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A Use of Multidimensional Scaling to
Examine a Social Representation of
Corporate Quality Programs Among
Knowledge Workers
The London School of Economics and Political Science
Institute of Social Psychology
MSc Organisational and Social Psychology
PS434 Research Report
Candidate 35750
13 August 2007
Word count 13,933
ii
To Deedre
in spite of everything
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Contents
Acknowledgements 1.0 Abstract 2.0 Int roduct ion 2.1 Six Sigma as management fashion 2.2 The common theme of Taylorism 2.3 Taylorism colonizes the knowledge industries 2.4 Old ideals versus new expectations 2.5 A critical view of entrepreneurial risk in the knowledge workplace 2.6 New economy ideology and psychological contracts 2.7 The professional and managerial class 2.8 The neo-Taylorization of the Taylorists 3.0 Statement o f Research Questions 4.0 Method: Mult id imensional Scal ing (MDS) 4.1 MDS and the theory of social representations 4.2 MDS and anchoring 4.3 MDS and objectification 5.0 Procedure: In ternet-based Survey 5.1 Selection of the stimulus set 5.2 Sort task 5.3 Presentation of the stimulus pairs 5.4 Subjective evaluations of properties of the stimuli 6.0 Resu lts and D iscussion 6.1 Multidimensional scaling results 6.2 Confirmation of the MDS solution through a cluster analysis 6.3 Interpreting the MDS solution through property fitting
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6.4 A two-domain solution: entitative processes and organizational processes 6.5 Comparison of knowledge-based and Taylorist workers 7.0 Conclusion 7.1 Limitations 7.2 Further research 7.3 Implications for practitioners Appendix A.1 Informed consent form for the pilot interviews A.2 Sample interview transcript, redacted A.3 Screenshots of the online survey A.4 Relevant statistical outputs A.5 Description of the normalization of regression coefficients References
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Acknowledgements
A great debt of gratitude is owed to the friends, co-workers, managers, and complete strangers who allowed me physical and electronic access to their worlds of work. Without the generosity of their time, this work would not be possible. Many thanks also go to Sarah for her comprehensive and substantive edits during a busy time at the Academy of Management Meeting. I am especially grateful for the patience of friends and family who celebrated my return home from a year abroad by leaving me to my work and tolerating stultifyingly monological conversation over hasty meals until this work was complete. Thank you, Kristen, for enduring it all.
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1.0 Abstract During the 1980s, a great deal of business and popular press ink was devoted
to announcements of drastic changes in the worlds of work and career (e.g.,
Peters & Waterman, 1982). However, many of the management fashions
(Abrahamson & Fairchild, 1999) currently popular are rooted in a neo-Taylorist
tradition (Pruijt, 2003), which points directly back to the manufacturing industries
(Benders & van Veen, 2001) that the ‘new organization’ was predicted to
supposedly “bear little resemblance” (Drucker, 1988: 45). The workers
purported to populate this ‘new organization’ were “knowledge workers who
resist the command-and-control model” (Ibid: 45), yet quality programs
designed to measure and control work practices (Hosseini, 1993) were
“spread[ing] like wildfire” (Harry & Schroeder, 2000: 11) into what was claimed to
be virgin occupational territory. An investigation of possible tension between
fresh expectations and old methods was of great interest. Using the method of
multidimensional scaling, a social representation of neo-Taylorist quality
programs among a segment of knowledge workers at a multinational
manufacturing headquartered in the United States was examined. The salient
dimensions of this social representation were of particular interest, in light of
psychological contract theory (Rousseau, 1998; Thompson & Bunderson,
2003). It was the goal of the study to examine the dimensions of the social
representation of quality programs at this company to determine if it was at
odds with the values and purpose of the quality initiatives, possibly resulting in
feelings of violation and resistance among the workers. A comparison of the
assessments of Taylorist manufacturing and quality workers to those of
information technology and product development ‘knowledge workers’ was also
undertaken. Creative support, professional discretion, and an aversion to
bureaucracy were found to be salient dimensions of the social representation of
quality programs for these workers. The results did not evince widespread
resistance, as subjective assessments of the programs were all above the
average of the scale. No clear distinctions between Taylorist and knowledge
workers were found, though this may be due to relatively low sample size for
such a comparison and the lack of a distinction between enabling and coercive
2
bureaucracies (Adler & Borys, 1996), a critical distinction to make in research
going forward.
2.0 Introduction Although American industry is decades away from the zenith of its
manufacturing prowess in the 1950s and has long embraced its information-
based, service-oriented future (Drucker, 1988), corporations still rely quite
heavily on Scientific Management principles developed in manufacturing
settings, in order to continue to lead its economy away from the assembly line.
Contemporary quality management programs which promise ‘breakthrough’
and ‘revolutionizing’ results (Harry & Schroeder, 2000) in knowledge-intensive
workplaces, however, cannot be seen as revolutions, as they are reiterations of
previous quality gurus: Hammer & Champy of the 1990s, Joseph Juran of the
1960s, W. Edwards Deming of the 1950s, and Frederick Taylor of the 1910s.
The focus here is not to attempt to critique contemporary management
principles for their anachrony and lack of novelty, as these features have been
examined elsewhere (Benders & Van Veen, 2001; Kieser, 1997; Barley & Kunda,
1992). The concern here is the examination of any deleterious psychological
consequences for knowledge workers, as these programs find themselves far
outside both the occupational and historical context in which they were
developed. Some attention is paid below to any possible resistance or conflict,
which could result from the importation of management products and programs
with old economy roots into workspaces populated with workers possessing
new economy expectations of professional autonomy and entrepreneurial risk,
which have been established and perpetuated by the contemporary business
and popular presses. The goal of this inquiry is to examine the constitution of
knowledge economy workers’ conceptualizations of these former manufacturing
quality programs by taking up the theory of social representations (Moscovici,
1984) to determine if they are, in fact, compatible with workers’ needs and
expectations in the contemporary workplace.
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2.1 Six Sigma as management fashion One of the most visible and widespread of these quality programs currently
being touted by consultants and the business press is Six Sigma. Six Sigma is
described as a “breakthrough strategy” (Harry & Schroeder, 2000: 6) designed
to tie the improvement of manufacturing quality directly to financial results.
However, there is little within the body of the strategy that can be identified by
an experienced reader as a novel ‘breakthrough’. Rather its place within the
“lifecycle of fashions in management techniques” (Abrahamson & Fairchild,
1999: 714) can be easily identified as Six Sigma’s creators attempt to distance
Six Sigma from prior incarnations of similar ideas where they articulate “the
difference between previous total quality approaches and the Six Sigma
concept” (Harry & Schroeder, 2000: 10-11).
Alfred Kieser identified this distancing from past fashions as an important
component of the promulgation of new fashions (1997). The ostensible
similarities must be acknowledged merely because they are normally so obvious
that they demand recognition (Benders & Van Veen, 2001). In order for a new
management fashion to be embraced, the minor improvements upon the past
quality initiatives must be highlighted and magnified in order to distance the new
program from the costly failures of previous attempts of the implementation of a
scheme remarkably similar to the one now being promoted (Kieser, 1997). In
the years prior to the publication of Six Sigma (Harry & Schroeder, 2000), the
widespread lack of financial effectiveness of the total quality management (TQM)
and business process reengineering (BPR) methods was being published in the
popular and academic presses (Willmott, 1994; Ittner & Larcker, 1997). Six
Sigma, then, needed to distance itself from these fiscal shortcomings to install
itself as the new fashion within a rhetorical campaign of “bandwagon discourse”
(Abrahamson & Fairchild, 1999) that enumerated the corporations which had
successfully achieved double-digit performance improvements (Harry &
Schroeder, 2000) by following its tenets. According to Kieser, then, that Six
Sigma explicitly attempts to distance itself from the total quality management
programs, which preceded it, betrays its ancestry.
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2.2 The common theme of Taylorism Contemporary quality programs, like Six Sigma, can be traced, via TQM and
BPR, to Taylorist management principles, and even to Taylor himself. Six
Sigma, itself, was created by engineers at Motorola on the manufacturing floor
of their pager division in the early 1990s (Harry & Schroeder, 2000), but its roots
in scientific management run well into the past. Business process reeingineering
was being developed at the same time as Six Sigma as another alternative to
total quality management. The aim of business process reengineering’s founder
was to “use the power of modern information technology to radically redesign
our business processes in order to achieve dramatic improvements in their
performance” (Hammer, 1990: 104). Like Six Sigma, BPR attempts to
simultaneously distance from and align with TQM. In the same paragraph within
Reengineering the Corporation, BPR leaders assert “nor is reengineering the
same as quality improvement, total quality management (TQM), or any other
manifestation of the contemporary quality movement,” yet “quality programs and
reengineering share a number of common themes” (Hammer & Champy, 1993:
49).
These common themes have their root in scientific management. Hosseini
(1993) goes so far as to assert that BPR expands Taylorism contemporarily as
standard, routinized procedures are measured and controlled by information
technology apparatuses. Though BPR, too, describes itself as a strategy for
“business revolution” (Hammer & Champy, 1993), Benders & van Veen find that
it is simply a ‘repackaging’ of management practices that can be traced back to
a 1925 American Society of Mechanical Engineers (ASME) paper (2001: 47).
The ASME is a technical organization of which Frederick W. Taylor himself was a
past president. Willmott, too, indicates that
…the parallels between BPR and Taylorism are quite striking. Like Taylor, who rose to become chief engineer at the Midvale Steel Company, Hammer, the computer scientist, is quick to transfer the language of computing, and recent developments in parallel processing, to the complex and frequently perverse world of human relations (1994: 41).
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Six Sigma, too, was developed on the manufacturing floor under the rubric of
electrical engineering (Harry & Schroeder, 2000). Thus, both BPR and Six
Sigma, while they advocate “multidisciplinary integration of business processes,
[they are] largely driven by the logic and language of computer science and
production engineering” (Willmott, 1994: 39). It is no surprise, then, that Pruijt
describes these current quality programs trajectories as “neo-Tayloristic” (2003:
95). One of the quality programs’ key assumptions is that “employees are
assumed … to be infinitely malleable” (Willmott, 1994: 40). This assumption of
malleability is needed to encourage workers to follow the expressed
prescriptions of these algorithmic programs, a key underlying principle of
classical Taylorism (Hosseini, 1993). Indeed, because of the mimetic processes
of management fashion dissemination (Abrahamson & Fairchild, 1999), “Six
Sigma began to spread like wildfire to other industries—and beyond
manufacturing divisions alone” (Harry & Schroeder, 2000: 11).
2.3 Taylorism colonizes the knowledge industries The trajectory of these neo-Tayloristic quality programs and initiatives is now far
from manufacturing and its product development functions. They are currently
finding their way into such knowledge-oriented disciplines as banking and health
care. A combination of BPR and Design for Six Sigma, characterized as
process complexity reduction, has been implemented at various multinational
banks (George & Wilson, 2004). In an effort to stem the rising costs in American
health care, hospitals have hired industrial engineering efficiency experts from
industrial disciplines, such as valve manufacturing, to improve process
performance (Herper, 2007). In some cases there have been performance gains
made by this interdisciplinary cross-fertilization of skills and knowledge, but one
financial services company in particular is facing “tremendous resistance” as
they deploy old economy ideals in their new economy workplace
(Knowledge@Wharton, 2006). That there would be resistance at the interface of
two occupational life-worlds is not uncommon. Even within a single analog
device manufacturer deploying TQM, product development engineers resisted
the agenda of the manufacturing division, as they “didn’t think TQM could
6
improve product development and thought it interfered with their autonomy”
(Sterman, Repenning, & Kofman, 1997: 511). Because resistance between
divisions in the same company surrounding quality program deployment has
been shown to develop within the same company due to the different needs for
occupational autonomy, it is the goal of this inquiry to examine any similar
effects, as quality programs like TQM ‘spread like wildfire’ outside of their home
disciplines and take hold in industries that have been so widely reported to be
radically different from the American manufacturing sector of the mid-twentieth
century.
