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Integrating concepts from goal theories to understand the achievement of
personal goals
Article in European Journal of Social Psychology · January 2005
DOI: 10.1002/ejsp.233
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European Journal of Social Psychology
Eur. J. Soc. Psychol. 35, 69–96 (2005)
Published online 25 October 2004 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/ejsp.233
Integrating concepts from goal theories to
understand the achievement of personal goals
THOMAS L. WEBB* AND PASCHAL SHEERAN
University of Sheffield, UK
The aim of this research was to integrate concepts from goal theories to understand the factors that
distinguish between successful versus unsuccessful achievement of a personal goal. In two studies
participants completed a questionnaire that measured 17 constructs identified from research on goals
in relation to a recent situation in which they either succeeded or failed to see a task through to the end
(Study 1) or in relation to their performance on an undergraduate course (Study 2). Factor analyses of
the responses revealed 12 factors underlying self-regulatory efforts. Discriminant analysis showed
that self-regulatory success was associated with high levels of motivation and task-focus, and with
forming an implementation intention. Copyright # 2004 John Wiley & Sons, Ltd.
Goal-directedness is ‘a cardinal attribute of the behaviour of living organisms’ (Locke, 1969, p. 991).
But what influences whether we succeed or fail to achieve a personal goal? Goal theorists have
proposed numerous explanations for why people fail to achieve personal goals. However, to date no
study has addressed conceptual overlap amongst these explanations, or simultaneously examined the
range of constructs identified by previous research. The present research used factor analysis to
determine the structure of self-regulatory efforts and employed discriminant function analysis to
identify the most important predictors of goal attainment. Below, we review six key models of goal
achievement, asking what each can add to an understanding of how people achieve their personal goals.
MODELS OF GOAL ACHIEVEMENT
As Gollwitzer and Moskowitz (1996) noted, there are a wealth of different theories on goals. The
present research employs the ‘Rubicon Model’ of action phases (Heckhausen, 1987; Heckhausen &
Gollwitzer, 1986, 1987) as a basis for categorizing different models. The model delineates four distinct
phases in goal striving. The first is the predecisional phase in which people deliberate over which goal
to pursue and then form an intention. Second is the preactional, postintentional (i.e. volitional) phase
in which people decide when, where, and how to act. The outcome of this deliberation is a specific
behavioural plan, known as an implementation intention (Gollwitzer, 1993, 1996, 1999). In the third
*Correspondence to: T. Webb, Department of Psychology, University of Sheffield, Sheffield S10 2TN, UK.
E-mail: t.webb@shef.ac.uk
Copyright # 2004 John Wiley & Sons, Ltd.
Received 27 October 2003
Accepted 1 June 2004
70
Thomas L. Webb and Paschal Sheeran
stage of the model, the action is initiated and maintained if necessary. Finally, the outcome of the
action is evaluated against what was desired.
The starting point in the present research was the dominant model of attitude-behaviour relations—
the theory of planned behaviour (TPB; Ajzen, 1991) and models of goal setting (e.g. Locke & Latham,
1990), which relate to the predecisional action phase. Next, in an attempt to explicate the processes by
which intentions are translated into action, proposed extensions to the TPB such as Bagozzi’s (1992)
theory of self-regulation were included. The concept of implementation intentions was also included
because empirical evidence suggests that forming an implementation intention increases the likelihood that a positive intention will be translated into action (e.g. Gollwitzer, 1993, 1996, 1999;
Sheeran & Orbell, 2000). Next, in relation to the actional phase, Baumeister, Heatherton, and Tice
(1994) suggested that people fail to achieve their goals because they have inadequate self-regulatory
strength to attempt goal striving. Similarly, high levels of emotion may interfere with goal striving
(Luminet, Zech, Rimé, & Wagner, 2000) as may conflicting goals (Emmons & King, 1988). Finally, to
acknowledge that goal pursuit is unlikely to be determined entirely by personal factors, models of
social support were also included.
THEORY OF PLANNED BEHAVIOUR
The TPB (Ajzen, 1991) is a model of deliberative decision making and assumes that people make
decisions based on careful consideration of the available information. The model proposes that the
proximal predictor of a person’s behaviour is a behavioural intention or decision to exert effort in
order to perform a behaviour. Thus, the person who intends to reduce their fat intake is more likely to
do so compared to a person who has not formed this intention. The TPB suggests that three constructs
predict intention: attitude, subjective norm, and perceived behavioural control (PBC). Attitudes reflect
the individual’s global positive or negative evaluations of performing a particular behaviour (e.g. ‘I
enjoyed trying to do this task’). Subjective norms are people’s beliefs about whether significant others
think they should engage in the behaviour (e.g. ‘people important to me disapproved of my doing this
task’). Finally, the TPB suggests that PBC is predictive of intentions, and, also predicts behaviour
when PBC accurately reflects actual control over the behaviour (Sheeran, Trafimow, & Armitage,
2003). PBC is similar to Bandura’s (1977) concept of self-efficacy, and reflects a person’s appraisal of
their ability to perform the behaviour. A recent meta-analysis of 185 studies (Armitage & Conner,
2001) found that the TPB explained 27% of the variance in behaviour, on average; thereby lending
support to the efficacy of the TPB as a predictor of behaviour.
GOAL SETTING THEORY (LOCKE & LATHAM, 1990)
The achievement of many personal goals requires the maintenance of a particular level of behaviour
over time. For example, in order to reduce the risk of heart disease, one needs to exercise on a regular
basis. Thus, the intention to exercise on a regular basis serves as a standard of comparison against
which the individual must regulate her behaviour (Carver & Scheier, 1981, 1982; Hyland, 1988).
Notably, it seems that more challenging goals have a greater impact on behaviour than do easy goals.
For example, Locke, Shaw, Saari, and Latham (1981) reviewed three experimental field studies and 25
experimental laboratory studies which all suggested that difficult goals are more likely to effect
performance than easy goals. Thus, the present research measured how ambitious were participants’
personal standards.
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
Integrating goal theories
71
The second important construct in goal setting theory is performance feedback. Performance
feedback refers to information gained, usually from other people, about task performance. According
to Locke and Latham, the relationship between performance feedback and behaviour is inseparably
linked to goal setting—neither construct in isolation can influence performance. This dependent
relationship parallels Carver and Scheier’s (1981) control theory, which suggests that self-regulation is
the process of monitoring one’s performance with respect to a particular goal. In sum, goal setting
theory suggests that personal goals can be achieved by setting an ambitious standard and by receiving
feedback about one’s progress towards that standard.
THEORY OF SELF-REGULATION
Bagozzi (1992) argued that it is important to go beyond the TPB and models of goal setting to
explicate the processes that occur between intention formation and goal achievement. Bagozzi
proposed that two motivational constructs—commitment and effort—are crucial for translating
intentions into action. Commitment is defined as the importance of the personal goal for the individual
and is similar to Abelson’s (1988) notion of conviction. People who are committed to achieving their
intentions are thought to be more likely to succeed than are people who are not committed. Effort can
be defined as the amount of time and energy invested in order to perform an action. Latham and Locke
(1991) accord effort a central role in their theory of task performance and suggest that the impact of
personal goals on behaviour is mediated by the direction and intensity of effort. In sum, people who are
highly committed and invest a lot of effort should be especially likely to achieve their goals.
IMPLEMENTATION INTENTIONS
Implementation intentions (Gollwitzer, 1993, 1996, 1999) are subordinate to goal intentions and
involve specifying when, where, and how one will perform behaviour(s) that lead to goal attainment.
For example, whereas a goal intention might state ‘I intend to keep to a fitness regime’, the
corresponding implementation intention might be ‘If it is Monday evening, then I will jog home
from work’. Evidence suggests that implementation intentions are effective in promoting goal
achievement over and above goal intentions to perform the behaviour. A meta-analysis of 15 studies
by Sheeran (2002) found that implementation intentions had a ‘medium’ effect size on behaviour
(r ¼ 0.33). Moreover, the effectiveness of implementation intentions has been demonstrated across
a range of contexts, from discrete ‘one-off’ behaviours such as attendance for cervical cancer
screening (Sheeran & Orbell, 2000) to repeated behaviours that are performed daily such as vitamin
supplement use (Sheeran & Orbell, 1999), and across a range of samples and measures of behaviour
(Sheeran, 2002).
