Our Orthodox Methods and Tools Are 100 Years Old and Due for
Replacement
Ronald Stamper
Formerly University of Twente and London School of Economics, Now 38 London Court, 9-13 London Road,
Oxford OX3 7SL, U.K.
Keywords: Information, Analysis Methods, Specification Tools, Scientific Paradigms, Scientific Method, Refutationism,
Law, Norms, Signs, Organisational Semiotics, Taylorism, Semantics, Affordances.
Abstract This paper is intentionally provocative. The analysis methods and specification tools we use today are derived
from the century-old Taylorism via office work-study. If that was our scientific foundation, many obvious
anomalies should have forced us to find a new paradigm. Rejecting information-flow in favour of a
knowledge-field paradigm, we can build a rigorous science of organisational semiotics to underpin the
engineering of information systems, taking account of the essentially human and social aspects of information:
semantics/meaning, pragmatics/intention and social products/value, while reaching the level of rigorous
formality needed for the technical aspects of the system. Practical case studies have demonstrated the
advantages of this new approach, which reduces costs, especially over a long period while making the system
easier for the users to understand.
1 INTRODUCTION
We need a sound scientific foundation for
engineering organisational information systems that
encompasses the organisational as well as the
technical.
How do we compare? Hardware evolves
phenomenally fast; software less so; and, 60 years on,
AI still threatens, like Shakespeare’s King Lear, to
“do such things, what they are, yet I know not; but
they shall be the terrors of the earth.” As an example,
Stephen Hawking told the BBC: "The development
of full artificial intelligence could spell the end of the
human race." But we are slower still. IS systems
analysis and design clings to Taylor’s 100-year-old
scientific management. Today’s UML, looks modern
but it embodies the same old ideas.
1.2 Machines
UML, 1960s’ ISAD tools and Taylor’s 1890s work-
study tools all track the flow of parts and materials
and sequences of operations performed on them.
Usually, in factories these are mechanical products,
but in offices, documents and in computer systems,
structured data. Taylor’s science concerns only the
movements of and operations upon objects and
materials. So, importing his science into our domain
limits ‘information science’ to some purely technical
aspects and forces us to treat every organisation as a
kind of machine.
Is that enough? Probably not!
1.3 Organisations
Back in the 1960s, the steel industry had an acute
shortage of systems analysts, and they asked me to
create courses to address the problem. Computer
manufacturers providing the only other training at
that time, taught how to introduce computers into a
business. That technical bias and lack of
understanding of the human and social aspects of
information systems seemed to explain the alarming
project failure rate. We should be equally alarmed
today because the failure rate is still high.
1.4 Mystical Fluids
Instead, hoping to teach how to improve an
organisation as an information system, using
technology where appropriate, I searched for a
scientific understanding of the role information plays
in the functioning of organisations. To start with,
566
Stamper, R.
Our Orthodox Methods and Tools Are 100 Years Old and Due for Replacement.
In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 1, pages 566-571
ISBN: 978-989-758-187-8
Copyright
c
 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

scientific language must denote things precisely. But
to understand “information” were we offered a
hierarchy of mystical fluids – data> information>
knowledge>wisdom – each distilled from its
predecessor in a chemical engineering metaphor.
Even in the 1960s I was scornful of this idea, except
as an imaginative point of departure
1
. Without a
terminology with precise operational meanings, we
cannot conjecture testable hypotheses from which to
formulate theories for understanding and predicting
the behaviour of systems employing that wonderful,
new economic resource: information.
2 SIGNS AND SEMIOTICS
“Information” is a useless primitive concept; it has so
many different meanings. Armed with the criterion
“Take me to see some.” I searched for a better
primitive (Stamper, 1973). And there it stood: the sign.