2.4 Old ideals versus new expectations A great deal of academic and popular press has been dedicated to describing
how vast, substantial, and fundamental the shift in the American economy from
durable goods to information services would be. Peter Drucker predicted that
the new organization “will bear little resemblance to the typical manufacturing
company, circa 1950” (1988: 45). This new organization will be staffed with
“knowledge workers who resist the command-and-control model that business
took from the military 100 years ago” (Drucker, 1988: 45). If Drucker’s
prediction is indeed correct, then the great majority of today’s workers will resist
the underlying ideology of contemporary neo-Taylorist quality programs. One of
the main principles of Taylorist scientific management was “the separation of
execution of the work from its design” (Hosseini, 1993: 533). This separation of
execution from design in scientific management fits squarely within the
militaristic tradition of command-and-control, wherein the commanders issue
pre-designed orders to subordinates whose actions they control from across a
separation of rank and hierarchy. Moreover, it finds its roots in a time period,
the first decade of the twentieth century, close to that which Drucker predicts
knowledge workers will resist. In fact, it was the command-and-control nature
of the TQM initiative that the product development engineers in the Sterman et
al. (1997) study were resisting, as they felt their professional autonomy impinged
upon. Drucker identifies professional autonomy as an important commodity for
the knowledge worker as “they cannot be told how to do their work” (1988: 49).
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Therefore, the new organization must place a “greater emphasis on individual
responsibility” (Drucker, 1988: 47) and dispose of former command-and-control
structures.
Drucker was not the only pundit to prognosticate sweeping changes in
employment due to shifts in global economic forces. Peters and Waterman
called for organizations to become more “simple and lean” in order to be more
responsive to heightened global competition (1982: 306). They, too, called for a
departure from the layered hierarchy of command-and-control structures,
suggesting, instead, a focus on experimentation, autonomy, and
entrepreneurship. This change would result in the increase of ‘individual
responsibility’ predicted by Drucker as authority moved from institutional
apparatuses established and maintained via scientific management to more
humanistic processes in what Peters and Waterman call “productivity through
people” (1982: 235).
2.5 A critical view of entrepreneurial risk in the knowledge workplace But this new focus on worker autonomy and personal responsibility was not
seen as an urgent requirement for productivity and job satisfaction by all
observers. The social theory of Anthony Giddens would seem to indicate that
the institutional consequences of these structural corporate changes, while a
boon for financial efficiency, could erode workers’ psychological well-being. The
dismantling of static command-and-control structures could potentially act,
according to Giddens, as a disembedding mechanism (1991). The construct
was defined by Giddens to be “mechanisms which prise social relations free
from the hold of specific locales, recombining them across wide time-space
distances” (1991: 2). As organization charts are de-layered and flattened, social
ties are fractured and reoriented in physical space and desks move and entire
organizations are relocated or disbanded entirely. Moreover, as workspaces
become increasingly experimental, independent, and autonomous, they run the
risk of becoming more risky and lonely. Indeed, the word used by Peters and
Waterman to describe the new world—entrepreneurial—is laden with
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connotations of risk. As institutions core to human identity—as the workplace is
core to occupation identity—are rendered more dynamic, notions of ontological
security will be rarefied and replaced with feelings of doubt and existential
anxiety (Giddens, 1991). In fact, Ulrich Beck goes so far as to say that the
institutionalization of risk and insecurity will be cataclysmic. He asserts that
“work society is coming to an end” (2000: 2) and is being replaced by the risk
regime. This “risk regime firmly rules out, beyond any transition period, any
eventual recovery of the old certainties of standardized work, standard life
histories, an old-style welfare state, national economic and labour polices”
(2000: 70).
Regardless of whether these swift and totalizing predictions materialize or
knowledge workers resist scientific management en masse, these predictions
do indicate what must be recognized as signs of a new ideology of work.
Academics, pundits, and social critics, no matter the content of their predictions
or the scope of their timeline, seem to agree that corporations have entered a
period of unprecedented dynamism and structural change, societal forces on
par with a true revolution, forces labeled by Giddens as institutional reflexivity
(1991: 2). However, the managerial forces which have rhetorically positioned
themselves to ‘revolutionize’ the fortunes of business seem to be rooted in the
anachronistic ideology and practices which institutional reflexivity is set to
transform. It is not the intent here to examine the overall effectiveness of these
quality programs in the face of a new era in the late modern age, as this has
been undertaken elsewhere (e.g., Ittner & Larcker, 1997). Still, this body of new
economy ideology embedded in the popular and academic presses may have
had an effect upon the expectations that employees in these late modern times
have of their workplace, one that is distinct from the command-and-control
assumptions of the nineteenth and early twentieth century.
2.6 New economy ideology and psychological contracts As workers, especially those in the knowledge professions as highlighted by
Drucker (1988), internalize this rhetoric about the new world of work ahead
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(Beck, 2000), their anticipations and conceptualizations of their work experience
may be altered to include those predicted vital components of work in the late
modern age, such as increased personal responsibility, more professional
discretion, the promotion of entrepreneurial risk, and the erosion of bureaucratic
hierarchies. These expectations may then become line items in an individual’s
psychological contract with her employer. Thompson and Bunderson (2003)
expand the traditional economic and socioemotional contents of psychological
contracts to include ideological content. They refer to it as an “ideology-infused
contract” (Thompson & Bunderson, 2003: 574). The authors use a rather
restricted definition of the term ‘ideology’ to refer to the pursuit of a social cause.
An example they give of the violation of an ideology-infused contract is a
Catholic hospital that accepted the buy-out offer of a for-profit healthcare
conglomerate. Employees saw this as a violation of their ideology-infused
psychological contract with the organization, which included the line items of a
community mission and a “commitment to patients” (Thompson & Bunderson,
2003: 572), which were violated by a profit-seeking business transaction,
thereby decreasing morale and causing unfavorable press coverage. Denise
Rousseau makes clear that the violation of a psychological contract is “distinct
from unmet expectations” (emphasis in original; 1998: 667). That is, a
psychological contract violation produces a greater emotional reaction from an
employee than an unmet expectation. Indeed, the idignant letter-to-the-editor
written by an employee of the Catholic hospital featured in Thompson &
Bunderson’s (2003) work showcases a moral outrage intense enough to be
taken to the public forum of an op-ed page.
Thompson and Bunderson use a restricted definition of ideology in their
examination of ideology-infused psychological contracts. They focus exclusively
on ideology surrounding the “pursuit of a cause” (2003: 573), an example being
a mission of faith-based community health care. However, the list of possible
definitions of ideology provided by Terry Eagleton shows a far wider scope to
the term. The list includes such definitions as “the process of production of
meanings, signs and values in social life” and “identity thinking” (1991: 1-2).
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Thus, if the ideas of Drucker (1988) and Peters and Waterman (1982) have
sufficiently saturated everyday discourse in order to constitute what it means to
be a worker in today’s workplace, or identify oneself as a financial analyst,
registered nurse, software developer, or product development engineer, those
ideas, according to Eagleton (1991), would constitute the ideology of work.
Notions of autonomy, professional discretion, and personal responsibility might
then be line items of an employee’s ideology-infused psychological contract, to
the extent that these ideals inform what it means to be an effective and
respected worker in the late modern knowledge economy. The extent to which
neo-Taylorist quality programs abridge these needs could then trigger an
experience of psychological contract violation with heightened negative
emotional and moral consequences, including possible deleterious effects on
“subsequent employee citizenship behavior” (Rousseau, 1998: 667). The
resistance of workers in the financial services industry (Knowledge@Wharton,
2006) and the product engineers in the Sterman et al. (1997) TQM study may
have been reactions to the violation of their ideology-infused psychological
contracts, as they felt Six Sigma and TQM impinge upon their need for
autonomy, a component of their professional identity. It is important, therefore,
to understand the composition of workers’ conceptualization of these
contemporary quality programs to determine the extent to which any
expectations or perceived contractual obligations about work in Drucker’s
(1988) new organization are violated by contemporary quality programs, such as
Six Sigma.
Another definition of ideology provided by Eagleton is “a body of ideas
characteristic of a particular social group or class” (1991: 1). This raises an
interesting question of the possibility of the rhetoric surrounding the new
organization serving as an ideological base to substantiate and legitimate a
particular class of worker, namely those who master the mechanisms of
institutional reflexivity, versus those who are excluded by them. Giddens
indicates that participation in these late-modern practices is segmented by
socioeconomic class. As part of the disembedding mechanisms of institutional
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reflexivity, “individuals are forced to negotiate lifestyle choices among a diversity
of options” (Giddens, 1991: 5). However, “lifestyle refers only to the pursuits of
the more affluent groups or classes. The poor are more or less completely
excluded from the possibility of making lifestyle choices” (Ibid: 5). Moreover,
“modernity, one should not forget, produces difference, exclusion, and
marginalization” (Ibid: 6; emphasis his). Valerie Fournier (1998) described a
similar situation of a body of rhetoric serving to exclude a particular group of
workers. Her study found that the prevailing careerist discourse in the enterprise
segmented workers along disciplinary and class lines. The computer
information systems (CIS) graduates who rejected the discourse of
entrepreneurial independence in favor of clearer progression along a track of
technical mastery “tended to come from more modest backgrounds” (Fournier,
1998: 74-5). Comparatively, the Finance graduates who embraced the rhetoric
of careerism as a way to “manage your own destiny” (69) were all “issued from
the professional and managerial class” (75).
2.7 The professional and managerial class The professional and managerial class (PMC) was offered as a third class, a true
middle class, to expand orthodox Marxism. The class is defined as “salaried
mental workers who do not own the means of production and whose major
function in the social division of labor may be described broadly as the
reproduction of capitalist culture and class relations” (Ehrenreich & Ehrenreich,
1979: 12), or, more plainly, “cultural workers, managers, engineers, and
scientists” (12). These workers are the knowledge workers whom Drucker
predicted would populate the new organization. The PMC is thus independent
from both the bourgeoisie and the working class. The bourgeoisie is defined as
“that class which owns and controls the means of production and is thus able to
exploit and dominate the activities of workers” (Wright, Hachen, Costello, &
Sprague, 1982: 710). The PMC cannot claim to be among the bourgeoisie
because they do not own the means of production. Nor can the PMC be
among the working class, as it is the PMC who is charged with carrying out the
organizational policies and processes which “exploit and dominate the activities
12
of workers” (Wright et al., 1982: 710) through their roles as managers and
engineers. Thus, these engineers and managers of the PMC, specifically of the
American Society of Mechanical Engineers, were the primary physical force
behind the “forced Taylorization of major industries” (Ehrenreich & Ehrenreich,
1979: 17). As the PMC proliferated the tenets of scientific management, they
were placed in a position which was “objectively antagonistic” (17) to the
working class. If it was the bourgeoisie who dispossessed workers of
ownership of the means of production through capitalist control, it was the PMC
who further disposed the working class, through Taylorist scientific
management, of the very ways and means of their work.
2.8 The neo-Taylorization of the Taylorists Because the professional and managerial class is comprised of the “managers,
engineers, and scientists” (Ehrenreich & Ehrenreich, 1979: 12) who
administrated the “forced Taylorization of major industries” (17) in the American
economy’s previous industrial era, a sense of irony is palpable as they are now
the target of Taylorist policies amidst the contemporary neo-Taylorization of their
knowledge domain through policies and initiatives such as business process
reengineering, total quality management, and Six Sigma. Sewell and Barker
(2006) suggested that an appreciation of irony would be critical for a robust
understanding of the re-creation of an oppressive bureaucracy during an effort
to institute lean and nimble self-managing teams in an electronics manufacturing
facility. Here, too, the irony of the Taylorist role reversal can be appreciated as
the design and labor of knowledge work is being separated: the programmatic
quality framework determines the design and execution of knowledge work, as
the worker is subject to its optimized prescriptions. Naturally, these neo-
Taylorist policies “may encounter resistance” (Willmott, 1994: 42) as managers
may experience the violation of the new economy promises issued by the
business press, being now placed at the other end of the scientific managerial
machine that they themselves engineered in a previous era.
