Approaches to learning at work and workplace climate

International Journal of Training and Development 7:1
ISSN 1360-3736
Approaches to learning at
work and workplace climate
John R. Kirby, Christopher K.
Knapper, Christina J. Evans, Allan E.
Carty and Carla Gadula
Three studies are reported concerning employees’ approaches
to learning at work and their perceptions of the workplace
environment. Based on prior research with university students, two questionnaires were devised, the Approaches to
Work Questionnaire (AWQ) and the Workplace Climate
Questionnaire (WCQ). In Studies 1 and 2, these questionnaires
were administered to two different samples of employees, and
the factor structure of the questionnaires was explored. In
Study 3, the two data sets were combined, and a random half
of it was used to develop reduced sets of items that addressed
selected factors for each of the questionnaires. The other half
of the data was used to test the scales developed. For the
AWQ, three factors are proposed: deep, surface-rational, and
surface-disorganised. The first of these is consistent with the
student learning literature, but the other two represent a
division of a unitary surface factor. The three components of
the WCQ are good supervision, choice-independence, and
workload. Correlations between scales indicated that the deep
approach is positively associated with good supervision and
choice-independence, whereas the surface-disorganised
approach is negatively associated with these two constructs
and positively associated with workload. Surface-rational is
negatively, though less strongly associated with choiceindependence. Suggestions are presented for use of these
instruments in future research and practice.
❒ John R. Kirby, Christopher K. Knapper, Christina J. Evans, Allan E. Carty and Carla Gadula,
Queen’s University, Kingston, Ontario, Canada K7L 3N6. Email: [email protected].
 Blackwell Publishing Ltd 2003, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St., Malden, MA
02148, USA
Approaches to learning at work and workplace climate
31
Introduction
As the basis of the economy changes from manufacturing products to creating and
managing knowledge, and as organisations reduce the number of managerial layers,
greater skill levels and greater capacity to develop new skills are expected of more
and more employees. Rapidly changing business environments, characterised by
greater competitiveness in global markets, a need to reduce costs, and changes in
technology place substantial demands on organisations and their employees
(Watkins and Marsick, 1993). Organisations will meet these demands in many ways,
but one approach will be to empower employees, encourage self-directing team
work, and support participatory management. In addition to remaining current in
their own fields, employees will need to learn many generic skills including speaking
to groups, writing technical correspondence, negotiating with others, and making
decisions in situations where traditional practice no longer applies. Watkins and
Marsick argued that now, more than ever, employees must undertake continuous
and collective learning. Learning is now seen as a lifelong endeavour (Knapper and
Cropley, 1991). In this changing world, it becomes essential to understand how
employees learn and think in the workplace. In this article we report the development
of measures of employees’ conceptions of their learning at work and of their learning
environment at work, and report the relations observed among these measures in
two studies.
Learning in the workplace can only be successful if there is a commitment from
employers. Watkins and Marsick (1993) argued that, in addition to providing job
training, organisations must consider factors such as the way in which work is
designed, external environmental conditions, reward systems, and governing policies. Organisations which attend to these areas and develop them in light of their
mission are called ‘Learning Organisations’. Watkins and Marsick indicated that
Learning Organisations do the following:
(a)
(b)
(c)
(d)
(e)
connect the organisation to the environment;
promote discussion, team learning and collaboration;
empower employees towards a collective vision;
develop systems to record and share learning; and
create continuous learning opportunities.
Clearly, organisations are creating new and varied learning challenges for their
employees. The pace at which organisations are evolving is necessitating an approach
to learning which involves integrating material from multiple sources, evaluating
new information in relation to previous knowledge, making connections to form
deeper levels of understanding, and applying knowledge differentially according to
the circumstances of the situation.
Yet little research has actually been conducted regarding how employees approach
workplace learning. Most of the advice which does exist (for instance, Senge, 1990)
is based upon opinion and conjecture, or worse, fads, bandwagons, and buzzwords
(see Hilmer and Donaldson, 1996, for a critique). Although little is known about how
workers approach learning on the job, or about the factors that encourage or discourage deeper workplace learning, a great deal is known about how students approach
learning and about the factors which lead to various forms of learning. We chose
the student learning research as our starting point.
Students’ approaches to learning and perceptions of the
learning environment
The ways in which students approach their learning in academic settings has been
studied extensively (e.g. Biggs, 1985, 1987, 1993; Entwistle and Ramsden, 1983;
Kember et al., 1999). According to Biggs (1985), the construct ‘approach to learning’
comprises a set of motives and strategies used by the student to achieve desired
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learning outcomes. The ‘motive-strategy set’ comprises the learner’s motivation
towards the task and the strategies used to accomplish learning goals. Biggs observed
that students approach learning with specific expectations which serve as the motives
for engaging in the learning. Obtaining a qualification (extrinsic motive), learning
material because it is interesting (intrinsic motive), and achieving high grades
(achievement motive) are common motives for undertaking learning.
Motives tend to be associated with types of learning strategies (Biggs, 1987). Persons who are motivated by the prospect of gaining qualifications employ strategies
which aim to reproduce essential information. Learners who take a course based on
interest tend to use strategies which help them understand the material, while those
who are motivated by high grades focus on optimising their study effort. ‘Approach
to learning’, then, refers to the learner’s motives towards, and conscious use of strategies in pursuit of, recognised learning goals. Three broad dimensions of learning
approaches are known as ‘surface’, ‘deep’, and ‘achieving’. The following descriptions of these dimensions are based upon Biggs (1987) and Entwistle and Ramsden
(1983).
Learners who take a surface approach to learning are motivated to meet minimum
task requirements and generally put forth enough effort to avoid failing. The corresponding strategy is one of reproduction based on the tendency of the learner to
memorise factual information without regard for what it might mean.
Individuals who take a deep approach to learning seek meaning and understanding. They are intrinsically motivated towards learning the subject and interested in
achieving competence in the area. For deep learners, knowing more about the subject
under study is in itself rewarding. These learners will employ strategies such as
identifying underlying arguments, reading widely, and relating new information to
previous knowledge.
Students who take an achieving approach to learning are motivated by competition
and ‘self-enhancement’. They place priority on obtaining high grades irrespective of
how interesting the material is, or how well they understand it. Achieving learners
also wish to ‘look good’ in front of their teachers and peers. Learners motivated by
the achieving orientation will employ strategies that are concerned with organising
study time and ‘work space’.
Some researchers have also identified a fourth factor, termed ‘non-academic orientation’ by Entwistle and Ramsden (1983). This represents both the fact that some
students are in school to socialise rather than to learn, and also the alienation that
some students feel for academic learning. The latter characteristic may be related to
surface learning, in that it represents an intention to avoid academic learning.