Semiotics (
Nöth, 1990 ), the study of signs takes its
name from the ancient Greek for a symptom (the sign
of a disease), which must be something physical. From
its roots in philosophy, semiotics has an extensive
literature that few were bothered to read. John Locke
(1690) had identified the “doctrine of signs” as the
bridge between the physical and social worlds: our
technical and organisational domains. Signs are things
standing for other things that we want to communicate
about. So, to displace DIKW’s four mystical fluids, I
wrote a book about information as a number of
precisely defined properties of signs, all of them
capable of empirical investigation. Three categories of
them are well established in the literature
2,
but I drew
attention to two others
3
and added the social products
of using signs to form a “semiotic framework” to
divide an empirical science of organisational
information into distinct areas of investigation.
Incidentally, it serves as a checklist when working on
any information system because, to be effective, it
must function correctly on all six levels.
There is nothing mystical about signs. They
always have a physical form, which may be
investigated empirically in different ways, as
indicated in this table. Technical properties do not
depend on any human agent whereas the others
always involve signs in relation to individuals or
communities.
1
DIKW comes from TS Eliot’s 1934 poem, The Rock. Science
may start from imaginative ideas but must develop them with
criticism and imaginative tools of other kind. However, a
scientist who has access to poetic ideas gives me more
confidence than one of constrained imagination.
2.1 A Broader Focus
Can this broader understanding of information help
us to improve upon the disgraceful track record for
project failure? Every enterprise is coy about failures,
so figures are very difficult to obtain, but trawling the
web, as I last did in 2012,
suggests, roughly speaking,
that 25% succeed, 50% fail to meet functional
requirements, budget or timing, while 25% are totally
written off: a disgrace! Will a broad, unifying,
scientific foundation help to eliminate or reduce those
failures?
Each technical branch of semiotics has its own
scientific support. Physics underpins hardware
engineering; statistics and probability theory support
work on the empirics of signs; while the formal
sciences of logic and mathematics, as adapted by
computer science, deal with the syntactic aspects of
signs. Those excellent foundation disciplines tempt
us to retreat into the safe hands of software
engineering, well away from the messy domains of
human and social behaviour. But the problems of
engineering software for computers differ
fundamentally from those of engineering information
systems for organisations, unless you treat
organisations as though they were computers with
various information fluids flowing through them.
A software engineer need not differentiate
between a game about dungeons and dragons and a
system affecting the lives or livelihoods of real
people. Ensuring the safety of an atomic power
station or providing social security for a population
entail problems of meaning, intentionality and the
social value of the signs. Only in relation with
2
Eg: Syntactics, semantic and pragmatics in the writings of
Charles Morris (1946) and CS Peirce (1931-35).
3
CS Peirce included their physical properties and Colin Cherry
(1957) their statistical properties.
Our Orthodox Methods and Tools Are 100 Years Old and Due for Replacement
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those properties. Working on the analysis and
design of an enterprise, with or without a computer
application, one must deal rigorously with the real
world (not formal) meanings of all the data, the
intentions they express, and the agents who bear
responsibility for their personal and social effects.
2.2 A Unifying Science
Organisational information systems engineering
needs a unifying scientific discipline. To the technical
branches of semiotics we must add appropriate
treatments of semantics, pragmatics and the social
properties of signs but also with the essential
precision and formality for our work. Whereas the
Taylor’s 100 year-old tools serve the technical
domains, they do not help us with meanings,
intentions or the social properties of information,
unless one counts adding informal comments to the
documentation. The challenge is to clarify the
essential human and social concepts and handle them
in precise formal terms. Until we have without a
rigorous science behind us, one that deals with
organisations as well as computers, we shall continue
to work on organisations as skilled artisans like the
craftsmen who built early Rolls Royce cars, but
unable to keep pace with change because
organisations as they evolve to equate with Rolls-
Royce aero-engines
2.3 Phases of Scientific Progression
How can we move forward? Thomas Kuhn (1970)
has shown that science progress in two ways: in a
Normal phase, while everyone works on a set of
problems determined by a fixed paradigm with its
dominant metaphor, taught from similar texts, until
anomalies undermine the shared body of theory and a
revolutionary phase is precipitated. Taylor’s late 19th
century techniques dominate our education and our
practice but its anomalies are only beginning to
disturb a few of us. Perhaps we imagined that
fundamental changes were taking place while all we
had were continuous, incremental adaptations of
Taylor’s methods and tools, via O&M of the interwar
years, their adaptation for computer systems,
followed by numerous modifications by software
engineers that were unified in UML; but, beneath the
surface, the old ideas remained in place.
