Leading in the Digital Age: Old-timers, by-the-book players, and lone wolves.
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Competitive Differentiation
- Sustainable Growth and Tech Trends
Publication | Update: Sep 2020
Melissa Swift, who leads Korn Ferry’s Digital Advisory for North America and Global Accounts, contests that three groups of employees tend to slow transformation momentum: Old-timers, by-the-book players, and lone wolves.
Any one of these behaviors may seem benign in isolation. But in aggregate – across large numbers of employees across large numbers of organizations – they may be the driving force behind the estimated 84 percent of digital transformations that fail.
Companies must not ignore but engage these three groups, or face obstacles to digital transformation
How to do that?
Her first suggestion: Think about your population in a segmented fashion, and work to meet different segments where they are.
“Many organizations, have rolled out the digital journey in a highly uniform manner, with the same messages and techniques deployed throughout,” Melissa Swift writes,. “Re-skilling for everyone! New teams! Welcome to the new world!”
From a change management perspective, companies should consider both digital experience and behavioral preferences to hit the right starting point and realistic end point for different groups.
The first, and perhaps largest, group is composed of older workers. This is not an insignificant group – for instance, Americans over 65 (and thus past the classic age for retirement) are actually the fastest-growing segment of the workforce at the moment.
Organizations would be far better served by bringing these workers on the journey with them.
Organizations often dismiss older workers’ ability to participate in digital efforts. “They’re obstinate.” “They don’t care.” “They didn’t grow up with this technology and will never get it.”
And these workers themselves sense they are not being included, and inject biases of their own as well: “I don’t need to be a part of digital. It doesn’t concern me.” “This is just another bogus transformation. I’ve seen a million of them and I can wait it out.” Or most poignantly: “I don’t have anything to contribute here.”
This is a disturbing dialogue – and a terrible waste. Given the large and growing fraction of the workforce that older workers represent – the valuable knowledge and experience they possess across industries – and, importantly, the leadership roles that many of these folks occupy – organizations would be far better served by bringing these workers on the journey with them.
It’s not just the pure technological journey that’s leaving workers by the wayside, though. The new, agile ways of working promoted as core to the digital journey leave the many employees who prefer a more structured way of operating feeling a bit lost at sea.
While the best digital leaders actually prefer unstructured environments – according to psychometric data we assembled from more than 500 best-in-class digital transformation leaders – they are challenged to manage teams that are often composed of folks with a heavy preference for structure.
For every employee cheering the slow death of the old-fashioned Cartesian organization, there’s one plunged into high anxiety by the loss of comforting guardrails.
When employees who prefer structured ways of operating are asked to operate in a new-look digital environment, they may feel that everything that allows them to perform well has been taken away: a clear chain of command, a highly codified job description, and step-by-step processes with well-defined beginnings and ends. For every employee cheering the slow death of the old-fashioned Cartesian organization, there’s one plunged into high anxiety by the loss of comforting guardrails. Some of these employees have even made a career on navigating within this structure – particularly in matrixed environments – and feel their primary skill is being devalued.
One critical point of clarification: The older workers and the workers with a high preference for structure are not necessarily the same people. The behaviors helpful to the digital journey are not owned by a particular age group or another. Picture, for instance, a 20-something who has traveled very risk-averse paths: This person may have a far worse reaction to “test and learn” iterative environments than a 50-something who has a more diverse collection of experiences in their history.
The lone wolves
Let’s add to the mix a third group that often gets left behind on the digital journey, but due to their very nature can be easy to ignore: introverts. While we might traditionally place them in the winners’ column on the digital journey – due to the prevalence of introverts in certain kinds of highly technical roles – the same ways of working that may fluster folks with a high preference for structure can also be tough for introverts.
For an introvert, being forced to discuss things in real time, constantly, may be energy-draining.
Consider, for instance, the focus on in-person collaboration that many companies believe is critical to agile progress. For an introvert, being forced to discuss things in real time, constantly, may be energy-draining. Increasingly, team-based environments take away the opportunities for solo focus that introverts may value highly. And let’s not even get started on how introverts may feel in open-plan environments, which many companies view as the natural habitat of the digital journey!
Getting everyone on board: 3 tips
So what can companies do to engage these groups better as they seek to make digital progress? Consider these concrete suggestions:
- Think about your population in a segmented fashion, and work to meet different segments where they are. Many organizations have rolled out the digital journey in a highly uniform manner, with the same messages and techniques deployed throughout. “Re-skilling for everyone! New teams! Welcome to the new world!” From a change management perspective, this is pure folly – and a misuse of investment dollars that might be spent more strategically targeting smaller groups. Companies should consider both digital experience and behavioral preferences of different sub-populations within their organization, and they should craft messaging, programs, and even environments to hit the right starting point and realistic end point for different groups.
- Use resistance to digital as a learning tool. We are not so far into the digital journey that the playbook is finished. Far from it: Think of the 84 percent of digital transformations that fail! Accordingly, when different employee populations put up resistance to this journey, you can gain a host of valuable learning from their negative reactions. For instance, there is a huge amount of calibration going on within organizations right now around what the right model of digital collaboration looks like. So organizations may be well-served by listening to their introverts, and emphasizing a model of agile collaboration with greater elements of “go away and think, then we’ll discuss.”
- Begin with the assumption that every group has something powerful to give to the journey. Organizations have spent decades learning about the value of a diverse workforce, with an array of viewpoints included in decisions small and large. Then the rhetoric of digital blew this up, emphasizing job loss for workers in either “the wrong” jobs, or those who couldn’t “get there” fast enough. We need to remember what we’ve learned over so many years and approach the digital journey from the point of view that different populations will contribute differently. (Microsoft’s efforts around hiring employees on the autism spectrum is a lovely example of this phenomenon in practice.)
Objectives and Study Scope
This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.
The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges.
The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period.
The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players. This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.
Research Process & Methodology
We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.
We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.
Research Portfolio Sources:
Global Business Reviews, Research Papers, Commentary & Strategy Reports
M&A and Risk Management | Regulation
The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.
Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:
As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.
Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.
Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pretax revenue and its total boughtin costs (costs excluding wages and salaries).
Forecasts of GDP growth: GDP = CN+IN+GS+NEX
GDP growth estimates take into account:
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.
Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.
Industry Life Cycle Market Phase
Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:
The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.
The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.
Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from ofﬁcial sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reﬂect different assumptions about their relative importance.
The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.