach’s alpha can be considered an adequate index of
the inter-item consistency reliability of independent and dependent
variables (Sekaran, 1992). Nunnally (1978) suggests that constructs
have reliability values of 0.7 or greater. There were only a few
cases where a loading value was less than 0.7 and, in those extreme
cases, the item was removed from further analysis. Only two out of
the 82 items
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did not reflect their underlying construct adequately enough, since
they received low loading values and were therefore removed.
Once the test for content validity was complete, items were
reviewed for construct validity. This test examines whether or not
the item is closely related to the underlying construct it purports
to measure. The item to total construct correlation was calculated
for this test. Typically, a score of 0.5 or greater is required and
was met by every item. Finally, the reliability of each construct
was calculated by using the Cronbach alpha measure. Constructs are
deemed to be reliable, when alpha values are 0.7 or greater. Each
construct had a Cronbach alpha value of greater than 0.8, which
means that respondents can answer these items over and over again
with a high probability of receiving similar scores for the
underlying construct.
Correlation matrix of constructs A factor score was calculated for
each of the perceptual constructs based on their underlying items.
A correlation matrix was then calculated for the constructs (see
Table IV).
It is important to note that, for the three areas of most concern
as identified by the respondents (i.e. management leadership,
business performance, and retention of key people), the highest
correlation values were with the following two constructs
each:
(1) Management leadership – value alignment (0.771), retention of
key people (0.722).
(2) Business performance – employee motivation (0.566), employee
commitment (0.560).
(3) Retention of key people – employee commitment (0.724),
management leadership (0.722).
Management leadership was most highly correlated with value
alignment and the retention of key people. These results are to be
expected, since employees look up to their senior managers for
guidance as to what values they should possess. Retention of key
people is also related to senior management’s leadership
capability, since exit interviews typically show that poor
relationships with supervisors tend to explain why an employee has
left an organization. It is also important to note which
relationships were not statistically significant in their
correlations.
Integrating the qualitative and quantitative measures One of the
key objectives of this study was to integrate both qualitative and
quantitative measures, so that a more holistic and comprehensive
understanding of human capital management could be realized. The
perceptual items were joined with their respective quantitative
metrics by associating respondents with their corresponding
organizational membership.
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Structural equation model (causal map) Partial least squares (PLS)
is a structural equation modeling technique typically chosen for
handling relatively small data samples. PLS has been used as a
research tool in a variety of settings such as business disciplines
(Hulland and Kleinmuntz, 1994); cooperative ventures (Fornell et
al., 1990); global strategy (Johansson and Yip, 1994); risk-return
outcomes (Cool et al., 1989); geographic scope (Delios and Beamish,
1999) and in intellectual capital research (Bontis, 1998; Bontis et
al., 2000). Although not so well-known a modeling technique as
LISREL, for instance, PLS has as its primary objective the
minimisation of error (Hulland, 1999). The degree to which any
particular PLS model accomplishes this objective can be determined
by examining the R-squared values for the dependent (endogenous)
constants. PLS was used to test the model within its nomological
network. The 15 latent constructs in this study derive their
meaning from both their underlying measures and their antecedent
and consequent relations, giving a researcher the benefit of
examining the constructs in an overall theoretical context.
A partial least squares structural equation (PLS) conceptual model
was developed, so that both constructs and measures could be
simultaneously examined within their nomological network. The final
conceptual model was developed by exploring a variety of potential
configurations among constructs until statistically significant
paths were reached and the explanatory power of the causal map was
maximized. The final conceptual model depicts a comprehensive
collection of relationships among constructs that are all
statistically significant at the 0.05 level (see Figure 6).
The values along each path are identified as the direct structural
relationship between two constructs and can range from a value of
–1.00 to + 1.00 (these values are more accurate than correlations,
since they account for mediating and indirect causal paths). For
example, there is a statistically significant and direct path of
0.506 magnitude from managerial leadership to retention of key
people. Values underneath key constructs are equivalent to
R-squared scores, which depict the explanatory power of the model.
For example, the R-squared value of human capital effectiveness is
28.5 percent, which means that this model can explain over 28
percent of the variance in that construct.