STRENGTH MODEL OF SELF-CONTROL
The strength model of self-control suggests that people have a finite capacity for self-control (Muraven
& Baumeister, 2000). The exertion of self-control on a primary task leads to a temporary depletion of
the self-control resource known as ego-depletion. This temporary depletion is observed as a
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
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Thomas L. Webb and Paschal Sheeran
performance deficit on a subsequent self-control task. Baumeister, Bratlavsky, Muraven, and Tice
(1998) employed a number of ego-depletion paradigms to demonstrate the limited nature of the selfcontrol resource. For example, Experiment 1 showed that being told to eat radishes, rather than
available chocolate, reduced participants’ subsequent persistence with unsolvable puzzles. In Experiment 3, regulating one’s emotional reaction to movie clips reduced performance on a subsequent
anagram task. These experiments suggest that if multiple demands are placed on the self-control
resource, then this may have an adverse effect on attainment of the focal goal.
The experience of ego-depletion is also linked with two other factors that may influence selfregulatory success; physical fatigue and acquiescence. Baumeister et al. (1998) proposed that while
the exact nature of the self-control resource is unknown, it is linked to physical tiredness. Evidence
suggests that participants who have been ego-depleted tend to report greater tiredness than do their
non-depleted counterparts (Baumeister et al., 1998; Muraven, Tice, & Baumeister, 1998; Webb &
Sheeran, 2003). Second, Baumeister et al. (1994) argued that the depleted individual is more likely to
exhibit acquiescence. That is, when people are tired, they may choose to allow self-regulation to fail
because the exertion of self-control does not appeal. For example, a person who is very busy at work
may allow their exercise programme to fall behind schedule.
CONFLICTING STANDARDS
At any one point in time it is likely that an individual will have numerous personal goals; for example,
to finish writing a report, to be on time for lunch with one’s partner, and to eat more healthily. It is also
likely that some of these goals will conflict (for a review, see Wilensky, 1983); for example, report
writing and social commitments tend to be mutually exclusive. Emmons and King (1988) found that
conflicting goals (e.g. ‘to appear more intelligent than I am’ and ‘to present myself in an honest light’)
produced rumination rather than action, and so people fail to make progress towards either goal (see
also van Hook & Higgins, 1988). Thus, the present research measured whether participants’ had
conflicting goals.
EMOTION
Research by Luminet et al. (2000) suggests that the experience of emotion (the extent to which an
individual experiences an affect-laden state, cf. Reber, 1995) also results in ruminative thoughts. That
is, when people experience a highly emotional event (e.g. a serious accident), thoughts, memories, and
mental images related to the event repetitively surface in consciousness. Thus, if striving to achieve
personal goals causes emotional experiences, then the resulting ruminative thoughts may inhibit action
(Martin & Tesser, 1988, 1996). However, many functional accounts of emotion emphasize the
beneficial consequences of emotions (Keltner & Gross, 1999). For example, emotions may serve to
prioritize and organize behaviour in ways that optimize the individual’s adjustment to the demands of
the environment (e.g. Lazarus, 1991; Levenson, 1994; Tooby & Cosmides, 1990). Similarly,
Personality Systems Interactions (PSI) theory (Kuhl, 1996, 2000; Kuhl & Kazen, 1994, 1999)
suggests that ‘down regulating’ negative affect produced by task demands produces increased access
to memory and facilitates task performance. In sum, the intensity of emotion experienced while
striving to achieve a personal goal is likely to influence goal achievement, though whether emotion has
a positive or a negative influence is uncertain.
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
Integrating goal theories
73
SOCIAL SUPPORT
Finally, social support may be important in determining whether we succeed or fail to achieve our
personal goals. Social support can be defined as the help or assistance one receives when trying to
perform, or not to perform, a particular behaviour (Povey, Conner, Sparks, James, & Shepherd, 2000).
Although the majority of studies of social support relate to depression (e.g. Hays, Steffens, Flint,
Bosworth, & George, 2001), or to the correlates of social support receipt (e.g. Dunkel-Schetter,
Folkman, & Lazarus, 1987) there have been a few studies of the impact of social support on goal pursuit.
In a study of married cigarette smokers, Mermelstein, Lichtenstein, and McIntyre (1983) found a
positive relationship between partner support and the success of quitting attempts. Similarly, in a metaanalysis of the role of social support in predicting adherence to medical treatment, DiMatteo (2004)
found significant relationships between adherence and both practical and emotional social support.
RATIONALE FOR THE PRESENT RESEARCH
The present research was motivated by the idea that it is necessary to integrate insights from a wide
variety of goal theories in order to fully understand influences on people’s achievement of personal
goals. To date, there have been very few attempts to integrate or compare goal theories (e.g. Bagozzi &
Kimmel, 1995), and comparative studies have been very selective about which theories were
compared (e.g. Fredricks & Dossett, 1983; Valois, Desharnais, & Godin, 1988). As Weinstein
(1993) noted, the lack of comparison studies means that there is little consensus about whether
some variables are more influential than others. For example, on the basis of extant research, we do not
know the relative importance of attitude towards goal striving (as conceptualized by the TPB) and egodepletion (as described by the strength model of self-control) as determinants of goal attainment. The
present research tests whether all of the goal theory constructs reviewed above are needed to
understand goal achievement, or whether a more parsimonious model is sufficient.
The present research also addresses the issue of conceptual overlap between factors from goal
theories. For example, subjective norm may be similar to social support. A person’s beliefs about
whether significant others think he/she should engage in the behaviour may provide little additional
information once we know that the person has received help in order to perform the behaviour.
Similarly, the constructs of commitment and intention may overlap; if we know a person is committed
to achieving a goal, we may be able to infer that he intends to achieve that goal. No formal tests of
conceptual overlap amongst models of goal achievement have been conducted heretofore, even though
such tests are crucial for understanding whether and how different theoretical perspectives can be
integrated.
STUDY 1
Study 1 aimed to explore the conceptual structure of factors from goal theories and compare the
relative importance of these factors in distinguishing successful from failed goal attainment. The study
adopted a retrospective approach in which participants were asked to think about a situation where
they had either succeeded or failed to achieve a personal goal. Next, participants were asked to
complete a number of questions about this situation. Seventeen constructs from goal theories were
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
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Thomas L. Webb and Paschal Sheeran
measured; behavioural intentions, attitude, subjective norm, self-efficacy, personal standards, conflicting standards, commitment, effort, ego-depletion, physical fatigue, mental fatigue, acquiescence,
intensity of emotion, implementation intentions, performance feedback, social support, and perceived
task demand. Perceived task demand served as a control variable to ensure that participants who failed
were not doing so simply because they chose harder tasks than participants who succeeded. Responses
to the questions were then analysed using factor analysis to examine conceptual overlap and
discriminant function analysis was used to compare the predictive validity of the derived factors.
Method
Participants and Design
The study employed a one-factor (condition: failure vs. success) between-participants design.
Participants were randomly allocated to one of the two conditions when they completed a
questionnaire on the internet. In the failure condition, participants were asked to ‘think about a
situation in the last year or so where you failed to see a difficult task through to the end.’ Participants in
the success condition, on the other hand, were asked to ‘think about a situation in the last year or so
where you succeeded in seeing a difficult task through to the end.’ N ¼ 646 participants completed the
questionnaire (failure n ¼ 307, success n ¼ 339). Participants were predominantly female (71%),
students (84%), and aged between 18 and 55 years (M ¼ 23.3, SD ¼ 7.4). None of the demographic
variables differed between conditions.
Measures
Questionnaire measures were taken of the 17 constructs (See Appendix for the complete list of items).
Each construct was measured using two, three or four items assessed on 7-point bipolar scales
(strongly disagree-strongly agree). Scale reliabilities were acceptable (median Cronbach’s ¼ 0.76,
range 0.47–0.90), with the exception of acquiescence ( ¼ 0.61), conflicting standards ( ¼ 0.55),
personal standards ( ¼ 0.57), and subjective norm ( ¼ 0.47). Wherever possible, the questionnaire
measures had been validated in previous research.
Procedure
Participants were contacted by e-mail (sent to distribution lists of university departments) and asked if
they would take part in an online study. Interested participants were asked to visit a start page that
contained the instructions. Participants were informed that the study investigated ‘factors that affect
people’s behaviour’ and that they would be presented with a number of statements to which they
should respond on 7-point agree-disagree scales (an example was given). Participants were also told
that the study would take no longer than 15 minutes to complete and that all responses would be
treated in confidence. Participants were then asked to click a link at the bottom of the page. This link
randomly allocated them to one of the two conditions and presented the relevant questionnaire
(participants were unaware of this allocation procedure).
Participants were first asked to indicate their age, gender, and occupation, and were then asked to
provide a brief description of their success/failure situation. Next, a number of questions were asked
about the nominated situation to which participants responded by clicking radio buttons with the
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
Integrating goal theories
75
mouse. Once participants had submitted their responses they were directed to a debrief page which
thanked them for their participation, explained the purpose of the study, and gave contact details
should they require further information.