Students’ approaches to learning do not exist in a vacuum, but rather are influenced by the environments in which the students are studying. Ramsden and
Entwistle (1981) devised the Course Perceptions Questionnaire (CPQ), and found it
to comprise dimensions describing how considerate and supportive instructors are,
whether they respond to student interests and needs, whether students feel they have
freedom to choose what they learn and how they learn it, and how heavy the
required workload is. The surface approach has generally been associated with little
academic freedom (Ramsden and Entwistle, 1981), heavy workloads (Bertrand and
Knapper, 1991), attitudes of the instructor towards knowledge transmission versus
knowledge facilitation (Christensen et al., 1995; Gow and Kember, 1993), and curricula which facilitate the transmission of facts and details (Hattie et al., 1996). Deeper
learning, by contrast, has been associated with lighter workloads (Bertrand and
Knapper, 1991; Entwistle and Ramsden, 1983), greater academic freedom (Ramsden
and Entwistle, 1981), and good teaching (Bertrand and Knapper, 1991; Ramsden and
Entwistle, 1981).
Approaches to learning in the workplace
Until recently, research on approaches to learning has only been aimed at students
in secondary and post-secondary environments. Very little is known about learning
 Blackwell Publishing Ltd 2003
Approaches to learning at work and workplace climate
33
approaches in the workplace. Until the last decade or so, it might have been said
that there was no relationship between academic learning and work—students learn,
workers work. And in fact there are many important differences: students often have
more choice in what they do than many workers have; academic learning is abstract
rather than performance-oriented, and often written in books rather than being created on the spot; and at work the bottom line is more immediate and apparent to
both employee and employer than it is to either student or teacher (Candy and
Crebert, 1991). However, as the nature of work changes, workers may have to adopt
some of the characteristics of students, without losing the critical focus on performance and competitive success.
Candy and Crebert claimed that workplace learning differs from academic learning
in four regards. They suggested that learning in academic environments involves
propositional knowledge, is decontextualised, encourages elegant solutions, and
tends to be individualistic and competitive. Workplace learning was said to involve
procedural knowledge, be contextualised by the nature of the organisation, aimed at
problem solving, and seen as encouraging collaborative teamwork. These different
orientations, it was asserted, require different types of skills (Crebert, 1995). University learning encourages learners to understand information from different disciplines and to make connections among them in a relatively well-structured context.
Many of the ideas are abstract and developed without ‘real-world’ constraints. Workplace learning, on the other hand, according to Crebert, encourages critical and creative thinking in response to ill-defined problems. Owing to these differences, Crebert
argued that emphasis should be placed on assisting employees in developing multiple skills which would enable them to transfer knowledge from specific situations
to broader contexts. Such learners would be able to integrate new information with
previous knowledge, synthesise new material and make connections to form a wider
perspective. Each of these characteristics is involved in the deep approach to learning.
Pearn et al. (1992) argued that conceptual and thinking skills will be at a premium
as jobs requiring physical skills become automated. Pearn et al. declared that trends
such as delayering, participatory management, multi-skilling and increased decisionmaking at lower levels of the organisation will place greater demands on the average
worker. Only an adaptable, thinking workforce will produce more with fewer
resources. If anything, the deep approach to learning would appear to be more necessary and desirable in the workplace than in school.
There are two issues here. First, is the nature of workplace learning fundamentally
the same as academic learning, that is, are the dimensions of learning the same? And
second, are the relations between these dimensions and other factors the same? With
respect to approaches to learning and the first question, it seems likely that the deep
and surface approaches will exist in all contexts, as they seem to represent
fundamental human processes. The achieving dimension also seems relevant, but in
the workplace it may be indistinguishable from learning, whether surface or deep,
because of the basic orientation to performance in the workplace. The non-academic
dimension may show up as disaffection for work, but employees should show more
maturity than do some students in understanding the nature of their position.
The distinctive nature of the workplace may affect the relationship between
approaches to learning and performance. For example, whereas the surface and
achieving approaches to learning have enabled students to meet a variety of learning
objectives in academic environments, only the deep approach has been associated
with high quality learning (Biggs, 1979; Marton and Saljo, 1976; Trigwell and Prosser,
1991). This may have a different result in a workplace in which only a minority of
employees have the opportunity, or even the need, to learn deeply. Many work tasks
may have a necessary surface component (for example, completing forms accurately,
filing information correctly, applying formulas, and so on), and some work environments may discourage, rightly or wrongly, more creative solutions. Some aspects of
surface learning may be more respected, and some aspects of deep learning less
respected, in the workplace than in the academy.
With regard to the employees’ perceptions of their workplace environment and its
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support for learning, many of the issues found with students should be relevant to
the workplace, but the greater maturity of the employees and other factors may
temper some of the effects found for students. For instance, most workers will have
chosen their place of work with at least some sense of the type and amount of work
required, and most will see some relationship between the amount of effort expended
and their performance and rewards. In many cases workers will see that there are
not many different ways to perform tasks, and in any case the alternative ways may
require resources which are not available. It would be an error to generalise the
student research findings to the workplace without appropriate empirical investigation.
Few have attempted to operationalise and study employee approaches to learning
in work settings. Knapper (1995) investigated whether learning approaches used by
students in academic settings transferred to the workplace. He adapted the Entwistle
and Ramsden (1983) Approaches to Studying Inventory (ASI) so that it addressed
issues related to workplace learning, and renamed it the Approaches to Work Questionnaire (AWQ). This was a direct adaptation done by rewording the items on the
short form of the ASI to make them suitable for descriptions of work experiences.
For example, ‘I find it easy to organise my study time effectively’ was changed to
‘At work, I find it easy to organise my time effectively’ (Knapper, 1995). The Workplace Climate Questionnaire (WCQ) was similarly adapted from the CPQ.
Knapper (1995) administered the AWQ to 226 students from six university departments. These students were all in cooperative programmes which included study
and work terms. These students had recently finished a four-month work placement
and they completed the questionnaire with respect to their work placement. Of these
226 students, 114 also completed the WCQ, and 118 completed the ASI at the end
of the following academic term.
Knapper (1995) found that students were consistent in their approaches to learning
across work and university settings, indicated by significant correlations between the
ASI and AWQ scales. However, factor analysis of the WCQ showed a structure different from that observed by Entwistle and Ramsden for the CPQ. Knapper obtained
three factors underlying the WQC which he labelled Good Supervision and Concern
for Staff, Consultation/Openness, and Work Climate and Motivation. Knapper did
not factor analyse the items of the AWQ.
Purpose and hypotheses
The aim of this research was to better understand employees’ approaches to learning
at work and their perceptions of the work environment by determining the dimensions which underlie the AWQ and WCQ, and by exploring the relationships
between these two measures. Whereas Knapper’s (1995) participants were students
on temporary work placements, the present research investigated regular employees.
Study 1 included participants from a variety of organisations, while Study 2 focused
upon a single, large organisation. We hypothesised that the deep and surface factors
found in the student literature would emerge from a factor analysis of the AWQ.
We further expected to observe the same three factors underlying the WCQ that
Knapper (1995) had found.
Based on the literature relating learning approaches to departmental climate in
academic settings, we expected to find relations between conditions at work and the
employees’ approach to learning. Specifically, we predicted positive relationships
between the deep approach and positive aspects of the work climate
(consultation/openness, good supervision and concern for staff), and between the
surface approach and negative aspects. We also predicted negative relationships
between deep and the negative aspects of the work climate, and between surface
and the positive aspects.
