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LSP Journal, Vol.3, No.2 (2012) / http://lsp.cbs.dk
51
A genre analysis of PhD dissertation acknowledgements
across disciplinary variations
Wenhsien Yang
Department of Applied English,
National Kaohsiung University of Hospitality and Tourism,
Kaohsiung, Taiwan
yangwenhsien@mail.nkuht.edu.tw
Keywords: genre analysis, dissertation acknowledgements, disciplinary variations, keyword
analysis
Abstract
This study examined PhD dissertation acknowledgements (DA) written by EFL authors
in an English-speaking context. A total of 120 texts from six different disciplines were
collected as the study corpus. The study attempted to investigate whether or not the
variable of discipline would exercise influences on the construction of DA in terms of
their generic structure and linguistic choices made to modify thanking acts. It is found
that subtle variations existed in employing strategies of writing DA between soft science
and hard science PhD students. A number of factors contributed to the diversity,
including the area of research, academic conventions, exposure to English, language
proficiency, and socio-cultural norms or expectations. In addition, the study also suggests
that ESP practitioners attend to genre analysis of DA at both macro and micro levels in
order to develop ESP learners’ awareness of broad socio-cultural and narrow linguistic
perspectives as they learn to construct appropriate dissertation acknowledgements.
LSP Journal, Vol.3, No.2 (2012) / http://lsp.cbs.dk
52
1 Introduction
Expressing gratitude in academia is a common practice and is also commonly seen in
academic texts, in particular, in dissertation acknowledgements (DA). However, writing
acknowledgements does not simply involve listing the individuals acknowledged for their
assistance; rather, “acknowledgements are sophisticated and complex textual constructs which
bridge the personal and the public, the social and the professional, and the academic and the
lay” (Hyland, 2003: 265). Acknowledgements not only provide writers with space to signify
interpersonal relationships by employing rhetorical devices, but reflect writers’ personal
identity and socio-cultural, contextual or conventional values. Compared with other academic
texts such as the introduction, literature review, methods, results, discussion and conclusion in
dissertations and journal research articles, researching DA is generally regarded as marginal
and thus has received relatively less attention (Cheng, 2012; Hyland, 2004a).
To express appropriate personal gratitude through rhetorical elements relies much on what
identities writers adopt in different situations; that is, how writers position themselves through
elaborated language use in their DA. Nevertheless, acknowledgements are not entirely
personal but can also be context-embedded. Language users in different contexts may have
various thought patterns, and these affect writers’ preferred patterns of rhetoric. Use of the
full range of one specific language will not occur with equal frequency across different
contexts (Kaplan, 1987; Nkemleke, 2006). In addition, writing acknowledgements also
involves social and cultural pragmatism. Socio-cultural variations and preferences could
affect how the expressions of thanking acts are arranged and realised (Cheng, 2012). In other
words, personal identities and language use in DA are inevitably influenced by the contexts
writers are exposed to.
Previous research on DA has been conducted from two main perspectives. A majority of the
research, mainly following Hyland’s (2004a) model, has examined the compatibility between
their corpora and Hyland’s universal three-tier structure, and has attempted to identify
whether new localised moves/steps existed due to socio-cultural differences; meanwhile,
another direction has compared the differences of DA written by non-native (NNS) and native
English speakers (NS). Very few studies have compared and contrasted DA written by EFL
learners studying in English-speaking countries across different disciplines. Moreover, the
existing research has seldom addressed the issue of the keywords used to modify thanking
acts in DA. Therefore, the present study endeavours to compare and contrast PhD dissertation
acknowledgements written by EFL learners in an English-speaking country, to be specific,
Taiwanese students studying in the US, across various disciplines, namely, hard sciences and
soft sciences, in order to investigate how DA are structured, sentence patterns and lexical
elements chosen in the expressions of thanking acts, and whether disciplinary conventions or
the targeted culture (i.e. the US) will affect the above across the two major science areas.
