Business Performance Measurement

At the Crossroads of Strategy, Decision-Making,

Learning and Information Visualization

February 2003

Vince Kellen

Faculty, E-Commerce
DePaul University
Chicago, IL
U.S.A.
http://www.depaul.edu

President, CRM Strategy

Blue Wolf
Hoffman Estates, IL
U.S.A.
http://www.bluewolf.com

+1 (312) 543-0589
vince@bluewolf.com
Abstract
Business Performance Measurement (BPM) systems have grown in use and popularity over the past
twenty years. Firms adopt BPM systems for a variety of reasons, but chiefly to improve control over the
firm in ways that traditional accounting systems have not allowed. Several approaches, or frameworks,
for building and managing BPM systems have evolved with the balanced scorecard as the dominant
framework in use today. Despite the growing use of BPM systems in organizations of all kinds,
significant problems cause firms to experience difficulty in implementing BPM systems. The problems
range across a variety of topics: excessive diversity in the field of study, data quality and information
system integration problems, lack of linkage to strategy, fundamental differences in how a strategy is
formulated and executed in the firm, ill-defined metrics identification processes, high levels of change in
BPM systems, analytical skills challenges, knowledge as a social and non-deterministic phenomenon,
judgment and decision biases (from prospect theory literature) and organizational defenses that can
undermine successful BPM systems use. To help address these problems, a set of critical success factors
for BPM projects, derived from the literature, are identified. A minimal set of four criteria for designing
successful BPM systems along with 12 BPM system factors to be considered when building BPM
systems are discussed. Forty (40) software vendors with BPM related solutions are listed and the role of
data visualization and metaphor is discussed as a potential means for addressing cognitive problems with
BPM systems. Given the continual information processing and computing power improvements coupled
with the advances in business strategy theory, analysis of decision-making, organizational learning and
BPM systems overall, BPM systems are at a crossroads of difficult problems and interesting
opportunities.

Introduction
While this paper discusses business performance measurement (BPM), two larger questions must be
looked at in order to understand the core issues and opportunities in BPM:

1. How do we know?
2. What moves us to act?

In these questions, the “we” and the “us” referred to can stand for individuals, teams, business units,
corporations and organizations and cultures. As encompassing as this definition of “we” is, it is necessary
because of the ubiquity of communication technology, most notably the Internet, which is causing us (as
individuals, teams, business units and companies) to again wrestle with understanding how it is we know
who our competitors and collaborators are, what our core capabilities are, what makes us unique and what
we should be doing. The technology is allowing a radical increase in the potential gathering and
disseminating of information worldwide. The second question also follows from the first. Knowing
without acting properly can be fatal. For businesses, these epistemological concerns are increasingly
important as markets, competitors and customers are changing faster and causing businesses that had
previously been thought of as enduring to turn up as ephemeral. Business performance measurement is an
integral component to how businesses do know things and how it is they cause themselves to act in a
manner that helps them survive and thrive. It is natural, then, that businesses would show an increased
interest in performance measurement, especially since Internet technology makes diffusion of
performance measurement across the business or across businesses much simpler than in the past. The
measurement mantra continues to reverberate throughout nearly every corridor of business life.

In order to survive and succeed, firms need to set strategic directions, establish goals, execute decisions
and monitor their state and behavior as they move towards their goal. Once a firm becomes large enough
that a single manager cannot sense the firm’s current state and cannot control its behavior alone, the firm
must use performance measurement and control systems to replace the eyes and ears of the beleaguered
manager. Over the past few decades, firms have used information technology to provide this “sense and
control” capability. Several dozen vendors provide business performance measurement information
technology solutions. These tools have leveraged the latest advancements in data and application
integration approaches, web-based charting and reporting, statistical analysis, artificial intelligence,
machine learning and expert system technology.

Yet despite the technology’s improvement, availability and increasing adoption rate, many challenges to
successful adoption and use abound. The challenges in implementing performance measurement arise in
the following areas:

Technical Data quality & latency, application usability, visualization of data

Organizational Business culture, leadership, processes, strategic control and intent
Individual Gesturing, biases, framing and decision-making abilities

Overcoming these challenges are not as simple as finding the right software, establishing the set of best
practices and implementing a BPM system. Issues in each of these areas are teased out of some of the
recent BPM and related literature and discussed here.

What are Business Performance Measurement and Control

Systems?
Specifically, business performance measurement and control systems are the formal, information-based
routines and procedures managers use to maintain or alter patterns in organizational activities (Simmons
2000). A typical performance measurement helps businesses in periodically setting business goals and
then providing feedback to managers on progress towards those goals. The time horizon for these goals
can typically be about a year or less for short-term goals or span several years for long-term goals
(Simmons 2000). Since a BPM system measures performance, it is important to define what performance
is. Lebas and Euske (2002) provide a good definition of performance as “doing today what will lead to
measured value outcomes tomorrow.” BPM then is concerned with measuring this performance relative
to some benchmark, be it a competitor’s performance or a preset target.

Measurement systems are comprised of multiple measures. A measure (or metric) is a quantitative value
that can be used for purposes of comparison (Simmons 2000). A specific measure can be compared to
itself over time, compared with a preset target or evaluated along with other measures. Since a measure is
used for the purpose of comparison, it need not represent an absolute value. For example, in measuring
customer profitability, knowing the relative distance in profitability between two customers may be as
valuable (and more easily gotten) than knowing the absolute value for a customer’s profitability.
Moreover, many BPM systems normalize a measure into a value that promotes comparison not just with
itself, but also with other measures.

Following Simmons (2000), measures can be objective or subjective. Objective measures can be
independently measured and verified. Subjective ones cannot. Measures are also typically classified as
financial or non-financial. Financial measures are typically derived from or directly related to chart of
accounts and found in a company’s profit and loss statement or balance sheet, such as inventory levels or
cash on hand. Non-financial measures are measures not found in the chart of accounts, such as customer
satisfaction scores or product quality measures. Measures are also leading or lagging. Lagging measures
give feedback on past performance, such as last month’s profit, and typically do not provide insight into
future performance. Leading indicators, in contrast, are designed to measure future performance, and
more often than not, future financial performance. Some leading indicators to future performance might
include customer defection rate, customer satisfaction scores or changes in consumer confidence.

Measures are either complete or incomplete. Complete measures capture all the relevant attributes of
achievement, whereas incomplete measures do not. Measures are also responsive or not responsive.
Individuals can influence responsive measures, whereas non-responsive measures are outside the
influence or control of an individual (such as consumer confidence). Measures may be related to inputs
into a process, feedback on the performance of a process itself or they may be related to the outcomes or
outputs from the process. Measures may be related to human performance, process performance or
market conditions. Some, but not all, measures are directly related to the firm’s strategy and are critical
for its successful execution of its strategy. These are called critical or key performance indicators. Finally,
measures can refer to tangible things, often recorded in the chart of accounts, such as inventory levels,
accounts receivable balances, employee headcount, or can refer to intangibles such as level of skill or
knowledge, creativity and innovation.

In summary, below is a listing of attributes that can be useful in examining, selecting, designing and using
measures:

 Objective / subjective
 Financial / non-financial
 Lagging / leading
 Complete / incomplete
 Responsive / non-responsive
 Inputs / process / output
 Critical / non-critical
 Tangible / intangible

When discussing performance measurement, most practitioners (and software vendors) refer to the type
of measurement that helps companies monitor its current and past state. Thresholds, both low and high,
for key performance indicators (KPIs) are set and managed by exception. When data begins to move
outside the threshold limits, the performance measurement system can alert management, who then
attempt to diagnose the problem and address its causes. This type of measurement is referred to as
diagnostic control systems (Simmons 2000). While this type of measurement provides management with
basic control over the firm and an “auto-pilot” capability that can keep the firm on target with its goals, it
is frequently insufficient for success.

Interactive control systems provide additional control capabilities to help the firm deal with strategic
uncertainties. According to Simmons (200), interactive control systems “are the formal information
systems that managers use to personally involve themselves in the decision activities of subordinates.”
Interactive control systems help managers integrate new data and learning into the decision-making
process. Diagnostic and interactive control systems are not disjoint. In fact, an important synergy may
exist between the two as multiple diagnostic control systems serve as a basis for dialog between levels in
the firm (de Hass & Kleingeld 1999). This strategic dialog can aid in managers questioning the validity of
its control system, constituting double-loop learning which challenges controlling assumptions or
variables for the process, the business unit or the firm.

BPM systems need to provide insight into different units or levels of analysis. Performance can be
ascribed to corporations, business units, support or functional units, teams and workgroups and
individuals. One key benefit of BPM systems lies in their ability to help align these different levels of
analysis in the firm. Many corporations consist of several business units or divisions that compete in
different markets with differing strategies. A corporate-wide BPM system can help articulate the theory of
the firm (why different business units exist within the corporation) and improve overall performance by
exploiting synergies between the business units (Kaplan & Norton 2001). At the lowest level of analysis
lies measurement of human performance, for which the literature and examples are rich and long. In
between the business unit and the individual lie other layers, such as the functional or service group,
workgroup or team and the business activity. BPM systems are often designed to be a vehicle for strategic
dialog within the firms. Therefore, performance metrics and scorecards scattered horizontally and
vertically across a corporation, need to be coherent so that the conversations between people about the
strategy is consistent and all the different measurement units contribute to the performance of the
corporation overall (de Haas & Kleingeld 1999). BPM systems can help provide this firm-wide
coherency.

Why Measure Business Performance?