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3.0 Statement of Research Questions The goal, then, is to understand the effects of the current ironic role of neo-
Taylorist quality practices, as it seems industries of all ilk are now undertaking
the neo-Taylorization of the Taylorists. Using the theory of social
representations, via the method of multidimensional scaling (MDS), the shared
understanding of contemporary neo-Taylorist corporate quality initiatives will be
examined among a small segment of the professional and managerial class at a
major American design and manufacturing company. The solution space of the
MDS method will then be examined to determine if engineers’ understanding of
and experience with these programs is inconsistent with the promises of
professional discretion and autonomy in the workplace made to them by the
rhetoric and ideology of Drucker’s (1988) new organization.
4.0 Method: Mult idimensional Scaling (MDS) 4.1 MDS and the theory of social representations As Serge Moscovici describes social representations as composed of both
structure and content (McKinlay & Potter, 1987), a method of analysis was
chosen that can elucidate both the structure and content of conceptual material.
Multidimensional scaling was chosen because of its apt capabilities on both
fronts. From similarity data generated from multiple comparisons of a list of
stimuli, the multidimensional scaling method creates a map of stimulus objects
in a coordinate space (Schiffman, Reynolds, & Young, 1981). In this way, the
multidimensional scaling technique can reproduce the structure of a social
representation. Once the coordinates of the stimuli have been established by
the statistical algorithm underlying the technique, the social representation can
be examined by fitting property vectors onto the coordinate matrix of the
stimulus objects. Through this process, described by Purkhardt & Stockdale
(1993), the possible meanings underlying this spatial map can be determined
and the content of the social representation can be revealed.
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4.2 MDS and anchoring This two-step methodological process is also consistent with the two-step
process underlying the creation of the social representation itself (Purkhardt &
Stockdale, 1993): anchoring and objectification (Moscovici, 1984). It is for this
reason that Purkhardt and Stockdale (1993) describe multidimensional scaling
as a way to “uncover the underlying dimensions which describe the relations
among a representative set of social objects pertaining to the social
representation of interest” (273). To anchor an object, in the most literal sense,
is to fix it in space. The initial outcome of the multidimensional scaling technique
is a coordinate matrix, which anchors each stimulus object in a space, assigning
coordinates to each object. But in Moscovici’s use of the word, an object is
anchored not with an absolute location in space, but with a relative one.
Through the process of social representations theory, an object is situated in “a
familiar context” (Mosciovici, 1984: 29); that is, situated in relation to other
objects. Anchoring situates an object “into our particular system of categories
and compares it to the paradigm of a category which we think to be suitable”
(29; emphasis added). Moscovici further elaborates on this process of
categorization as “choosing a paradigm from those stored in our memory and
establishing a positive or negative relation with it” (31; emphasis added).
Fittingly, Moscovici’s description of the process of anchoring can be read as a
restatement of the multidimensional scaling method itself.
Focusing on specific components of Moscovici’s statements, the physical
construction of the multidimensional scaling coordinate matrix will be reviewed.
The first step in the construction of a stimulus coordinate matrix is a series of
multiple comparisons of the stimuli themselves. The respondent examines each
stimulus object and compares it to every other stimulus object. The task given
to the respondent, specifically, is to establish a positive or negative relation
between each pair of stimulus objects; she must determine whether they are
similar or dissimilar to each other. Moreover, the respondent is given the latitude
to judge the similarity of the objects based on any criteria which she thinks to be
suitable. The respondent is instructed to assess the objects according to any
15
attribute she considers to be important, as no specific criteria are required by
the experimenter. Below is a screenshot of a representative comparison task
given to each respondent in the online survey portion of this study. Complete
screenshots of the survey are provided in Appendix A.3.
Figure 4.1 Multiple comparison task
Here one can begin to see how the coordinate matrix is constructed with each
object in relation to the other, one comparison at a time. It is the resultant
coordinate matrix that serves as the basis for the MDS solution, a reconstruction
of the particular system of categories which underpins the social representation
of interest.
4.3 MDS and objectification The second component of social representations theory, which can be
examined using the multidimensional scaling method, is the process of
objectification. Moscovici states that “to objectify is to discover the iconic quality
of an imprecise idea or being” (1984: 38). Moscovici later calls this ‘iconic
quality’ of a social representation its “figurative nucleus, a complex of images
that visibly reproduces a complex of ideas” (1984: 38). The multidimensional
scaling method can be easily understood as a way to discover the ‘figurative
nucleus’ of an ‘imprecise being’. In fact, all of the examples in the Schiffman,
Reynolds and Young (1981) monograph, a key element of the multidimensional
16
scaling literature, are drawn from the body of research of taste and smell, an
‘imprecise idea’, indeed.
While it is in the first step of the multidimensional scaling method where the
relationships between the social objects are established and the number of
dimensions underlying them is determined, it is the second step of fitting
subjective properties to these coordinates that illuminates the ‘iconic quality’ of
each dimension identified. The possible salient properties of these quality
initiatives posed to the respondents in this study came from Spreitzer and
Quinn’s (1996) work on the study of programmatic change initiatives for middle
managers at the same American multinational corporation of interest in this
study. Dimensions from the Spreitzer and Quinn survey to managers were used
to create the questions about the stimulus objects posed to respondents of the
online survey. Below is a screenshot of a representative collection of questions.
Figure 4.2 Presentation of evaluative statements
The results from the property questions were then mapped onto the stimulus
coordinate matrix generated by the multidimensional scaling algorithm using
17
multiple linear regression procedure, as described by Purkhardt and Stockdale
(1993), to give possible meaning to the ‘figurative nucleus’ of social
representations of quality programs at this organization.
“Indeed, a representation is, basically, a system of classification and denotation,
of allotting categories and names” (Moscovici, 1984: 30). The task of the
multidimensional scaling method is to first discover the coordinates of that
classification system and then determine the denotation of its dimensions. While
the computer-driven algorithm might seem too mechanical of a method for the
examination of a social process which Moscovici himself described as “mobile
and circulating” (18), the technique is theoretically consistent with the
constituent components of the theory, and yields output that is equally rich and
multidimensional. The specific steps taken to generate this output are reviewed
below, and the output itself will later be reproduced and examined.
5.0 Procedure: Internet-based Survey To explore the social representations of quality programs across various
professional disciplines, a web-based survey of 24 respondents was conducted
in a multinational manufacturing corporation headquartered in the United States.
Because Taylor’s work began in the discipline of mechanical engineering in
manufacturing settings and his ideas are an important component of
mainstream corporate quality strategies, workers in the divisions of
manufacturing and quality were assumed to be sympathetic to contemporary
neo-Taylorist management initiatives (Niepce & Molleman, 1998; Hosseini,
1993). Six workers were surveyed in these divisions and were categorized as
Taylorist workers. As the focus of the study was to examine the social
representations of workers outside of the realms in which these programs were
developed, 18 of the 24 respondents were from product development,
research, information technology, and marketing divisions. These workers were
categorized as knowledge workers. This yielded a ratio of three-to-one of those
outside traditional Taylorist disciplines versus those who work within that
tradition. The details of the sample used is shown in Table 5.1 below.
18
A snowball sampling technique was employed through the use of e-mail queries.
This email was then forwarded by recipients to other current and former
employees of the organization multiple degrees of separation away from the
author. Thus, the response rate is impossible to compute, as it is impossible to
know the number of individuals to whom the solicitation email was sent. It is
important to note that the organization under study had undertaken massive
headcount reductions immediately prior to the solicitation of the participants.
Some departments experienced reductions in their salaried, white-collar
workforce of 10 to 30 percent. At the time of the survey, these workforce
actions were entirely voluntary and included cash buyout inducements. This
‘rightsizing’ was understood by all employees to be necessary, and was not at
all surprising or even unwelcome. Most of the negative feelings surrounding the
event were centered on upper management’s lack of proactive resource
management and planning, in the face of steadily declining market share, to
prevent such a drastic action with palpable effects on the local economy and the
difficult business of doing the same amount of work with far fewer people.
Because of the voluntary and predictable nature of the program, fear was not
rampant, however spirits were obviously low amidst an outlook to a bleak future.
Most of the evidence of the sullen business climate was apparent only in the
pilot interviews used to develop the online survey questions, as the programs,
tools, and initiatives were rated uniformly higher than average. These surprising
results will be explored further in the section 6.5 below. Still, this human
resource disruption made sample solicitation difficult and forced the author to
solicit both current and former employees. This variable had no obvious effect
on the survey results, though the current versus separated attribute was not
captured, so one cannot be certain.
19
Table 5.1 Composition of the subject sample Worker Category Corporate Div ision Count in Sample Taylorist Workers Manufacturing 2 Quality 4 Total 6 Knowledge Workers Information Technology 3 Marketing & Sales 2 Product Development 10 Research & Development 3 Total 18 Grand Total 24
The sample was somewhat evenly split along supervisory lines, with 14
respondents occupying management roles and 10 without managerial
responsibilities. Stockdale, Dockrell, and Wells’ (1989) study of mass media
representations of HIV and AIDS using the multidimensional scaling technique
utilized a sample size of 24 subjects, so this number was used as guidance for a
suitable sample size. Fortunately, even with the sizable headcount reductions,
the target sample size was achieved.
5.1 Selection of the stimulus set After collecting the demography of the respondent’s tenure with the corporation
in years, corporate division, gender, and managerial classification, each
respondent was presented with a list of the stimuli used for the multidimensional
scaling procedure. The list of stimuli contained 11 commonly used quality
programs, initiatives, and tools. Within the company, they are commonly
referred to by their acronyms. A list of the acronyms used throughout the data
presentation, accompanied by a brief description of each, is provided below in
Table 5.2.
20
Table 5.2 The stimulus set
Name Descript ion GPDS Corporate product development guidelines DMAIC Six-Sigma, a popular contemporary manufacturing quality initiative DMADV Design for Six Sigma, the product development analog of DMAIC G8D 8-D (Eight Disciplines) problem-solving methodology FPS Corporate manufacturing guidelines also used in business processes Lean Any variation on the theme of ‘lean thinking’ popularized by Toyota QPS Quality Process Sheets used to standardize process steps ISO / TS International quality standards SOx Sarbanes-Oxley regulations for business financial processes FMEA Failure Mode and Effects Analysis TRIZ/SIT Structured Inventive Thinking based on the Russian TRIZ technique Eleven stimuli were chosen as a compromise among the needs for accuracy in
the technique, limitations of a particular respondent’s exposure to all of the
stimuli, and respondent time constraints. In order to have the requisite number
of multiple comparisons from which to construct the coordinate matrix of the
stimulus objects, it is necessary to include a sufficient number of stimuli for the
respondent to compare. It was recommended, at minimum, to have nine stimuli
for a coordinate matrix with two dimensions and 13 stimuli for a coordinate
matrix with three dimensions (Schiffman, Reynolds, & Young, 1981: 24). It was
warned that the “use of a small data set can lead to loss of the more subtle
nuances which can only be observed in higher dimensionalities” (24). ‘Subtle
nuances’ were not of primary concern, however; as Parker (1987) maintains that
“Moscovici … is actually quite explicit that the theory of ‘social representations’
focuses on form” (464) over content. The focus of this study, therefore, was not
the precise calculations of the coordinates of the stimulus objects contained
within a multi-dimensional conceptual space, but an understanding of the
composition of the dimensions that underlie and give form to that conceptual
space. It was, thus, decided to follow the wisdom of splitting the difference
between the nine stimuli recommended for a two-dimensional and the 13
suggested for a three-dimensional one. The 11 stimuli featured in Table 5.2
above were used throughout the study.
Pilot interviews were conducted with nine informants to determine, prior to the
creation of the web-based survey, the extent to which any potential respondent
21
was familiar with all of the stimulus objects. The pilot interview informants were
comprised of workers who used the tools listed in Table 5.2 on a daily basis,
those who used one or two of them on occasion, and four managers who had
an enterprise-wide view of the strategy and implementation surrounding them.