Results
Reported Tasks
Participants’ responses to the question ‘please give a brief description of your chosen situation’ were
coded using Wadsworth and Ford’s (1983) seven life domains. An independent rater coded a
subsample (N ¼ 100) of the responses. Inter-rater reliability was satisfactory (84% agreement, Cohen’s
Kappa ¼ 0.71) and disagreement between the raters was resolved by discussion. Sixty-one per cent of
the responses were related to work and school (e.g. implementing a database at work), 4% to family
life (e.g. sorting out home life and marriage), 10% to social life (e.g. asking a girl out), 12% to leisure
(e.g. performing in a musical), 11% to personal growth and maintenance (e.g. getting back into my car
after a car accident), and 1% to material/environmental concerns (e.g. redecorating my bedroom);
the remainder (2%) were classified as other/general (e.g. sorting my life out), or were missing. Chisquare analysis of the frequency of each life domain by condition did not reveal significant differences
between the conditions, 2(7) ¼ 12.61, ns. Thus, participants who failed to achieve their goals did not
do so because they chose qualitatively different tasks than did participants who succeeded.
Factor Analysis
Factor analysis is applied to a set of variables in order to determine which variables form coherent
subsets that are relatively independent of one another (Tabachnick & Fidell, 1996). There are a number
of extraction techniques, but principal factor extraction is the most appropriate for deriving a
theoretical solution uncontaminated by unique and error variability, and was therefore used here.
Similarly, a number of rotation techniques are available. Oblique rotation was employed because it
was reasonable to expect that the factors would be correlated. A delta value of 0 (direct quartimin
rotation) was adopted so as not to constrain the correlations between the factors.
The factor analysis was performed through SPSS on 53 items for the sample of N ¼ 646
participants. Twelve factors were extracted (based on Kaiser’s, 1958, criterion) that explained 65%
of the variance in participants’ responses.1 In order to ascertain the extent to which the factor structure
was invariant across conditions, the same factor analysis was repeated for each condition separately.
Pearson’s correlations were then computed between all pairs of factor vectors between samples (see
Barrett, 1986). Barrett suggests a congruence of 0.80 as an acceptable lower bound to assume
reliability between two samples. According to this criteria the factors were well matched across the
two samples (median congruence ¼ 0.98, range 0.86 to 1.00), which speaks to the stability of the factor
structure. Table 1 shows the loadings of variables on factors (for the conditions separately and for the
overall sample), the amount of variance explained by each factor, and the alpha coefficient
representing the reliability of each factor. Variables are ordered by size of loading to facilitate
interpretation. Loadings less than 0.30 are not reported.
1
It is notable that interpretative and statistical difficulties were encountered with both 11 and 13 factor solutions. Extracting 11
factors meant that the items designed to measure subjective norms were scattered across the solution—one factor was defined by
a single subjective norm item, while the other two items did not load ( > 0.30) on any factor. The 13 factor solution failed to
converge in 50 iterations.
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
76
Thomas L. Webb and Paschal Sheeran
Table 1.
Principal axis factoring with direct oblimin rotation by condition and for the total sample (Study 1)
Factor/items
F1: Motivation
I planned to see this task through to the end
I definitely intended to see this task through to the end
I told myself to try to see this task through to the end
I felt very dedicated to completing the task
I felt capable of succeeding in this situationa
I felt that my trying to do this task was worthwhile
I felt very committed to acting in this situationa
I felt that it was pointless trying to do this task (r)
I did not care about the outcome of this situation (r)
I set my sights very low in this situation (r)
I was very ambitious in this situation
R2
F2: Emotion
I was not at all emotional in the situation (r)
I was very emotional in this situation
The situation aroused very little emotion in me (r)
R2
F3: Feedback
No-one remarked about how they thought I was doing
during this task (r)
No-one commented on my progress with the task (r)
Other people told me a great deal about how they thought I was
doing in this situation
R2
F4: Ego-depletion
I had other competing commitments at this time
My life was very hectic at the time of this situation
Very few other things were happening at the time of this situation (r)
I felt a lot of demands were being placed upon me about this time
I had other priorities in this situation
R2
F5: Implementation intentions
I told myself where and when I would complete this task
I did not set myself a time and a place to complete this task (r)
I committed myself to performing the task in a specific situation
R2
F6: Task demands
This would have been a difficult task for anyone
By anyone’s standards the task was very demanding
Most people would have found this task very easy (r)
R2
F7: Acquiescence
I just went with the flow in this situation
I just went along with things in this situation
R2
Failure
Success
Total
0.71
0.72
0.56
0.42
0.47
0.49
0.83
0.71
0.56
0.64
0.46
0.35
0.39
0.79
0.77
0.61
0.51
0.45
0.42
0.36
0.35
0.35
0.84
0.17
0.84
0.20
0.88
0.89
0.84
0.78
0.26
0.90
0.84
0.79
0.82
0.10
0.90
0.90
0.85
0.83
0.11
0.90
0.75
0.72
0.82
0.69
0.77
0.73
0.58
0.07b
0.81
0.68
0.07
0.81
0.68
0.06
0.82
0.84
0.73
0.76
0.54
0.52
0.05
0.80
0.76
0.73
0.64
0.46
0.33
0.04
0.71
0.80
0.73
0.70
0.51
0.42
0.05
0.76
0.68
0.65
0.50
0.05
0.75
0.79
0.72
0.37
0.03
0.71
0.76
0.72
0.46
0.04
0.76
0.91
0.84
0.76
0.03
0.87
0.77
0.87
0.65
0.04
0.79
0.86
0.86
0.73
0.04
0.84
0.77
0.80
0.03
0.79
0.74
0.79
0.03
0.78
0.82
0.82
0.03
0.79
Continues
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
Integrating goal theories
Table 1.
77
Continued
Factor/items
F8: Subjective norms
I felt that people who are important to me wanted me to
succeed in this task
People important to me disapproved of my doing this task (r)
I felt under social pressure to succeed at this task
R2
F9: Self-efficacy
Physically, I felt I was in an excellent condition in this situation (r)
Physically, I felt I was in a bad condition in this situation
Physically, I felt I could take on a lot in this situation (r)
Physically, I felt only able to do a little in this situation
I felt able to handle this situation
I was not very confident in my ability in this situation (r)
R2
F10: Affective beliefs
I disliked trying to do this task (r)
I enjoyed trying to do this task
R2
F11: Task focus
When I was doing the task I could keep my thoughts on it (r)
My thoughts easily wandered in this situation
I tried very hard in this situationa
I put very little effort into the task (r)
I could concentrate well in this situationa (r)
I put a great deal of energy into being successful in this situation
I never felt tempted to do other things (r)
I did not let things slide in this situation (r)
It took a lot of effort to concentrate on this task
R2
F12: Social support
I received no help from others in this situation (r)
I received a lot of assistance from others in this situationa
I felt I had no support from other peoplea (r)
R2
Failure
Success
Total
0.65
0.65
0.55
0.36
0.44
0.02
0.44
0.03
0.47
0.34
0.12
0.79
0.67
0.67
0.62
0.61
0.50
0.41
0.03
0.81
0.76
0.73
0.58
0.58
0.40
0.37
0.02
0.80
0.95
0.80
0.02
0.85
0.83
0.71
0.03
0.87
0.84
0.81
0.02
0.86
0.50
0.36
0.48
0.53
0.43
0.39
0.42
0.32
0.56
0.60
0.49
0.42
0.43
0.44
0.32
0.31
0.54
0.52
0.50
0.49
0.47
0.41
0.39
0.34
0.16
0.79
0.07
0.78
0.02
0.82
0.70
0.74
0.68
0.07b
0.83
0.75
0.73
0.53
0.02
0.83
0.70
0.56
0.51
0.02
0.85
0.43
0.02
0.43
0.81
0.76
0.47
0.57
Note: Loadings < 0.30 are suppressed. (r) indicates that the item was recoded prior to factor analysis.
a
Complex item: loadings > 0.30 on more than one factor. bItems measuring social support and feedback loaded on the same
factor for the failure condition.
Factor 1 had high loadings from intention, commitment, personal standards, and two attitude items
and was labelled as motivation. All three emotion items loaded on Factor 2. All three feedback items
loaded on Factor 3. Factor 4 had high loadings from items intended to measure ego-depletion and
conflicting standards and was interpreted as ego-depletion. Items measuring implementation intentions
loaded on Factor 5. Task demand items, acquiescence items, and subjective norm items, loaded on
Factors 6, 7, and 8, respectively. Factor 9 had high loadings from physical fatigue and self-efficacy
items and was labelled self-efficacy. The two attitude items not included in Factor 1 loaded on Factor
10, which was interpreted as affective beliefs. Factor 11 had high loadings from items measuring
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
78
Thomas L. Webb and Paschal Sheeran
mental fatigue and effort, and seemed to capture the concept of task focus. Finally, all three social
support items loaded on Factor 12. Reliability for all factors was satisfactory (median ¼ 0.82), with
the exception of Factor 8, subjective norm ( ¼ 0.47).