The purpose of Study 3 was to refine the AWQ and the WQC for future use. To
do this we combined the data from Studies 1 and 2, divided the resulting data ran Blackwell Publishing Ltd 2003
Approaches to learning at work and workplace climate
35
domly into two sets, refined the scales in one data set, and confirmed the final scales
in the second data set.
In order to simplify the presentation of the results and maintain continuity, we
first present all of the results for the AWQ (Study 1, then Study 2, then Study 3),
before doing the same for the WCQ. The method for each of the studies is described
at the beginning of each of the AWQ sections. Finally, we present the correlations
between the scales of the two newly developed questionnaires.
Study 1 (AWQ)
Method
Inventories were mailed to a random sample of 2,000 alumni from a large Canadian
university. The sample was restricted to alumni who had obtained a bachelor’s
degree at least five, but no more than 15, years previously. A total of 339 responses
were received. One had failed to complete the WCQ and was eliminated. A further
33 were eliminated because they were self-employed. This left 305 responses, representing 139 men and 166 women. Of these, 81 reported working in organisations
with fewer than 50 employees, 229 reported working in organisations with between
50 and 500 employees, and 99 reported working in organisations with more than
500 employees.
The 64-item Approaches to Work Questionnaire and the 40-item Workplace Climate Questionnaire were completed by all respondents. All responses were provided
on a five-point Likert-type scale with the following choices: (5) definitely agree, (4)
somewhat agree, (3) doesn’t apply or find it impossible to give a definite answer, (2)
somewhat disagree, and (1) definitely disagree.
Results
Missing data represented 0.17 per cent of the items on the AWQ and 0.14 per cent
of the items on the WCQ. These missing data were replaced with group means for
the items.
Exploratory factor analysis of the 64 items in the AWQ was conducted in SPSS
using Maximum Likelihood (ML) extraction with direct quartimin rotation. The selection process for the-factor solution was based on three criteria: (1) interpretability
based on previous research; (2) variance accounted for; and (3) goodness of fit as
indicated by the root mean square error of approximation (RMSEA). RMSEA is an
estimate of the discrepancy between the model and the data per degree of freedom
for the model (Fabrigar et al., 1999). A value of less than .05 indicates good fit; .05
to .08 indicates acceptable fit. Scree plots were also used to get an initial indication
of the approximate number of factors to extract.
The scree plot pointed to a solution in the range of three to five factors. Three-,
four-, and five-factor solutions were obtained.1 In each solution, surface items loaded
on two separate factors. One of these factors consisted of surface items combined
with a few deep and achieving items, and suggested a rational, step-by-step approach
to work with an emphasis on memorising important information (e.g. ‘When I learn
something new at work I put a lot of effort into memorising important facts’). The
other surface factor was a mixture of surface items with most of the non-academic
items, and suggested an individual who was confused or overwhelmed by task
demands (e.g. ‘When I start on a job and things start to go wrong I tend to panic’).
A deep factor was identifiable and remained relatively consistent across solutions.
When four factors were extracted, an achieving factor emerged as the fourth factor,
although only six achieving items obtained loadings of at least .300 on that factor.
1
Due to space limitations, factor loadings for Studies 1 and 2 are not reported here. They are available
from the first author.
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The main difference between the four- and five-factor solutions was that the factor
consisting of surface and non-academic items split in two. One of these retained the
sense of being confused or overwhelmed, while the other suggested a worker who
was disaffected or not interested in the job (e.g. ‘When I look back, I sometimes
wonder why I ever decided to work here’). However, the five-factor solution offered
no improvement in terms of underlying theory.
The three-factor solution accounted for 18.77 per cent of the variance, the fourfactor solution accounted for 21.65 per cent of the variance, and the five-factor solution accounted for 23.95 per cent of the variance. Values for RMSEA were calculated
using the program FITMOD (Browne, 1992). Values of RMSEA (with 95 per cent
confidence intervals) were obtained as follows: 3 factors, RMSEA = .043 (.039, .047);
4 factors, RMSEA = .038 (.034, .042); 5 factors, RMSEA = .035 (.031, .039). This indicates good fit for all three solutions, and only modest improvements for each of the
more complex solutions.
On the basis of all of this information, the four-factor solution seems optimal. It
has good fit, and is closest to the underlying theory with a deep factor, an achieving
factor (albeit weak), a predominantly surface factor, and a factor with most of the
non-academic items (plus a number of surface items). Given the split of surface items,
it seems advisable to rename the two factors on which surface items loaded. Owing
to the rational, logical nature of the items on the one factor, it has been labelled
‘surface-rational’. The other factor, with its sense of confusion and being overwhelmed, has been labelled ‘surface-disorganised’. The four factors were minimally
correlated, with the largest correlation occurring between deep and surfacedisorganised, r = -.138.
Study 2 (AWQ)
Method
The participants were 172 employees of a large Canadian financial institution (62
males, 105 females, 5 did not indicate gender) who were undergoing one week of
instruction at the institution’s training centre. The majority of the participants (n =
109) were in Personal and Commercial Financial Services, in jobs ranging from clerical and counter staff to customer service representatives, assistant managers and
branch managers. The next largest group (n = 37) were employed in Operations,
including computer systems and institutional processes. The remaining participants
were spread across a variety of corporate positions.
Participants were asked to participate by their training facilitator. Participation was
voluntary. Those who agreed completed both the AWQ and the WCQ. All responses
were indicated on a five-point scale ranging from ‘definitely agree’ (5) to ‘definitely
disagree’ (1). In addition, a sixth option, ‘not applicable to me’, was offered at the
request of senior officers in the participating institution.
Results
Five participants failed to answer more than 10 per cent of the items on the AWQ
and were therefore removed from the study. Responses of ‘not applicable to me’
were treated as missing data for the purpose of the analyses. For the remaining 167
participants, missing data represented 0.86 per cent of the items on the AWQ and
0.34 per cent of the items on the WCQ. Missing data were replaced with group means
for the respective items.
Exploratory factor analyses were conducted on the 64 items from the AWQ. As
before, analyses were done in SPSS using ML extraction and direct quartimin
rotation. Once again, the scree plot suggested three to five factors.
As with Study 1, surface items split regardless of the number of factors extracted.
When three factors were extracted, a similar pattern arose as had occurred in Study
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Approaches to learning at work and workplace climate
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1. The first factor was similar to surface-disorganised, consisting largely of surface
and non-academic items and with a similar sense of confusion and disorganisation.
The second factor was similar to the surface-rational factor, consisting largely of surface items which suggested a methodical, step-by-step approach and memorisation.
The third factor consisted largely of deep items. This solution accounted for 20.60
per cent of the variance.
A four-factor solution was attempted, and accounted for 23.79 per cent of the variance. However, although the first three factors remained largely the same as for the
three-factor solution, the fourth factor was uninterpretable, containing a mixture of
surface, deep, and achieving items.
A five-factor solution was also attempted, and accounted for 26.30 per cent of the
variance. It was also uninterpretable, with deep items dividing into two factors and
surface items dividing among the other three factors. In addition, one item achieved
a communality greater than 1.0.