Thus, not all interview informants had knowledge of every stimulus object, but
some had experience with them all. Table 5.3 features the demographic details
of the pilot interview informants. To accommodate for varying degrees of
knowledge, the online survey respondents were given a screen on which they
could deselect any stimulus object about which they had no knowledge. This
step ensured that subsequent questions would only be posed regarding quality
programs and tools which were cogent to the respondent. Thus, it was decided
to choose accuracy of responses over the breadth of the stimulus set.
Table 5.3 Composition of the pilot interview informants Respondent Worker Category Corporate Div ision Manager Gender S1 Knowledge Product Development No Female S2 Knowledge Product Development Yes Male S3 Knowledge Research & Development Yes Female S4 Knowledge IT Yes Male S5 Knowledge IT No Male S6 Knowledge IT No Female S7 Taylorist Quality Yes Female S8 Taylorist Quality Yes Female S9 Taylorist Quality No Female Because the multidimensional scaling technique requires pairwise comparison
for the construction of the similarity matrix, which then serves as the input for the
statistical algorithm, the number of tasks faced by the survey respondent can
grow quickly with each additional stimulus included in the set. The number of
pairwise comparisons required for N stimulus objects to be paired with each
other object is
[ N * (N-1) ] / 2. (1) Respondents were solicited via email at their workplace. With a relatively
tedious task of repetitive parings on the online survey, it created concerns both
about the intrusion on the respondents’ time and the possible effects of the
22
fatigue during a busy workday. Moreover, in order to fit subjective properties
onto the stimulus coordinate matrix, to give its axes meaning once it is
calculated, each respondent must respond to a series of questions about each
stimulus object, in addition to the comparison tasks. The literature recommends
that there be no more than 55 pairings presented to a respondent in any single
sitting (Schiffman, Reyonolds, & Young, 1981: 20). Fifty-five pairings was
therefore used as the upper bound of equation (1), above, to give an N of 11,
the number of stimulus objects used in the survey. As each respondent was
asked six questions about each object, an additional 66 questions, the survey of
over 100 questions was enough of a time-consuming chore for the already
taxed respondents. It was, therefore, decided to err on the side of brevity out of
deference to the respondents’ time and work demands, rather than lengthen the
survey for completeness and risk a lower completion rate. The specific content
of these six property questions will be examined in section 5.4 below.
5.2 Sort task The free sort task was suggested by Purkhardt and Stockdale (1993) as a way
to overcome any potential limitations that the multidimensional scaling technique
may present to the examination of social representations. Because a social
representation is, at heart, a social process, they suggest that “a comprehensive
investigation of social representations would thus not only describe the content,
structure, and evolution of social representations, but also the social dynamics
by which they evolve and function as well as describing their relationships with
groups and social identity” (295). While this study, in particular, does not
present itself as a ‘comprehensive’ one, it still endeavored to employ multiple
methods to overcome the limitation inherent in any single technique. Certainly, a
simple, individual sort task cannot reconstitute ‘social dynamics’, but the sort
task does provide another method of classification for the stimulus objects of
interest, in addition to the multidimensional scaling technique, through which to
understand the “system of classification and denotation” (Moscovici, 1984: 30)
which is at the heart of a social representation. Clearly, given the social and
‘circulating’ nature of a social representation, these static and computer-driven
23
classification tasks must be enlivened with data that is more social in nature.
Given that Parker (1987) suggests that problems of reification in the examination
of social representations can be remedied by a “determined return to
‘conversation’” (458), the pilot interviews will serve as an indispensable
corroboration with and elaboration of the free-sort and multiple comparison
tasks.
Once the respondent had selected a subset of the stimulus set with which she
was knowledgeable, she was presented with instructions for a free sort task.
The respondent was asked to sort the stimuli into as many relevant categories
as he wished, by placing a letter in a text box next to each stimulus object that
denoted the category to which the object belonged. On the instruction screen,
names of cities were given as an example, with various possible sorting
arrangements to demonstrate what was being asked. In Figure 5.4 below, is a
partial screenshot of the instructions given to each respondent. The complete
screenshots of the online survey are provided in Appendix A.3.
24
Figure 5.4 Sort task instructions, partially reproduced
The sort task was an area for potential confusion for three respondents as the
data revealed that they ordinally ranked the stimuli, rather than sorting them into
distinct categories. These misinterpretations of the survey instructions left 21
sort task results, from which to compute the cluster analysis result discussed
below in section 6.2. In future research, it will be imperative to administer the
sort task face-to-face, rather than through an electronic medium, in order for the
investigator to instruct the respondent through this sorting exercise.
The results of the free-sort task were converted into a proximity matrix
according to the procedure outlined by Miller (1969), in order to prepare the
sorted categorical groupings for analysis with the hierarchical cluster analysis
25
technique. The sort task results were first compiled into an 11 x 11 incidence
matrix. Each of the eleven stimuli were placed on the rows and columns of the
matrix in a symmetrical fashion. For each combination of two objects placed in
a group together, a ‘1’ was entered into their intersecting cell in the incidence
matrix. For example, if DMAIC, DMADV, and G8D were grouped together by
the respondent, there would be three different entries of ‘1’ in the incidence
matrix: ‘1’ in the cell at the intersection of DMAIC and DMADV, ‘1’ in the cell at
the intersection of DMADV and G8D, and ‘1’ in the cell at the intersection of
DMAIC and G8D. An incidence matrix was generated for each respondent’s
sort task results. An example of a single incidence matrix, along with its
representative sort task grouping is provided below in Table 5.5.
Table 5.5 Preparation of the incidence matrix Exemplary sort task grouping:
{ DMAIC, DMADV, G8D }; { ISO, SOx }; { Lean } Corresponding incidence matr ix:
GPDS DMAIC G8D FPS Lean QPS DMADV ISO SOx FMEA TRIZ GPDS DMAIC 1 1 1
G8D 1 1 1 FPS Lean 1 QPS
DMADV 1 1 1 ISO 1 1 SOx 1 1
FMEA TRIZ
As shown in Table 5.5, the conversion from the groups to the incidence matrix is
both tedious and elegant. ISO was placed into a group of two, SOx and itself.
A glance to either to ISO row or column in the above incidence matrix will reveal
two entries, one at ISO and one at SOx. The trivial case of a group of one can
be seen by the single entry in the Lean row and column. The conversion of the
group of three can be investigated by inspecting the three entries in the DMAIC
row and column.
26
The individual incidence matrices were then summed according to the rules of
matrix addition to form a proximity matrix, where entries of larger value represent
more proximate pairs, or, equivalently, more frequently grouped stimulus
objects. The proximity matrix was then reverse coded to create a distance
matrix with zeroes forced down the diagonal, because diagonal entries of the
matrix reflect a comparison of an object with itself, and the distance between an
object and itself should be zero. This distance matrix was then used as the
input for the hierarchical cluster analysis procedure in SPSS using the average-
linkage similarity measure, as suggested by the literature (Aldenderfer &
Blashfield, 1984).
5.3 Presentation of the stimulus pairs After the sort task, the respondent was then presented with a series of multiple
comparisons as shown in Figure 4.1 above. The task with which the
respondent was confronted was to assess the similarity of the two quality
programs or tools with respect to any criteria of her choosing. The similarity
measure was recorded on a nine-point Likert scale, with ‘Very Similar’ coded as
1 and ‘Very Dissimilar’ coded as 9. These codes were then converted into a
distance matrix, where the average similarity measure for each pair was
arranged in an 11 x 11 matrix with each stimulus object placed symmetrically in
the rows and columns (Schiffman, Reynolds, & Young, 1981), similar to Table
5.3 above.
In the presentation of pairs it is, first, desirable to avoid space and time errors,
second, to prevent repetition which could bias the respondent, and, third, to
achieve optimal spacing between the sequential presentation of the same object
(Ross, 1934). In addition, a later amendment balances the subsequent
presentation of the same object, alternating from left to right (or top to bottom) in
the pairing (Ross, 1939). These adjustments to the presentation order are
preferred to a strictly random presentation of pairs, which could be susceptible
to unlucky streaks of repetition. In order to achieve a balanced and well-spaced
presentation of pairs to an already inundated respondent, the pairs followed the
27
“optimal order” scheme given by Ross (1939: 417), commonly known as a Ross
order.
5.4 Subjective evaluations of properties of the stimuli After determining the conceptual similarity of the stimulus pairs, the respondent
was asked to evaluate each of the stimuli that she selected according to six
different properties: creative support, bureaucratic waste, sufficiency of
management resources, effective structure, latitude for professional discretion,
and overall business satisfaction. Table 5.6 shows the FMEA tool in the question
text to serve as an example.
Table 5.6 Evaluative statements regarding the stimulus objects Property Name Quest ion Text P1 Creativity FMEA provides a framework to work creatively. P2 Structure FMEA provides structure for work to be done more effectively. P3 Resources Management provides the resources necessary to execute FMEA well. P4 Discretion FMEA allows the freedom to use professional discretion. P5 Bureaucracy FMEA is needlessly bureaucratic. P6 Business
Effectiveness FMEA should be a part of how we do business.
The same questions were asked regarding each stimulus object. Because the
survey respondents were working professionals solicited during business hours
over email, a balance needed to be struck between completeness and brevity.
The six themes were chosen to cover a variety of issues addressed in the
literature (Amabile, Conti, Coon, Lazenby, & Herron, 1996; Spreitzer & Quinn,
1996) and, also, comments made by informants during the pilot interviews. But,
because these questions needed to be asked of each stimulus due to the
demands of the technique, there was not the luxury to ask multiple questions on
each theme to achieve a “balanced” pool of evaluative statements (c.f.
Oppenheim, 1992). Instead, one simple, clear statement was made about each
theme, allowing the respondent to easily assume either an affirmative or contrary
stance. The questions were displayed to the respondents in the manner as
shown Figure 4.2 above. The responses were coded on a 7-point Likert scale,
with ‘Strongly Agree’ coded as 7 and ‘Strongly Disagree’ coded as 1.
28
The creativity property (P1 in Table 5) was selected because of the current
implementation of these neo-Taylorist quality programs in service-oriented
sectors, such as banking, health care and information technology, where
creativity may be required to deliver quality service. An IT manager interviewed
(S4 in Table 5.3) expressed the fear that these neo-Tayloristic quality
methodologies have the potential to become “too prescriptive” and dogmatic,
thus hampering the flexibility needed for creative work. Proponents of these
quality programs, on the other hand, suggest that these programs do not stifle
creativity, but provide “a framework to channel it” (Pestorius, 2007: 20). In order
to assess if the organized framework of these qualities does increase their
appeal, the structure property (P2) was also included.
The resources property (P3) came from Teresa Amabile’s work on organizational
impediments to creativity in the workplace. She and her colleagues found that
“perceptions of the adequacy of resources” can affect the level of creativity and
innovation in the workplace (Amabile et al., 1996: 1161). In addition, Spreitzer &
Quinn (1996) included the variable of “top management support” (258) in their
study of the transformational change practices of middle managers in the same
company as this study. The managerial facet of resource allocation was
incorporated into the resources property by making ‘management’ the subject
of the question text.
Spreitzer and Quinn (1996) also found that there was an ambient feeling in the
organization that “no one wants middle managers to be leaders” (240). This
sentiment was also present in interviews at the same company, where some felt
that the programs were too prescriptive to allow for managerial discretion. As
this feeling of decreased autonomy is at odds with Drucker’s prognostication
that the knowledge economy would feature an “even greater emphasis on
individual responsibility” (1988: 47), a discretion property (P4) was included in
the study. A bureaucracy property (P5) was also featured in the Spreitzer &
Quinn (1996) paper, where they asked their participants about feelings of there
being “too much paperwork and red tape” (258) in their organization. Because it
also appeared as a salient theme in pilot interviews, a statement about
29
bureaucracy was included in this study as well. A statement encompassing
personal satisfaction with the property of the business effectiveness (P6) of the
quality methods under study was necessary, so the final statement of ‘____
should be a part of how we do business’ was included to serve as an overall
satisfaction measure.