Correlations between the factors are shown in Table 2. Factor 1, motivation, had moderate
(0.32 < r < 0.42), positive correlations with implementation intentions, task focus, affective beliefs,
and task demands. Further, motivation was negatively correlated with acquiescence. Only three other
correlations exceeded r ¼ 0.30. Self-efficacy was positively correlated with levels of emotion, and
negatively correlated with affective beliefs. Finally, task focus was positively correlated with affective
beliefs. In sum, the discriminant validity of the factors is further supported by the correlational
findings.
Discriminant Analysis
Discriminant function analysis is the most appropriate statistical technique for determining the factors
that differentiate between participants belonging to two or more different groups (Tabachnick &
Fidell, 1996). Correlations between predictors and discriminant function(s) indicate the combination
of predictors that is associated with group membership and the adequacy of discrimination is
determined by the ability of the discriminant function to classify participants into groups.
Direct entry discriminant analysis was performed through SPSS using the factor scores (derived
using the regression method) for the 12 factors as predictors of successful versus failed achievement of
personal goals. One discriminant function was identified, 2(12) ¼ 268.06, p < 0.001. The first column
in Table 3 presents the correlations between the factors and the discriminant function. The best
predictor of success versus failure was motivation. In order of discriminatory importance, participants
in the success condition also reported being more focused on the task, were more likely to have formed
an implementation intention, and were more likely to have received social support and believe that
people important to them approved of their doing the task.
Next, we conducted Roy-Bargmann Stepdown Fs to compare the mean score on each factor by
condition, using previously entered factors as covariates. The results revealed that five factors
(motivation, task focus, implementation intentions, social support, and subjective norm) differed
significantly between the groups. Although the means for the other seven factors were all in the
expected direction, none of the differences were significant; the additional factors failed to explain
additional variance beyond the initial five factors. Importantly, there were no differences between the
conditions on the task difficulty factor, indicating that—even for equally difficult tasks—the
identified factors have discriminatory power.
The discriminant function correctly classified 505 participants (78.3%), compared to 324 (50.0%)
who would have been correctly classified by chance alone (see Table 4). In other words, the five
predictors of goal achievement led to a 28% improvement in classification over chance. The stability
of the classification procedure was checked by a cross-validation run (Tabachnick & Fidell, 1996).
Approximately 25% of the cases were withheld from the calculation of the classification functions. For
the 75% of the cases from which the functions were derived, there was a 77.0% correct classification
rate. For the cross-validation cases, classification was virtually identical at 77.4%. This indicates a
high degree of consistency in the classification scheme.
Finally, to ensure that the variables derived from the factor analysis better predicted success versus
failure than did the original goal theories from which the constructs were derived, we conducted
discriminant analyses testing each of the original models (see Table 5). To test the efficacy of the TPB
(Ajzen, 1991) scale scores for attitude, subjective norm, intention, and self-efficacy entered
the discriminant function. Bagozzi’s (1992) theory of self-regulation was tested by entering the
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
Copyright # 2004 John Wiley & Sons, Ltd.
Note: *p < 0.05; **p < 0.01; ***p < 0.001.
0.10*
0.18***
0.07
0.42***
0.33***
0.34***
0.25***
0.22***
0.33***
0.38***
0.12**
F1
0.16***
0.08*
0.08*
0.19***
0.24***
0.15***
0.44***
0.20***
0.15***
0.01
F2
Correlations among factors (Study 1)
F1: Motivation
F2: Emotion
F3: Feedback
F4: Ego-depletion
F5: Imp. intentions
F6: Task demands
F7: Acquiescence
F8: Sub. norms
F9: Self-efficacy
F10: Affective beliefs
F11: Task focus
F12: Social support
Table 2.
0.02
0.26***
0.10*
0.06
0.15***
0.02
0.01
0.07
0.26***
F3
0.02
0.12**
0.02
0.01
0.20***
0.13***
0.17***
0.08*
F4
0.18***
0.14***
0.13***
0.04
0.06
0.15***
0.03
F5
0.26***
0.06
0.02
0.07
0.21***
0.02
F6
F8
F9
0.11**
0.06
0.00
0.12** 0.05
0.46***
0.27*** 0.01
0.22***
0.10
0.17*** 0.10
F7
0.36***
0.11**
F10
0.06
F11
Integrating goal theories
79
Eur. J. Soc. Psychol. 35, 69–96 (2005)
80
Thomas L. Webb and Paschal Sheeran
Table 3. Correlations with discriminant function, means, and Roy-Bargmann Stepdown Fs for factors by
condition (Study 1)
Factor
Motivation
Task focus
Implementation intentions
Social support
Subjective norm
Acquiescence
Affective beliefs
Feedback
Task demands
Self-efficacy
Emotion
Ego-depletion
r
Failure
Success
F
0.83
0.55
0.52
0.38
0.37
0.33
0.32
0.28
0.27
0.24
0.12
0.10
0.51
0.35
0.33
0.24
0.22
0.26
0.22
0.22
0.22
0.12
0.13
0.10
0.46
0.32
0.30
0.22
0.20
0.24
0.20
0.20
0.20
0.11
0.12
0.09
230.36***
18.34***
11.43*
33.01***
7.81y
1.03
0.00
0.63
0.26
0.85
0.06
1.20
Note: Bonferroni correction has been applied.
y
p < 0.06; *p < 0.05; ***p < 0.001.
Table 4. Discriminant function classification table (Study 1)
Actual
Predicted
Success
Failure
Table 5.
Success
Failure
84.7% (n ¼ 287)
15.3% (n ¼ 52)
28.8% (n ¼ 88)
71.2% (n ¼ 218)
Comparisons between the five-factor model and the original goal theories (Study 1)
Model
Five-factor model
Theory of self-regulation
Theory of planned behaviour
Strength model of self-control
Implementation intentions
Goal-setting theory
Social support
Mental fatigue
Conflicting standards
Task demand
Emotion
Wilk’s Lambda
0.66
0.73
0.75
0.87
0.89
0.90
0.90
0.94
0.95
0.97
0.98
Approx. F
12.37***
16.43***
41.18***
43.31***
45.24***
45.44***
54.72***
55.49***
60.13***
62.45***
***p < 0.001.
commitment and effort scales whereas entering ego-depletion, acquiescence, and physical fatigue
tested the strength model of self-control (Muraven & Baumeister, 2000). Scale scores for personal
standards and task feedback were used to assess the discriminant validity of goal-setting theory (Locke
& Latham, 1990). Finally, a number of additional constructs that did not form part of larger models
were examined individually; implementation intentions, conflicting standards, emotion, mental
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
Integrating goal theories
81
fatigue, social support, and task demand. We computed the significance of the difference between the
Wilk’s Lambda derived from the original model of goal achievement and the Wilk’s Lambda from our
own five factor (motivation, task focus, implementation intentions, social support, and subjective
norm) using the procedure described by Tabachnick and Fidell (1996, p. 545). This procedure derives
an approximate F for the difference between the two lambdas which is evaluated with three degree of
freedom parameters: p the number of predictors in the larger model; dfeffect, the number of conditions
minus 1; and the dferror from the larger model. As Table 5 shows, our model was significantly better
than all of the original goal theories at discriminating successful versus unsuccessful goal pursuit.
Discussion
Study 1 examined the conceptual overlap between, and predictive validity of, 17 constructs identified
from research on goal achievement. Factor analysis suggested that 12 factors provided the best fit to
the data as a whole, and proved to be invariant across the conditions. The 12 identified factors were
then entered into a discriminant analysis to determine their relative importance in discriminating
successful achievement of a personal goal versus failure. The findings revealed that success in
achieving personal goals is associated with high levels of motivation, greater task focus, the formation
of implementation intentions, and high levels of social support and subjective norm.
It is important to acknowledge that the design of Study 1 was retrospective, which leaves open the
possibility that self-serving or memory biases may have influenced participants’ responses. For
example, participants who were assigned to the goal failure condition may have been more likely to
attribute their failure to external factors than to personal factors. However, two findings speak against
this explanation. First, the content of the tasks and perceived task difficulty did not differ between the
groups. Second, the three most important predictors of goal achievement were personal factors
(motivation, task focus, and implementation intentions). Notwithstanding this evidence, it seemed
important to attempt to replicate the model proposed in Study 1 and to consider the predictive power of
the obtained factors using a prospective design.
