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The Hard Side of Change Management

5/5/2014

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When French novelist Jean-Baptiste Alphonse Karr wrote “Plus ça change, plus c’est la même chose,” he could have been penning an epigram about change management. For over three decades, academics, managers, and consultants, realizing that transforming organizations is difficult, have dissected the subject. They’ve sung the praises of leaders who communicate vision and walk the talk in order to make change efforts succeed. They’ve sanctified the importance of changing organizational culture and employees’ attitudes. They’ve teased out the tensions between top-down transformation efforts and participatory approaches to change. And they’ve exhorted companies to launch campaigns that appeal to people’s hearts and minds. Still, studies show that in most organizations, two out of three transformation initiatives fail. The more things change, the more they stay the same.

Managing change is tough, but part of the problem is that there is little agreement on what factors most influence transformation initiatives. Ask five executives to name the one factor critical for the success of these programs, and you’ll probably get five different answers. That’s because each manager looks at an initiative from his or her viewpoint and, based on personal experience, focuses on different success factors. The experts, too, offer different perspectives. A recent search on Amazon.com for books on “change and management” turned up 6,153 titles, each with a distinct take on the topic. Those ideas have a lot to offer, but taken together, they force companies to tackle many priorities simultaneously, which spreads resources and skills thin. Moreover, executives use different approaches in different parts of the organization, which compounds the turmoil that usually accompanies change.

In recent years, many change management gurus have focused on soft issues, such as culture, leadership, and motivation. Such elements are important for success, but managing these aspects alone isn’t sufficient to implement transformation projects. Soft factors don’t directly influence the outcomes of many change programs. For instance, visionary leadership is often vital for transformation projects, but not always. The same can be said about communication with employees. Moreover, it isn’t easy to change attitudes or relationships; they’re deeply ingrained in organizations and people. And although changes in, say, culture or motivation levels can be indirectly gauged through surveys and interviews, it’s tough to get reliable data on soft factors.

What’s missing, we believe, is a focus on the not-so-fashionable aspects of change management: the hard factors. These factors bear three distinct characteristics. First, companies are able to measure them in direct or indirect ways. Second, companies can easily communicate their importance, both within and outside organizations. Third, and perhaps most important, businesses are capable of influencing those elements quickly. Some of the hard factors that affect a transformation initiative are the time necessary to complete it, the number of people required to execute it, and the financial results that intended actions are expected to achieve. Our research shows that change projects fail to get off the ground when companies neglect the hard factors. That doesn’t mean that executives can ignore the soft elements; that would be a grave mistake. However, if companies don’t pay attention to the hard issues first, transformation programs will break down before the soft elements come into play.

That’s a lesson we learned when we identified the common denominators of change. In 1992, we started with the contrarian hypothesis that organizations handle transformations in remarkably similar ways. We researched projects in a number of industries and countries to identify those common elements. Our initial 225-company study revealed a consistent correlation between the outcomes (success or failure) of change programs and four hard factors: project duration, particularly the time between project reviews; performance integrity, or the capabilities of project teams; thecommitment of both senior executives and the staff whom the change will affect the most; and the additional effort that employees must make to cope with the change. We called these variables the DICE factors because we could load them in favor of projects’ success.

We completed our study in 1994, and in the 11 years since then, the Boston Consulting Group has used those four factors to predict the outcomes, and guide the execution, of more than 1,000 change management initiatives worldwide. Not only has the correlation held, but no other factors (or combination of factors) have predicted outcomes as well.

The Four Key Factors

If you think about it, the different ways in which organizations combine the four factors create a continuum—from projects that are set up to succeed to those that are set up to fail. At one extreme, a short project led by a skilled, motivated, and cohesive team, championed by top management and implemented in a department that is receptive to the change and has to put in very little additional effort, is bound to succeed. At the other extreme, a long, drawn-out project executed by an inexpert, unenthusiastic, and disjointed team, without any top-level sponsors and targeted at a function that dislikes the change and has to do a lot of extra work, will fail. Businesses can easily identify change programs at either end of the spectrum, but most initiatives occupy the middle ground where the likelihood of success or failure is difficult to assess. Executives must study the four DICE factors carefully to figure out if their change programs will fly—or die.

The Four FactorsThese factors determine the outcome of any transformation initiative.

D. The duration of time until the change program is completed if it has a short life span; if not short, the amount of time between reviews of milestones.

I. The project team’s performance integrity; that is, its ability to complete the initiative on time. That depends on members’ skills and traits relative to the project’s requirements.

C. The commitment to change that top management (C1) and employees affected by the change (C2) display.

E. The effort over and above the usual work that the change initiative demands of employees.


Duration.