Values of RMSEA were calculated using FITMOD, and were as follows: 3 factors,
RMSEA = .045 (.039, .050); 4 factors, RMSEA = .040 (.034, .046); and 5 factors, RMSEA
= .037 (.030, .043). This shows that all three solutions have good fit, and improvements
in fit for the more complex models are, at best, only modest.
Consequently, the three-factor solution was chosen as the best, on the basis of
interpretability. The three factors were essentially uncorrelated, with all correlations
having absolute values less than .100.
Study 3 (AWQ)
The results of Studies 1 and 2 showed reasonable consistency, even though they did
not reproduce the hypothesised four factors underlying the ASI. Both results showed
a deep factor, a surface-rational factor, and a surface-disorganised factor. The difference was that Study 1 also showed an achieving factor, although only six of the 11
achieving items loaded on it. The fact that the achieving factor was not found in
Study 2 was perhaps not surprising. According to a review by Richardson (1994),
evidence for the existence of the achieving or strategic factor has generally been
ambiguous. Therefore, the decision was made to focus on the three-factor solution
which was common to Study 1 and Study 2.
Method
The data from Studies 1 and 2 were combined. They were then divided using a
simple odd- and even-numbered method. This provided two data sets each with
equal numbers from each of the original data sets. There were 236 cases in each of
the two new data sets.
The odd-numbered set was factor-analysed and the solution was used to select
items for the final version of the AWQ. The even-numbered data set was then used
to test the structure of the resulting questionnaire.
Results
The scree plot for the factor analysis of the odd-numbered data again suggested
between three and five factors. Based on the results of Study 2 and Richardson’s
(1994) findings with respect to the achieving scale, a three-factor solution was
obtained, which accounted for 20.18 per cent of the variance. RMSEA for this solution
was .044 (.040, .048), indicating good fit.
The three factors were again identifiable as surface-rational, deep, and surfacedisorganised. Correlations between factors were minimal, with the largest correlation, r = ⫺.124, occurring between deep and surface-disorganised. The factor loadings are shown in Table 1. Abbreviated versions of the items have also been included
for clarity.
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Table 1: Factor loadings for the AWQ, 3-factor solution, Study 3, odd-numbered cases
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
Item
Surfacerational
Deep
Surfacedisorganised
Difficulty organising time
Relate ideas from work areas
Prefer overview not details
Enjoy competition
Read to understand
Ideas start chain of thought
Present job prepares for future jobs
Present job just happened
Like being told what is expected
Question things at work
Tackle tasks in order
Work pressure makes me tense
Difficulty switching tracks
Too much catching up
Important to do well
Managers make things complicated
Distractions make difficult to work
outside office
Follow instructions even though conflict
No time to think about what I read
Managers give hints
Use imagination to understand
puzzling ideas
I’m here to get career experience
Wonder if my work is worthwhile
Lot of effort to understand difficult
things
Prefer clearly structured work
Panic when things go wrong
Prefer well-tried approaches
Usually get through work at home
Relate new ideas to real life
Memorise important facts
Play with own ideas
Choose jobs to enhance career
Draw conclusions based on evidence
Ask myself questions about new tasks
More interested in salary than learning
Read without understanding
If conditions aren’t right I change them
My reports present many conclusions
Learning new things is main attraction
of job
My explanation of new material differs
from others
Lot of effort in memorising new facts
Important to do better than peers
Start with details to build overview
Ideas produce vivid images
⫺.148
⫺.093
⫺.099
⫺.112
.377
⫺.030
.056
⫺.041
.514
⫺.155
.457
.103
.392
⫺.043
.094
.116
.055
.025
.209
.130
.306
.130
.364
.440
⫺.138
⫺.131
.252
⫺.067
⫺.046
⫺.189
.010
.290
⫺.023
⫺.157
.392
⫺.020
.552
⫺.076
ⴚ.333
.333
⫺.255
.218
.055
.213
⫺.152
.458
.225
.448
⫺.175
.364
.324
.458
.134
.158
⫺.050
⫺.115
⫺.103
.112
.393
.187
.469
.042
.191
.290
.068
.249
.213
⫺.036
.340
.222
.338
⫺.177
.594
.228
.457
.346
.055
.467
⫺.113
.338
.394
.032
.273
.065
⫺.106
.046
⫺.227
⫺.134
⫺.195
ⴚ.424
.205
.416
.158
.563
.126
.080
.368
⫺.244
.011
.327
.154
.481
.035
.430
⫺.014
⫺.087
.048
.066
.207
⫺.151
ⴚ.311
⫺.009
.057
.473
⫺.221
⫺.018
⫺.116
.128
.367
.477
.535
.232
.452
.083
.174
.219
⫺.020
.525
.042
⫺.044
.054
.265
[continued on next page]
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Approaches to learning at work and workplace climate
39
Table 1 (continued): Factor loadings for the AWQ, 3-factor solution, Study 3, odd-numbered
cases
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
Item
Surfacerational
Deep
Surfacedisorganised
I get materials needed to do work
Criticised for irrelevant report material
Studying for tasks is exciting
Remember textbook definitions
OK to scrape through
Read widely around subject
Difficult fitting facts to form overall
picture
Do my job but no more
Speaking at meetings is ordeal
Work through problems to logical
conclusion
Use spare time to learn about work
Map out topic to see how ideas fit
Too quick in jumping to conclusions
Hate admitting defeat
Look at problems rationally not
intuitive jumps
Remember better when I concentrate on
order
Look at evidence to see if conclusion is
justified
Managers think I should use more of
my ideas
Wonder why I ever worked here
Pursue interesting issues even if not my
job
.104
.073
.050
.326
⫺.089
.265
.222
.258
.000
.384
⫺.009
⫺.047
⫺.069
⫺.059
⫺.149
.448
⫺.075
.053
.381
.138
.539
.171
.335
⫺.018
ⴚ.396
⫺.189
.292
.198
.130
⫺.133
.196
.038
⫺.066
.059
.451
.538
.350
.047
.071
⫺.127
.076
⫺.100
.510
.083
⫺.240
.431
⫺.021
.105
.241
.235
⫺.129
.219
.057
.176
.040
⫺.068
⫺.126
.503
.453
.053
1.000
.025
.085
1.000
⫺.124
1.000
Correlations
Surface-rational
Deep
Surface-disorganised
Note: Loadings of .3 or more have been bolded to emphasise factor structure.
To make up the final version of the AWQ, ten items were selected from each of
the three factors. Items were selected to have high loadings on their own factor, but
low loadings on the others, and to fit the three constructs. Alpha coefficients for the
three resulting scales are shown in Table 2 and are all above .70. The items are shown
in the Appendix.
The next step was to factor-analyse only the 30 selected items, using the evennumbered data set. The scree plot supported a three-factor solution, and this solution
accounted for 25.51 per cent of the variance. Factor loadings are shown in Table 3.
The three factors (deep, surface-rational, and surface-disorganised) were reproduced.