STUDY 2
The principal aim of Study 2 was to replicate and extend the findings of Study 1 using a prospective
design. Students taking an introductory psychology course (PSY101) were asked to complete a
questionnaire that asked about their perceptions of ‘doing well on PSY101.’ Two months later their
exam grades were recorded and the predictive capacity of the model identified in Study 1 was tested.
Method
Participants and Design
N ¼ 166 participants completed the questionnaire at Time 1. Participants were predominantly female
(69%) and ages ranged from 17 to 36 years (M ¼ 18.94, SD ¼ 2.23). At Time 2, exam grades for 78%
of the sample were obtained (N ¼ 129). MANOVA revealed that participants in the final sample and
participants for whom exam grades could not be obtained (N ¼ 37), did not differ on the demographic
variables or questionnaire items, F(60, 95) ¼ 1.39, ns.
Copyright # 2004 John Wiley & Sons, Ltd.
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82
Thomas L. Webb and Paschal Sheeran
Measures
Questionnaire measures of the 17 constructs were the same as Study 1 except that each item now made
reference to the focal behaviour, ‘doing well on PSY101.’ The only other change was the use of
different ego-depletion items (e.g. ‘Lots of situations other than PSY101 require my willpower’ and ‘I
feel a lot of demands other than PSY101 are being placed upon my willpower’). The original measure
of ego-depletion was changed due to a concern that the measure should reflect self-control demands
(Baumeister et al., 1998) rather than simply general busyness. Scale reliabilities were acceptable
(median Cronbach’s ¼ 0.72, range 0.46–0.86), with the exception of personal standards ( ¼ 0.55),
acquiescence ( ¼ 0.53), and subjective norm ( ¼ 0.46).
Procedure
Participants were approached during a PSY101 lecture and asked if they would complete a questionnaire
about their perceptions of the course. Participants were informed that all responses would be treated in
confidence, that the study was part of a larger research programme concerned with how people achieve
their goals, and that his or her data would not be shown to anyone involved with the PSY101 course. In
addition, participants consented to their exam results being obtained for research purposes.
Participants were first asked to give their student number, age, and gender. Next, participants
defined what mark represented ‘doing well’ for them on the PSY101 exam (out of 100). Participants
were asked to keep their answer in mind during the questionnaire as each question referred to ‘doing
well on PSY101.’ Next participants were asked to complete measures of the 17 constructs.
Results
Defining Successful Goal Achievement
In order to allow direct comparison with the findings from Study 1, participants’ scores on the PSY101
exam were used to define two conditions, success and failure. Successful performance was defined as
achieving a first or 2.1 grade on the exam paper whereas all other grades were classified as failed
performance. This definition is consistent with participant’s own perceptions of success since 90% of
participants defined ‘doing well on PSY101’ as achieving either a first (53.3%) or a 2.1 (36.7%).
According to this definition, n ¼ 70 participants were classified as successful, and n ¼ 59 participants
were classified as having failed.
Factor Analysis
The factor analysis was performed through SPSS on 53 items for the sample of N ¼ 166 participants.
Twelve factors were extracted (based on Kaiser’s, 1958, criterion) that explained 67% of the variance
in participants’ responses. Table 6 shows the loadings of variables on factors, the amount of variance
explained by each factor, and the alpha coefficient representing the reliability of each factor. As before,
variables are ordered by size of loading to facilitate interpretation and loadings less than 0.30 are not
reported.
Factor 1 had high loadings from intention, commitment, and two attitude items and was interpreted
as motivation. Self-efficacy items loaded on Factor 2. Factor 3 had high loadings from items intended
to measure task feedback and social support. All three emotion items loaded on Factor 4 (along with
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
Integrating goal theories
Table 6.
83
Principal axis factoring with direct oblimin rotation for the total sample (Study 2)
Factor/items
Loading
F1: Motivation
I definitely intend to do well on PSY101
I plan to do well on PSY101
I feel that my trying to do well on PSY101 is worthwhilea
I tell myself to try and do well on PSY101
I feel very committed to doing well on PSY101
I do not care about doing well on PSY101 (r)
I feel that it is pointless trying to do well on PSY101 (r)
I do not put much effort into doing well at PSY101 (r)
R2
F2: Self-efficacy
I feel capable of doing well on PSY101
I feel I am able to do well on PSY101
I do not feel very confident that I can do well on PSY101 (r)
R2
F3: Social support/task feedback
I receive a lot of assistance from others with PSY101
I feel I have no support from others with PSY101 (r)
Other people give me a great deal of feedback about how they think I am doing in PSY101
No-one comments on my progress with PSY101 (r)
Very few other people help me in terms of doing well on PSY101 (r)
I am not in a position to know how well I am doing with PSY101 (r)
R2
F4: Emotion
I am very emotional about doing well on PSY101
I am not at all emotional about doing well on PSY101 (r)
Doing well on PSY101 arouses very little emotion in me (r)
I just take it as it comes in terms of doing well on PSY101
R2
F5: Ego-depletion/conflicting standards
I feel a lot of demands other than PSY101 are being placed on my willpower
Lots of situations other than PSY101 require my willpower
I have competing commitments that prevent me doing well on PSY101
Other things always seem to tempt me away from PSY101
I have more important priorities than doing well on PSY101
R2
F6: Implementation Intentions
I do not set myself a time and a place to work on PSY101 (r)
I tell myself where and when I will do work for PSY101
I have committed myself to working on PSY101 in specific places at specific times
Physically, I often feel only able to do a little towards doing well on PSY101a
I do not let things slide with PSY101 (r)
I try and make things happen so that I do well on PSY101
R2
0.71
0.69
0.53
0.52
0.51
0.47
0.46
0.35
0.23
0.89
0.80
0.77
0.67
0.08
0.83
0.79
0.65
0.60
0.59
0.47
0.39
0.06
0.76
0.90
0.78
0.60
0.46
0.05
0.82
0.81
0.65
0.61
0.34
0.33
0.04
0.68
0.81
0.81
0.73
0.30
0.30
0.26
0.04
0.83
Continues
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
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Thomas L. Webb and Paschal Sheeran
Table 6. Continued
Factor/items
Loading
F7: Physical fatigue
Physically, I feel I am in an excellent condition to do well on PSY101 (r)
Physically, I feel I am in too bad a condition to do well on PSY101
It takes a lot of effort to concentrate on doing well on PSY101
Very few things other than PSY101 require me to exert self-control (r)
I felt under social pressure not to do well on PSY101 (r)
R2
F8: Mental fatigue
When I am doing PSY101 I can keep my thoughts on it (r)
Physically, I can take on a lot of work for PSY101 (r)
I can concentrate well on PSY101 (r)
R2
F9: Subjective norms
I feel that people important to me would want me to do well on PSY101
People important to me would approve of my doing well on PSY101
R2
F10: Task demands
Most people would find doing well on PSY101 very easy (r)
Doing well on PSY101 would be a difficult task for anyone
By anyone’s standards doing well on PSY101 is very demanding
R2
F11: Personal standards
My target for doing well on PSY101 is very low (r)
I have been very ambitious in my definition of doing well on PSY101
R2
F12: Task focus
I put a great deal of energy into doing well on PSY101
I try very hard to do well on PSY101a
I am very dedicated to doing well on PSY101a
My thoughts easily wander from doing well on PSY101a
I enjoy trying to do well on PSY101
I dislike trying to do well on PSY101 (r)
R2
0.67
0.64
0.38
0.28
0.27
0.03
0.60
0.67
0.59
0.59
0.03
0.78
0.83
0.82
0.03
0.79
0.66
0.63
0.60
0.02
0.70
0.45
0.41
0.02
0.55
0.57
0.42
0.40
0.35
0.34
0.33
0.02
0.83
Note: Loadings < 0.30 are suppressed, (r) indicates that the item was recoded prior to factor analysis.
a
Complex item: loadings > 0.30 on more than one factor.
one acquiescence item), which was labelled emotion. Factor 5 had high loadings from questions
concerning ego-depletion and conflicting standards. Factor 6 had high loadings from items intended to
measure implementation intentions. Physical fatigue and mental fatigue items loaded on Factor 7 and
Factor 8, respectively. Factors 9, 10, and 11 were loaded on by subjective norm items, task demand
items, and items designed to measure personal standards, respectively. Finally, Factor 12 had high
loadings from questions measuring effort, mental fatigue and affective beliefs, and seemed to capture
the concept of task focus. Reliability for all factors was satisfactory (median ¼ 0.78), with the
exception of Factor 11, personal standards, ¼ 0.55.
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
Integrating goal theories
85
In order to ascertain the extent to which the factor structure identified in Study 2 matched the
structure identified in Study 1, Pearson’s correlations were computed between all pairs of factor
vectors between studies (Barrett, 1986). The factor structure was well matched across the two studies
(median congruence ¼ 0.92, range 0.75 to 0.99), which reinforces the validity of the model.