2 Literature Review
In studying acknowledgements, most researchers have adopted the genre analysis approach
(Swales, 1990). According to Bhatia (1993), a genre is highly structured and
conventionalised, and has specific constraints such as lexis and moves exploited by the
members in a community to achieve communicative purposes. Studies on conventionally
recognisable texts of a genre can better attend to the dynamic/negotiated aspects of situated
language use (Lee, 2001). Analysing a genre helps ESP practitioners and writers identify how
texts are structured and distinguished in conventional and socio-cultural contexts in order to
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realise their communication purposes (Hyland, 2004a). Moreover, analysing texts in the genre
approach offers researchers “explicit and systematic explanations of the ways language
functions in social contexts” (Hyland, 2004a: 18), which also helps writers acquire the
specialist culture (Bhatia, 1997).
Giannoni (2002), as the first genre analyst studying acknowledgements, analysed
acknowledgements in journals and concluded that their generic structure not only reflects the
varieties of different disciplines but is affected by national patterns of the disciplinary
communities. However, it was Hyland (2003, 2004a) and his colleague (Hyland & Tse, 2004)
who started to analyse dissertation acknowledgements systematically and established the
three-tier generic structure of expressing gratitude in DA. In their model, DA mainly consist
of three moves, namely, one obligatory move, the thanking move (Move 2) where writers
map credit to individuals and institutions, and two optional moves, the reflective move (Move
1) in which writers introspectively comment on their research experience, and the announcing
move (Move 3) where they make a public statement of responsibility and inspiration. In the
thanking move, there are four sub-divided steps, namely, presenting participants (Step 2.1),
thanking for academic assistance (Step 2.2), thanking for resources (Step 2.3), and thanking
for moral support (Step 2.4). There are two sub-divided steps in Move 3, namely, accepting
responsibility (Step 3.1) and dedicating the thesis (Step 3.2). Hyland (2003: 266) also
acknowledges that DA not only “play an important role in promoting a competent, even
rhetorically skilled, scholarly identity“ of the acknowledgers, but also reveal their social and
cultural characteristics in situated settings.
The above three studies (Hyland, 2003, 2004a; Hyland & Tse, 2004) opened a window for
subsequent research to scrutinise DA in more detail. Zhao and Jiang (2010) examined DA
written by Chinese speakers in China using a corpus from English-related disciplines, and
found that the structure generally follows Hyland’s model. However, subtle differences were
still identified. In their corpus, Moves 1 and 2 are absent, especially Step 3.2, and the writers
were prone to excessively use the bare mention form and modifiers in their thanking acts.
Zhao and Jiang contributed these differences to cultural, mental and academic diversities in
various contexts. Similarly, Cheng and Kuo (2011) investigated DA in the applied linguistics
discipline written by Chinese speakers in Taiwan. Their study found that Taiwanese writers
tend to express their gratitude explicitly and use more complex strategies to thank their
advisors, while Yang (2012a) compared DA in the same single discipline written by
Taiwanese students studying in both Taiwan and the US. He argued that the rhetorical
language in his samples was relatively direct, emotional and precise, and that academic
conventions, institutional preferences, the language context and socio-cultural factors were
the likely cause of this tendency. In addition, Yang (2012a) identified a unique step in Move 3
from his corpus, Making a confession, where writers confessed themselves to those who had
made sacrifices due to their postgraduate study.
Variations in arranging moves/steps and employing strategies to thank others for their
assistance are also commonly seen in other contexts. For instance, a new step was found in
Muslim cultures. Al-Ali (2006, 2010) identified a step named Thanking Allah (God), which is
caused by Arabic writers’ academic and social conventions. Furthermore, these writers tend to
use contextualised components to specifically realise their thanking acts. In Nkemleke’s
(2006) study, writers are apt to employ nativised deferential strategies and nominal phrases to
display good manners to their advisors and superiors. Afful and Mwinlarru (2010a. b.) also
argue that writers use different lexical, grammatical and discoursal elements to construct their
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identities and signify particular relationships with various individuals who are thanked. All of
these four studies confirm that the construction of dissertation acknowledgements has a
feature of hybridism and is dynamic because they are shaped and appropriated to
accommodate newly accepted practices and localised socio-cultures (Bhatia, 2004).