With two exceptions, all items achieved a loading of at least .300 on the hypothesised
factor. One item (‘Although I generally remember facts and details, I find it difficult
to fit them together into an overall picture’) which was intended to load on surfacedisorganised loaded on surface-rational instead. The other item (‘I certainly want to
get a good performance appraisal, but it doesn’t really matter if I only just scrape
40
International Journal of Training and Development
 Blackwell Publishing Ltd 2003
Table 2: Cronbach’s alpha for the final version of the AWQ
Scale
Alpha
Odd-numbered
cases
Even-numbered
cases
N = 236
N = 236
.72
.72
.74
.71
.73
.75
1. Deep (10 items)
2. Surface-disorganised (10 items)
3. Surface-rational (10 items)
through’) was intended to load on surface-disorganised, and it did achieve its highest
loading on that factor, but the loading was only .201. Four of the items loaded on
more than one factor. Three of these were deep scale items that also loaded negatively
on surface-disorganised. The fourth was from surface-rational and also loaded negatively on deep. The factors were again minimally correlated, the largest being
between surface-rational and surface-disorganised, r = .219. The fit of the model was
tested using FITMOD. RMSEA for the model = .047 (.037, .057), suggesting good fit.
Cronbach’s alpha was calculated for each of the scales (using even-numbered
cases) and values are shown in Table 2 along with alphas from the odd-numbered
data. Again, all values were greater than .70.
Results (WCQ)
Study 1
The 40 items from the WCQ were factor-analysed in SPSS using ML extraction and
direct quartimin rotation. The scree plot suggested approximately three or four factors. Three-, four-, and five-factor solutions were obtained.
The three-factor solution accounted for 34.14 per cent of the variance. The first
factor was made up of items which described a supportive and receptive work
environment, consisting of good supervision combined with clear work expectations
(e.g. ‘Supervisors here make a real effort to understand difficulties employees may
be having with their work’, and ‘It’s always easy here to know the standard of work
expected of you’). The second factor consisted of just five items, all of which related
to heavy and demanding workloads (e.g. ‘There seems to be too much work to get
through here’). The third factor consisted mainly of items which concerned choice
and independence in the workplace (e.g. ‘We seem to be given a lot of choice here
in the work we have to do’).
The four-factor solution accounted for 37.83 per cent of the variance. The first two
factors (supportive environment and workload) were essentially unchanged from the
three-factor solution. Two items which had previously loaded with choiceindependence separated to form a new factor related to vocational relevance (e.g.
‘The work I do here will definitely improve my future employment prospects’).
Although this factor consisted of only two items, both had very high loadings,
above .900.
The five-factor solution accounted for 40.46 per cent of the variance. It was similar
to the four-factor solution, except that the first factor split into the two content areas
previously identified: good supervision and clear expectations.
Fit of the various models was assessed by calculating RMSEA, using the program
FITMOD. Values were as follows: 3 factors, RMSEA = .064 (.059, .070); 4 factors,
RMSEA = .054 (.048, .059); 5 factors, RMSEA = .047 (.041, .053). This suggests good
fit for the five-factor solution.
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Approaches to learning at work and workplace climate
41
Table 3: Factor loadings for final version of the AWQ, even-numbered cases
Item
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
Difficulty organising time (SD)
Prefer overview not details (SD)
Present job prepares for future jobs (D)
Like being told what is expected (SR)
Tackle tasks in order (SR)
Work pressure makes me tense (SD)
Too much catching up (SD)
Managers make things complicated
(SD)
Follow instructions even though conflict
(SR)
Use imagination to understand
puzzling ideas (D)
Prefer clearly structured work (SR)
Prefer well-tried approaches (SR)
Relate new ideas to real life (D)
Play with own ideas (D)
Read without understanding (SD)
If conditions aren’t right I change them
(D)
Learning new things is main attraction
of job (D)
Lot of effort in memorising new facts
(SR)
Start with details to build overview
(SR)
Studying for tasks is exciting (D)
Remember textbook definitions (SR)
OK to scrape through (SD)
Difficult fitting facts to form overall
picture (SD)
Use spare time to learn about work (D)
Map out new topic to see how ideas fit
(D)
Too quick in jumping to conclusions
(SD)
Look at problems rationally not
intuitive jumps (SR)
Remember better when concentrate on
order (SR)
Wonder why I ever worked here (SD)
Pursue interesting issues even if not my
job (D)
Surfacerational
Deep
Surfacedisorganised
.011
.170
.075
.484
.474
.164
.066
.121
⫺.068
.131
.316
.019
⫺.030
.026
⫺.002
.063
.438
.455
ⴚ.317
.064
⫺.028
.526
.356
.527
.544
⫺.111
.023
⫺.178
.471
.295
.624
.465
.169
⫺.200
.292
.032
⫺.095
ⴚ.312
.358
.435
⫺.042
.321
.120
.105
.047
.212
.480
⫺.279
.037
.407
ⴚ.326
.474
.097
.054
.370
.141
.092
.026
.454
⫺.040
.383
.501
.065
⫺.033
⫺.021
ⴚ.349
.076
.201
.229
.008
.080
.453
.456
⫺.061
.027
⫺.083
.097
.473
.331
⫺.074
⫺.107
.529
.122
.030
.054
⫺.081
⫺.148
.523
.487
⫺.142
1.000
⫺.046
.219
1.000
⫺.122
1.000
Correlations
Surface-rational
Deep
Surface-disorganised
Notes: D = Deep item; SD = Surface-disorganised item; SR = Surface-rational item
Loadings of .3 or more have been bolded to emphasise factor structure.
42
International Journal of Training and Development
 Blackwell Publishing Ltd 2003
Overall, taking into account interpretability, fit, and variance accounted for, the
five-factor solution appears to be optimal for these 40 items. The largest factor correlation was between good supervision and clear expectations, r = .440. The next largest
correlation was between vocational relevance and choice-independence, r = .322. All
other correlations had absolute values less than .300.
Study 2
The 40 items from the WCQ were factor-analysed in SPSS using ML extraction and
direct quartimin rotation. The scree plot suggested about three factors. As in Study
1, three, four, and five-factor solutions were obtained.
The three-factor solution accounted for 30.86 per cent of the variance. This solution
was similar to that of Study 1, but less clear. The workload factor was very similar.
However, the choice-independence factor also contained vocational relevance items,
and the supportive environment factor contained good supervision, clear expectations, items related to social networks (e.g. ‘Employees in this organisation often
get together socially’), and others.
The four-factor solution accounted for 34.20 per cent of the variance, but the factors
were not interpretable.
The five-factor solution accounted for 36.88 per cent of the variance. The workload,
vocational relevance, and choice-independence factors were similar to Study 1. However, the factor containing the good supervision items also included some of the
items relating to clear expectations. The clear expectations factor from Study 1 disappeared and was replaced by a social network factor.
Values of RMSEA were obtained as follows: 3 factors, RMSEA = .061 (.053, .069);
4 factors, RMSEA = .055 (.047, .064); 5 factors, RMSEA = .050 (.040, .059). Though the
overlap in confidence intervals is substantial, the five-factor solution appears to have
the best fit.
Based once again on fit, interpretability, and variance accounted for, the five-factor
solution appears optimal, although slightly different from Study 1. The largest correlation among factors was between vocational relevance and choice-independence, r =
.317. The next largest was between good supervision and social network, r = .303.
All other correlations had absolute values less than .300.