Business performance measurement has a variety of uses. Bititci, Carrie and Turner (2002) list the
following reasons companies measure business performance:

  To monitor and control

  To drive improvement
  To maximize the effectiveness of the improvement effort
  To achieve alignment with organizational goals and objectives
  To reward and to discipline

Simmons (2000) looks at business performance measurement as a tool to balance five major tensions
within a firm:

1. 1. Balancing profit, growth and control

2. 2. Balancing short term results against long-term capabilities and growth opportunities
3. 3. Balancing performance expectations of different constituencies
4. 4. Balancing opportunities and attention
5. 5. Balancing the motives of human behavior

Looking at the firm as a complex organism seeking to survive or thrive in its competitive environment,
performance measurement systems serve as a key contributor to the perceptual and coordination/control
capabilities of the firm. Firms use BPM systems to help monitor and control specific activities; to predict
future internal and external states; to monitor state and behavior relative to its goals; to make decisions
within needed time frames; and to alter the firm’s overall orientation and/or behavior.

Related terms
While performance measurement is frequently used to refer to systems that track and provide feedback on
strategy execution and implementation, other related concepts touch upon performance measurement in
some manner. The field of cybernetics, now an interdisciplinary study of organization, regardless of the
form or material representation of the organization, touches upon measurement systems (Principia
Cybernetica). Firms today are more frequently intertwined with information technology that collects and
delivers data that is significant to the control of the organization. Cybernetic systems are autonomic; they
are self-regulating. Other writers and experts frequently point to BPM systems as providing businesses
with this autonomic capability. Beer (1966) uses cybernetics as a means to describe management control
as a way of coaxing a system towards optimal performance, and even better, arranging for the system to
regulate itself.
In 2001, Gartner coined the term business activity monitoring (BAM), which is “the provision of real-
time access to critical performance indicators” (Flint 2002). BAM delivers alerts and business metrics in
real-time or near-real time to increase efficiency of business processes, monitor shifts of priority and
conflicting goals, increase customer satisfaction through improved product and service quality. Gartner
restricts the term BAM to refer to “systems that draw upon and support the management of several major
business processes” (Flint 2002).

Similar to BAM, event management and alerting (EM&A) is a term that several software vendors have
used to describe information technology that deals more specifically with how business process events are
managed and how alerts are distributed to management. Not specifically a performance measurement
tool, EM&A is used to deliver performance measurement data throughout an enterprise, typically through
messaging middleware and out to different devices, including cell phones, pagers, email boxes, web sites
and database entries. Depending on the breadth of each vendor’s offering, both BAM and EM&A
technology can be used in combination with performance measurement software. The BAM or EM&A
technologies become the integration and messaging layers of technology getting data to and from the
performance measurement application.

BPM Frameworks and Reference Models

Different frameworks and reference models for measuring business performance have evolved from a
variety of origins. Frameworks are approaches to measurement that businesses frequently adopted, often
with significant diversity in their design and use. Reference models are more rigorous standards, typically
around specific performance metrics and associated business processes, adopted by an industry or by a
common functional unit. Discussed in this paper are:

Frameworks Reference models

Balanced Scorecard Supply chain management

Economic Value Added New product development
Activity-based costing
Quality Management
Customer Value Analysis
Action-Profit Linkage Model

The Balanced Scorecard

Perhaps the most widely used BPM framework is the balanced scorecard. Introduced by Robert S. Kaplan
and David P. Norton in 1992, balanced scorecards have found widespread adoption in Fortune 1000
companies. Initially focused on finding a way to report on leading indicators of a business’s health rather
than traditional accounting measures which are lagging indicators, the balanced scored was refocused to
measure the firm’s strategy. Instead of measuring anything, firms should measure those things that
directly relate to the firm’s strategy (Kaplan & Norton, 2001). Normally (although not required) the
balanced scorecard is broken down into four sections, called perspectives:

The financial perspective The strategy for growth, profitability and risk from the shareholder’s
perspective.

The customer perspective The strategy for creating value and differentiation from the perspective of
the customer.
The internal business The strategic priorities for various business processes that create customer
perspective and shareholder satisfaction.

The learning and growth The priorities to create a climate that supports organizational change,
perspective innovation and growth.

Economic Value Added

Developed by the Stern Stewart Corporation as an overall measure of financial performance, EVA is both
a specific performance measure and the basis for a larger performance measurement framework (Otley
1999). According to its creators, EVA is a financial performance metric that is most directly linked to the
creation of shareholder value, over time (Stern Stewart 2002. EVA is net operating profit less an
appropriate charge for the opportunity cost of all capital invested in an enterprise. Mathematically it is:

EVA = ( Net Operating Profit After Taxes ) – ( Capital X Cost of Capital )

It is designed to give managers better information and motivation to make decisions that will create the
greatest shareholder wealth. The EVA framework is typically used as a manager incentive plan. Since
EVA is a single metric (although it can cascade down and across an enterprise to evaluate the
performance of specific investments) it is complementary to the balanced scorecard and can be included
in one (Otley 1999). Using EVA alone can cause managers to invest in less risky, cost-reducing activities
rather than in growth activities and as a pure financial model, EVA cannot serve as a vehicle for
articulating a strategy. But coupled with the BSC, the trade-offs between short-term productivity
improvements and long-term growth goals can be managed (Kaplan 2001).

Activity-Based Costing

Activity-based costing (ABC) was developed to provide better insight into how overhead costs should be
allocated to individual products or customers. Typically, businesses make simple adjustments to allocate
overhead costs that do not accurately model how the product or cost consumes those overhead activities.
ABC links expenses related to resources supplied to the company to the activities performed within the
company. Expenses flow from resources to activities and then to products, services and customers. Using
the approach, companies get insights into profitable and profitless activities based on a customer or a
product viewpoint (Kaplan 2001). ABC, then is a way of measuring which of the firm’s activities
generate revenues in excess of costs and as a result, provide keen insight into what is really providing
value for customers (Meyer 2002).

Again, just as with EVA, ABC can be complementary to the BSC. Companies with large and growing
indirect and support expenses may benefit from an ABC measurement scheme first. Companies with a
low return on capital and a weak financial structure may start with EVA first. If the organization wishes
to implement a major change in its strategy, a BSC scheme may be embarked upon first. Over time, EVA
and ABC metrics can find their place within the BSC (Kaplan 2001).

Quality Management

Over the past few decades, many firms have adopted various quality programs, such as Total Quality
Management (TQM), Six Sigma, European Foundation Quality Management (EFQM) and The Baldridge
National Quality Program to improve the quality of the manufacturing and service offerings. A central
tenant for all of these programs is business performance measurement. For example, The Baldrige
National Quality Program measures businesses in seven categories and the EFQM in nine (Kaplan &
Lamotte, 2001):
Baldrige categories EFQM Criteria

Leadership Leadership
Human Resource Focus People
Strategic Planning Policy and Strategy
Process Management Partnerships and Resources
Customer and Market Focus Processes
Information and Analysis People Results
Business Results Customer Results
Society Results
Key Performance Indicators
Kaplan and Lamotte (2001) argue that quality program performance measurement need not be exclusive of
balanced scorecard measurement systems. They point out differences and synergies between the
frameworks:

 The BSC emphasizes explicit causal links through strategy maps ad cascaded objectives more than
the quality programs do.
 The BSC targets breakthrough performance whereas the quality programs rely on benchmarking
approaches
 The BSC sets strategic priorities for process enhancements.
 The BSC integrates budgeting, resource allocation, target-setting, reporting and feedback on
performance into ongoing management processes.

Quality programs, while grounded in product quality improvement and applied to many other business
processes (Wruck & Jensen, 1998), are continuous improvement frameworks that might not be best suited to
help manage discontinuities in business strategies (Kaplan and Lamotte, 2001). Despite the differences
between quality programs and the BSC, Kaplan and Lamotte (2001) see a symbiosis between the two
frameworks. They point out similarities in the four perspectives of the BSC and the four sub-categories
(customer-focused results, financial and market results, human resource results and organizational
effectiveness results) in the Baldrige Business Results category. The EFQM is also converging on more
detail in assessing the organization’s results. Proponents of the Baldrige frameworks also note the shift over
the years to integrated measurement. In 1997, the Baldrige criteria moved “further away from a perceived
narrow focus on ‘managing quality’ to a comprehensive framework for improving overall organizational
performance excellence” (Evans, 1997).

Customer Value Analysis

With the recent strong focus on customers, businesses have begun to deploy technologies, and measurement
systems, to manage business activities that directly or indirectly interact with the firm’s customers. These
customer relationship management (CRM) technologies are providing firms with better data integration and
hence better measurement regarding customers. With the obvious strategic importance of customers, it is
natural for businesses to begin exploring more robust ways of measuring customers and the related business
activities. Gale (1994) explains the role of managing customer value in the context of the Baldrige National
Quality Award’s customer focus and satisfaction criteria, which comprises about 30% of the overall score
for the award. Customer value analysis is sufficiently rich and complex to require more robust analytical
tools and frameworks for measurement and Gale (1994) offers seven tools:

1. 1. The market-perceived quality profile which breaks down the firm’s offering(s) into a set of
attributes that are scored and weighted from the customers’ perspective.
2. The market perceived price profile which breaks down the firm’s offering price (in many businesses
and markets price is composed of multiple aspects) into a set of attributes that are scored and
weighted from a customers’ perspective.
3. The customer value map depicts the firm’s relative perceived offering price and relative perceived
offering performance along and 2-dimensional grid against competitors.
4. Won/lost analysis researches further reasons and facts about why a customer defected or decided to
buy the firm’s offering.
5. Head-to-head area chart of customer value is a graphic display of how the firm’s offering is
performing against a single competitor.
6. Key events timeline depicts how the firm’s and competitors’ actions change the markets perception
of performance of each offering attribute.
 7. A what/who matrix is a tool for tracking who is responsible for what actions that will change the
firm’s ability to improve its ability to manage customer value.