Correlations between the factors are shown in Table 7. Factor 1, motivation, had moderate positive
correlations with emotion, implementation intentions, subjective norm, and personal standards
(0.28 < rs < 0.41). Factor 2, self-efficacy, had moderate negative correlations with physical fatigue
and mental fatigue. Factor 6, implementation intentions, was negatively correlated (r ¼ 0.33) with
mental fatigue and positively correlated (r ¼ 0.42) with task focus. Finally, Factor 7, physical fatigue,
was positively correlated (r ¼ 0.33) with mental fatigue, which, in turn, was negatively correlated
(r ¼ 0.33) with task focus. None of the other correlations exceeded r ¼ 0.30.
Discriminant Analysis
In order to test whether the five factor model identified in Study 1 could successfully discriminate
between successful and unsuccessful exam performance, a hierarchical discriminant analysis was
conducted using SPSS. In the first step of the analysis, the five factors that differed significantly
between the conditions in Study 1 (motivation, task focus, implementation intentions, social support,
and subjective norm) entered the equation. In the second step, the remaining factors were entered to
see if they improved the classification.
At step 1, the discriminant function was significant, 2(5) ¼ 12.33, p < 0.05. The first column of
Table 8 presents the correlations between the factors and the discriminant function. The predictor that
best distinguished between success and failure was whether the participant had formed an implementation intention specifying when and where they would work on PSY101. In order of discriminatory importance, participants in the success condition were also more focused on PSY101, had
greater motivation, and were more likely to have received social support, task feedback, and believe
that people important to them approved of their doing the task.
In the second step of the analysis the remaining factors were entered: self-efficacy, emotion, egodepletion, physical and mental fatigue, task demand, and personal standards. As in Study 1, entering
additional factors only decreased the within-groups degrees of freedom and the discriminant function
was reduced to non-significance, 2(12) ¼ 16.70, ns. In order to determine whether classification
improved as a result of the additional predictors, McNemar’s repeated measures chi-square was
computed (see Tabachnick & Fidell, 1996). This statistic compares the success of classification for
each case in the two analyses and uses chi-square to compare the number of participants successfully
classified in each analysis. Chi-square for the difference in classification between the five-factor and
the 12-factor model was non-significant, 2(1) ¼ 0.04, ns, which indicates that the additional
predictors did not improve the prediction of exam success versus failure. The second column of
Table 8 presents the correlations between the factors and the second discriminant function.2
Finally, we used ANOVA to compare the mean score on each of the five factors that made up the
discriminant function by condition. The results showed that only the first three factors; implementation
intentions, task focus, and motivation differed significantly between the conditions (see Table 8).
Subjective norm and feedback/social support produced effects in the expected direction, but these
were non-significant. Next, in order to test whether scores on the additional seven factors differed by
2
It is notable that the groups differ more on both task demand and ego-depletion than on subjective norm and task feedback from
the original model. However, replacing these factors in the original model (i.e. subjective norm and task feedback were removed
and replaced with task demand and ego-depletion) did not significantly improve the discriminant ability of the model,
2(1) ¼ 0.57, ns (McNemar’s chi-square test).
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
Copyright # 2004 John Wiley & Sons, Ltd.
Motivation
Self-efficacy
Feedback/support
Emotion
Ego-depletion
Imp. intentions
Physical fatigue
Mental fatigue
Subjective norm
Task demand
Personal standards
Task focus/affective
0.28***
0.02
0.32***
0.04
0.36***
0.25**
0.24**
0.41***
0.28***
0.30***
0.23**
F1
0.19*
0.03
0.22**
0.26**
0.32***
0.36***
0.18
0.15
0.13
0.19*
F2
Correlations among factors (Study 2)
Note: *p < 0.05; **p < 0.01; ***p < 0.001.
F1:
F2:
F3:
F4:
F5:
F6:
F7:
F8:
F9:
F10:
F11:
F12:
Table 7.
0.13
0.20*
0.18*
0.05
0.18*
0.09
0.03
0.01
0.10
F3
0.06
0.24**
0.02
0.14
0.28***
0.06
0.21**
0.15
F4
0.12
0.15
0.15
0.01
0.02
0.11
0.19*
F5
0.13
0.33***
0.18*
0.14
0.18*
0.42*
F6
0.33***
0.10
0.01
0.09
0.06
F7
0.17*
0.05
0.10
0.33***
F8
0.11
0.22**
0.16*
F9
0.21**
0.14
F10
0.17*
F11
86
Thomas L. Webb and Paschal Sheeran
Eur. J. Soc. Psychol. 35, 69–96 (2005)
Integrating goal theories
Table 8.
87
Correlations with the two discriminant functions, means, and Fs for factors by condition (Study 2)
Discriminant function
Factor
Implementation intentions
Task focus
Motivation
Subjective norm
Feedback/social support
Task demand
Ego-depletion
Physical fatigue
Self-efficacy
Personal standards
Emotion
Mental fatigue
r1
r2
0.64
0.35
0.31
0.27
0.17
0.44
0.31
0.34
0.27
0.08
0.33
0.29
0.23
0.22
0.18
0.05
0.02
Means
Failure
0.29
0.22
0.14
0.01
0.09
0.01
0.12
0.06
0.07
0.10
0.03
0.05
Success
F
0.24
0.14
0.18
0.13
0.11
0.12
0.17
0.13
0.30
0.14
0.12
0.25
10.20***
5.71*
4.08*
0.11
1.61
1.76
1.45
0.12
1.36
0.49
0.09
0.18
Note: Bonferroni correction has been applied.
*p < 0.05; ***p < 0.001.
condition once scores on the original five factors had been taken into account, we conducted RoyBargmann Stepdown Fs for each of these factors using the original factors as covariates. None of the
other seven factors explained any additional variance beyond implementation intentions, task focus,
motivation, subjective norm, and feedback/social support.
The discriminant function made up of the five factors from Study 1 correctly classified 79
participants (59.7%) compared to 65 (50.0%) that would have been correctly classified by chance
alone (see Table 9). Finally, in order to ensure that the variables derived from the factor analysis
were a better predictor of success versus failure than the original goal theories from which the
constructs were derived, we conducted several additional discriminant analyses testing each of the
original models (see Table 10). As before, we computed the significance of the difference between
the Wilk’s Lambda derived from the original model of goal achievement and the Wilk’s Lambda
from our own five factor (motivation, task focus, implementation intentions, social support, and
subjective norm) using the procedure described by Tabachnick and Fidell (1996, p. 545). As
Table 10 shows, although our model was the best discriminator of successful versus unsuccessful
exam performance, it was not significantly better than any of the other models. It is likely that this
finding is attributable to a floor effect— the discriminant validity of all the models was reduced
considerably from Study 1 to Study 2. These findings are consistent with evidence that it is
notoriously difficult to predict students’ exam performance at least in the UK (even based on
demographic variables or absenteeism, Roddan, 2002).
Table 9.
Discriminant function classification table (Study 2)
Actual
Predicted
Success
Failure
Copyright # 2004 John Wiley & Sons, Ltd.
Success
70.0% (n ¼ 49)
30.0% (n ¼ 21)
Failure
52.5% (n ¼ 31)
47.5% (n ¼ 28)
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Table 10.
Comparisons between our five-factor model and the original goal theories (Study 2)
Model
Five-factor model
Goal-setting theory
Strength model of self-control
Conflicting standards
Theory of planned behaviour
Theory of self-regulation
Implementation intentions
Mental fatigue
Social support
Task demand
Emotion
Wilk’s Lambda
0.91
0.91
0.93
0.93
0.93
0.93
0.94
0.96
0.99
1.00
1.00
Approx. F
0.22
0.52
0.60
0.71
0.76
0.95
1.33
2.28
2.47
2.50
Discussion
The purpose of Study 2 was to replicate the factor structure obtained in Study 1 and to test the predictive
capacity of the identified constructs in a prospective design. The results conformed to expectations.
Factor analysis revealed that a 12-factor solution provided the best fit to the data and that the factor
structure was extremely similar to the structure obtained in Study 1. Moreover, three of the five factors
identified in Study 1 as determinants of goal achievement (motivation, task focus, and implementation
intentions) successfully predicted exam performance. In sum, Study 2 provides additional support for
the factor structure of constructs from models of goal achievement and the importance of a subset of
these factors in determining whether or not people will achieve a personal goal.
However, there were two important differences between the findings of Study 1 and Study 2. First,
implementation intentions were a better predictor of goal achievement in Study 2 than in Study 1.