From the final conceptual model generated in PLS a total of five
key research findings have been uncovered.
Research implication I: managerial leadership is the key antecedent
Managerial leadership is the foremost antecedent construct in human
capital management. It has a substantive and significant direct
path to both the retention of key people (+ 0.506) and value
alignment (+ 0.751). Value alignment in turn has a path to the
reduction of human capital depletion (–0.233) via knowledge sharing
(+ 0.285).
Research Implication I: The development of senior management’s
leadership capabilities is the key starting ingredient for the
reduction of turnover rates
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243
and the retention of key employees. Effective management leadership
acts as a spark for organizational knowledge sharing, which in turn
allows senior management to align values throughout the
organization.
Research implication II: intellectual capital management yields HC
ROI Recall that human capital effectiveness was measured with four
metrics: revenue factor, expense factor, income factor, and HC ROI.
This variable is a key outcome of the overall model. In essence,
organizations constantly strive to generate more revenue and income
per employee. One predictor of this construct is the reduction of
human capital depletion – 0.337), which makes intuitive sense,
since lower turnover rates will yield a higher base of
organizational knowledge and less deterioration of experiential
learning (note: the key factors predicting human capital depletion
were discussed above). The other predictor of human capital
effectiveness comes from a collection of constructs that emanate
from the intellectual capital literature. The intellectual capital
literature states that there exist three primary components of
intellectual capital: human capital, structural capital, and
relational capital. Research conducted at the Institute for
Intellectual Capital has shown that these three constructs are
interdependent in their positive effects. This model bears out the
same result. Note that human capital has a positive effect on
relational capital (+ 0.326) and that structural capital also has a
positive effect on relational capital (+ 0.307). Relational capital
is the key determinant of human capital effectiveness (+
0.360).
Many of these sub-models are interdependent as well. Note that
training (+ 0.530) and employee satisfaction (+ 0.358) have
positive effects on human capital, which is to be expected.
Structural capital also has a positive influence on process
execution (+ 0.543), which is a natural deduction as well.
Research implication II: The effective management of intellectual
capital assets will yield higher financial results per employee.
The development of human capital is positively influenced by the
education level of employees and their overall satisfaction.
Research implication III: employee sentiment drives many factors
There are three constructs that describe general employee sentiment
in an organization: employee satisfaction, employee commitment, and
employee motivation. As expected, these constructs positively
reinforce one another. Satisfaction leads to both commitment (+
0.734) and motivation (+ 0.456) and commitment further influences
motivation (+ 0.429). Interestingly, these three variables play
important roles in other sub-models as well. Satisfaction leads to
human capital (+ 0.358), as described above. Employee motivation
leads to knowledge sharing (+ 0.430), which basically means that
employees who are motivated to work will also tend to share their
knowledge among their peers, as opposed to hoarding it. Finally,
employee commitment is a very important
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244
predictor of three different variables: the retention of key people
(+ 0.442), knowledge generation (+ 0.491), and ultimately business
performance (+ 0.439).
Research implication III: employee sentiment, as defined by
satisfaction, motivation and commitment, has far-reaching positive
impacts on intellectual capital management, knowledge management
and ultimately business performance.
Research implication IV: knowledge management is a critical
initiative Knowledge management activities encompassed three
constructs: knowledge generation, knowledge integration, and
knowledge sharing. The model outlines the importance of coupling
knowledge management activities with general HR policy. Employee
commitment has a positive influence on knowledge generation (+
0.491). Knowledge integration is preceded by process execution (+
0.394) and is followed by knowledge sharing (+ 0.262). Finally,
knowledge sharing will occur, if value alignment (+ 0.285) is
evident, and this can lead to a reduction of human capital
depletion. In other words, individuals will be more prone to
improvisation, creativity and knowledge generation, if they are
committed to an organization. An organization can integrate this
new knowledge into its systems, if the execution of its
technological processes is efficient. Finally, if employees’ values
are aligned so that they are motivated to share knowledge, turnover
will decrease.
Research implication IV: Knowledge management initiatives can
decrease turnover rates and support business performance, if they
are coupled with HR policies.