Rust, et al (2000) decompose the customer problem down to three top-level areas (with further
decomposition beneath each of the three):

1. Value equity refers to the customers’ perceptions of value

2. Brand equity refers to the customers’ subjective appraisal of the brand
3. Retention equity refers to the firm building relationships with customers and encouraging repeat-
purchasing

These three areas correspond to three distinct disciplines in the CRM and marketing literature (brand
management, customer value analysis and customer loyalty analysis), each with its own detailed
measurement approaches. Numerous other CRM measurement frameworks exist (Kellen, 2002). The
implications for BPM systems are clear: measuring business activities and outcomes regarding customers is
becoming increasingly complex and increasingly important to the firm’s strategy.

Action-Profit Linkage

Epstein and Westbrook (2001) developed the Action-Profit Linkage (APL) model to help firms identify,
measure and understand the causal links between company actions and profits. This framework is a
multiple-stakeholder behavior model that lays out the chain of effects as a result of changes in stakeholder
behavior. The APL model starts with the corporate strategy and moves to the four main components:
company actions, delivered product/service, customer actions and economic impact. Behaviors (and
perceptions and attitudes) are measured in each of the components.

For example, management and employee behavior can be measured as activities in the company action
component by measuring learning, workload, reward and recognition and culture. Product/service
characteristics such as price and quality can be measured along with employee or customer perceptions of
the product/service. Customer behaviors such as purchase rates, share of requirements, repeat purchases,
cross-sell rates and new referrals can be measured as well as attitudes such as customer satisfaction and
intent to purchase. Finally, economic impact such as customer revenues and profitability, number of new
customers and market share can be measured.

By presenting managers with an explanation of behavioral linkages between strategy, company actions,
product/service improvements, customer behavior and economic impact, Epstein & Westbrook (2001) claim
that firms can show some dramatic improvements. Their model shares features in common with BSCs and
ABC. Like ABC, APL looks at a firm as a collection of activities. Unlike ABC, APL describes these
activities in a strict behavioral linkage. Like the BSC, the APL model accommodates measures from
different aspects of a firm and combines internal and external measures and lagging and leading indicators.
While the BSC does have this notion of causal linkages between elements within the BSC, the APL model
ascribes behavioral causal linkages and because of this focuses more on the company’s actions (Epstein &
Westbrook, 2001). Despite the differences between APL and the BSC, Epstein & Westbrook stress the
compatibility between the APL and the BSC frameworks.

Reference models

Well-established standard measures for financial performance already exist. Not so for non-financial
measures. Different standards groups are developing standard approaches to non-financial measures. TQM,
Six Sigma and the EFQM Excellence model are methodologies and management systems that can provide
leading indicators of financial performance. The International Organization for Standardization (ISO), along
with the large public accounting firms, is working on the processes that generate these non-financial
numbers. The Supply Chain Council and the Product Development and Management Association are
developing reference models for business processes, which also include specific performance measurements
(Smith, 2001).

Just a few BPM reference models exist. Most likely this is due to popularity of the balanced scorecard as a
strategic measurement tool and the various quality programs that have become integrated business
performance measurement systems more closely supporting the business strategy. Since strategies are often
highly differentiated, BPM systems that support the strategy become highly differentiated and propriety as
well, including specific sequences of metrics. Hence, reuse on specific metrics and arrangement of metrics
is less likely to occur in these strategically aligned frameworks. A reference model is more likely to cover
non-core processes or processes that are highly consistent and diffuse across many companies, such as new
product development and supply chain management. While many practitioners may desire reference models
to ease the initial design and implementation of a BPM, they do not address how to construct a BPM system
that is tightly integrated with a differentiated strategy.

Issues with BPM

Commentary on performance measurement can be placed along a continuum ranging from views supporting
conventional approaches based on a rational view of decision-making at one end to unconventional
approaches that derive from a view based on ambiguity, irrationality or bounded-rationality. In addition,
commentaries cover a wide range of topics from data integration and quality, best practices to cognitive
psychology that address multiple levels of analysis – including the individual, the workgroup and team, the
firm and cultures.

Diversity
Perhaps the first and most important issue with BPM is its diversity. Neely (2002) cites 12 million web sites
dedicated to performance measurement, up from 200,000 in 1997, a significant rise in the number of
conferences world-wide on the subject and wide-spread adoption of the BSC in large organizations. He also
cites tremendous diversity in the academic field as well, with experts in accounting, economics, human
resource management, marketing, operations management, psychology and sociology all exploring the
subject independent of each other. In 1998 at a multi-disciplinary conference on performance measurement
in the U.K., the 94 papers presented cited 1,245 books and articles, of which less than 10% were cited more
than once and only 0.3% were cited more than five times. More importantly, Neely argues that there is little
agreement on what are the most important themes and theories in performance measurement.

Adoption rates
Citing studies by technology research firm Gartner, Frigo and Krumwiede (2000) report that the balanced
scorecard approach is in use at about 40% of Fortune 1000 companies. In the public sector, only 33% of
U.S. counties with populations of more than 50,000 were using performance measurement in any form with
a similar adoption rate among cities (Berman, 2002). Performance measurement may be following similar
diffusion patters as other productivity improvements, which can take a generation to achieve widespread
acceptance. Wide variation in the use of information technology may be part of the problem as well.
Performance measurement efforts may have more success in measuring activities and outputs, versus
outcomes. Outcomes require stakeholder or customer perceptions of timeliness, quality and usefulness of
services, which involve data not widely gathered (Berman, 2002).
Data quality
BPM systems typically draw their data from data warehouses that in turn draw their data from source
enterprise systems and numerous ancillary software and data sources throughout an enterprise. Bad data
quality is affecting the usefulness for data warehouses in general. The Data Warehousing Institute (TDWI)
reports in its study of 647 companies on data quality reports that 40% of the companies surveyed have
suffered losses, problems or costs due to poor data quality (Eckerson, 2002). Sources of data quality are:
lack of validation routines in data entry systems or in system loading; mismatched syntax (first name, last
name versus last name, first name), data formats (6-byte versus 4-byte data fields) and code structures
(male/female versus m/f); unexpected changes in source systems; the number and complexity of system
integration interfaces; poor system design; data conversion errors (Eckerson, 2002).

Strategy and measurement

BPM systems come in two distinct flavors: strategic and operational. The balanced scorecard is an example
of a strategic BPM system. Operational BPM systems help managers with specific operational process
control issues that may or may not be directly related to the strategy. Numerous researchers have discussed
the link between strategy, measurement and success.

Measurement plays a crucial role in translating business strategy into results (Lingle & Schiemann, 1996).
In the area of executive management, only 6 in 10 executives place confidence in the data presented to
them. Factors that prevent successful measurement include fuzzy objectives (more precise objectives
needed), unjustified trust in informal feedback systems, and existing entrenched measurement systems.
Those that measure gain agreement on the strategy, clarity of communication, focus and alignment and
organizational culture advantages (Lingle & Schiemann, 1996). The survey included 203 executives, 72
percent top executives. 50% of the respondents were from companies with more than 500 employees.

Not all measures are good ones to include in a strategic measurement system. Strategy and performance
measurements need to be intertwined, and as such are likely to be unique for each company. Companies
should measure how parts of their value chain actually fit together for an overarching advantage instead of
relying on process-by-process metrics (Porter, 2002).

Three forces are at work in shaping BPM: increased demands from capital markets for forecasting accuracy;
shorter product cycles, quicker market shifts and expanding partnerships; and the growing sophistication
and availability of information technology, including ERP software, and improved database and analytic
capabilities (Krell, 2002). Despite the widespread understanding of the link between strategy, measurement
and success and the need for some balance between internal/external, leading/lagging indicators, internal,
lagging metrics dominate performance measurement, rather than external and leading indicators (Krell,
2002).

BPM system satisfaction

The Institute of Management Accountants (IMA) conducted an annual survey of its 1,300 members on
performance measurement systems, practices and trends and performed follow-up interviews with
respondents. The 2001 IMA survey reported that 80% of its respondents reported making changes in their
performance measurement systems during the last three years. Changes ranged from radical (dumping an
existing system) to incremental (adding or deleting performance measures, refining the mix of measures).
33% reported the change as a major overhaul. Overall, only 31% of the respondents reported their existing
performance measurement system as less than adequate to poor in supporting management’s business
objectives and initiatives. Only 15% considered their PM systems as very good to excellent in
communicating strategy. Balanced Scorecard (BSC) users fared much better in perception.

Percent rating PM system

Either “very good” or “excellent”
BSC users Non-users
Supporting management’s objectives and initiatives 52% 5%
Communicating strategy to employees 48% 3%
Supporting innovation 22% 2%

The key challenges for performance measurement is in intangible assets (human and information capital)
and innovation. (Frigo, 2001). In the IMA survey, 60% of the respondents said that innovation was a key
part of the firm’s mission statement yet more than 50% rated the BPM system as poor or less than adequate
in this area. Overall, less than 10% of the respondents rated performance measures for intangible assets as
very good or excellent (Frigo, 2002).