Let us call to mind some of those anomalies, They
include: an appalling project failure rate; persistence
of sloppy ideas such as DIKW, inadequate treatment
of meaning and intentionality, a weak understanding
of how information delivers any value; high cost of
system maintenance; obscure documentation that
prevents an organisation’s management from
exercising control over projects; obscure mountains
of documentation that make it difficult to involve an
organisation’s members from contributing to a
system’s design and development; a long lead time
before a project can deliver benefits; and so on.
Where is our scientific motivation?
If we had a serious scientific tradition and noticed
that so much is wrong, we should be out on the
proverbial streets in protest. Which makes me suspect
that a lack of scientific spirit in the Information
Systems community is holding back progress. Below
I show that the comments of programme committee for
another conference that expose their unawareness of
scientific method and their responsibility to apply it.
My position is that it is time for a scientific
revolution in our field. It is time for a new dominant
metaphor and a better paradigm. Why doesn’t
everyone share my disquiet?
2.4 Resistance to Change
Perhaps Kuhn’s explanation is enough: people who
have expended decades acquiring expertise in some
orthodox methods, for which they are hired at
comfortable salaries, react against the threat of having
to learn another way of working. Certainly, when
consultancies build computer applications that need
their expertise to maintain them, they benefit from a
long-term, reliable cash flow; if all their competitors
work within the same antiquated paradigm, their
government and industrial clients have no alternative
but to buy similar orthodox-style products from
another consultancy. So why upset the boat? Those
who teach the long-established orthodoxy react in a
similar manner.
New ideas that threaten a comfortable way of life
will nearly always come from a rather isolated
maverick, so the opposition is easily attacked. When
Max Planck’s quantum theory encountered this
treatment he said that science progresses one funeral
at a time. We may feel great sympathy for him but
should acknowledge the difficulty we all encounter
when adopting a new paradigm.
So, having called for a revolution, I shall do
something that you will probably consider even more
foolish: I assert that there is a radically better
paradigm for our work that can vastly improve our
tragically bad project failure rate and it is based on a
more suitable metaphor, one that embraces both the
technical and the social aspects of the engineering
problems we are required to solve.
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568

3 CONJECTURE AND
REFUTATION
Of course, I express myself this way to provoke you
to attempt to falsify my risky assertion. Why?
Because my research team and I adopted Karl
Popper’ scientific method of Refutationism: science
progresses by bold imaginative leaps that formulate
new universal hypotheses that must be expressed
precisely enough to be capable of falsification by
even a single particular empirical observation or
experiment although no proof of a universal
hypothesis will result from any number of particular
empirical tests. In order that I may learn, I invite your
criticism.
When the courses I established for the steel
industry became the basis for the UK’s national
programme run by the British Computer Society and
the National Computing Centre, I became an
academic at the London School of Economics and my
chance to apply a radically new paradigm had arrived.
3.1 A New Paradigm
Instead of the information flow paradigm, I adopted a
different metaphor from physics: field instead of flow.
It became rather obvious when examining the
computerisation of the Department of Health and
Social Security. I noticed that a single shelf for books
could house all the Acts of Parliament and Statutory
Instruments containing the legal norms defining what
that huge organ of state must do. Only a minority of
the legal norms governed routine bureaucracy and
only some were worth automating. If we could
express that small percentage in a suitable formalism,
a computer might be able to interpret them, in effect
turning the legal norms into the programs for
supporting computer applications. The actual
procedure was to translate the 1m shelf of legislation
into library of 400 thick volumes of “clerical codes”
that were then translated in orthodox flow
specifications.