Research implication V: business performance has a feedback cycle
There are three antecedents to business performance in the model:
two being positive relationships with employee commitment (+ 0.439)
and knowledge generation (+ 0.327). In effect, an organization will
sustain levels of strong performance, if its employees are
committed to success and it continually innovates and renews
itself. The third intriguing path to business performance is
actually in reverse and is a negative feedback loop to human
capital depletion (– 0.372). In other words, a strongly performing
organization can influence human capital depletion by reducing
turnover rates and thus positively affecting individual employee
financial contributions (– 0.337).
Research implication V: business performance is positively
influenced by the commitment of its organizational members and
their ability to generate new knowledge. This favourable level of
performance subsequently acts as a deterrent to turnover, which in
turn positively affects human capital management.
Finally, the endogenous constructs, as specified by the senior
executives, all had significant and substantive r-squared values,
denoting a model with high explanatory power. The r-squared values
ranged from 28.5 percent for human
Intellectual capital ROI
capital depletion and effectiveness, to 44.1 percent for business
performance and as high as 68.2 percent for retention of key
people.
Conclusion All in all, these results suggest that the measuring and
modelling of human capital are critical. This view can be
attributed to the growing strategic importance of intellectual
capital management and the need for HR managers to establish their
credibility by making the function more accountable in financial
terms.
The difficulties of human resource managers in achieving this
should not be underestimated. It is perceived that they do not have
the necessary expertise to carry out appropriate measurement and
that many of the measures used lack precision and are too
difficult.
Nevertheless different measurement approaches are used. Whether
they are actually providing information that establishes the
importance of human capital in financial terms or its credibility
is a moot point. The difficulties are made more difficult by the
attitudes of others in the organization, particularly those
accounting and finance managers who are less likely to see the
importance of such measurement. Nevertheless the importance of
measuring human capital is established. Fitz-enz describes the
future as follows:
The accounting function does a fine job of telling the state of our
past and present financial health. But it says nothing about the
future. Additionally, it does not speak to human capital issues. To
see the future, we need leading indicators. These indicators tell
us the state of our human capital, as we prepare for the future
(2000, p. 249).
The benefit of establishing a causal map of human capital
management is clear. Senior management can visually comprehend the
antecedents and consequents of various quantitative and qualitative
proxies of human capital, thus making clear executive management
decisions with expected outcomes.
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© 2008 nGenera Corporation© 2008 nGenera Corporation © 2008 nGenera
Corporation
Raw data was collected from two recent employee survey
administrations at a large Canadian Telco. A causal model was
developed using structural equation modeling techniques, which
compared the results from one survey administration to another. The
resultant report yielded a longitudinal examination of how direct
links among constructs changed over time. Most importantly, the
causal model provided senior HR decision makers with a clear action
plan for moving forward with Net Gen employees.
Bontis
2 Why Human Capital Measurement Matters
2 A Brief Tutorial: Correlations vs. Causal Models
3 Working With Causal Models
4 Survey Data
6 Further Results 6 Strategic clusters 8 Isolating Net Geners
8 Applying Results to Action
9 Conclusion
11 Endnotes
© 2008 nGenera Corporation
Traditionally, statistics derived from companies’ internal employee
surveys only report how things are, not why they are. Furthermore,
most employee survey results are presented in the form of mean
scores, trend analyses, and perhaps, correlations. These
statistics, while useful, still do not provide the type of insight
that human capital strate- gists require.
This paper introduces an approach to analyzing human capital
statistics called causal modeling (also known as structural
equation modeling and path analysis). This methodological approach
allows decision makers to identify causal relationships behind the
trends observed in a firm using path analysis. By examining direct
links from one construct to another, organizations can determine
how to optimally allocate scarce resources among different
initiatives by testing a variety of hypothetical relationships.
However, causal models are not meant to replace traditional
approaches. Instead, they are to be used as a powerful extension of
existing statistical practices.
This case study applies the causal model approach to human capital
measurement and will demonstrate how working with advanced
statistics can provide actionable insights into human resource
management. The study, conducted by Dr. Nick Bontis and nGenera
Insight, was designed to explain the antecedents and consequents of
effective human capital management. The raw data was supplied by a
Canadian Telco, which conducted two internal surveys in 2007; one
in the spring and a subsequent one in the fall.