The results of these six evaluative statements were then fit to the stimulus
coordinate matrix provided by the multidimensional scaling (MDS) algorithm
using multiple linear regression, according to the equation
!
"1 j #$1
+ "2 j #$2
+ "3 j #$3
+% j =&^
j (2)
where Υj is a column vector of the average responses to the evaluative
statement j for each of the eleven stimulus objects, and j ranges from 1 to 6
(Kruskal & Wish, 1978: 36), for each of the six property statements in Table 5. Χj
is a column vector of the 11 coordinates of the jth dimension of the MDS
solution matrix. If a coefficient is significant for a given evaluative statement, then
“the attribute measured by that unidimensional scale should provide a
satisfactory interpretation of that dimension” (Purkhardt & Stockdale, 1993:
287). This procedure is enumerated in detail with the MDS solution in Appendix
A.4 below.
6.0 Results and Discussion 6.1 Multidimensional scaling results The ALSCAL algorithm was used to calculate the MDS coordinate solution
matrices, as the algorithm was readily available in the SPSS ‘Scale’ menu and
documentation was plentiful. In order to determine if the multidimensional
stimulus coordinate matrix was a fair representation of the distance matrix, a
goodness-of-fit measure was calculated. STRESS is the common measure of
goodness-of-fit from MDS solutions, but ALSCAL uses the S-STRESS modified
calculation as a termination criterion for each iteration of the algorithm (Davison,
1983). When the improvement in S-STRESS from one iteration to the next is
30
less than 0.001, the algorithm stops and the stimulus co-ordinate matrix is
produced. Both two- and three-dimensional solutions were considered, as it
was hypothesized that there were three possible dimensions underlying social
representations of neo-Taylorist quality programs: latitude for creativity, business
task effectiveness, and resources available for implementation.
Though ALSCAL uses S-STRESS as a termination criterion, STRESS is reported
as the goodness-of-fit diagnostic. The lower the STRESS value, the better the
fit of the MDS solution to the proximity matrix. STRESS was 0.14 for the two-
dimensional solution and 0.06 for the three-dimensional solution. It is tempting
to immediately conclude that the three-dimensional solution is preferred
because its STRESS is lower. However, the addition of an additional dimension,
de facto, reduces STRESS in general, so the literature must be consulted for
guidance. Davison (1983) follows the Kruskal & Wish (1978) guidance of not
accepting a solution with a STRESS greater than 0.10. This guideline indicates
that the two-dimensional solution’s STRESS measure is too high to be an
adequate fit. The three-dimensional solution’s STRESS is below this upper
bound. Furthermore, “Kruskal and Wish conclude that it is seldom necessary to
add dimensions beyond the number needed to reduce STRESS below 0.05”
(Davison, 1983: 91-2). Adding the third dimension brings the STRESS down to
0.057 from above 0.10, so the three-dimensional solution is an optimal solution.
Adding a fourth dimension would hinder the ease of visualization and
interpretation, as none of the evaluative statements asked of the respondents
mapped to a fourth dimension. Moreover, the three-dimensional solution is
sufficiently close to the 0.05 target as not to warrant further reduction in
STRESS. Both the matrix and graphical form of the three-dimensional MDS
solution is provided below. The coordinates in Table 6.1 below correspond the
to Cartesian coordinates of each stimulus in the graphical representation in
Figure 6.2. The 3-dimensional graphical solution is presented as two separate
2-dimensional graphs to facilitate a visual interpretation of the MDS solution.
31
Table 6.1 Three-dimensional MDS coordinate matrix
St imulus Dimension 1 Dimension 2 Dimension 3 DMADV 1.3602 -0.0964 -0.6018 DMAIC 1.1754 0.0707 0.7101 FMEA 0.9038 -1.1493 0.1559 FPS -1.1647 1.0465 0.1328 G8D 0.7777 -0.8734 1.1593 GPDS -0.7540 0.6751 -1.2597 ISO / TS -1.6435 -0.4948 -0.3380 Lean 0.2658 1.3262 0.4733 QPS -0.8173 1.0064 0.7603 SOx -2.0094 -1.4746 0.1126 TRIZ / SIT 1.9060 -0.0363 -1.3047
32
Figure 6.2 Graphical representation of the MDS solution
33
6.2 Confirmation of the MDS solution through a cluster analysis Dimension 1 can be seen to cluster into two groups when plotted against
Dimension 2. The cluster analysis performed on the results of the free sort task
roughly confirms the grouping circled on the MDS solution in Figure 6.2 above.
Below is the dendrogram produced by the SPSS hierarchical cluster analysis
procedure.
Figure 6.3 Dendrogram of the hierarchical cluster analysis results
When compared to the clustering on Dimensions 1 and 2 of the MDS solution, it
can be seen that there is a high degree of agreement between the groupings.
Figure 6.4 Comparison of the MDS and sort task clusters MDS clusters
{ DMAIC, DMADV, G8D, FMEA, TRIZ }; { GPDS, FPS, ISO, SOx, Lean, QPS } Sort task clusters
{ DMAIC, DMADV, G8D, FMEA, TRIZ, QPS }; { GPDS, FPS, ISO, SOx, Lean } The only disagreement is the placement of QPS. The corroborating results are
reassuring as they point to a result reproduced on two different, though
complementary, methods. Moreover, using a property fitting technique with the
34
evaluative statements about each stimulus object, as well as the pilot interviews
for further information, richer qualitative meaning can be imputed to the clusters
and dimensions found with the quantitative algorithms. Triangulating the results
from various research methods yields a more robust and substantial
understanding of the social representations of these quality programs (Purkhardt
& Stockdale, 1993). The exploration of the possible meanings of these
groupings and dimensions follow.
6.3 Interpreting the MDS solution through property fitting For each of the 11 stimuli, the results of the evaluative statements shown above
in Table 5.5 were used as response variables in six separate multiple linear
regression procedures, according to the equation (2) above, with the dimensions
of the MDS coordinate matrix in Table 5.6 as predictors.
The normalized coefficients of the regression equations are provided below in
Table 6, along with the multiple correlation coefficient (R) of the regression
model. Kruskal and Wish (1978) recommend that the regression coefficients (βij)
be normalized such that “their sum of squares equals 1.000 for every scale”
(37). This normalization allows the coefficients to be interpreted meaningfully as
the cosine of the angle between the property vector and its primary dimension in
the MDS solution. The details of the normalization calculation are provided in
Appendix A.5.
In order for a property to meaningfully mapped onto a dimension in the MDS
solution, both the multiple correlation coefficient of the regression model and the
regression coefficient for that dimension must be sufficiently high (Kruskal &
Wish, 1978). The 0.01 significance level of the multiple correlation coefficient
was suggested by Kruskal and Wish as a “minimal requirement” for the
interpretation of the dimensions (1978: 39). The dimension with the highest
regression coefficient is then the best fit for that property. The themes with a
multiple correlation significant at the 0.01 level are shaded in gray. The highest
regression coefficient for each significant theme is shaded in gray, as well, to
indicate to which dimension each significant theme maps.
35
Table 6.5 Normalized coefficients from property fitting to the MDS solution
Theme R Dimension 1 Dimension 2 Dimension 3
Creativity 0.913 0.880 0.405 -0.250 Bureaucracy 0.884 -0.937 -0.348 -0.023 Resources 0.880 -0.335 -0.120 0.935 Structure 0.854 0.733 0.556 0.391 Discretion 0.903 0.878 0.445 -0.178 Business 0.828 0.641 0.502 0.581 No evaluative statement asked of the respondents had a statistically significant
fit to the second dimension of the MDS solution. However, creativity,
bureaucracy, and discretion mapped to the first dimension, while the resources
property was a better fit to the third dimension. To facilitate the interpretation of
the dimensions, the rank order of each of the stimulus objects on the first and
third dimensions will be reproduced below in Table 6.6. Negative coordinates
are shaded in order to indicate where on the scale the sign changes.
Table 6.6 Conversion of the MDS coordinate matrix to an ordinal scale
Rank Dimension 1 Dimension 3 1 TRIZ / SIT G8D 2 DMADV QPS 3 DMAIC DMAIC 4 FMEA Lean 5 G8D FMEA 6 Lean FPS 7 GPDS SOx 8 QPS ISO / TS 9 FPS DMADV 10 ISO / TS GPDS 11 SOx TRIZ / SIT Because the creativity property has a positive sign in Table 6.5, it is positively
correlated with the Dimension 1 list in Table 6.6. Thus, TRIZ / SIT and DMADV
allow for the most creativity, whereas ISO / TS and SOx allow for the least. The
property of creativity along this dimension follows the purpose and content of
each of the quality methodologies, so the fit of the creativity property along this
dimension is reassuringly cogent. For example, the SIT portion of the TRIZ / SIT
acronym stands for ‘Structured Inventive Thinking’ and the intent of the
36
framework is to assist with a creative solution to novel problems. Thus, it is not
surprising to see it atop a creativity dimension. Likewise, DMADV is the
acronym for the steps in the Design for Six Sigma process, a methodology used
in product design where creativity must be allowed for the innovative and robust
satisfaction of customer requirements. On the other end of the dimension, there
are SOx and ISO / TS, which are government regulations and international
quality standards, respectively. It is not surprising that these stimulus objects
would rank lowest on the creativity scale, because they are not intended to aid
in problem solving, but enforce consistency and compliance.
Because the bureaucracy property has a negative sign in Table 6.5, it is
negatively correlated with the Dimension 1 list in Table 6.6. Moreover, it is fitting
that the normalized coefficient for the bureaucracy property (-0.937) is close to
the opposite polarity of the creativity property (0.880). This arrangement would
place the regulatory SOx and ISO / TS as the most bureaucratic, and the
creative problem-solving tools, TRIZ / SIT and DMADV, as the least. The
opposing trajectory of the bureaucracy and creativity unidimensional scales is
consistent with the finding that “rigid, formal management structures within
organizations will impede creativity” (Amabile et al., 1996: 1162). The pilot
interviews also corroborate the mapping of bureaucracy on to dimension 1.
GPDS and SOx were referred to, in interviews, as “paperwork”, “redundancy”,
and “cumbersome”, alluding to their lack of value in the business and
engineering work-stream. Both GPDS and SOx can be found in the negative
(shaded) region of dimension 1, indicating that they are highly bureaucratic
initiatives.
The other theme to fit on dimension 1 was discretion. Because the discretion
property has a positive sign in Table 6.5, it is positively correlated with the
Dimension 1 list in Table 6.6. It is not surprising to find this property on the
same dimension as bureaucracy and creativity. In the same exploration of
creativity in the workplace as cited above, it was found that “creativity is fostered
when individuals have relatively high autonomy in the day-to-day conduct of the
work and a sense of ownership and control over their own work and their own
37
ideas” (Amabile et al., 1996: 1161). There is a modicum of evidence of this
theme in the pilot interviews. GPDS, in the region of lesser discretion on
dimension 1, was described in interviews as “too prescriptive”.
Because the resources property has a positive sign in Table 6.5, it is positively
correlated with the Dimension 3 list in Table 6.6. There is a great deal of
support for the resources property in the pilot interviews. For example, TRIZ /
SIT, at the bottom of dimension 3, was described in interviews as something
“that we don’t see as much, uh maybe five, ten years ago. There was a big
push for it, a lot of people got trained in it”. Consequently, because TRIZ
“ended up getting lower priority”, it seems reasonable to find it at the bottom of
a scale that measures management resources. At the top end of the resources
scale is G8D, which was one of the most consistently mentioned quality tools in
pilot interviews, as it was used across the corporation, from manufacturing to
product development to information technology. In an interview with IT workers,
G8D was identified by one respondent as “the big thing … that we’ve probably
seen a few times”. In product development, engineers concurred in an
interview: “We use [Global 8Ds] all the time”. With G8D mentioned as a
frequently used tool, it is not surprising to find it at the top of a dimension that
measures support with management resources.