In addition to the legal norms, the people involved
in the health and social security work also make use
of the numerous social norms belonging to their
shared background knowledge. So we recast our task:
to define the knowledge people in this activity
domain must share if they are to collaborate in an
organised way.
Knowledge (note this precise definition) consists
of social norms (culturally evolved informally as well
as enacted as legal norms by Parliament) that express
what things they deal with (perceptual norms), how
that world functions (conceptual norms), how to
judge things (evaluative norms) and how to act in
different situations (behavioural norms). This
knowledge field binds together the community
involved into a system or institution that governs how
they collaborate on the relevant, shared activity.
3.2 Refutable Hypotheses
That broad idea led to the evolution of
F: a formalism that can express any of the norms in
question; and
P: a program to interpret the formalised norms
Conjecturing a version of F and its associated version
of P, the research proceeded iteratively by pitting F
and P against bodies of norms of increasing
complexity, until they failed, as a result of which
learned enough to make improved versions of F and
P. The scientific investigation never ends because the
latest hypotheses always invites attempts to refute
them, but one may apply the formalism and
interpreter as soon as they seem acceptable for an
engineering task.
4 RESULTS
We have achieved more than we initially hoped for
and we have been able to test the results on
innumerable desktop case studies but only two
substantial actual organisational applications. (From
the point of view of the refutationist method, we
should be attempting many such real applications but
the opportunity to do so is not readily offered by
businesses that, contrary to all the propaganda, are
seldom entrepreneurial enough to take any risk.)
4.1 Two Business Applications
Case-I: University Administration In one country, we
built their administrative system (A) using our
methods and tools for the first time and, over ten
years, compared it with a corresponding system (B)
in a different country in the same region. B employed
modular software of orthodox design, perfected on
200+ similar applications worldwide. System-A was
bespoke and did all and exactly what the organisation
required; system-B, on the other hand, forced the
organisation
• to change to suit the available software and/or
• to pay for additional expensive software
modules and/or
• to have clerical staff process data in the margins
of printouts.
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Such solutions make adaptation to changing
requirements even slower, and more costly. Over ten
years, the comparative costs for System-A were [I
hesitate to say this, lest I be disbelieved] 80% lower
than for System-B. Adapting to changing
requirements was quick, easy and cheap, turning a
sclerotic organisation into an agile one. Moreover,
because everyone found System-A easy to
understand, experienced users with detailed
knowledge could contribute to the design and on-
going improvement of the system. Additionally, the
sound theoretical foundations of our methods meant
that many desirable feature were inbuilt whereas
orthodox systems, must treat them as optional extras
at additional cost: a full historical database; explicit
semantic structures and associated error detection;
specification of responsibilities; traceable records of
all error treatment; multi-lingual facility (English and
one other language but any number of others could be
added easily).
Case II: A Complex Expert System - This
system was being developed using the best of
orthodox methods but was on the point of being
totally written off because the experts commissioning
it could not understand what was being constructed
for them. The orthodox documentation had grown to
its usual gargantuan volume; with its impenetrable
style, the experts could not understand much of it; it
was boring to read and difficult to verify. So they
invited two members of our team to apply our
methods.
The documentation shrank to about one-twentieth
of its original volume. The expert commissioners
found the new formalism succinct and easy to read.
They could see what the system designers were
proposing and were able to steer the emerging system
toward their goals. Implementation went through
smoothly and successfully,
4.2 Criteria of Progress
You may not think that I have described anything
resembling a revolution in our scientific field. That
is exactly the right attitude. Refutationism demands
permanent scepticism on the part of its practitioners.