The American Institute of Certified Public Accountants and Lawrence Maisel, from the Balance Scorecard
Collaborative, conducted a study to determine current perceptions and practice regarding performance
measurement systems. The study included 2000 respondents to a survey and on-site interviews with a
smaller number of companies. Only 35% rate their BPM systems as effective and 80% consider the
information from their BPM systems as merely “adequate” if not “poor.” The study cited many other points.
Many respondents indicated using BSCs even though their BSC system fails to meet the criteria set by the
BSC creators (Kaplan and Norton). Performance measurement involves change management, and therefore
staff buy-in, education and leadership are all required. Benefits include improved organizational
development and leadership, financial performance, operating performance, decision-making, and strategy
and alignment. Common barriers cited include issues related to buy-in, leadership, education and the
measurement process itself. Better information quality and technology was cited as one of the areas of
needed improvement. Financial professionals see PM systems as more effective than their accounting
counterparts (Maisel, 2001)

A recent KPMG study of U.S and European business and government executives revealed that one of the
most common disappointments reported was the lack of data integrity and the inability of their system to
product meaningful information to support decision-making. The study also discovered that BPM systems
are not aligned with strategic business measures, dependant on lagging, not leading indicators, are poorly
integrated with internal and external information and rely too heavily on financial measures. Some factors
for failed BPM systems included: measuring things that are easily measured versus what should be
measured, data inaccuracy; measures that were too complicated, and users didn’t understand the system and
its measures (KPMG, 2001).

Building measurement systems

BSCs work best if employees have input into the formation of its parameters (Ho & McKay, 2002). The
number and type of parameters must be well thought out in advance. Firms should avoid an unmanageable
number of parameters so that the using the BSC does not become too cumbersome and time-consuming.

Van Aken & Coleman (2002) identify a process for building BPM systems. After defining the need for
measurement and improvement, the process proceeds through the following steps:

  Creating a common understanding of what the organization does (its mission, key processes,
and key outputs).
  Defining key performance areas and understanding the metrics (so everyone knows if the process
was successful) are next.
 After a balanced and focused set of metrics has been defined, the measurement system must be
implemented, taking into account required resources, technology, training, and communication.
 In the remaining steps, the management team must systematically use the measurement system to
assess performance, determine improvement actions, and review the impact of these actions.

Van Aken & Coleman (2002) also report that the time it takes to design and implement a measurement
system varies and can take as little as one to two months if no major technologies or tools are needed.
Kaplan and Norton (2001) also report that the time to create a BSC can be, on average, eight weeks, which
is down from the 16 weeks setting up a BSC took in 1996. The use of industry templates for strategy maps
and more experience with the BSC contribute to the improvement in time. However, Kaplan and Norton
(2001) report that many companies find the BSC harder than it initially looked. Many companies do not
implement a complete BSC. Many measures are not collected and over time measures are added and
refined. Kaplan and Norton (2001) encourage firms to think of the BSC as a living document that evolves
over time.

Jensen & Sage (2000) describe a process for selecting and refining metrics (depicted in Figure 1). Since
metrics in a BPM system do change over time, firms need to establish the process for accommodating this
change.

Begin

Identify/revise set of organizational mission, goals, objectives

Identify/revise set of internal and Identify/revise set of organizational Identify/revise set of information
External organizational entities Inputs, outputs, influences Technology resources

Evaluate requirements for candidate metrics

Identify candidate metrics for inclusion in metrics set

Identify candidates in metrics set if requirements are met

No

Assess complete set of metrics against refinement criteria

Is metrics set sufficiently refined?

Implement metrics set

No
Have goals or environment changed?
Figure 1. Metrics identification and refinement process flowchart (Jensen & Sage, 2000)

Kaplan & Norton (2001) make the argument that BSCs are superior to other forms of performance
measurements, such as key process indicators (KPI scorecards) and stakeholder scorecards. BSCs link
performance measurement to the strategy, and cause and effect mapping (strategy maps) helps firms identify
the indirect links between business activities and overall performance. They warn that BSCs should not just
be a collection of financial and nonfinancial measures organized into three to five perspectives. Metric
selection should be driven by the firm’s strategy. While formulating a strategy is an art form, describing the
strategy (which BSCs do) should not be (Kaplan & Norton, 2001).

Causal and correlative measurement

A key attribute for the BSC and other BPM frameworks is the support for identifying and communicating
causal linkages between components of the business that fulfill the strategy. Identifying the causal linkages
may be a bottleneck. It might take firms too much time to statistically prove causal linkages between
performance metrics, thus severely impairing the causal diagnostic benefit that BPM systems are supposed
to supply (Maisel, 2000).

While some firms have begun establishing closed loop measurement approaches, which by their nature seek
to understand causality in a deeper fashion, many firms struggle with the more subjective correlative
measurement that might provide insight with less effort and in less time. Nail (2002) points out that
marketing departments need to master correlative measurement, not just closed loop measurement.
Complexity in integrated marketing campaigns is making closed loop measurement harder to do. However,
correlative measurement requires a more complex data store and higher level of analytical skills than causal
analysis (Nail, 2002).

Successful violations of accepted BPM principals

The widely accepted model of performance measurement adheres to three basic principles: performance
should be clearly defined; performance should be accurately measured; rewards should be contingent upon
measured performance (Austin & Gittell, 2002). However, continued acceptance on these three principles
may be misplaced. Austin and Gittell document cases where firms deliberately violated one or more of these
three principles but still exhibited high levels of performance relative to competitors. The widely accepted
model of performance is based on compliance and extrinsic motivation. Following Austin & Gittell, these
approaches tend to give rise to undesirable outcomes including the distortion of information quality, the
displacement of employee effort from real organizational goals to ones that can be more easily pre-specified
and measured and a deterioration of performance. In contrast, the firms in the anomalous cases used a
performance measurement approach that was based on ambiguity and intrinsic motivation. In these
approaches, information quality tends to be high and require dialog across levels in the organization to
determine what is the right thing to do in a particular context. Moreover, since individuals are frequently not
identified in specific performance situations, the “information-reducing” effect of fear is minimized.

Intrinsic motivations have other benefits. Intrinsic motivation is often required to transfer tacit information.
Intrinsic motivation enhances learning and is needed for creativity. Intrinsic motivation can help overcome
those situations where it is impossible to specify all the relevant employee behaviors and outcomes needed.
Intrinsic motivation is also not without its faults, however. Changing intrinsic motivation is hard and the
outcomes are less certain. Intrinsic motivation has its ugly side as well. Envy, vengeance and the desire to
dominate are intrinsic motivations. Also, manipulating extrinsic motivations enables more flexible firm
behavior (Osterloh & Frey, 2002).
Discriminatory power and coherence
Almost all measures lose their ability over time to discriminate between good and bad performance (Meyer,
2002). Causes include: improved performance, learning how to meet the measure without improving the
performance that is sought (perverse learning or gaming); replacing low performers with high performers
(selection); and withholding performance data when differences persist (suppression). This requires firms to
change measures and search for new measures that can discriminate better.

Activity-based approaches might provide measurement coherence. Meyer (2002) suggest that we think of a
firm as a bundling of activities which incur costs and may or may not add value to the customer. “The
problem for the firm is finding those activities that add value for the customer and generate revenues in
excess of costs, extending those activities, and reducing or eliminating those activities that incur only costs,”
(Meyer, 2002). He has proposed activity-centric performance measurement as an approach that can aid in
establishing the right measures and improving lateral and horizontal measurement coherency in the
organization. His notion of activity-centric performance measurement is derived from activity-based
costing, and is referred to as activity-based revenue (ABR). ABR renders individual accountability
independent of organization design. This approach is not without problems either. Gathering activity-based
measures can be difficult. ABR is also more suitable for complex situations such as when a firm supplies
many products to many customers and the product specifications adding value to the customer are not fully
understood.

Bounded Rationality
Technology and process considerations aside, decision-making based on measurement data is fraught with
individual biases, depending on how the measurement data and problem is presented within the relevant
decision-making context. Most managers make decisions about future hypothesized choices that make little
sense from the utility theory perspective of a rational assessment of probabilities (Kahneman & Tversky
2000). Prospect theory attempts to explain why people make what appear to be unsound decisions,
especially under uncertainty and risk. How decision problems are described (framed) can lead to decision
outcomes that deviate from standard decision-making theory (utility theory). These issues around framing
and biases can affect decision-making based on performance measurement data. Managers consistently
exhibit unwarranted risk aversion and a propensity to look at decisions in narrow terms often isolated from
future or past decisions (narrow framing), quite possibly leading to, in aggregate, incorrect management
choices. This narrow framing and excessive risk aversion may be unintended consequences of excessive
insistence on measurable short-term successes (Kahneman & Lovallo, 1993). A performance measurement
system project that proceeds unaware of the framing issues, the heuristics people employ when making
judgments under uncertainty and the cognitive biases that even statistical experts possess and employ, could
be headed for little impact on the business, or worse still, accelerated faulty decision-making.

When making decisions, many organizations are often overly optimistic (the optimism bias). Despite the use
of measurement, analysis of data and worst-case scenarios, decision-makers paint rosy pictures that give an
ungrounded illusion of control (Kahneman & Lovallo, 1993). Realism, however, may have its costs.
Kahneman and Lovallo (1993) point to research that indicates “the deeply disturbing conclusion that
optimistic self-delusion is both a diagnostic indication of mental health and well-being, and a positive causal
factor that contributes to successful coping with the challenges of life.”

While the work of Kahnamen, Tversky and others in the prospect theory strain have contributed much to
understanding judgments and decision-making, some of the biases and heuristics that prospect theory
“uncovers” may be an artifact how information in these probabilistic reasoning tasks is represented
(Gigerenzer, 2000). Some of the biases that prospect theory exposes disappear when the information in the
problem is represented as a frequency problem rather than a probability problem. Gigerenzer (2000) argues:
“While the standard probability format has become a common way to communicate information, … it is
only one of many mathematically equivalent ways of representing information.”