Other stimulus objects in the negative region of dimension 3 appeared to be
well-placed with reference to the resources property. Second and third from the
bottom, GPDS and DMADV, were both mentioned in pilot interviews as excellent
tools, in theory, but workers did not feel that they were allowed adequate time
by their management to support the initiatives. For example, a pilot interview
informant from the research and development division felt she was without the
resources to follow the DMADV method: “Ok, so you want me to actually do the
Design for Six Sigma [DMADV], or whatever buzzword they’re using these days,
but you really don’t give me the support”. The last object in the negative region
of dimension 3 is ISO / TS, which was identified in a pilot interview with a quality
manager as something receiving fewer and fewer resources from management:
“ISO / TS, we’ve tended to walk away from that [as a company]”.
38
The interpretation of the dimensions of the MDS solution, then, has benefited
from the combination of quantitative and qualitative research techniques,
certainly resulting in “a more comprehensive picture” of the social
representations of quality programs than would have been obtained with one
technique alone (Purkhardt & Stockdale, 1993: 281). Based on the MDS result,
the social representation of quality programs shared by employees of the
corporation under study could be thought of as a plane intersected by a line, as
shown below in Figure 6.7, where the stimulus objects themselves have been
omitted for readability.
Figure 6.7 An interpretation of the MDS solution space
The plane is composed of dimensions 1 and 2, as seen in Figure 6.3. This plane
clusters the quality programs based on the programs own intrinsic attributes:
their support of creativity, the extent of their bureaucratic nature, and the latitude
they allow for professional discretion. These properties were identified through
the regression of the creativity, bureaucracy, and discretion themes on to the
coordinates of the MDS solution and the clustering was confirmed by the
hierarchical analysis of the free-sort task. The line perpendicular to this plane is
39
dimension 3, which denotes the amount of organizational resources devoted to
each quality program. This interpretation of dimension 3 is supported by the
regression of the resources theme on to the MDS solution, as well as the
content of the pilot interviews.
The property vectors themselves are situated according to the method
suggested by Kruskal and Wish (1978), where the arccosine of the standardized
regression coefficient determines the angle at which the property lies from a
given dimension. For example, the normalized regression coefficient for the
creativity property was 0.880 for dimension 1 and the arccosine of 0.880 is 28
degrees. Thus, the creativity vector lies at an angle of 28 degrees from
dimension 1. Because the creativity and discretion properties generated
functionally equivalent normalized regression coefficients, 0.880 and 0.878,
respectively, their vectors lie atop each other at angles of 28 and 29 degrees,
respectively.
Figure 6.7 above can be conceived of as a visual map of the social
representation of quality programs among the workers studied (Purkhardt &
Stockdale, 1993). Latitude for creativity, professional discretion, the
preponderance of bureaucracy, and the management support of these
programs are salient dimensions around which workers structure and make
sense of their environment. The cluster analysis serves to the confirm this
structure, as the cluster identified by the dendrogram in Figure 6.3 above can be
interpreted as a grouping of quality programs which afford a worker a high
degree of creativity and discretion with a low amount of bureaucracy, versus
those which have the opposite characteristics.
6.4 A two-domain solution: entitative properties and organizational processes Succinctly, then, the social representation of quality programs at this particular
organization is likely composed of both entitative and process-oriented
components (Hosking & Morely, 1991). The entitative component consists of
attributes of the quality programs themselves, whereas the process-oriented
component consists of the organizational processes that support the quality
40
programs. In an interview with a quality manager, the administration of these
quality processes was characterized as dynamic and contingent upon
organizational variables: “…deployment has what looks like a sine wave over
time depending upon who management is and the amount of support they are
giving to it”. Thus, dimension 3, the resources dimension, is very dynamic,
contextual, and temporal, whereas the plane of dimensions 1 and 2 can be seen
as more stable and fixed, based on the enduring facets of the tools themselves.
6.5 Comparison of knowledge-based and Taylorist workers One of the aims of this research is to examine any differences in the conceptions
of these quality programs between Taylorist and knowledge-oriented workers.
As the industry imports these programs from the manufacturing shop-floor
environments in which they were developed and into ‘new economy’ settings, it
would be profitable to understand any differences in perspective these two
classes of workers might have on their use and role in the workplace. In Table
6.8 below, each theme that had a significant correlation with the MDS solution
dimension is examined using the Taylorist and knowledge worker distinction
drawn in Table 5.1. The Kruskal-Wallis non-parametric median test was used
on the Likert scale responses to the property evaluations of each respective
property, using the MINITAB statistical computer program.
Table 6.8 Median rating by worker type
Property Knowledge Workers Taylorist Workers P-va lue
Creativity 4 5 < 0.001 Bureaucracy 3 5 < 0.001 Discretion 5 5 0.067 Resources 4 4 0.344 Naturally, some degree of circumspection should inform the analysis of the
above results, as the sample sizes behind them are both modest and lopsided.
There are 18 respondents in the knowledge worker group and six in the Taylorist
group. Still, profitable conclusions can be drawn if it is assumed that this is a
representative result, while readily accepting that it, likely, is not. The only
statistically significant differences found at the 0.05 level between the knowledge
41
and Taylorist worker groups were for the creativity and bureaucracy properties.
Examining the results for the bureaucracy property, it is surprising to find that
knowledge workers rated the bureaucracy of these quality programs with a
median value of 3, which is lower than the median rating of 5 given by the
Taylorist workers. Based on the findings of the Sterman et al. (1997) work
where the product development engineers felt the formalism from the
manufacturing division limited their autonomy, it was expected that knowledge
workers would rate the quality programs higher than Taylorist workers on the
bureaucracy property. However, recent research reveals that not all
professionals view bureaucracy as a uniformly negative phenomenon.
Briscoe (2007) found that increasing the formalism of rules and procedures had
positive effect on worker discretion and effectiveness. The construct under
study was what he described as “temporal flexibility to mean the extent to which
workers have an ability to control the timing of their work” (emphasis in original;
298). The increased process and role clarity that comes with a highly formalized
bureaucracy allowed workers to better hand-off responsibilities to one another,
giving an individual increased freedom in the construction and negotiation of
their work day. Here, bureaucracy had the opposite effect on the construal of
autonomy of that found in the Sterman et al. (1997) study. Indeed, Briscoe
indicates that this finding “inverts the typical relationship between bureaucracy
and flexibility, in which bureaucracy is associated with greater constraint on
individuals and reduced flexibility” (emphasis in original; 2007: 297).
Briscoe’s (2007) work employs the theory of Adler and Borys (1996) to clarify
how bureaucracy can both help and hinder the effectiveness of workers. Adler
and Borys (1996) conceive of two different types of bureaucracy: enabling and
coercive. In the case of Briscoe’s (2007) study, the professional service workers
viewed the bureaucratic processes as an enabling force as it “increase[d] a
worker’s options for handling contingencies that arise in their work” (301). But in
the Sterman et al. (1997) study, the product development engineers felt the
bureaucracy of TQM to be coercive as it limited their feelings of professional
autonomy. The distinction between enabling and coercive bureaucracies can
42
also help to explain the puzzling lack of an inverse relationship between
bureaucratic processes and creativity found in the results of the Taylorist
workers, where both the creativity and bureaucracy properties were rated with a
median score of 5. Given the work of Amabile et al. (1996), creativity and
bureaucracy should be inversely related. However Adler and Borys indicated
that “even the frequently asserted negative impact of formalization on
innovativeness is not uncontested” (1996: 64). Indeed, if the composition and
implementation of quality programs constituted an enabling rather than coercive,
the formalization may provide a framework to channel creativity, as purported by
Pestorius (2007).
In summary, through creativity, discretion, bureaucracy, and resources were
identified as salient dimensions of a social representation of neo-Taylorist quality
programs, no clear differences were found between the evaluations of these
programs by Taylorist and knowledge workers. Statistically significant
differences were found between Taylorist and knowledge workers with respect
to the properties of bureaucracy and creativity. However, without taking into
account the difference between enabling and coercive bureaucracies, the
bureaucracy question on the online survey was likely misspecified (Adler &
Borys, 1996), thus clear conclusions between these two segments of workers
should not be drawn from this data. While it was intended to frame bureaucracy
in a coercive light on the online survey, by including the phrase ‘needlessly
bureaucratic’ in the question text, the results of the Briscoe (2007) study
highlight the necessity of capturing both coercive and enabling aspects of
bureaucratic practices. Still, having identified salient aspects of quality programs
for workers, regardless of a Taylorist versus knowledge distinction, can assist
practitioners in the deployment and institutionalization of these programs in
various industries, as will be discussed further below.
43
7.0 Conclusion 7.1 Limitations The greatest limitation of this study was the sample size. Though the size of the
sample of respondents of 24 is comparable to previously published studies
using the MDS technique (e.g., Stockdale, Dockrell, and Wells, 1989), the
knowledge about any particular stimulus object varied greatly among the
respondents. As a result, none of the multiple comparisons of the stimulus
objects have 24 measures. Table 7.1 below shows the number of respondents
familiar with each of the stimulus objects, in descending order.
Table 7.1 Respondent knowledge of stimulus objects St imulus Count of Respondents DMAIC 24 G8D 23 ISO / TS 22 FMEA 21 GPDS 20 FPS 20 DMADV 20 Lean 16 QPS 14 SOx 12 TRIZ / SIT 10
The MDS solution itself is likely not as adversely affected by the low sample size
as the Taylorist versus knowledge worker comparisons above in Table 6.8. The
interpretation of the MDS solution benefited from the triangulation with the free-
sort task cluster analysis and the content of the pilot interviews (Purkhardt &
Stockdale, 1993). Together, those three data sources corroborated to form a
cogent and consistent interpretation of the dimensions of the MDS solution.
However, no such corroboration could be used to overcome the inconsistency
found in the comparative analysis of the creativity and bureaucracy properties
between Taylorist and knowledge workers. Any differences therein must be
considered inconclusive because of the small sample size of this study.
The misspecification of the bureaucracy question likely contributed to the
inconclusiveness of any difference between Taylorist and knowledge workers.
44
Without making some distinction between coercive and enabling bureaucracies,
“the attitudinal outcomes are likely very different” (Adler & Borys, 1996: 62). It
will be imperative to capture both types of bureaucratic formalism in any future
research on neo-Taylorist quality programs.
Another limitation of this study includes the lack of longitudinal data. These data
are merely a snapshot in time, and a tumultuous time, indeed. Because of the
unprecedented breadth of salary employee buy-out packages and the
consequent reorganization to cope with the substantial decrease in the
workforce, it would be advantageous to obtain repeat measures at a future date.
Not only would the analytic strength of the conclusions be strengthened, but any
effects of the disruptive ‘rightsizing’ action could be better controlled. In
addition, Moscovici highlights the “mobile and circulating character” (1984: 18)
of social representations, indicating that they do change over time. A
longitudinal study would be apt to capture these dynamic nuances.
Due to the study’s focus on one corporation, the results may not be
generalizable. In future work, investigations should be made in various
industries to facilitate the analysis across different companies. It would be
advantageous to conduct these studies face-to-face with respondents, as it was
found in this investigation that 13% of the respondents misinterpreted the
requirements of the free-sort task. Because the MDS and sort tasks require
responses that differ from typical opinion or attitude surveys, the accuracy of the
responses would likely be enhanced with an experimenter on-hand to clarify the
procedure with the participant. Additional guidance for further research is
provided below.