Despite that, Popper advises one to conjecture “bold
hypotheses” that shift one’s perspective in a
surprising way. Better still they should preferably:
• explain as much as the hypothesis it is intended
to replace;
• do so more succinctly,
• replacing a large obscure model with one that is
simpler and easier to understand; and
• in a way that explains more about the domain;
• preferably bringing to light new invariants in the
domain; while
• raising new, exciting lines of enquiry and
application
4.3 Success? or Not yet?
The question: does the “knowledge field” paradigm
achieve all that?
Given an organisation specified as a knowledge
field, any number of suitable information flows can
be derived from it but not the reverse.
Case II achieved a massive reduction in the
documentation while making it easier to understand,
thus reversing the plan to write off the project;
• a flow model tells you a lot about boring
bureaucratic activity whereas the field model
tells one what should happen for business
reasons, especially who is responsible and
mostly why; it contains a semantic model, it
accounts for human intentions and, by showing
the intended changes of attitudes, deals with the
valuable products of the information;
• the semantics for the domain are contained in a
Semantic Normal Form that is largely invariant
over time and between cultures; the
classification of norms enables one find a stable
organisational kernel that remains invariant over
all bureaucratic revisions that do not change the
essential business activities;
• the computer-interpretable specification opens
up a range of organisational research
opportunities and practical products such as a
touchstone to test any new computer
application; with a Parliamentary Counsellor we
have tested the method for legal drafting and
parallel design of supporting software; it leads
to ERP solutions based on ‘atomic’ modules;
etc. etc.
5 SCIENTIFIC CRITICISM
WELCOME
In conclusion, I present my position to you and
explicitly ask for your critical questioning. In the best
scientific tradition, I want you to take my request
seriously and make your comments rationally and,
therefore, capable of rational response. Recently,
from another conference, the reviewers of my paper
made unhelpful comments that were:
• value judgements to which no rational response
was possible; or
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• assertions that a statement or explanation is
wrong or questionable without even a hint of
why; or
• complaints that I did not cite their favourite
authors who, in fact, we had read but found
irrelevant to our work; or
• complaints about missing explanations that
were actually in the text; while others
• complained that I had relied on their appropriate
prior knowledge to keep my explanations to a
length appropriate to a book rather than a
conference-length paper;
This made me sceptical about our community
having a well-established scientific tradition. If one,
as a scientist (PC member, for example) writes a
criticism of a scientific document, then one has a duty
to abide by the same standards of discourse we
impose on the authors.
Now is the time for some refutations! I hope I
have provoked you into having interesting
discussions. It would be unwise of colleagues
younger than me to be so controvertial but I have
reached a point in life when worrying about my future
career would be pointless. Have fun!
ACKNOWLEDGEMENTS
Many members of the research team since 1971
deserve acknowledgement but I only have space to
mention: Kecheng Liu and Yasser Ades who were
responsible for the two major practical case-studies.
REFERENCES
Cherry, Colin, 1957, On Human Communication,
Cambridge Mass, MIT Press
Kuhn, Thomas S., 1962, 1970, The Structure of Scientific
Revolutions, Chicago, Chicago University Press.
Locke, John, 1690/1959, Essay Concerning Human
Understanding, unabridged edtion 1959, Dover, New
York.
Morris C., l946, Signs, Language and Behaviour, New
York, Prentice Hall - Braziller.
Nöth, W. 1990, Handbook of Semiotics, Bloomington,
Indian University Press
Pierce C. S., l93l-35, Collected Papers, (6 volumes),
Hartshorne C. & P. Weiss (eds.), Cambridge, Mass.
Harvard U.P.
Popper, Sir Karl, 1934/1959, The Logic of Scientific
Discovery, London, Hutchinson.
Popper, Sir Karl, 1963, Conjectures and Refutations,
London, Routledge and Kegan Paul
Stamper, R. 1973 Information in Business and
Administrative Systems, Batsford, London & Wiley,
New York.
Stamper, R, 2012 “A New Framework for IS Thinking and
a Game for Teaching Organisational IS Rather than
Business Applications of IT,” Proc. UKAIS, New
College, Oxford
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