scientist’s perspective dr. nick bontis

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!
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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.
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@NickBontis
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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:
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www.NickBontis.com Thank You
CPHR caught up with keynote 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 processes in the SECI model as opposed to just one. SECI is a model of how organizational 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-emphasize 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 available in electronic media.
The second step, externalization, means we have to automate processes in HR so that they remain in organizational 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 resident in that HR person’s brain and the risk is that when she leaves, that knowledge 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
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finance to talk about the expenditure 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 compensation 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 initiatives for knowledge and document
sharing are going on in the organization, 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, document 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 socialization and are critical to the development of rapport among each other.
Generally speaking, our HR departments 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 concept 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 competitive instincts
• Courses taught at McMaster: strategy for undergrads, knowledge management for MBAs, digital transformation for EMBAs, advanced statistics for PhDs
• Teaching awards: 12, plus the 3M National Teaching fellow for top professor 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, audience 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 services 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 starting 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 measures 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 organization effectively by diagnosing it so infrequently. My advice is this, survey one twelfth of your employees 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 executives 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 measuring 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 strategic 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 directly 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 obsolescence, 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 obsolescence rate and adjust the budget accordingly.
CPHR: You have published many academic research papers in the area of HR measurement and benchmarking,
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Intellectual capital ROI
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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.
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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
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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,
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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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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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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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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
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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
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**
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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
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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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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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
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
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 representation 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
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 predisposition? 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
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

scientist’s perspective dr. nick bontis

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