Indeed, specifying when and where to study was a more important predictor of examination success
than was motivation to do well on PSY101. One possible explanation of this finding might be that
implementation intentions are more suited to the pursuit of specific study goals (e.g. Sheeran et al.,
2003, Study 1) than some of the more general goals described by participants in Study 1 (e.g. sorting
out home life and marriage). Alternatively, our first study may have underestimated the importance of
implementation intentions because people’s naı̈ve theories on the factors that determined their success
or failure may neglect the beneficial effects of planning. For example, because plans specify how one
will behave in a future point in time, people may associate planning with negative properties of goal
pursuit such as inflexibility (Sheeran, Webb, & Gollwitzer, in press). Furthermore, the positive benefits
of planning are a relatively recent discovery in the psychology of goal pursuit and may not have
infiltrated pop science and the media to the same extent as more traditional constructs such as
motivation.3
The second difference between the two studies was the diminished importance of social support
and subjective norm in Study 2, when compared with Study 1. The non-significant effect of subjective
norm may be attributable to a ceiling effect—it seems likely that important others would uniformly
approve of the student doing well on his/her PSY101 exam. This assertion is supported by the data; the
mean score for the three subjective norm items was 5.89 (7-point scale), with just 3% of students
endorsing a response below the midpoint of the scale. However, lack of variability cannot explain the
reduced importance of social support in Study 2. One possibility is that exam success is essentially a
3
We thank an anonymous reviewer for bringing this point to our attention.
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very personal goal; thus, personal factors such as motivation, planning, and task-focus prevail over
more ‘social’ constructs such as support. Alternatively, it may be easier to retrospectively assess the
impact that others (and beliefs about the behaviours that others would approve or disapprove of) have
on goal pursuit than to assess this impact prospectively. For example, the latter may be biased because
people are unrealistically optimistic about the likelihood that others will help them. In sum, the model
identified in Study 1 should be seen as a general model applicable to many different types of goals;
however, the relative importance of each factor might differ as a function of the type of goal and study
design (prospective or retrospective).
GENERAL DISCUSSION
Studies 1 and 2 analysed both the conceptual structure of constructs from goal theories and their
relative importance in predicting goal attainment. Both retrospective (Study 1) and prospective (Study
2) designs suggested that motivation, task focus, implementation intentions, and, in some instances,
social support and subjective norm distinguish when people succeed in achieving personal goals from
when they fail. This discussion will consider the implications of the factor model for the distinction
between motivational and volitional processes and then consider the predictive findings.
The factor structure supports the model of action phases (Heckhausen, 1987; Heckhausen &
Gollwitzer, 1986,1987). The first factor—motivation—embraced concepts relating to goal selection;
intention, commitment, and attitude towards the goal (perceived utility). Thus, the first factor neatly
parallels the predecisional phase of action, which consists of deliberating wishes and setting
preferences (Gollwitzer, 1990). The factor structure also supports the distinction between the
predecisional (goal setting) and preactional (planning) phases of action—variables measuring
implementation intentions were distinct from motivational constructs. The model of action phases
also acknowledges that achieving a personal goal is not simply about initiation of the relevant
behaviour, but also requires maintenance of the behaviour over time. Thus, the actional phase refers to
ideas of goal striving that are reflected in the construct of task focus obtained in the factor analysis. For
example, goal striving requires that one ‘puts energy into the task’ and ‘does not allow thoughts to
wander.’ In sum, the identified factor structure discriminates between constructs that are presumed to
influence goal pursuit during different action phases.
Two other findings from the factor analyses also warrant discussion. First, the analyses supported
the distinction between subjective norms and social support as qualitatively different constructs
(Courneya & McAuley, 1995). Subjective norm reflects a person’s perception of what other people
important to them would want them to do, whereas social support reflects actual help or assistance that
the person receives from others in achieving his/her goal. Indeed, the finding that subjective norm and
social support were independently predictive of a person’s success in achieving their goals in Study 1
further strengthens this distinction. Second, the factor analysis revealed an interesting dichotomy
among the attitude items. Four attitude items were employed, two of which (relating to whether the
task was worthwhile or pointless) loaded on the motivation factor. The other two attitude items,
relating to enjoyment and liking of the task, loaded separately, and were labelled ‘affective beliefs’.
Thus, the present data support the distinction between affective and cognitive beliefs in the structure of
attitudes (e.g. Rosenberg, 1960, 1968; Trafimow & Sheeran, 1998).
The discriminant analyses showed that motivation was the most important predictor (Study 1) and
the third most important predictor (Study 2) of success versus failure in achieving personal goals. The
study of motivation has a long history in psychology (see e.g. Gollwitzer & Moskowitz, 1996, for an
overview), and its importance here is not surprising. Motivation reflects the strength of a person’s
Copyright # 2004 John Wiley & Sons, Ltd.
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Thomas L. Webb and Paschal Sheeran
decision to achieve a goal and this construct appears to underlie three specific constructs; intention,
commitment, and cognitive attitudes. The importance of motivation has a number of implications for
models of goal achievement. First, it provides support for the proposal made by several models
including the TPB, protection motivation theory (Rogers, 1983), and the model of interpersonal
behaviour (Triandis, 1980) that intentions are the most important predictor of behaviour (see Sheeran,
2002, for a review). Second, contrary to the theory of self-regulation, the findings indicate that
intention and commitment are not empirically distinct constructs. Third, the importance of cognitive
attitudes lends further support to research that suggests people perform a cost-benefit analysis when
committing themselves to a personal goal (e.g. van der Pligt & de Vries, 1998).
The finding that implementation intention formation increased the probability that the person
would be successful in achieving his/her goal is consistent with previous studies on implementation
intentions (Gollwitzer, 1999; Sheeran, 2002). However, this finding is especially important because
previous studies of implementation intentions have been experimental—one half of a sample were
asked to form implementation intentions whereas the other half were not. In contrast, the present study
shows that people spontaneously form implementation intentions when trying to achieve their goals
and that these implementation intentions are effective in promoting performance. Further research is
needed to examine the conditions under which people naturally form implementation intentions. For
example, Bargh and Gollwitzer (1994) argued that people only form an implementation intention
when they anticipate problems in achieving their goal. In support of this assertion, positive correlations
between perceived task demand and implementation intention formation were obtained in both Study
1 (r ¼ 0.18, p < 0.001) and Study 2 (r ¼ 0.14, p < 0.07). Participants were more likely to form an
implementation intention if they felt that the task was difficult.
Task focus was also an important factor in determining goal achievement, and reflects the amount
of effort exerted and the person’s level of mental fatigue. Interestingly, task focus is not explicitly
included in the theories of goal achievement outlined in the introduction. Bagozzi’s (1992) theory of
self-regulation points to the importance of expending effort, but the main idea behind task focus is
directed effort; the ability to concentrate on the task and ignore distractions. However, the issue of task
focus has been considered in relation to volitional strategies. For example, Schaal (1993, cited in
Gollwitzer & Schaal, 1998) addressed the issue of how people stay focused on a boring but strenuous
task, while being exposed to attractive distractions. Schaal tested the idea that two different forms of
implementation intentions may prove effective. In the first, a task facilitating implementation
intention, participants planned to work harder on the focal task in the presence of distractions. In
the second, a temptation inhibiting implementation intention, participants planned to ignore the
distractions. The results suggested that both plans improved performance on the focal task; however,
the plan directed at temptation inhibition produced the strongest performance enhancing effects.
Implementation intentions may, therefore, prove an effective strategy for overcoming problems with
task focus.
The final two discriminating factors—social support and subjective norm—speak to the idea that
striving to achieve a personal goal may involve support and approval from significant others. The
importance of social support is consistent with research on the health-protective qualities of social
support (for a review, see Rutter, Quine, & Chesham, 1993). Further research may usefully consider
how social support influences the achievement of personal goals. For example, social support could
prove useful both in the initiation of a task (e.g. starting a fitness regime might be easier if a friend
drives you to the gym), and during goal striving (e.g. verbal encouragement may improve persistence
on the running machine). The importance of subjective norm was less predictable, as the normative
component of the TPB is typically its weakest predictor (Armitage & Connor, 2001), and is usually
presumed to have an indirect influence on behaviour through behavioural intentions. However,
Armitage and Connor found that the relationship between subjective norm and intention was
Copyright # 2004 John Wiley & Sons, Ltd.
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Integrating goal theories
91
moderated by measurement considerations, with multi-item measures of normative beliefs providing
stronger correlations than other measures. Perhaps the use of a multi-item measure in the present study
increased reliability and thereby enhanced the predictive validity of subjective norm. An alternative
explanation for the effect of subjective norm is that most studies of the TPB examine the incidence or
frequency of behavioural performance (cf. Sheeran, Connor, & Norman, 2001) whereas the present
study was concerned with persistence in the pursuit of a goal. Perhaps subjective norm is more
important for the maintenance of behaviour over time than it is for behavioural initiation.