7.2 Further research The future for increased study on the social representations of neo-Taylorist
quality programs seems promising, as recent research in operations
management has focused on social differences between corporate functions
and their implications for the implementation and administration of quality
programs. One particular inquiry investigated the educational differences
45
between production employees and manufacturing engineering staff (Lu,
Sankaran, & Mouly, 2007), and saw contemporary ‘process improvement’
initiatives as possible causes for inter-group conflict because current
implementation strategies ignore salient differences between departments. The
investigators maintained that these social differences are rarely investigated in-
depth because management routinely attributes the conflict to universal
resistance to change (Ibid). The researchers hypothesize that the two
occupational functions differ in their occupational goals, the tension in their
prioritizing innovation versus throughput, and a conflict in values between
technical sophistication and simplicity (Ibid). Moreover, these two functions are
usually lumped in one department, so this organizational consolidation facilitates
the sweeping assignment of cause. However, the theory of social
representations may have some contributions to make to this body of research
as it could be a way to discern the “socially shared aspects of beliefs” (Billig,
1993: 54) of quality programs and process management in these two
occupational groups, possibly leading to a richer understanding of this common
workplace phenomenon which exceeds that of the pedestrian ‘resistance to
change’ wisdom (c.f. Dent & Goldberg, 1999).
The distinction between enabling and coercive bureaucracy (Adler & Borys,
1996) is also relevant in the current research of another realm of enterprise-wide
program implementation where bureaucracy, irony, and paradox are operative:
the administration of corporate ethics programs (Stansbury & Barry, 2007).
While enabling and coercive control result in different deployments of corporate
ethics programs, Stansbury and Barry (2007) believe that both can lead to
reactance. “Coercive control mechanisms can be expected to elicit substantial
reactance, especially for stipulations that entail a persistent threat to the exercise
of valued freedoms” (Ibid: 249). However, “enabling control mechanisms may
also arouse reactance…. If [the values that the ethics program promotes] are
controversial then reactance may be greater, because of the perceived intrusion
upon employees’ freedom of thought” (Ibid: 250). The study of social
representations among employees subject to new corporate ethics programs
46
may illuminate this field in two ways. Firstly, key dimensions in employees’
values can be identified through the use of the MDS technique, possibly using
brief ethical vignettes as stimulus objects. With an MDS solution, it can then be
determined if the values espoused by a given ethics initiative will be construed
as controversial to the employees. Secondly, the social representations of
ethical conduct of management can be compared and contrasted to those of
employees to determine if there are “conflicting interests between management
and employees” (Ibid: 255). The greater the conflict in interest between
employees and management, the more likely it is that a corporate ethics
program will be deployed using coercive control rather than enabling control
(Ibid). In this arena, too, the study of social representations could result in a
more detailed and nuanced understanding of possible resistance to a major
change program.
7.3 Implications for practitioners Understanding the salient dimensions of workers’ conceptualizations of quality
programs is critical for a successful change initiative. Beer, Eisenstat, and
Spector (1990: 162) found that it was important to “foster consensus for the
new vision” among managers in order to successfully deploy a transformational
change program. Before consensus can be established, however, it must be
determined what primary constructs it will be necessary to include in any
program strategy in order to achieve a ‘new vision’. In this study, it was
determined that professional discretion, facilitation of creativity, perceptions of
bureaucracy, and the adequacy of management support each constituted the
respondents’ view of the corporate quality programs and tools. Each of these
dimensions would likely need to be addressed before consensus among
divisions can be achieved, otherwise the tension between departments
described by Sterman et al. (1997) may result.
In the Sterman et al. (1997) study, the lack of consensus may have been
surrounding the discretion and bureaucracy dimensions, which would then
trigger the feelings of diminished autonomy experienced by the product
47
development engineers. Knowledge of the fundamental conceptual dimensions
of employees could then serve to provide a framework for the content and
construction of a “comprehensive communications program” (Hall, Rosenthal, &
Wade, 1993: 129) involving two-way formats, such as small group discussion
with robust feedback mechanisms to ensure clearer understanding between
departments and a successful quality program deployment. When a dimension
of possible disagreement is identified, a Lewinian perspective can be assumed
in order to deconstruct and possibly remove any organizational obstacles to
consensus and change (Dent & Goldberg, 1999).
One of the largest challenges for quality management practitioners will be
minding both the quality policy and the deployment process throughout the life
of the initiative. Policy content and compliance usually receives more attention
than the maintenance and examination of organizational processes and contexts
(Hosking and Morley, 1991). The resources dimension found in this study
indicates that the managerial context in which a program is administered and
the resources allocated for its deployment could be key dimensions in
employees’ collective perceptions of corporate quality programs, above and
beyond the program’s bureaucratic content.
This study also indicates that what program is supported by management may
not be as important as how it is supported by management. If a quality program
is deployed in a way to “enable employees to master their tasks,” it would be
considered an enabling bureaucracy, but if the program is “designed to coerce
effort and compliance from employees,” then it would be considered a coercive
bureaucracy (Adler & Borys, 1996: 62). Flexibility in the quality program is one of
the key factors differentiating a coercive from an enabling program. A hallmark
of flexibility in a quality program is the incorporation of “practices developed by
employees in course of their work that were not deliberately instituted by
superiors” (Ibid: 76). However, the policy versus context distinction is operative,
as “flexibility in the implementation context [is] distinct from the flexibility of the
procedures themselves” (Ibid: 77). Admittedly, striking this balance of flexibility
and process standardization is not a trivial task. Many corporations deploy both
48
TQM and Lean initiatives simultaneously, but their primary focuses can be
contradictory. While TQM emphasizes process standardization, Lean promotes
“employee discretion” (Ibid: 61). The inherent tension of these two quality
programs can be seen in the MDS solution created in this study, bureaucratic
formalization and employee discretion are at opposite ends of Dimension 1.
Perhaps, then, an ideal goal of a quality manager would be to use an
understanding of the prevailing social representations of quality programs in their
organization to implement a program, regardless of its policy content, which
would help employees to master those dimensions of work that are most salient
and valuable to them. In this way, a quality program deployment would be
experienced as an enabling bureaucracy, rather than a controversial coercive
force liable to trigger reactance and resistance.
49
Appendix A.1
Informed consent form for the pilot interviews
50
Research Project Information and Informed Consent Respondent: Thank you for your participation in this research project supporting the requirements of the MSc programme in Organisational and Social Pscychology at the London School of Economics and Political Science. The aim of this interview is to discuss your experiences with the use of Lean Manufacturing techniques in knowledge-intensive work tasks. This interview will be recorded and transcribed into a written form. The content of this will be used to construct a larger web-based survey of knowledge workers to be administered this spring, in order to better understand knowledge workers’ feelings and perception of these techniques on their workplace effectiveness and job satisfaction. However, information that is considered personal or personally identifiable will be kept strictly confidential and will not be published. Your participation in this study is voluntary and you may refuse participation without explanation. This portion of the study will consist of a simple unstructured, conversational interview which will last between 60-90 minutes. As an increasing number of problem-solving and work-task structuring techniques developed on the factory floor find their way into front- and back-office processes, the information and experiences you share will help to create a better understanding of how they are understood and implemented by knowledge workers. Thank you for your participation. _______________________________________________ Signed _______________ Date If you would like to be kept informed of all relevant information that becomes available during the course of the study, please provide an email address. ____________________________________________________ Email
51
Appendix A.2
Sample interview transcript, redacted
69
Appendix A.3
Screenshots of online survey
70
MSc Research Project For the MSc in Organisational and Social Psychology at The London School of Economics and Poltical Science
Informed Consent
By clicking below, you affirm that you understand:
This questionnaire is part the of research component that partially fulfills the requirement of the MSc programme in Organisational and Social Psychology at The London School of Economics and Political Science, Houghton Street, London WC2A 2AE, United Kingdom.
The purpose of the survey is to determine the psychological perceptions of various quality programs and problem-solving tools. The aim is not to evaluate any technical aspects of these programs, as these critiques abound elsewhere. I wish to capture the subjective aspects of these systems. As a result, you need only have a rough knowledge of any particular program. If you have merely read or heard about a particular initiative, I am keenly interested in the impression it left. So please do respond to a question, even if you lack direct experience. Hearsay, opinions, rumors, illusions, personal biases, bad tastes in your mouth, or even a pet conspiracy theory would be valid and of interest here.
There are five sections to the survey: 1. Four brief demographic questions
2. An opportunity to select from a list the initiatives of which you have some anecdotal knowledge
3. A sorting task, where you are asked to group similar systems 4. Six qualitative statements to evaluate about each tool or initiative
5. A series of pairwise comparisons to determine the similarity of each pair of initiatives
Your participation in this project is entirely voluntary. You may withdraw your participation in this study at any time before June 1, 2007 without reason or explanation. Simply send an email to the address below indicating that you wish to withdraw survey number 42. Information generated from this survey may be published; however, no details will be divulged from which the participant could be identified. Furthermore, any personal information will be treated as strictly confidential and will not be made publicly available or given to any other person.
If you wish to participate, please provide an email address. I request your email address only in case of the event of an incomplete survey. The survey can take up to an hour to complete. Should you be called away from your computer during this time, entering your email address below will allow you to continue after your last completed
71
question. To preserve the anonymity of your responses, your email address will be deleted from the database upon completion of the survey.
Email:
Please check the box if you are returning to finish an incomplete survey.
I Agree
W. Cory Sherb [email protected]
72
MSc Research Project For the MSc in Organisational and Social Psychology at The London School of Economics and Poltical Science 1% Completed
Demographics
Number of years at Ford Motor Company
Please Select
Division
Please Select
Female
Male
Manager Role Continue
W. Cory Sherb [email protected]
73
MSc Research Project For the MSc in Organisational and Social Psychology at The London School of Economics and Poltical Science Back to Previous Screen - 3% Completed
You will be presented with questions regarding the following quality programs. Please deselect any item about which you have no knowledge. Direct experience with any program is not required. If you have heard or read about a program of system, please leave it checked.
GPDS - Global Product Development System
DMAIC - Six Sigma
G8D - Global 8D: problem-solving and presentation pro-forma
FPS - Ford Production System
Lean - Either on the factory floor or transactional business processes
QPS - Quality Process Sheets
DMADV - Design for Six-Sigma
ISO / TS - International quality standards
SOx - Sarbanes-Oxley regulations for business processes
FMEA - Failure Mode and Effects Analysis
TRIZ / SIT - Structured Inventive Thinking based on the Russian methodology
Continue
W. Cory Sherb [email protected]
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MSc Research Project For the MSc in Organisational and Social Psychology at The London School of Economics and Poltical Science Back to Previous Screen - 3% Completed
Confirm
Below are the quality programs about which you will be questioned:
GPDS - Global Product Development System
DMAIC - Six Sigma
G8D - Global 8D: problem-solving and presentation pro-forma
FPS - Ford Production System
Lean - Either on the factory floor or transactional business processes
QPS - Quality Process Sheets
DMADV - Design for Six-Sigma
ISO / TS - International quality standards
SOx - Sarbanes-Oxley regulations for business processes
FMEA - Failure Mode and Effects Analysis
TRIZ / SIT - Structured Inventive Thinking based on the Russian methodology
If this list is not correct, please go back to the previous screen. Otherwise, if the list is correct, please continue.
Continue
W. Cory Sherb [email protected]
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MSc Research Project For the MSc in Organisational and Social Psychology at The London School of Economics and Poltical Science Back to Previous Screen - 4% Completed
The first portion of the survey contains a sort task. You will be presented with a list of items preceded by an empty text box. Please sort these items in as many categories as you wish, marking items that are grouped together with the same letter. You may categorize these items based any criteria you desire.
The below list of cities will serve as an example.
San Francisco
Houston
Fresno
Crawford
San Antonio
Pasadena
Fort Worth
San Diego
La Jolla
You might choose to categorize them based on size: small, medium, and large.
A San Francisco
A Houston
B Fresno
C Crawford
A San Antonio
B Pasadena
A Fort Worth
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A San Diego
C La Jolla
Or you may wish to sort them by state.
A San Francisco
B Houston
A Fresno
B Crawford
B San Antonio
A Pasadena
B Fort Worth
A San Diego
A La Jolla
Or you could even choose to sort those whose name begin with 'San' against the rest.