Study 3
The aim of Study 3 was to select a smaller number of items for a new questionnaire.
As before, odd-numbered cases from the combined data set were used to select items,
and even-numbered cases were used to test the resulting new questionnaire. From
the combined data set, the odd-numbered cases were selected and the 40 items from
the WCQ were factor-analysed in SPSS using ML extraction and direct quartimin
rotation. The scree plot suggested three main factors and up to three additional lesser
factors. Solutions for three through six factors were obtained.
The three-factor solution accounted for 33.40 per cent of the variance. As before,
two factors were composites. Factor one contained good supervision, clear expectations, and social network items. Factor two was the previously identified workload
factor, and factor three combined choice-independence with vocational relevance
items.
The four-factor solution accounted for 36.99 per cent of the variance. It was virtually identical to the three-factor solution except that the two vocational relevance
items broke off from choice-independence to form their own factor. As with Study
1, their factor loadings were very high, .975 and .833.
The five-factor solution accounted for 39.93 per cent of the variance. The factors
were good supervision, workload, vocational relevance, choice-independence, and
clear expectations. Social network items did not load highly on any factor.
The six-factor solution accounted for 42.98 per cent of the variance. The previously
identified five factors emerged, plus a social network factor. Factor loadings are
 Blackwell Publishing Ltd 2003
Approaches to learning at work and workplace climate
43
shown in Table 4. Values of RMSEA were obtained for the various solutions as follows: 3 factors, RMSEA = .068 (.062, .074); 4 factors, RMSEA = .059 (.053, .066); 5
factors, RMSEA = .053 (.046, .060); 6 factors, RMSEA = .045 (.037, .053).
Based on fit, interpretability, and variance accounted for, the six-factor solution
appears optimal. Correlations between factors appear in Table 4 along with the factor
loadings. As can be seen, the largest correlations occur between vocational relevance
and choice-independence, r = .370, and between good supervision and social network, r = .331.
Because the aim of Study 3 was to produce a shorter and more useable version of
the WCQ, it was undesirable to retain too many factors. Given that the clear expectations and social network factors had not emerged consistently across all three studies, they were rejected for the final questionnaire. Additionally, since the vocational
relevance factor consisted of only two items, it was also discarded. Five items were
selected from each of the remaining three factors: good supervision, workload, and
choice-independence. The items are shown in the Appendix. Cronbach’s alpha coefficients were calculated for the odd-numbered data for each of the scales and are
shown in Table 5. Values are high, .80 or above.
The next step was to factor-analyse only the 15 selected items, using the evennumbered data set. A three-factor solution accounted for 48.95 per cent of the variance. With one exception, items loaded as hypothesised on the three factors. One
item (‘The organisation really seems to encourage us to develop our own workrelated interests as far as possible’) loaded on good supervision instead of choiceindependence as intended. In addition, one item from the choice-independence scale
also loaded on the good supervision factor (‘Employees here have a great deal of
choice over how they learn new tasks’), although it had its largest loading on the
intended scale, choice-independence. Factor loadings are shown in Table 6. The highest correlation between factors was between good supervision and choiceindependence, r = .436.
The fit of the model was tested using FITMOD. RMSEA for the model = .069 (.050,
.089), suggesting acceptable fit. Alpha coefficients for the even-numbered data were
calculated for each of the scales and are shown in Table 5, along with the coefficients
for the odd-numbered cases. Values remain relatively high, with the lowest being
.74 for workload.
Correlations between scales
Finally, correlations were calculated between the newly defined scales of the AWQ
and WCQ. In this case, the complete data set (combined cases from Study 1 and
Study 2) was used. The correlations are shown in Table 7. The deep approach is
positively associated with good supervision and choice-independence, whereas the
surface-disorganised approach is negatively associated with these two constructs and
positively associated with workload. Surface-rational is negatively, though less
strongly associated with choice-independence. The strongest correlation occurred
between surface-disorganised and workload, r = .438.
Discussion
The aim of these studies was to develop measures of approaches to workplace learning and workplace climate, as a step towards better understanding how employees
think about work, and to examine the relationships between the constructs identified.
In both cases we began with the student learning literature, and so had hypotheses
about what dimensions would emerge. The dimensions that emerged were broadly
consistent with the student learning literature, with some salient differences.
With respect to approaches to learning, we found consistent support for the existence of the deep factor, as hypothesised, and little support for achieving, but in
every analysis the surface factor separated into two distinct components. These two
44
International Journal of Training and Development
 Blackwell Publishing Ltd 2003
Table 4: Factor loadings for the WCQ, 6-factor solution, combined data, odd-numbered cases
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
Item
Good Worksuper. load
Vocation Choicerel.
ind.
Clear Social
expect. net.
Assigned tasks
Opportunity to choose
tasks
Supervisors have
nothing to learn
Clear idea of what’s
expected
A lot of employees are
friends
Workload is too heavy
Supervisors receptive
to suggest.
Work is good
preparation
Can learn from
instructions
Encouraged to develop
interests
Most supervisors well
prepared
Work standards well
known
Employees get together
socially
Too many different
things to do
Supervisors consult
employees
Professional training
pointed out
Expected to learn on
own
Lot of choice in work
Supervisors explain at
right level
Hard to know how
well doing
Friendly climate
fostered
Difficult to find time to
learn
Supervisors try to
know employees
Productivity
requirements
Encouraged to find out
things
Can work in ways/suit
learning
.054 .084
⫺.027 ⫺.029
⫺.010
.154
ⴚ.374
.523
⫺.034
⫺.009
.046
.161
ⴚ.516
.111
⫺.163
⫺.003
.050
⫺.025
⫺.014 ⫺.146
.114
.129
.594
.225
⫺.131
.092
.050
.019
.085
.625
.024 .820
.472 ⫺.080
⫺.062
.098
⫺.086
.159
⫺.004
.039
.033
⫺.177
⫺.066 ⫺.011
.962
⫺.092
.067
⫺.006
.001 ⫺.170
⫺.154
⫺.096
.107
.025
.052
.094
.511
.271
.088
.501 ⫺.038
.032
.057
.324
⫺.022
.223 ⫺.129
⫺.039
.053
.567
.138
⫺.042 ⫺.068
.037
.053
.067
.634
.508
⫺.050
.079
.098
.041
.627 ⫺.013
⫺.021
.173
.057
⫺.047
.122
⫺.081
⫺.012
.112
.140
.214
.295
.081
⫺.073
.489
.080
.118
⫺.025
.035
.003 ⫺.143
.452 ⫺.081
.048
.082
.568
.115
.011
.155
.133
.175
.164
⫺.262
.069
ⴚ.320
.026
.308 ⫺.123
.007
.016
⫺.117
.501
.361
⫺.086
ⴚ.346
.077
.016
.654 ⫺.018
.133
⫺.271
⫺.067
.280
ⴚ.310
⫺.053
⫺.067
.210
⫺.012
⫺.088
.183
⫺.078
.191
.237
.111
.350
.044
.004
.304
.093
.000
.584
⫺.177
.112
[continued on next page]
 Blackwell Publishing Ltd 2003
Approaches to learning at work and workplace climate
45
Table 4 (continued): Factor loadings for the WCQ, 6-factor solution, combined data, oddnumbered cases
Item
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
Good Worksuper. load
Supervisors/understand .713 ⫺.029
difficulties
Supervisors tell what
.185 ⫺.134
to do
Meetings are well
.328 .174
attended
Too much work to get
.161 .901
through
Supervisors are
.629 .037
friendly
Work improves future
.040 ⫺.070
employment
Told what to do/little ⫺.274 .009
discussion
Choice how to learn
.263 .055
tasks
Supervisors ready to
.582 ⫺.009
help
A lot of pressure
⫺.055 .649
Employees discuss
.024 .051
work
Requirements made
.308 ⫺.037
clear
Emphasis on ‘right’
.027 .246
attitudes
Supervisors take ideas
.754 ⫺.008
seriously
Vocation Choicerel.
ind.