INTrOduCTION
“Global competition, market volatility and declining labor pools
make investing in people a high-risk gamble. Still, future success
is dependent primarily on Hr’s ability to attract, retain and
productively manage human capital. However, the lack of a
strategic, unifying model limits Hr’s internal efficiency and
greatly inhibits its ability to positively affect the people and
the organization it services. Human capital measurement must
precede management. Only then can a model support effective
decision making.”
—dr. Jac Fitz-enz, founder and CEO, Human Capital Source, Godfather
of human capital measurement
2 | nGenera Insight: Talent 2.0
© 2008 nGenera Corporation
Why human Capital measurement matters
Human capital measurement can provide valuable insight into a
firm’s direction and climate with regard to its employees. There
are ten primary reasons why analysis based on human capital
measurement is important:
What isn’t measured doesn’t count.• It’s not possible to talk about
something without some sort of unit of measurement. Not measuring
leads to having nothing to say.
What isn’t measured can’t be improved. • The proper identification
of change—be it positive or negative— relies completely on
standardized benchmarks. Without them, it’s very hard to say in any
detail how things have changed, let alone why. There are three
types of benchmarking: internal (comparing one unit to another),
external (comparing one organization to another), and longitudinal
(comparing data over time).
Making business decisions based on empirical • evidence and
analytics is a critically important executive competence. Advanced
statistical analysis brings greater consistency, and a much more
holistic perspective regarding HR policies and practices and their
link to performance outcomes.
Hr empiricists are rare and advanced human capital • analytics is
not a common skill. Not everyone can find, use and present solid,
statistic-based data in an easy to understand format—especially
when C-level executives demand it.
Measurement provides transparency. • As Don Tapscott recommends, in
the age of transparency, if you’re going to be naked, you had
better be buff. When metrics are being tracked and exposed, there’s
incentive for everyone to ensure they look good. Part and parcel of
this is a greater awareness of the firm’s overall status. A healthy
measurement process can help you diagnose and strategize with more
confidence.
Measurement provides control.• Facts based on figures allow for the
precise allocation of resources. Alternatives to capital
measurement usually amount to guesswork, or extrapolations based on
old guesswork. Advanced statistics provide confidence for a
longer-term view.
Analytics provide legitimacy.• With minimal translation, the
numbers speak for themselves.
C-level executives and boards of directors will never • stop
wanting metrics; not ever.
Measuring talent is a top CEO priority. • When employees are viewed
as valuable investments, human capital measurement provides a
wealth (and breadth) of information about staff productivity,
engagement and satisfaction.
decision making requires empirical evidence.• More accurately, good
decision making requires valid empirical evidence provided by
strong statistical analysis.
a Brief tutorial: Correlations vs. Causal models
Before examining the application of causal modeling in human
capital data, it will be useful to introduce the concept and how it
differs from correlation analysis. Traditional correlation analysis
is often misinterpreted and can lead to erroneous implications. To
illustrate this, we will employ a straightforward example drawn
from healthcare.
Assume that data from 50,000 random patients was collected that
included the following three metrics:
whether or not they had a heart attack•
their age (in years)•
their degree of obesity or not (based on their Body • Mass
Index)
Traditional statistical analysis would show the obvious, which is
that the probability of a heart attack is positively correlated to
both age and obesity (Figure 1). But the real question is does age
and/or obesity cause heart attacks?
Age
Heart Attack
BELow Figure 1: Correlations, Source: nGenera Insight
Research
Correlation analysis is not an accurate or complete repre-
sentation of the truth. In fact, as people grow older, their bodies
tend towards obesity. In fact, it is obesity that leads to heart
attacks, not aging itself. With first generation statistical
analyses (correlation), it is easy to reach faulty
What’s Measured Counts | 3
© 2008 nGenera Corporation
conclusions and fake misguided actions. In this example, if one
wanted to solve the heart attack problem, they would either solve
the age problem or obesity problem since they are both positively
correlated. This, of course, is absurd.