The discussion thus far has focused on the factors that discriminated successful from failed attempts
to achieve personal goals. However, findings pertaining to factors that failed to distinguish the groups
also merit discussion. It is notable that self-efficacy did not discriminate between participants who
failed and participants who succeeded. One explanation for this finding (at least in Study 1) is that selfefficacy may influence performance through the selection of personal goals, rather than influencing goal
striving itself (Locke & Latham, 1990). For example, people are unlikely to try to achieve behavioural
targets over which they feel little control (Bandura & Wood, 1989; Earley & Lituchy, 1991; Gibbons &
Weingart, 2001). Alternatively, self-efficacy could have influenced goal achievement indirectly through
task focus. For example, there is evidence to suggest that high self-efficacy leads to greater focus on the
task whereas low self-efficacy directs attention to self-evaluation and increases self-doubt (Gibbons &
Weingart, 2001). In sum, self-efficacy may not have affected performance directly because its effects
were mediated by the selection of personal goals and/or task focus.
Ego-depletion did not influence self-regulatory success, contrary to the prediction from the strength
model of self-control (Muraven & Baumeister, 2000). One possibility is that ego-depletion is a
difficult concept for people to reflect upon, and our questionnaire measure may not have captured what
is going on in the experimental studies that are usually used to investigate ego-depletion (see, for
example, Baumeister et al., 1998). Alternatively, the effects of ego-depletion could have been
mediated by reduced motivation. Martijn, Tenbült, Merckelbach, and de Vries (2003) showed that
altering expectancies about tiredness in the standard ego-depletion paradigm was sufficient to
overcome the negative effects of ego-depletion. Similarly, Muraven and Slessareva (2003) found
that when participants believed that the task was important (i.e. the incentives were high enough) egodepletion had little effect on performance. Finally, ego-depletion may not have influenced goal
attainment due to the temporary nature of its influence. By definition, ego-depletion refers to the
temporary depletion of the self-control resource. However, many of the goals chosen by participants in
Study 1 (and the goal of ‘doing well on PSY101’ in Study 2) require goal striving over prolonged
periods of time. Thus, although people may lack the self-control required to ‘strive’ on Monday, by the
next day the self-control resource will have been replenished. Moreover, Muraven, Baumeister, & Tice
(1998) argued that ‘it is good to exert self-control on a regular basis because in the long run, these
exercises will strengthen self-control and make a person less susceptible to the depleting effects of a
single exertion’ (p. 456). This suggests that the effect of ego-depletion on performance may follow a
temporal pattern wherein ego-depletion undermines performance in the short term but, paradoxically,
may serve to enhance performance in the long term (cf. Muraven & Slessareva, 2003).
Conclusion
In conclusion, only three previous studies (Bagozzi & Kimmel, 1995; Fredricks & Dossett, 1983;
Valois et al., 1988) have tried to integrate goal theories, and there have been no previous attempts to
integrate recent concepts (e.g. ego-depletion and implementation intentions) with more traditional
models (e.g. the TPB). Thus, the present research provides an important analysis of both the
conceptual structure of factors from goal theories, and their relative importance in predicting goal
Copyright # 2004 John Wiley & Sons, Ltd.
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Thomas L. Webb and Paschal Sheeran
attainment. Two studies demonstrated that motivation, task focus, and implementation intentions
distinguish when people succeed in achieving personal goals from when they fail. In addition, the
research suggested a number of directions for future research on goal achievement. For example,
further studies are needed: (a) to determine how social support and subjective norm combine to
influence goal achievement; (b) to confirm the importance of task focus in promoting goal
achievement; and (c) to delineate the conditions under which people spontaneously form implementation intentions.
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Wilensky, R. (1983). Planning and understanding: A computational approach to human reasoning. Reading, MA:
Addison-Wesley.
APPENDIX: QUESTIONNAIRE ITEMS
Acquiescence was measured with three items: ‘I just went with the flow in this situation’, ‘I just went
along with things in this situation’, and ‘I did not let things slide in this situation’ (Cronbach’s ¼ 0.61
and 0.53, for Study 1 and Study 2, respectively).
Attitude was measured using four items: ‘I felt that my trying to do this task was worthwhile’, ‘I
disliked trying to do this task’, ‘I enjoyed trying to do this task’, and ‘I felt that it was pointless trying
to do this task’ (Cronbach’s ¼ 0.74 and 0.72).
Behavioural intentions were measured with three items: ‘I definitely intended to see this task
through to the end’, ‘I planned to see this task through to the end’, and ‘I told myself to try and see the
task through to the end’ (Cronbach’s ¼ 0.84 and 0.80).
Commitment was measured using three items: ‘I felt very committed to acting in this situation’, ‘I
did not care about the outcome of this situation’, and ‘I felt very dedicated to this situation’
(Cronbach’s ¼ 0.76 and 0.78).
Conflicting standards were measured using three items: ‘I had other priorities in this situation’, ‘I
had other competing commitments at this time’, and ‘I never felt tempted to do other things’
(Cronbach’s ¼ 0.55 and 0.57).
Effort was measured using three items: ‘I put very little effort into the task’, ‘I tried very hard in this
situation’, and ‘I put a great deal of energy into being successful in this situation’ (Cronbach’s
¼ 0.88 and 0.84).
Ego-depletion was measured using three items: ‘Very few other things were happening at the time
of this situation’, ‘My life was very hectic at the time of this situation’, and ‘I felt a lot of demands
were being placed upon me about this time’ (Cronbach’s ¼ 0.71 and 0.58).
Emotion was measured with three items: ‘I was not at all emotional about the situation’, ‘I was very
emotional in this situation’, and ‘The situation aroused very little emotion in me’ (Cronbach’s
¼ 0.90 and 0.82).
Feedback was measured using three items: ‘Other people told me a great deal about how they
thought I was doing in this situation’, ‘No-one remarked about how they thought I was doing during
this task’, and ‘No-one commented on my progress with the task’ (Cronbach’s ¼ 0.82 and 0.69).
Implementation intentions were measured with three items: ‘I told myself where and when I would
complete this task’, ‘I did not set myself a time and a place to complete this task’, and ‘I committed
myself to performing the task in a specific situation’ (Cronbach’s ¼ 0.76 and 0.86).
Mental fatigue was measured by four items from the Multidimensional Fatigue Inventory (MFI-20,
Smets, Garssen, Bonke, & De Haes, 1995) phrased in the past tense, and measured on 7-point, rather
than 5-point scales: ‘When I was doing the task I could keep my thoughts on it’, ‘I could concentrate
well in this situation’, ‘It took a lot of effort to concentrate on this task’, and ‘My thoughts easily
wandered in this situation’ (Cronbach’s ¼ 0.73 and 0.73).
Personal standards were measured using two items: ‘I was very ambitious in this situation’, and ‘I
set my sights very low in this situation’ (Cronbach’s ¼ 0.57 and 0.55).
Physical fatigue was measured by four items from the MFI-20 (Smets et al., 1995) rephrased into
the past tense, and measured on 7-point, rather than 5-point scales: ‘Physically I felt only able to do a
little in this situation’, ‘Physically I felt I could take on a lot in this situation’, ‘Physically I felt I was in
Copyright # 2004 John Wiley & Sons, Ltd.
Eur. J. Soc. Psychol. 35, 69–96 (2005)
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Thomas L. Webb and Paschal Sheeran
a bad condition in this situation’, and ‘Physically I felt I was in an excellent condition in this situation’
(Cronbach’s ¼ 0.77 and 0.68).
Self-efficacy was measured using three items: ‘I was not very confident in my ability in this
situation’, ‘I felt able to handle this situation’, and ‘I felt capable of being successful in this situation’
(Cronbach’s ¼ 0.77 and 0.83).
Social support was measured using three items: ‘I received no help from others in this situation’, ‘I
received assistance from others in this situation’, and ‘I felt I had no support from other people’
(Cronbach’s ¼ 0.85 and 0.72).
Subjective norms were measured using three items: ‘I felt that people who are important to me
wanted me to succeed in this task’, ‘I felt under social pressure to succeed at this task’, and ‘People
important to me disapproved of my doing this task’ (Cronbach’s ¼ 0.47 and 0.46).
Task demands were measured with three items: ‘Most people would have found this task very easy’,
‘This would have been a difficult task for anyone’, and ‘By anyone’s standards the task was very
demanding’ (Cronbach’s ¼ 0.84 and 0.70).
Copyright # 2004 John Wiley & Sons, Ltd.
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