Gigerenzer shows how the dramatic removal of one such fallacy, the conjunction fallacy, is achieved with a
frequentist representation of the information. Following Gigerenzer (2000), the original conjunction fallacy
problem is shown below:

Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student
she was deeply concerned with issues f discrimination and social justice, and also participated in
antinuclear demonstrations.

Participants are asked with of the two alternatives was more probable:

1) 1) Linda is a bank teller

2) 2) Linda is a bank teller and is active in the feminist movement

His transformed problem asks participants in the study the following:

There are 100 persons who fit the description above (i.e., Linda’s). How many of them are:

a) a) bank tellers
b) b) bank tellers and active in the feminist movement

In Gigerenzer’s (2000) research, the conjunction fallacy largely disappears with this reframing.

While the classic depiction of the fallacy above is constrained and not usually naturally found, as is, in BPM
environments, this type of reasoning employed by people may in fact be behind many decision-making
problems. The implications for business performance measurement is that information representation can
have a significant impact in the number of decision errors made as a result of common cognitive limitations.

Use of Metaphor
A further source of errors in reasoning may lie in how the human brain actually processes information and
reasons. Assuming that to reason is to construct and manipulate symbolic representations in a language of
thought may be wrong. Based on our biology and evolution, our use of visual, spatial and kinesthetic
cognitive abilities to process information may be the basis for reason and language (Potts, 2001). The role of
metaphor (image schemas and perceptuo-motor mappings) is to provide a structural environment for this
reasoning.

Citing Lakoff & Johnson (1999), Potts enumerates some metaphors and associated abstract concepts:

Metaphor Abstract Schema Concrete Schema

Important is big Significance in situation Seen/felt size
Difficulties are burdens Obstacle to intention Felt weight
More is up Quantity or degree Seen or felt elevation
Categories are containers Classification Seen or felt containment
Similarity is closeness Diagnostic/predictive similarity Seen or felt proximity
Help is support Assistance toward intention Felt firmness underneath
 Time is motion Passage of time Seen/felt movement
States are locations Situational equivalence Seen/felt place
Change is motion Variation over time Seen/felt movement
Action is self-propulsion Autonomous activity Intentional movement
Purposes are desired objects Intention Reinforcing object
Causes are physical forces Causes and origins Felt pressure and weight
Relationships are enclosures Relational dependency Seen/felt enclosure
Controlling is being above Causal dependency Vertical alignment
Seeing is understanding Knowledge Objects seen
Understanding is grasping Knowledge and comprehension Objects actively felt

Potts (2001) suggests that “if all cognition, from everyday preconscious thinking to abstract professionalized
mathematical reasoning is grounded in perceptual and performance schemas rather than arbitrary symbolic
codes, perhaps we should consider the ways in such schemas can be used in professional descriptions of
desired and actual computing and information artifacts.”

Indeed. While Potts was considering the use of metaphor to enrich requirements specification of computing
systems, the approach is especially noteworthy with regard to BPM systems. Visualization and metaphor, if
is the basis for reasoning skills, may be the best form of further reducing errors in decision-making due to
problems of cognition.

Others have classified metaphors differently. Nesbitt (2000) classifies metaphors into five categories:

1. Spatial metaphors. These relate to scale, location and structure and can carry quantitative
information. Relationships can be described by a position on a map or a two or three-dimensional
grid. Structures such as tree maps or data maps can carry broader overview information.
2. Temporal metaphors. These are concerned with how data changes over time. It includes concepts of
movement, animation, rhythms and cycles.
3. Sight metaphors. These use direct mappings from information to the attributes of sight. These
include color, light, shape and surface texture. Icons are an example of how abstract shapes can be
used to convey information using intuitive symbols.
4. Sound metaphors. These deal with direct mappings of typical sound properties such as pitch,
amplitude, timbre and also more musical qualities such as rhythm and melody. Auditory symbols
are less common, but good examples exist, such as the Geiger counter.
5. Touch metaphors. These relate to tactile properties such as force, inertia and vibration. Other
properties such as weight, density, hardness and surface texture can be used to encode information.

With abstract data (data that does not conform to the three-dimensional world, such as business
information), metaphor classifications would first help guide the analysis and transformation of the data
(Nesbitt, 2000).

Defensiveness and Organizational Learning

More danger lurks still. Cognitive limitations and problem framing is just one component of the overall
issues in BPM. How organizations deal with threatening information is another substantial concern. It is one
thing to have the problem framed appropriately and have the performance data represented properly. It is
another to get human beings to discuss the information and its implications and take appropriate action.
Argyris and Schön (1996) examine the role defensiveness plays in organizational learning. Organizational
inquiry (the intertwining of thought and action that proceeds from doubt to the resolution of doubt) can
produce a change in thinking and acting within an organization. This learning, however, has two distinct
“flavors” – single-loop and double loop learning. Single loop learning produces a change in behavior, but
not a change in the underlying assumptions within the organization. For example, normal product process
improvement inquiry may yield changes in the manufacturing process, but not impact basic assumptions
about the organization. Double-loop learning on the other hand results in a change in the strategies and
assumptions governing the activities (Argyris and Schön, 1996). The relationship between the two forms of
learning is depicted in Figure 2.

Governing No
Actions Consequences Mismatch?
variables
Yes
Single-loop learning

Double-loop learning
Figure 2. Double-loop and Single-loop learning (adapted from Argyris, 1999).

Argyris and Schön describe two models of theories in action. These are theories that organizations actually
put in to use (theories of action put to use, or action theory), versus theories that organizations say they put
into action (theories of action espoused, or espoused theory). The first model, (called Model-I), is one they
claim to have observed in many organizations in many environments throughout the world. Model-I theory
in use has four main governing variables or values and the accompanying action strategies:

1 Define goals and try to Design and manage the environment unilaterally (be persuasive,
achieve them appeal to larger goals.
2 Maximize winning and Own and control the task (claim ownership of the task, be
minimize losing guardian of the definition and execution of the task).
3 Minimize generating or Unilaterally protect yourself (speak in inferred categories with
expressing negative little or no directly observable data, be blind to impact on others
feelings and to incongruity; use defensive actions such as blaming,
stereotyping, suppressing feelings, intellectualizing)
4 Be rational Unilaterally protect others from being hurt (withhold information,
create rules to censor information and behavior, hold private
meetings)

An organization with this learning model (which Argyris and Schön contend is a great many organizations)
is “highly unlikely to alter its governing variables, norms and assumptions,” (Argyris and Schön, 1996).
Argyris and Schön offer Model-II theory in use has three main governing variables and accompanying
action strategies:

1 Valid information Design situations where participants can be origins of action and
experience high personal causation.
2 Free and informed choice Task is jointly controlled
3 Internal commitment to the Protection of self is a joint enterprise and oriented toward growth
choice and constant (speak in directly observable categories, seek to reduce blindness
monitoring of its about own inconsistency and incongruity). Bilateral protection of
implementation others
If this model is followed, the degree of defensiveness between individuals and groups will tend to decrease
and double-loop learning will be enhanced (Argyris and Schön, 1996). Argyris contrasts the organizational
reasoning associated with these two models as defensive reasoning and productive reasoning. He points out
the productive reasoning in dynamic environments is especially difficult for people because it requires them
to “reexamine their basic assumptions and test their judgments against changing conditions” (Argyris,
1997). Interestingly, he comments on the need to move beyond a static conception of the world that is
reflected in deterministic causality to forms of probabilistic reasoning. “Because the world of action is
dynamic and uncertain, probabilistic reasoning is more realistic and accurate in assessing the likelihood of
accomplishing our intended result,” (Argyris, 1997). Action can more easily follow from probabilistic
reasoning for Model-II organizations. Organizations holding to defensive reasoning is more likely to dismiss
probabilistic evidence that challenges the organization’s espoused theory.

For BPM systems, organizational defensiveness has profound implications. While cognitive biases can be
overcome by reframing and representing problems so they are more intuitively understood, organizational
biases due to defensive behaviors are much harder to root out and change. Argryis points to some hope in
the form of management information systems. He states several benefits for these systems (Argyris, 1997):

1. Technology allows the design of information practices that support individual and organizational
learning
2. Storing and retrieving relevant actual performance information is relatively easy and timely
3. Individuals can use information technology tools to record and discover discrepancies between
stated goals and actual performance in a nonthreatening setting.
4. All members of the organization can have access to confirming and disconfirming data, lowering
the cloak of secrecy and control
5. By linking accurate, timely information to the sense of stewardship among decision makers, the
likelihood of learning increases. As organizations begin to change their practices, individuals within
them will feel less threatened and more willing to correct their mismatches between intent and
action as part of an ongoing development process.

To-date, most BPM systems excel in assisting single-loop learning, that is, correcting specific processes so
that they meet stated goals. BPM systems are currently not designed specifically or solely to help manage
the double-loop learning problem. Since organizational environments (markets and competitive situations)
can rapidly change and extinguish even the largest or seemingly durable entity, enhanced double-loop
learning is critical for long-term survival.