A San Francisco
B Houston
B Fresno
B Crawford
A San Antonio
B Pasadena
B Fort Worth
A San Diego
B La Jolla
Continue
W. Cory Sherb [email protected]
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78
MSc Research Project For the MSc in Organisational and Social Psychology at The London School of Economics and Poltical Science Back to Previous Screen - 6% Completed
Please evaluate the below quality program with regard to the following statements.
GPDS - Global Product Development System
GPDS provides a framework to work creatively.
Strongly Agree
Strongly Disagree
< - - | - - >
GPDS is needlessly bureaucratic.
Strongly Agree
Strongly Disagree
< - - | - - >
Management provides the resources necessary to execute GPDS well.
Strongly Agree
Strongly Disagree
< - - | - - >
GPDS provides structure for work to be done more effectively.
Strongly Agree
Strongly Disagree
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< - - | - - >
GPDS allows the freedom to use professional discretion.
Strongly Agree
Strongly Disagree
< - - | - - >
GPDS should be a part of how we do business.
Strongly Agree
Strongly Disagree
< - - | - - >
Continue
W. Cory Sherb [email protected]
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MSc Research Project For the MSc in Organisational and Social Psychology at The London School of Economics and Poltical Science Back to Previous Screen - 21% Completed
Please assess how similar these two programs are in terms of structure, intent, effectiveness, usability, or any other attribute you consider to be important.
GPDS - Global Product Development System
and
DMAIC - Six Sigma
Very Similar
Very Dissimilar
< - - - | - - - >
Continue
W. Cory Sherb [email protected]
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MSc Research Project For the MSc in Organisational and Social Psychology at The London School of Economics and Poltical Science
Survey Complete
Your email address has been deleted from the database to preserve the anonymity of your responses.
If you have any questions or wish to rescind your participation in the project, send an email to the below address, mentioning survey number 42.
Thank you for your time and participation in this project. The results will be posted at http://corysherb.com/lse after September 1, 2007.
W. Cory Sherb [email protected]
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Appendix A.4
Relevant statistical outputs
83
Format of the input data for the MDS solution property fit name dim1 dim2 dim3 creative bureau resources structure discretion business DMADV 1.36 -0.10 -0.60 5.00 3.10 3.50 5.55 5.55 5.90 DMAIC 1.18 0.07 0.71 4.36 3.60 3.72 5.68 4.84 5.92 FMEA 0.90 -1.15 0.16 5.19 2.62 4.38 6.10 5.24 6.38 FPS -1.16 1.05 0.13 4.29 4.29 4.00 5.00 5.05 5.33 G8D 0.78 -0.87 1.16 4.04 3.33 4.04 5.46 4.71 5.88 GPDS -0.75 0.68 -1.26 3.52 3.86 3.24 5.24 4.19 5.62 ISO -1.64 -0.49 -0.34 2.64 4.32 3.68 3.77 3.59 4.41 Lean 0.27 1.33 0.47 5.47 2.94 4.18 5.59 5.29 6.35 QPS -0.82 1.01 0.76 3.29 3.43 4.57 5.29 3.71 5.86 SOx -2.01 -1.47 0.11 2.00 5.33 4.58 2.92 2.67 4.42 TRIZ 1.91 -0.04 -1.30 6.50 2.60 2.50 5.00 5.80 5.30
Columns dim1, dim2, and dim3 are the columns of the MDS coordinate matrix shown in Table 6.1 above. These columns serve as the X i column vectors in equation (2) below.
!
"1 j #$1
+ "2 j #$2
+ "3 j #$3
+% j =&^
j (2)
where j goes from 1 to 6, for each of the six property statements. Each of the creative, bureau, resources, structure, discretion, and business columns contain the mean response to the respective evaluative property statement for each of the stimulus objects. They serve as the six Y j vectors in equation (2) above. The results of the six regression equations, one for each property, are shown below.
84
Multiple linear regression fit of the creativity property to the MDS solution Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
1 .913(a) .834 .763 .63788 a Predictors: (Constant), dim33, dim23, dim13 ANOVA(b)
Model Sum of
Squares df Mean Square F Sig. Regression 14.297 3 4.766 11.712 .004(a) Residual 2.848 7 .407
1
Total 17.145 10 a Predictors: (Constant), dim33, dim23, dim13 b Dependent Variable: creative Coefficients(a)
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig. (Constant) 4.209 .192 21.885 .000 dim13 .837 .152 .851 5.504 .001 dim23 .385 .215 .276 1.792 .116
1
dim33 -.238 .252 -.146 -.943 .377 a Dependent Variable: creative
85
Multiple linear regression fit of the bureaucracy property to the MDS solution Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
1 .884(a) .782 .689 .46021 a Predictors: (Constant), dim33, dim23, dim13 ANOVA(b)
Model Sum of
Squares df Mean Square F Sig. Regression 5.317 3 1.772 8.369 .010(a) Residual 1.483 7 .212
1
Total 6.800 10 a Predictors: (Constant), dim33, dim23, dim13 b Dependent Variable: bureau Coefficients(a)
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig. (Constant) 3.584 .139 25.827 .000 dim13 -.534 .110 -.861 -4.861 .002 dim23 -.198 .155 -.226 -1.278 .242
1
dim33 -.013 .182 -.012 -.070 .946 a Dependent Variable: bureau
86
Multiple linear regression fit of the resources property to the MDS solution Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
1 .880(a) .775 .679 .35229 a Predictors: (Constant), dim33, dim23, dim13 ANOVA(b)
Model Sum of
Squares df Mean Square F Sig. Regression 2.994 3 .998 8.041 .011(a) Residual .869 7 .124
1
Total 3.862 10 a Predictors: (Constant), dim33, dim23, dim13 b Dependent Variable: resources Coefficients(a)
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig. (Constant) 3.854 .106 36.280 .000 dim13 -.201 .084 -.429 -2.388 .048 dim23 -.072 .119 -.109 -.610 .561
1
dim33 .561 .139 .726 4.033 .005 a Dependent Variable: resources
87
Multiple linear regression fit of the structure property to the MDS solution Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
1 .854(a) .730 .614 .57205 a Predictors: (Constant), dim33, dim23, dim13 ANOVA(b)
Model Sum of
Squares df Mean Square F Sig. Regression 6.192 3 2.064 6.307 .021(a) Residual 2.291 7 .327
1
Total 8.483 10 a Predictors: (Constant), dim33, dim23, dim13 b Dependent Variable: structure Coefficients(a)
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig. (Constant) 5.055 .172 29.305 .000 dim13 .518 .136 .748 3.796 .007 dim23 .393 .193 .401 2.038 .081
1
dim33 .276 .226 .241 1.224 .260
88
Multiple linear regression fit of the discret ion property to the MDS solution Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
1 .903(a) .815 .736 .49258 a Predictors: (Constant), dim33, dim23, dim13 ANOVA(b)
Model Sum of
Squares df Mean Square F Sig. Regression 7.507 3 2.502 10.313 .006(a) Residual 1.698 7 .243
1
Total 9.205 10 a Predictors: (Constant), dim33, dim23, dim13 b Dependent Variable: discretion Coefficients(a)
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig. (Constant) 4.604 .149 30.997 .000 dim13 .607 .117 .842 5.168 .001 dim23 .308 .166 .302 1.856 .106
1
dim33 -.123 .194 -.103 -.632 .547 a Dependent Variable: discretion
89
Multiple linear regression fit of the business property to the MDS solution Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
1 .828(a) .686 .551 .44826 a Predictors: (Constant), dim33, dim23, dim13 ANOVA(b)
Model Sum of
Squares df Mean Square F Sig. Regression 3.070 3 1.023 5.092 .035(a) Residual 1.407 7 .201
1
Total 4.476 10 a Predictors: (Constant), dim33, dim23, dim13 b Dependent Variable: business Coefficients(a)
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig. (Constant) 5.579 .135 41.279 .000 dim13 .343 .107 .682 3.208 .015 dim23 .269 .151 .378 1.782 .118
1
dim33 .311 .177 .374 1.760 .122 a Dependent Variable: business
90
Non-parametric median tests of property ratings Taylorist vs. Knowledge workers Kruskal-Wallis Test: measure_Bureaucracy versus type_Bureaucracy Kruskal-Wallis Test on measure_ type_Bur N Median Ave Rank Z Knowledg 888 3.000 571.3 -7.81 Neo-Tayl 354 5.000 747.4 7.81 Overall 1242 621.5 H = 61.00 DF = 1 P = 0.000 H = 62.77 DF = 1 P = 0.000 (adjusted for ties)
Kruskal-Wallis Test: measure_Business versus type_Business Kruskal-Wallis Test on measure_ type_Bus N Median Ave Rank Z Knowledg 888 6.000 593.5 -4.36 Neo-Tayl 354 6.000 691.7 4.36 Overall 1242 621.5 H = 18.98 DF = 1 P = 0.000 H = 20.33 DF = 1 P = 0.000 (adjusted for ties)
Kruskal-Wallis Test: measure_Creativity versus type_Creativity Kruskal-Wallis Test on measure_ type_Cre N Median Ave Rank Z Knowledg 888 4.000 581.8 -6.18 Neo-Tayl 354 5.000 721.1 6.18 Overall 1242 621.5 H = 38.19 DF = 1 P = 0.000 H = 39.14 DF = 1 P = 0.000 (adjusted for ties)
Kruskal-Wallis Test: measure_Discretion versus type_Discretion Kruskal-Wallis Test on measure_ type_Dis N Median Ave Rank Z Knowledg 888 5.000 610.0 -1.79 Neo-Tayl 354 5.000 650.4 1.79 Overall 1242 621.5 H = 3.22 DF = 1 P = 0.073 H = 3.36 DF = 1 P = 0.067 (adjusted for ties)
91
Kruskal-Wallis Test: measure_Resources versus type_Resources Kruskal-Wallis Test on measure_ type_Res N Median Ave Rank Z Knowledg 888 4.000 627.6 0.95 Neo-Tayl 354 4.000 606.2 -0.95 Overall 1242 621.5 H = 0.90 DF = 1 P = 0.344 H = 0.92 DF = 1 P = 0.336 (adjusted for ties)
Kruskal-Wallis Test: measure_Structure versus type_Structure Kruskal-Wallis Test on measure_ type_Str N Median Ave Rank Z Knowledg 888 5.000 594.2 -4.25 Neo-Tayl 354 6.000 689.9 4.25 Overall 1242 621.5 H = 18.03 DF = 1 P = 0.000 H = 18.92 DF = 1 P = 0.000 (adjusted for ties)
92
Appendix A.5
Description of the normalization of regression coefficients
93
Normalized regression coefficients Kruskal and Wish (1978) recommend that the regression coefficients of the property fit procedure on the MDS solution should be normalized in order to facilitate their interpretation. When the regression coefficients, B ij in equation (2) below, are normalized, they represent the arccosine of the angle between the property vector and the fitted dimension.
!
"1 j #$1
+ "2 j #$2
+ "3 j #$3
+% j =&^
j (2)
For example, the regression coefficients for the creativity property are 0.837, 0.385, and -0.238 for dimensions 1, 2, and 3, respectively. In order to normalize them, one must first calculate their sum of squares. SS = (0.837)2 + (0.385)2 + (-0.238)2 = 0.905 (3) Next, the square root of the sum of squares is calculated. SS0.5 = 0.9050.5 = 0.952 (4) Each of the regression coefficients is then divided by result (4) to yield the normalized regression coefficient. Dimension Regression Coefficient Normalized Regression Coefficient 1 0.837 0.837 / 0.952 = 0.880 2 0.385 0.385 / 0.952 = 0.405 3 -0.238 -0.238 / 0.952 = -0.250 Because Dimension 1 has the largest normalized regression coefficient (and the multiple correlation coefficient for the model is statistically significant at the 0.01 level), the creativity property fit to Dimension 1. Moreover, because the normalized regression coefficient is the arccosine of the property vector, it can be calculated that the creativity property vector lies at an angle of 28 degrees from Dimension 1, as shown below in result (5). cos-1 0.880 = 28o (5) This is the angle from Dimension 1 at which the creativity vector is plotted in Figure 6.7 above.
94
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