Companies make the mistake of worrying mostly about the time it will take to implement change programs. They assume that the longer an initiative carries on, the more likely it is to fail—the early impetus will peter out, windows of opportunity will close, objectives will be forgotten, key supporters will leave or lose their enthusiasm, and problems will accumulate. However, contrary to popular perception, our studies show that a long project that is reviewed frequently is more likely to succeed than a short project that isn’t reviewed frequently. Thus, the time between reviews is more critical for success than a project’s life span.


source : change management

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  • Home
  • Applied Analytics
    • Analytics for Decision Making >
      • What is Cluster Analysis
      • Data Reduction and Unsupervised Learning
      • Preparing Data and Measuring Dissimilarities
      • Hierarchical and k-Means Clustering
      • Defining Output Variables and Analyzing the Results
      • Using Historical Data to Model Uncertainty
      • Models with Correlated Uncertain Variables
      • Creating and Interpreting Charts
      • Using Average Values versus Simulation
      • Optimization and Decision Making
      • Formulating an Optimization Problem
      • Developing a Spreadsheet Model
      • Adding Optimization to a Spreadsheet Model
      • What-if Analysis and the Sensitivity Report
      • Evaluating Scenarios and Visualizing Results to Gain Practical Insights
      • Digital Marketing Application of Optimization
      • Advanced Models for Better Decisions
      • Business Problems with Yes/No Decisions
      • Formulation and Solution of Binary Optimization Problems
      • Metaheuristic Optimization
      • Chance Constraints and Value At Risk
      • Simulation Optimization
    • Analytics for Marketing >
      • Marketing Analytics and Customer Satisfaction
      • Customer Satisfaction
      • Measurements and Scaling Techniques – Introduction
      • Primary Scales of Measurement
      • Comparative Scaling
      • Non-Comparative Scaling
      • Experiment Design: Controlling for Experimental Errors
      • A/B Testing: Introduction
      • A/B Testing: Types of Tests
      • ANOVA – Introduction
      • Example -Inspect Spray and Tooth Growth
      • Logit Model - Binary Outome and Forecastign linear regression
      • Text Summarization
      • Social media Microscope
      • N-Gram - Frequcy Count and phase mining
      • LDA Topic Modeling
      • Machine-Learned Classification and Semantic Topic Tagging
    • Data Engine >
      • Understanding The Growth Of Data
      • Evaluating Methods Of Data Access
      • Communication journey
      • Data Journey
      • Planning for data visualisation
      • Visualisation Component
      • Content Connection and Chart Legitibility
    • Customer Insights >
      • Introduction
      • What is Descriptive Analytics?
      • Survey Overview
      • Net Promoter Score and Self-Reports
      • Survey Design
      • Passive Data Collection
      • Media Planning
      • Data Visualization
      • Causal Data Collection and Summary
      • Asking Predictive Questions
      • Regression Analysis
      • Data Set Predictions
      • Probability Models
      • Results and Predictions
      • Perspective Analytics (Maximize Revenue and Market Structure Competitions)
    • Analytics for Advance Marketing >
      • Visualisation and statistics (Political Advertising,Movie Theater and Data Assembly)
      • Excel Analysis of Motion Picture Industry Data
      • Displaying Conditional Distributions
      • Analyzing Qualitative Variables
      • Steps in Constructing Histograms
      • Common Descriptive Statistics for Quantitative Data
      • Regression-Based Modeling
      • Customer Analytics
      • Illustrating Customer Analytics in Excel
      • Customer Valuation Excel Demonstration
  • Soft Skills
    • Adaptability
    • Confidence
    • Change Management
    • Unlearning and Learning
    • Collaboration and Teamwork
    • Cultural Sensitivity
  • Marketing
  • Finance
  • Economics
    • Introduction to Managerial Economics >
      • Basic Techniques
      • The firm: Stakeholders, Objectives and Decision Issues
      • Demand and Revenue Analysis >
        • Demand Estimation and Forecasting
        • Demand Elasticity
        • Demand Concepts and Analysis >
          • Formulation and Solution of Binary Optimization Problems
      • Scope of Managerial Economics
    • Prodution and Cost Analysis >
      • Production Function
      • Estimation of Production and Cost Functions
      • Cost Concepts and Analysis I
      • Cost Concepts and Analysis II
    • Pricing Decisions >
      • Pricing strategies >
        • Adding Optimization to a Spreadsheet Model
      • Market structure and microbes barriers to entry
      • Pricing under pure competition and pure monopoly
      • Pricing under monopolistic and oligopolistic competition
    • Narendra Modi Development Model of Gujarat
  • JBDON Golf
    • Digital Marketing Application of Optimization
  • Let's Talk
  • MBA Project Sharing
  • About Us
    • Good Read >
      • IIMC says PepsiCo CEO Indra Nooyi was an average student
      • India’s middle class figures in Fortune’s Top Ten list of those who matter
      • The Start-Up of you.
      • BUYING AND MERCHANDISING
      • HUMAN RESOURCE MANAGEMENT
      • Do You Suffer From Decision Fatigue?
      • New Page
      • About social media and web 2.0
      • Building Your Own Start-up Technology Company, Part 1
      • Building Your Own Start-up Technology Company, Part 2
      • Building Your Own Start-up Technology Company, Part 3
      • Building Your Own Start-up Technology Company, Part 4
      • Renewable energy is no longer alternative energy
      • What Makes an Exceptional Social Media Manager?
      • The Forgotten Book that Helped Shape the Modern Economy
      • Home
      • How to Think Creatively
      • A Lighthearted Looks at Project Management and Sports Analogies
      • Why Trust Matters More Than Ever for Brands
  • CET Knowledge Zone
    • Tips From JBIMS Students >
      • Prasad Sawant
      • Chandan Roy
      • Ram
      • Ashmant Tiwari
      • Rajesh Rikame
      • Ami Kothari
      • Ankeet Adani
      • Sonam Jain
      • Marketing Analytics and Customer Satisfaction
      • Mitesh Thakker
      • Tresa Sankoorikal
    • Speed Techniques
    • CET Workshops
  • Untitled
  • New Page
    • Cluster analysis using excel and excel miner
    • Chance Constraints and Value At Risk
    • Adding Uncertainty to a Spreadsheet Model