Competing Views of Strategic Intent, Strategic Control and Knowledge

In actual practice, companies craft their strategies differently. In some cases, strategy is a planned and
deliberate process. In others it is unplanned and emergent. Where one finds strategy crafted in a top-down
fashion, one can also examples of firms that created their strategy bottom, up, using grass-roots efforts
(Mintzberg, 1987). How a firm actually crafts, executes and controls its strategy can have a significant effect
on its BPM system. How firms construct their strategic BPM systems (ones that measure the strategy) will
most likely need to mirror how they constructed the strategy itself. Is it conceivable that a top-down strategy
in which the details of the strategy are known to a few at the top of the firm can be measured with a BPM
system built using bottom-up approaches that involve asking managers and directors closer to the front-lines
how to measure the strategy? Emergent strategies will have provisional BPM measurement that must evolve
over time, either using top-down and/or bottom-up conversations in the firm to describe how formulate the
strategy and how to best measure it
Business environments rarely change on a regular and orderly basis. Firms (and BPM systems) are obviated
when the environment shifts in subtle or profound ways. Turbulent changes are easy to detect Some argue
that subtle discontinuities are not and can only be dealt with by minds that are in tune with existing patterns
yet still able to perceive breaks in them (Mintzberg, 1987). With most knowledge of the business
environment today codified in digital data and presented as digital information, personal and direct
observation or trusted anecdote via word-of-mouth might be insufficient for detecting this change. Perhaps
BPM systems can play a significant role in not just monitoring changes in internal states, but also
understanding changes in the external environment as well.

Knowledge as a social phenomenon is emerging as a significant contribution to knowledge management

(Thomas, Kellogg & Erickson, 2001). Knowing what comprises the social and cognitive context that
contributes to individuals understanding things is critical for successfully managing knowledge in that
organization. Some social techniques for managing or creating knowledge include Bohm dialogs, systematic
use of metaphor, strategy mapping, story-telling and narrative, expressive communication. Thomas, Kellogg
& Erickson (2001) go so far as to re-label knowledge management as knowledge socialization. Viewed in
the light of the need for enhanced organizational learning, it is not surprising the social nature of knowledge
viewpoint is challenging more traditional thinking.

Stacey (2000) criticizes what he calls mainstream thinking on knowledge management for many
oversimplifications or inadequate explications. The key concepts in mainstream thinking – double-loop
learning, tacit and explicit knowledge, systems dynamics, sender-receiver models of knowledge
transmission from information theory, dialog as a special form of communication – have the following
problems: they treat individual learning different than organizational learning and hence require not one, but
two theories of how learning takes place, they fail to account for how new knowledge is created and they
cannot explain the unpredictable patterns of knowledge that may emerge outside of the control of the
managers. Stacey argues that “systems, databases, stored and written artifacts” are simply “records that can
only become knowledge when people use them as tools in their process of gesturing and responding to each
other.”

Stacey (2001) goes further, arguing that knowledge is not designed but emerges from the conscious and
unconscious interactions and gestures between individuals, and as such can be thought of as a complex
adaptive system. Systems thinking, which attempts to understand things by examining and reducing the
interactions between components of the overall system, is inadequate for understanding how knowledge
actually is created and diffuses. Stacey concludes that “from this perspective, it becomes impossible to think
of designing such a process and it makes no sense to think of managing it.” In this view, knowledge is
intractable.

What this means for BPM is that the art of identifying, linking and gathering data for a BPM system is only
part of the problem and not the thorny problem at that. Getting knowledge regarding the performance
measurement data diffused and used throughout an organization is at the core of what knowledge
management and BPM is all about. That, according to Stacey, may very well be an intractable problem.

Success and Fail Factors

Business performance measurement literature abounds with do’s and don’ts of measurement. Some classic
problems include the use of too many metrics; the use of primarily or exclusively financial and cost metrics;
the use of only short-term focused metrics; a lack of alignment between operational metrics and
organizational objectives and strategies; and metrics that drive the wrong behaviors and performance (Van
Aken & Coleman, 2002). In their literature review and industry research, Bititci, Carrie and Turner (2002)
identified the following aspects companies look for a performance measure system to address:
   Reflect stakeholder requirements to maximize their satisfaction
 Reflect the external/competitive position of an organization
 Focus on competitive criteria of the organization’s markets in order facilitate strategies and actions
to improve the organization’s competitive position
 Provide an input into strategy development
 To help ensure alignment if strategy, actions and measures
 Differentiate between control and improvement measures
 Focus on critical areas of the business
 Use locally meaningful terminology to encourage understanding and ownership
 Facilitate resource bargaining
 Promote proactive measurement by focusing on leading measures
 Accommodate both quantitative and qualitative measures
 Measure organizational capability and learning
 Ensure that measures are used at the correct levels
 Promote understanding of the causal relationships between various measures
 Facilitate simple reporting
 Be dynamic and change in response to the internal and external environments

Based on their experience, Kaplan and Norton (2001) identify the following key pitfalls for BSC
performance measurement systems:

  Changes in executive leadership due to mergers and acquisitions in which BSC loses executive
sponsorship or priority
 Design problems in which a poor BSC is built
o Too few measures (two to three) per perspective
o Too many measures without clear identification of the critical few measures
o Lack of linkage to the BPM system and sustainable competitive advantage
 Process failures in implementing a BSC
o Lack of senior management commitment
o Too few individuals involved
o Keeping the scorecard at the top of the hierarchy and not distributing it
o Too long of a development process, not incremental
o Treating the BSC as a systems project
o Hiring inexperienced consultants
o Introducing the BSC for compensation purposes only

Based on the current issues regarding BPM systems discussed above and elsewhere in this paper, key
success/fail factors that have been cited in the literature are summarized in Table 1.

1. BPM systems need to be understandable and not overly complex

2. Employees should be involved and leadership must be present. The BPM system needs to
be contextualized and relevant to all parties, up and down in the organization
3. The organizational learning and knowledge management environment must be consistent
with and enhanced by the BPM system.
4. Data integration, latency and quality must be acceptable
5. Intangible, predictive and outcome measurement practices need to improve relative to
current practices
6. BPM systems need to enhance and/or be preceded with some level of organizational
alignment or coherence
 7. BPM systems need help reveal linkages between the firm’s strategy and the activities the
firm must manage
8. 8. Problems related to lack of reference models and uniqueness of strategies must be
overcome
9. The BPM system needs to be easily changed
Table 1. BPM critical success factors

Since today performance measurement data is almost always derived from or communicated with computers
and information system technology, it is not surprising that some of the critical success factors are
essentially technology problems.

A Framework for Evaluating Performance Measurement Systems

Over the last 20 years or so, established frameworks and reference models, along with specific, lesser-
known measurement approaches, APL, IPMS, OPTIMAS (Jensen & Sage, 2000) and others, have populated
the business world. Companies have choices when building a BPM. Despite the large amount written about
how to measure, much less has been written on what are the key attributes that are desirable in a BPM
system. De Haas & Kleingeld (1999) mention seven pre-existing measurement system criteria from other
studies and add their concept of coherence (discussed below) to the list to make eight:
 1. Controllability 5. Specificity
2. Validity 6. Relevance
3. Completeness 7. Comprehensibility (Van Tuijl)
4. Cost-effective measurability 8. Coherence

In another study, Jensen & Sage (2000) enumerate metric design attributes (goals) and metric set goals
and measurement system infrastructure goals. The metric goals include:

1. 1. Cost-effectiveness 8. Reliability
2. 2. Strategic alignment 9. Repeatability
3. Acceptability (buy-in) 10. Believability
4. Usefulness 11. Timeliness
5. Acquirability and implementability 12. Responsiveness
6. Consistency 13. Known responsibilities
7. Accuracy 14. Security

The Jensen & Sage (2000) metric set goals include:

1. Balance across types of metrics 6. Standardization

2. Organizational coverage 7. Documentation
3. Completeness, minimum overlap 8. Coverage of strategic thrusts
4. Cost-effectiveness 9. Current status and trend measures
5. Total number, number per measurement 10. Communications to staff
area

The Jensen & Sage (2000) measurement system infrastructure goals include:

1. Automation
2. Repository, communications and other security (access to archival information)
3. Labor hour reduction
4. Information dissemination

As can be seen, design attributes vary from author to author. This paper attempts to enumerate a minimum
set of BPM system design attributes (comprised of criteria and factors) and foregoes discussion of
specific metric design attributes, metric set attributes or their linkages to a defined strategy. Metric and
metric set design attributes can be derived from the BPM system design attributes. In addition, some
useful and successful BPM systems are operational in nature and may not be designed to clearly link to
and communicate a firm’s strategy. In the interest of minimalism, the criteria and factors offered in this
paper are silent on this matter of strategic linkage. Underlying the following design attributes is the notion
that BPM systems provide a key component to a firm’s ability to sense and respond to its internal and
external environments. Data in them is often tied to key motivational aspects for both the firm and its
employees. In addition, the term BPM system refers to the information technology and the human process
that interact with the technology. The two are conceived as joined in a symbiotic relationship with each
other and hence design attributes must take into account both aspects. Using this biological organism
metaphor, this paper recasts the prior design criteria described elsewhere into the following four key
measurement criteria (Table 2). In addition, 12 factors that link to these four criteria are discussed (Table
3).
 1. 1. The BPM system should help the firm accurately perceive relevant internal and
external phenomenon. These include threats and opportunities, shortcomings in its
ability to perceive phenomenon as well as shortcomings in its ability to control its
actions (breadth, depth, coherence and predictability).
2. 2. Measurement information needs to be delivered, processed and acted upon within
the time frame needed for market survival (latency: propagation and response).
3. 3. The BPM system must aid the decision-making process (provability,
explainability, believability, communicability).
4. 4. The BPM system needs to operate self-reflexively and largely below the threshold
of the firm's awareness (adaptability, measurability, autonomic).
Table 2. Measurement system design criteria