Clear Social
expect. net.
⫺.028
.030
.217
⫺.146
⫺.026
ⴚ.368
.329
.072
⫺.052
.109
.112
.195
⫺.032
⫺.109
⫺.078
.029
⫺.003
⫺.113
⫺.078
.263
.852
⫺.045
⫺.033
.039
⫺.140
ⴚ.344
⫺.023
.022
⫺.055
.654
⫺.074
.048
.041
.039
.044
.196
.051
⫺.017
⫺.109
.010
⫺.050
⫺.001
⫺.024
.432
⫺.034
⫺.151
.525
.125
⫺.002
⫺.037
.437
⫺.028
⫺.020
.100
⫺.037
⫺.010
1.000
.370
.089
.180
1.000
⫺.045
.137
1.000
.187
Correlations
Good supervision
Workload
Vocational relevance
Choice-independence
Clear expectations
Social network
1.000
⫺.177 1.000
.251 .013
.242 ⫺.026
.289 .038
.331 .005
1.000
Note: Loadings of .3 or more have been bolded to emphasise factor structures.
components, which we termed rational and disorganised, make considerable sense.
Surface-rational consists primarily of surface strategies, and reflects a preference for
orderly, accurate, and detailed work. There is little in surface-rational that describes
the fear-of-failure or extrinsic motives usually associated with the surface factor in
the student learning literature (e.g. Biggs, 1987, 1993; Entwistle and Ramsden, 1983).
Surface-disorganised, on the other hand, is a combination of surface motives and
Entwistle and Ramsden’s non-academic orientation; it is more a reaction to work than
an approach to it. The surface construct is complex (Biggs et al., 2001; Evans et al.,
in press; Kember et al., 1999), perhaps too complex, consisting of motives and strategies that need not cohere. For instance, an extrinsic motive for studying may be
common in technical subjects, yet act in combination with meaningful learning; simi46
International Journal of Training and Development
 Blackwell Publishing Ltd 2003
Table 5: Cronbach’s alpha for the final version of the WCQ
Scale
Alpha
1. Good supervision (5 items)
2. Workload (5 items)
3. Choice-independence (5 items)
Odd-numbered
cases
Even-numbered
cases
N = 236
N = 236
.84
.80
.80
.87
.74
.78
Table 6: Factor loadings for the final version of the WCQ, even-numbered cases
Item
Good
supervision
1. Opportunity to choose tasks (CI)
2. Workload is too heavy (WL)
3. Required to do too many different
things (WL)
4. Expected to learn on own (WL)
5. Lot of choice in work (CI)
6. Supervisors try to know employees
(GS)
7. Supervisors try to understand
difficulties (GS)
8. Too much work to get through (WL)
9. Supervisors are friendly to
employees (GS)
10. Employees have choices of how to
learn tasks (CI)
11. Supervisors always ready to help in
learning (GS)
12. There is a lot of pressure on
employees (WL)
13. Supervisors take employee ideas
seriously (GS)
14. Encouraged to develop own work
interests (CI)
15. Can work in ways which suit your
learning (CI)
Workload Choiceindependence
⫺.003
.133
⫺.075
.024
.844
.469
.757
⫺.079
.002
⫺.084
⫺.020
.760
.308
⫺.112
⫺.009
.161
.848
⫺.078
.804
⫺.018
.042
.116
.761
.838
.043
⫺.038
⫺.027
.343
.044
.469
.716
⫺.049
⫺.018
⫺.078
.560
⫺.077
.712
⫺.065
.118
.382
⫺.067
.180
.291
.058
.441
1.000
⫺.218
.436
1.000
.044
1.000
Correlations
Good supervision
Workload
Choice-independence
Notes: CI = Choice-independence item; WL = Workload item; GS = Good supervision item
Loadings of .3 or more have been bolded to emphasise factor structure.
 Blackwell Publishing Ltd 2003
Approaches to learning at work and workplace climate
47
Table 7: Correlations between the AWQ and WCQ scales
AWQ scales
Deep
Surface-rational
Surface-disorganised
WCQ scales
Good
supervision
Workload
Choiceindependence
.186**
⫺.016
⫺.288**
.034
.030
.438**
.295**
⫺.157**
⫺.245**
Note: N = 472; ** p ⬎ .01
larly an extrinsically motivated student may not fear failure. It may be worthwhile
to re-examine student learning, to see if distinct surface factors can be obtained.
The three approach dimensions were essentially uncorrelated. This is consistent
with previous studies of deep and surface approaches, even though those previous
studies used less sophisticated factor analytic methods and frequently assumed
uncorrelated factors. It may seem odd that deep processing is uncorrelated with surface processing, when the constructs sound as though they should be negatively
related. The lack of relationship would be strange if the question was how a particular learner would approach a particular situation. The approach measures, however, ask how learners would approach situations in general, especially with respect
to their (in this study) current workplace. It is not impossible for a deep learner to
be disaffected (surface-disorganised) in the current workplace, nor is it impossible
for a deep learner to accept that a surface-rational approach may be optimal in a
given situation.
In the context of approaches to learning at work, the three-factor solution we found
makes sense. Deep and surface-rational represent complementary approaches that
are appropriate at different levels of responsibility or for different tasks. Surfacedisorganised is more pathological, representing disaffection with the work environment and a sense of incompetence in the work tasks. The failure to find consistent
support for the achieving factor may be due to the items we employed, but is more
likely due to the difference between the contexts faced by students and employees.
Whereas students and employees may easily see themselves in competition with their
peers, employees are also responsible for cooperating with their peers at work and
may be more likely to see their unit or organisation in competition with other units
or organisations. The individually-oriented achieving factor of the student learning
literature is less relevant in the context of work.
The structure of the Workplace Climate Questionnaire was broadly consistent with
that found previously by Knapper (1995) and to a reasonable degree with those for
the Course Perceptions Questionnaire (Ramsden and Entwistle, 1981). Good supervision, choice-independence, and workload are three logically-separable dimensions
of employees’ perceptions of their workplace. The positive relationship between the
first two dimensions is noteworthy.