By taking the exact same raw data and using path analysis with a
causal model, one can determine which variable is the actual
mediating driver of heart attacks. It turns out that age is
actually an antecedent variable to obesity which is, in fact, the
direct driver (Figure 2). One must address the obesity issue if one
wants to zero in on the target variable. In fact, in a causal
model, the direct path between age and heart attack is not
statistically significant even though it is correlated. Remember
the old adage: correlation speaks to association, not
causation.
Assume that more variables are added to the model described above.
Take for example, diet, fitness, and genetic predisposition. All of
these variables would also be correlated to the propensity for a
heart attack (Figure 3). But how do these variables interrelate to
each other in a causal model?
In this case, it turns out that genetic predisposition is as
significant a cause of heart attacks as obesity. The question
becomes: Can anything be done about genetic predispo- sition? Since
the answer is no, other avenues need to be explored. The next most
impactful risk factor is fitness with a beta value of 0.19. As
people endeavor to improve their level of fitness, their risk for
heart attack lowers accordingly through a reduction of obesity.
This methodology is then continued across all statistically
significant causes of heart attacks.
Each time a new cause is identified as part of the heart attack
risk example, the whole system must be reconfigured to take the new
cause into account, while recognizing that some of the newly
introduced elements may act as stronger
causes than others (fitness, diet, smoking, etc.). Adding new
elements to the system allows one to see how other elements are
interrelated. Interestingly, correlation values do not change at
all, regardless of what new variables are added to the analysis.
This means that these values would provide no intelligence about
the underlying cause of heart attacks or how to prevent them.
Working With Causal models
The structural equation modeling process is run by software (e.g.,
PLSgraph, SmartPLS, LISREL, AMOS). While it has no understanding of
what any of the numbers represent, it does provide a very useful
visual representation of data.
Once a model is established, it can be used to identify and improve
problem areas. Specifically, it can be used to isolate and
prioritize interventions (or action plans). In the healthcare
example previously cited, the first point of inter- vention would
be the development of a fitness plan (since we can’t affect age or
genetic predisposition). Once that antecedent driver is
established, a causal model will predict the follow-on
outcomes.
Furthermore, it is possible to see the degree to which one
antecedent driver influences another, so if there are multiple
causes for one problem, it is easy to determine which approach will
likely yield the most effective results.
With the concept of causal modeling introduced, we will now examine
the results of a company that conducted a survey that served as the
basis for causal modeling of human capital data.
BELow Figure 2: Causal Model, Source: nGenera Insight
Research
Age
Path between Obesity and Risk of Heart Attack
Heart Attack
Obesity
BELow Figure 3: Causal Model and New Variables, Source: nGenera
Insight Research
4 | nGenera Insight: Talent 2.0
© 2008 nGenera Corporation
survey data
An online survey was administered to several thousand employees at
a Canadian Telco. The survey focused on ten work-related themes.
Each of the following constructs had approximately two or three
survey items.
Autonomy & Job Challenge 1.
Effectiveness & Innovation 4.
Employee Satisfaction 5.
Information Sharing 6.
People Development 7.
Senior Leadership 8.
Strategic Pillars 9.
Supervisory Behaviors 10.
Each survey item was based on a Likert-type scale requiring the
respondents to agree or disagree with a statement. For example, the
construct People Development was evaluated in terms of the
following aspects of the respondent’s job:
I am given a real opportunity to improve my skills in this a.
company.
I have the opportunity for career mobility (internal b. transfer,
promotion, etc.) within this company.
In my work group, efforts to balance work and personal c. needs are
supported by our leaders.
The same survey was administered in spring 2007 and again six
months later, in the fall. The objective of the analysis was to
determine the interconnected nature of these ten constructs. The
following seven research questions were addressed—the first four
reflect traditional statistical analyses and the latter three
questions utilize causal models.
What behaviors are strongest now?1.
What behaviors are weakest now?2.
What’s changed for the better?3.
What’s changed for the worse?4.
The previous research questions are staples used in the analysis of
most organizations’ employee survey data.
What are the antecedent drivers of Effectiveness & 5.
Innovation, and Customer Focus?
What differences in antecedent drivers exist between 6. Net Gen
employees and the rest of the organization?
What causal links are getting better or worse over 7. tim