Breadth Refers to how much of the total set of activities needed to be measured are
actually measured. Breadth needs to be balanced between internal state
and activities inside of the firm and activities and items external to the
firm such as customers, suppliers, competitors, market conditions,
environmental conditions, etc.
Depth Refers to the unit of analysis. Levels of analysis, or granularity, can
include the employee, the workgroup or team, the functional unit, the
business unit, the product, the customer, the firm as a whole, the
marketplace or the economy at large. BPM systems can and typically do
cover multiple levels of analysis.
Coherency Refers to the how much breadth and depth factors combine together to
improve performance. How do lower levels of measurement contribute to
higher levels? How do units of measurement at the same level coordinate
together to contribute higher levels?
Predictability Refers to how accurately and far into the future a BPM system can
project.
Provability Refers to how the BPM system can show the relationship between causes
and effects. Identifying causes and effects helps managers better
understand where (which object) to apply attention.
Explainability Refers to how easily people in the firm can explain relationships between
measurements and how the BPM system functions.
Believability Refers to how much people in the firm trust the BPM system. Do people
in the firm believe what the BPM system is expressing? Data quality and
overall measurement trust (reliability, consistency, accuracy) are key
components.
Communicabilit Refers to how well can people in the firm communicate measures and
y discuss them amongst themselves?
Adaptability Refers how easily and completely the BPM system can be altered. Is the
BPM system automatically self-changing? How much intervention is
required to change it? Is the human component capable of changing?
 Measurability Refers to how the BPM system itself is measured (meta-measurement). Is
the BPM system working within normal parameters? What is the quality
of service? How effective is the BPM system? Where is improvement in
the BPM system warranted? Is it measuring the right things?
Autonomic How much does the BPM system help the firm self-correct? How much
management attention and effort does operating the BPM system require?
Table 3. Measurement system design factors

The measurement criteria are non-gradated; that is the BPM system either meets the criteria or it does not.
If anyone of the four BPM system design criteria is not met, the BPM system may not be successful in
contributing to the success of the firm or may fall into disuse. The 12 factors are gradated. Individually
they vary depending on the constraints inside or outside the firm but collectively they meet the criteria
threshold.

Austin & Gittell’s (2002) discussion of the three conventional attributes (performance should be clearly
defined; performance should be accurately measured; rewards should be contingent upon measured
performance) is unnecessary to explicitely include here. Nor is a discussion of intrinsic, ambiguous or
extrinsic and unambiguous metrics/motivations. The factors in Table 3 relevant to the topic of
ambiguous-unambiguous metrics and intrinsic-extrinsic motivations can be scaled to either direction to
satisfy the criteria. In addition, causality is folded into the model as a factor, not a criteria, under the
notion that it might be possible (albeit remotely) for a BPM system to satisfy all the four criteria without
the need for strict causal proof or even causal reasoning. While managers generally intend to do things
with a causal framework in mind, the BPM system may not be able to capture (or need to capture) the
causal linkages. These design attributes (criteria and factors) make a clear distinction between what
managers intend with regard to causality and what the system is capable of detecting.

BPM Software
Through literature review and web category searches, 40 vendor solutions have been classified in the
following categories:

1. Balanced scorecard or general business performance measurement systems (BSC/BPM)

2. Business activity monitoring (BAM)
3. Event alerting and management (EM&A)

Table 4 lists the vendors by type:

A) A) BSC/BPM vendors
1) 1) ABC Technologies, Inc. 12) 12) Hyperion
2) 2) Accrue Software 13) 13) Host Analytics
3) 3) ActiveStrategy 14) 14) IC Visions
4) 4) Cognos 15) 15) INPHASE
5) 5) ComShare 16) 16) Online Development
6) 6) CorVu 17) 17) Oracle
7) 7) Crystal Decisions 18) 18) Panorama Business Views
8) 8) Dialog Software 19) 19) PBViews
9) 9) Ergometrics 20) 20) PeopleSoft
10) 10) FiberFlexBI 21) 21) PerformanceSoft
11) 11) Gentia 22) 22) Pilot Software
23) 23) Procos Professional 27) 27) SAS Institute
Controlling Systems AG 28) 28) Show Business Software Ltd
24) 24) Prodacapo AB 29) 29) SIMPEL Systems
25) 25) QPR 30) 30) Solvision B.V.
26) 26) SAP AG 31) 31) Stratsys A
B) B) BAM Vendors
1) 1) Mentisys
2) 2) Quantive
3) 3) Accenx
4) 4) Presence
5) 5) Praja/TIBCO
6) 6) Catagoric Software

C) C) EM&A Vendors
1) 1) Adeptra
2) 2) Categoric
3) 3) Objective Edge <neXus/>

Table 4. Software vendors

Software Issues
Obviously BPM systems need data. Several researchers have pointed out the need for timely access to quality data.
Companies implementing BPM systems need to choose the appropriate data movement, integration and transformation
approach.

Data integration and transformation

Data for performance measurement systems can come from a variety of sources: enterprise systems (including supply
chain, demand chain, CRM, and point of sale systems), ad-hoc systems (including spreadsheets, desktop database
applications, word processing documents) or direct data entry into the BPM software. In addition, data architectures to
house the BPM data can vary. Most of the BPM software, especially the BSC niche vendors such as QPR and Dialog
Strategy, has specific data architectures into which BPM data must be entered or transferred. Enterprise software vendors,
like PeopleSoft, have a specific data warehouse architecture to support enterprise performance data that is, to some extent,
integrated with the transaction systems within the suite. BAM vendors are more likely to rely on other data architectures
for BPM data since their role is to disseminate BPM data, not aggregate it. In this case, the BPM system will use
integration and messaging techniques to present BPM data without the aid of a pre-built or custom BPM data warehouse.
The data architecture approaches are summarized here:

 Data architecture embedded within the BPM tool. Here the BPM tool contains all the necessary data tables
specifically for its purpose of producing output.
 Built-in BPM data warehouse architecture integrated with enterprise suites. Many ERP solutions, such as
PeopleSoft, have a general-purpose data warehouse architecture that is also used for the BPM system.
 Custom BPM data warehouse with custom integration to data sources. Companies that build their own
performance measurement systems need to design and build the data tables for the BPM system and determine the
best integration approach that will bring enterprise data into the BPM data architecture.
 Multiple application and database messaging and integration. The EM&A approach can use messaging and point-
to-point application integration to deploy performance measurement data without a significant repository for BPM
data. For specific process control-type measurement situations, this is often the model used.

Firms have a variety of approaches available to them to move data through the BPM system:

 Extraction, transformation and loading (ETL) tools that move large amounts of data into data warehouse, typically
in nightly batches
 Message-oriented middleware (MOM) tools that move smaller amounts of data between systems (and into a data
warehouse) in near-real time. MOM tools can provide transactional integrity benefits as well, which ETL tools do
not provide.
  XML and web services tools that move smaller amounts of data, often using MOM tools, between systems. (The
BSC has an XML standard for moving data into a BSC system.)

Moving data between systems nearly always involves transforming the data in some way. Transformations can occur at
many levels and for different reasons:

Computing Differences between representations of data due to different CPU

representation architectures, operating systems and programming languages.
Formatting Combining or splitting values (such as first name and last name), managing
different representations of dates, currencies, numbers, logical values and
codes.
Semantic meaning Data may not be directly comparable to each other due to differences in
level of aggregation (monthly versus weekly summaries), differences in
relational representation (how tables link to each other in primary key and
foreign key relationships), differences in meaning that users ascribe to data
(two users can have very different meanings for the term ‘customer’ or
different competing coding schemes may be in use for the same attribute)
Errors and omission Data quality may vary between systems with incorrect data entered or
missing data causing other problems.

BPM systems are suspect to all the normal data management problems found in other information processing systems. In
highly uncertain environments where the organization may be testing and changing basic assumptions, data quality and
trust in the data is critical. Fortunately there are data quality and data conversion tools that can aid in this aspect.

Data visualization

Tufte (1997) provides a compelling argument for proper data visualization to support decision-making in his analysis of
the documents NASA engineers used to unsuccessfully convince NASA managers to scrub the doomed Challenger launch
in January 1986. On the day of the launch, managers and engineers discussed canceling the launch due to unusually cold
weather. Engineers familiar with the problem had correctly anticipated the problem – the now infamous O-rings would
fail in the cold weather – but did not make an appropriate visual presentation of the data to management.

Data visualization, while practiced in some form for centuries (Tegarden, 1999), has developed rapidly since the 1980s,
starting out initially as visualization of scientific information (Card et al, 1999). Technological advances have also
contributed to development of data visualization as a field of research due to the widespread availability of low-cost, high-
performance workstations (DeFanti et al, 1999).

Visualization as a field is divided into two categories: visualization of scientific data that tend to be based on physical data
such as the human body, molecules, the earth, and so on, and visualization of abstract data that are based on financial data,
business information and, document collections (Card et al, 1999). This distinction is important for BPM systems because
the key challenge for visualization of abstract data has to do with representing more than 2 or 3 dimensions of data in an
intuitive form. Scientific data is more easily and more frequently depicted in 2 or 3 dimensions, often corresponding to the
dimensions of space. Abstract data, of which BPM data is an example, is not easily represented in the 2 and 3 spatial
dimensions. There is no physical geography to give the data an intuitive structure (Wright, 1999). Since no common
framework yet exists, most of the literature in business information visualization has attempts to provide new metaphors
and visualization approaches to abstract data rather than coalescing around a common framework for business information
visualization. With this distinction in mind, Card et al (1999) define information visualization as:

The use of computer supported, interactive, visual representations of abstract data to amplify cognition.

Cognition here is referred to as the acquisition of use of knowledge. Cognitive amplification is useful for tasks that are
“characterized by use of large amounts of heterogeneous data, ill-structured problem solving, but a relatively well-defined
goal requiring insight into information relative to some purpose,” (Card et al, 1999). These tasks are called “knowledge
crystallization” tasks that tend to follow the following six steps:
 1 Information foraging.
2 Search for schema (representation)
3 Instantiate schema with data. Residue is significant data that do not fit the
schema. To reduce the residue, go to step 2 and improve the schema.
4 Problem solve to trade off features.
5 Search for a new schema that reduces the problem to a simple trade-off.
6 Package the patterns found into some output product.