The correlations between scales indicate potentially important associations
between employees’ perceptions of their work environment and the approaches they
report to learning within it. As hypothesised, there was a positive association
between the deep approach and perceptions of positive aspects of the work environment (good supervision and choice-independence). This relationship is most likely
reciprocal, with the individual’s deeper learning being more productive and leading
to more managerial support and choice, and a more supportive and challenging
environment encouraging deeper learning. It may also be that deeper learners are
attracted to positions with more supportive supervision and choice-independence.
Future studies should investigate whether the perceptions of good supervision and
choice are valid, that is whether other observers would characterise the workplaces
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in those ways. Although it is not legitimate to argue from these results that good
(i.e. more supportive) supervision and choice produce deeper learning, they are more
likely to do so than their opposites.
The correlations involving workload were also consistent with our hypotheses,
though for the new surface-disorganised factor rather than a broader surface factor.
The strong positive relation between the workload and surface-disorganised scales
may also be reciprocal, those who are most alienated from their work being more
likely to perceive that there is too much of it, and those required to do too much
work being most likely to be overwhelmed by it. The pattern is completed by the
negative relations between surface-disorganised and the other two WCQ scales. It
seems unlikely that surface-disorganised workers would be attracted to positions
with poor supervision, little choice and high workload. However, future research
needs to assess the validity of the perceptions of workload—some employees may
actually have excessive loads whereas others may only perceive them. Both may be
prone to surface-disorganisation, but the remedies may be different.
Applications and future research
Although every effort was made to construct scales that are reliable and valid, these
scales require application in multiple settings before their nature can be fully understood. With that caveat, however, we can make some suggestions for how these
scales could be used. First, there is merit in encouraging deeper learning or thinking
in all employees, though in many cases a surface-rational approach may also be
useful. Profiles of desirable approaches could be developed, tailored to individual
positions, as a help in hiring and promoting employees. Employees could examine
their own approaches, and through vocational guidance and counselling consider
appropriate career paths and/or attempts to restructure their own approaches.
Employee instruction programmes should also be designed with approaches to learning considered both as a process (how will these employees best learn?) and an
outcome (what approach to learning is this instruction encouraging?).
Second, employers may wish to use these scales to guide restructuring of work
environments. The present results support the student learning literature in showing
that high workload (or at least its perception) is associated with the undesirable
surface-disorganised approach to learning. Although it is possible that there are types
of workers who can endure high workload without becoming surface-disorganised,
it may be more advantageous to consider workload reduction to encourage deeper
learning and higher quality work. Provision of supportive supervision, and as much
choice and independence as is feasible, also seems advisable. This would aim to
increase productivity through better work rather than through more work.
With respect to future research, the main requirements are to validate the beneficial
effects of deeper learning on work quality, and to test causal models of the relationships among approaches to work, workplace climate, and work outcomes. None of
these will be straightforward, because work is highly contextualised. For instance,
many employees have positions in which attention to detail is more important than
imagination and innovation; adoption of a deeper approach in these situations may
be ineffective or even counter-productive, leading perhaps to alienation resembling
surface-disorganisation. Similarly, the causal questions (Do approaches lead to workplace perceptions? Do workplace characteristics lead to workplace perceptions and
thus to approaches? Do approaches and workplace perceptions affect work quality?)
may have different answers in different situations. Finally, there is value in following
the developmental course of both approaches and workplace perceptions: How do
they change with experience? With retraining? With organisational change?
Just as the modern workplace requires greater learning by employees, modern
management requires greater understanding of that learning. The present research
represents a first step toward understanding employees’ conceptions of learning at
work and the factors that affect those conceptions. The scales developed here are
 Blackwell Publishing Ltd 2003
Approaches to learning at work and workplace climate
49
tools that can be used to explore these conceptions, their causes and effects further,
with the ultimate goal of improving work quality and satisfaction.
Appendix
Approaches to Learning at Work Questionnaire
Deep scale
1
The work I am doing in my present job will be good preparation for other jobs
I may have in the future.
2 In trying to understand a puzzling idea, I let my imagination wander freely to
begin with, even if I don’t seem to be much nearer a solution.
3 In trying to understand new ideas, I often try to relate them to real life situations
to which they might apply.
4 I like to play around with ideas of my own even if they don’t get me very far.
5 If conditions aren’t right for me at work, I generally manage to do something to
change them.
6 In my job one of the main attractions for me is to learn new things.
7 I find that studying for new tasks can often be really exciting and gripping.
8 I spend a good deal of my spare time learning about things related to my work.
9 I find it helpful to ‘map out’ a new topic for myself by seeing how the ideas
fit together.
10 Some of the issues that crop up at work are so interesting that I pursue them
though they are not part of my job.
Surface-disorganised scale
1
2
3
At work I find it difficult to organise my time effectively.
I prefer to have a good overview rather than focus on details.
The continual pressure of work—tasks to do, deadlines, and competition—often
makes me tense and depressed.
4 My habit of putting off work leaves me with far too much catching up to do.
5 Managers seem to delight in making the simple truth unnecessarily complicated.
6 Often I find I have to read things without having a chance to really understand them
7 I certainly want to get a good performance appraisal, but it doesn’t really matter
if I only just scrape through.
8 Although I generally remember facts and details, I find it difficult to fit them
together into an overall picture.
9 I seem to be a bit too ready to jump to conclusions without waiting for all the evidence.
10 When I look back, I sometimes wonder why I ever decided to work here.
Surface-rational scale
1
2
3
4
5
6
7
50
When I am given a job to do at work I like to be told precisely what is expected.
I generally prefer to tackle each part of a task or problem in order, working out
one at a time.
When I’m doing a piece of work I try to follow instructions exactly, even if they
conflict with my own ideas.
I prefer the work I am given to be clearly structured and highly organised.
I prefer to follow well tried approaches to problems rather than anything too
adventurous.
When I learn something new at work I put a lot of effort into memorising
important facts.
I find it better to start straight away with the details of a new task and build up
an overall picture in that way.
International Journal of Training and Development
 Blackwell Publishing Ltd 2003
8
The best way for me to understand what technical terms mean is to remember
the textbook definitions.
9 I think it is important to look at problems rationally and logically without making intuitive leaps.
10 I find I tend to remember things best if I concentrate on the order in which they
are presented.
Workplace Climate Questionnaire
Good supervision scale
1
2
3
4
5
Most of the supervisors really try hard to get to know employees.
Supervisors here make a real effort to understand difficulties employees may be
having with their work.
Supervisors in this organisation seem to go out of their way to be friendly
towards employees.
The supervisors in this organisation always seem ready to give help and advice
on the best way to learn something new.
Supervisors in this organisation generally take employees’ ideas and interests
seriously.
Workload scale
1
2
3
4
5
The workload here is too heavy.
It sometimes seems to me that my job requires me to do too many different
things.
In this organisation you’re expected to spend a lot of time learning things on
your own.
There seems to be too much work to get through here.
There’s a lot of pressure on you as an employee here.
Choice-independence scale
1
2
3
4
5
There is a real opportunity in this organisation for people to choose the particular
tasks they work on.
The organisation really seems to encourage us to develop our own work-related
interests as far as possible.
We seem to be given a lot of choice here in the work we have to do.
This organisation gives you a chance to go about your work in ways which suit
your own way of learning.
Employees here have a great deal of choice over how they learn new tasks.
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