Each of these steps has a cost (time, effort) associated with performing the step. Visualization can help reduce the cost for
each of these steps.

BPM tasks fit this description of knowledge crystallization. They often deal with large amounts of data. The data is
typically heterogeneous. The problem-solving context can be ill-structured and the goals are usually well-defined (revenue
growth, product quality, etc.). While some portions of BPM are routinized, especially operational measurement, strategic
BPM is not. The data can be collected and presented with a completely repeatable process but insight into the data cannot.
For this reason, strategic BPM needs to be supported with this kind of iterative process in which meaning is created by
refining schemas of analysis. Card, et al (1999) describe a reference model for visualization that might prove in
understanding issues with providing visual representations of BPM data (Figure 3).

Raw Data Visual

Views
Data Tables Structures
User / Task

Figure 3. Reference model for visualization (Card et al, 1999).

Raw data is transformed into one or more data tables (relational mappings of raw data). Tables themselves are iteratively
manipulated and then transformed into one or more visual structures (a base spatial design to which marks of various
kinds are added). Users manipulate the visual structure in a view. This is a highly interactive model for visualization.

Beyond the cost-savings benefit that visualization provides, visualization research over the past two decades has
illuminated many other benefits. Card et al (2000) lists six main benefit types:

1. Increasing the memory and processing resources available to the user

2. Reducing the search for information
3. Using visual representations to enhance the detection of patterns
4. Enabling perceptual inference operations
5. Using perceptual attention mechanisms for monitoring
 6. Encoding information in a manipulable medium

Several factors related to each type are listed below (Card et al, 2000):

Parallel perceptual processing Some attributes of visualizations can be processed in parallel

compared to text.
Offload work from cognitive to Some cognitive inferences done symbolically can be recoded into
perceptual system inferences done with simple perceptual operations.
Expanded working memory Visualizations can expand the working memory available for solving
a problem.
Expanded storage of information Visualizations can be used to store massive amounts of information in
a quickly accessible form (e.g., maps).
Locality of processing Visualizations group information used together, reducing searching.
High data intensity Visualizations can often present a large amount of data in a small
space.
Spatially indexed addressing By grouping data about an object, visualizations can avoid symbolic
labels.
Recognition instead of recall Recognizing information generated by a visualization is easier than
recalling that information by that user.
Abstraction and aggregation Visualizations simplify and organize information, supplying higher
centers with aggregated forms of information through abstraction and
selective omission.
Visual representations make Visualizations can support a large number of perceptual inferences
some problem obvious that are extremely easy for humans.
Perceptual monitoring Visualizations can allow for the monitoring of a large number of
potential events if the display is organized so that these stand out by
appearance or motion.
Manipulable medium Unlike static diagrams, visualizations can allow exploration of a
space of parameter values and can amplify user operations.

Since BPM data is a good candidate for visualization, BPM systems, if they support visualization, should enable user
manipulation of data tables, visual mappings and view transformations to support this reference model. For rote BPM
needs, such as operational BPM, user interaction with data transformations and visual mappings is not likely to be
frequent. However, providing these interactive visualization capabilities may be needed since metrics and measurement
systems undergo a continual change. In addition, manipulation of visual views for operational BPM might prove highly
useful since perceptual abilities can be useful for detecting patterns while monitoring data.

There may also exist a synergistic relationship between ongoing operational monitoring and more interactive visualization
tasks. For those BPM systems that let managers monitor operational activities, detection of problems often leads to further
interactive analysis into the causes and effects of the problem. Visualization not only can help in detecting operational
problems, it can be useful for researching the problem causes further.

Despite the numerous benefits for information visualization, none of the commercially available BPM systems offer
advanced information visualization capabilities of the kind hinted at here. Part of the challenge in providing visualization
in BPM systems has to do with the structure and dimensionality of the base data. Since business information is not
constraint by the law of physics, representing the data in an intuitive three-dimensional form is challenging. Which
dimensions of the business data should be mapped to the three vectors of spatial representation? Should dimensions
greater than three of business data be reduced to a three dimensional model? Mapping metaphors can make use of
multiple dimensions but at a cognitive cost. Conventional topographic maps can encode many dimensions of data into a
single two-dimensional display. However, for business data, of the many dimensions, which two dimensions should be
placed on the prominent x/y axes? Can other metaphors, richer in meaning, be useful? Perhaps to avoid these essentially
arbitrary difficulties, to bow before the sustained use and acceptance of simpler visualizations (bar charts, line charts,
scatter plots and the numerous variants), and to reduce the cost of developing the software, BPM software vendors have
not ventured too deep into the visualization waters.

Current BPM Visualization Approaches

To deal with the arbitrariness and multi-dimensionality of business data, visualizations in BPM software rely on simple
marks, charts and in some cases, metaphors. Current metaphors common in visualization software include.

Marks
Textual manipulation (color, font size and style, blinking)
Symbols (up/down arrow, +/-, empty/half/full circles, a.k.a. “harvey balls”)

Charts
Pie chart (2-D, 3-D)
Bar chart (2-D, 3-D)
Line chart
Scatter plot (2-D, 3-D)
Surface chart
Radar or spider web chart

Visual metaphors
Thermometer (high/low)
Fuel gauge (Empty/full)
Speedometer (fast/slow)
Cause-Effect Influence diagram (network, hierarchy)

Experimental approaches
Map (multiple dimensions coded in marks and colors)
Tree (as a visual metaphor showing time linkages)
Human face (because of our evolutionary enhanced visual apparatus for facial recognition)
Virtual reality
Animation, video

While many academics are researching some of these advanced metaphors and experimental approaches, none so far have
found their way into widely accepted BPM software.

Future directions
Business performance measurement suffers from the weaknesses of the antecedent disciplines of business strategy,
decision analysis, organizational learning and data visualization: relative newness and lack of a widely accepted
foundational theory to describe and predict. Business strategy has multiple disciplines, each using a different framework
to describe it, like the blind men describing an elephant (Mintzberg & Lampel, 1999). Decision and judgment analysis
from the prospect theorists’ perspective, while receiving plenty of attention from cognitive psychology and economics,
also may suffers from inconclusiveness. Gigerenzer (2000) characterizes prospect theory as an important but a provisional
step along an undermined path. Organizational learning and management has fragmentation of its own to contend with.
Argyris points out the need for an integrative approach to research in management problems (Argyris, 1996).
Visualization of abstract data, being quite new as a discipline, has been tied to the neurosciences or specific task-related
outcomes more than it has been tied to decision analysis as a whole and not at all to the problems that prospect theory
seeks to solve. It too seems to evolve without theoretical foundations and useful linkages to other disciplines. Gigerenzer
(2000) discusses the need for cross-discipline collaboration bluntly: “Intellectual inbreeding can block the flow of positive
metaphors from one discipline to another.” This form of territorial science, says Gigerenzer (2000), creates distrust and
disinterest in anything outside one’s subdiscipline. Is this Argyris’s Model-I theory in action?

The pieces, however, are in place. Business strategy is rapidly evolving to accommodate more complex and sophisticated
views of how firms determine and execute their strategies. Measurement related to those strategies is bringing multiple
pieces of the organizational puzzle together into an integrated framework, most likely enabled by recent advances in
distributed information technology. Prospect theory proponents and its critics have explored the inadequacies of human
cognition in decision and judgment making. Organizational learning has exposed the difficulties and problems in getting
organizations and individuals to learn. Information visualization researchers have demonstrated how we can utilize human
visual (and kinesthetic) intelligence for better understanding. Business performance measurement, which needs to address
and include the theories of strategy, decision-making, learning and cognition, needs to grasp these linkages and exploit the
synergies that lie in their convergence.

Conclusion
BPM systems capture and disseminate strategic information that matters most to the firm in the form of strategic process
and outcome measurement, and most to the individuals within the firm in the form of performance measurement,
incentives and motivation. Because of this, BPM systems are a primary means of “knowing” (coordinating what a firm
knows and learns) and “doing” (how it alters what it does). Over time, they may perhaps become the single most strategic
information system resource in the firm. Technological advances in data processing and integration, application
deployment via the web and in analysis and visualization have helped BPM systems advance significantly. However, the
best may be yet to come. While adoption rates of BPM systems are high in larger companies, actual success in larger
firms is more limited and adoption in smaller firms is embryonic. Measurement frameworks such as BSC, EVA and ABC
have found their way in to higher education curricula and into the accounting functions in those adopting firms.
Measurement reference models, which promise to diffuse BPM approaches even further – albeit at the expense of
competitive differentiation, are just beginning to sprout. Key software manufacturers are aboard the analytical and
performance measurement bandwagon. The market appears big enough to support a rather large number of very different
software vendors.

Despite the optimism, much work needs to be done. BPM systems could benefit from a dominant theory of judgment,
decision-making and organizational learning. Economic decision analysis researchers (prospect theory and its critics) need
to connect with data visualization researchers to see what impact using different mental “machinery” can have on
judgments and decision-making. Distributed and social knowledge gathering and knowledge creation activities, enabled
chiefly through distributed information technology (the Internet) needs to be understood, accepted and adopted by firms to
complement to historical tendencies towards top-down, engineered strategic planning. All of these areas need to connect
with the organizational learning discipline. Right now, BPM seems much more art than science. While this bodes well for
consultants and gurus, it does not for businesses. How to make BPM successful for businesses is the challenge for the next
thirty years.
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