To better clarify socio-cultural influences on employing strategies in expressing gratitude,
some studies have compared and contrasted DA written by native English speakers (NS) and
non-native English speakers (NNS) in diverse contexts. Lasasky (2011) collected DA in the
applied linguistics discipline written by NS and NNS Iranians, but found that statistically
there was no significant difference between the two groups in terms of constructing DA,
although the step Thanking Allah was identified. In contrast, by collecting texts from the
same discipline, Cheng (2012) found a number of subtle differences between NS Americans
and NNS Taiwanese in terms of employing thanking strategies. She discovered that
Taiwanese writers tend to use more explicit but fewer implicit thanking strategies than
American students. Taiwanese also use relatively more complex rather than simple strategies.
Furthermore, the two groups adopt different strategies and preferences in arranging the
thanked addressees. Cheng (2012) contributed this distinctness to diverse social norms and
expectations in Taiwan and the US.
In contrast to the above studies, Scrivener (2009) investigated DA written by PhD history
students between 1930 and 2005 in the US. Rather than adopting Hyland’s model, she
attempted to discover the academic and life evolutions from history majors’ dissertation
acknowledgements. She concluded that societal changes and features of an academic
discipline impact how DA are constructed. For example, librarians and archivists are the
second most frequently acknowledged groups, and these history majors are no longer lone
scholars as they once were because they have gradually included more and more individuals
to be thanked in their acknowledgements. Besides, the language use has also dramatically
changed from formal to less formal voice in terms of the authorial subject from the third
person s/he to the first person I. This study not only reflects the fact that acknowledgements
are not simply a fixed form, but bridge writers’ ways of living, communication and interaction
with the public, the social, the professional and the academic (Bazerman, 1997; Hyland,
2004a).
In sum, a number of factors can indeed affect the construction, the strategies and the linguistic
realisations used in DA such as discipline, cultural expectations, language background, social
norms, and academic conventions. Previous studies have undertaken the cross-examination of
the influences of these variations. However, some perspectives might still be overlooked, such
as the diversities of disciplines studied by a single ethnic group, the status of English use, and
the context in which English is used. To bridge the gap, the present study examines
dissertation acknowledgements written by a group of EFL learners with an identical ethnic
background, specifically Chinese-speaking Taiwanese, studying in an English speaking
country, namely the US, across a wide range of academic disciplines. This research attempts
to investigate whether disciplinary, socio-cultural and contextual differences affect the
structure construction and linguistic choices in realising the thanking acts.
3 Research methodology
3.1 Corpus
This study is based on 120 PhD dissertation acknowledgements written in English by native
Taiwanese students (TW) who studied their doctoral degrees in the US. In order to compare
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and contrast the similarities or differences of DA between the soft and hard sciences, the
corpus collected texts from 6 different disciplines. The texts from the soft sciences represent
the disciplines of applied linguistics (APL), business studies (BUS), and public administration
(PBA), while those from the hard sciences include the disciplines of medical science (MED),
electronic engineering (EEN), and biology (BIO). Each discipline equally contributes 20
texts, giving a total of 120 texts. All of the acknowledgements in the dissertations were
written between 1990 and 2011.
Due to the severely limited availability of English DA written by Taiwanese PhD students in
Taiwan, all 120 texts were collected in the US using the ProQuest Digital Dissertations
Database. Several measures were taken to ensure the native identity of the authors for
accurate representation of Taiwanese students, as follows: setting keywords to limit the topics
related to Taiwan only, checking the author’s name spelling system, reading the author’s
curriculum vitae, and screening from the content of abstracts and acknowledgements. The
present corpus consists of a total of 43,166 running words. The length of the DA ranges from
54 to 1,456 words with an average of 420.6 words in the soft sciences, and 50 to 1,669 words
with an average of 298.8 words in the hard sciences. Table 1 shows the detailed total and
average running words of the texts in each discipline, and compares the present corpus to that
of Hyland (2003).
Present corpus
Hyland’s corpus
Discipline
Texts
Words
Average
Texts
Words
Average
APL
20
7,917
395.9
20
7,718
385.9
BUS
20
7,298
364.9
19
2,512
132.2
PBA
20
10,022
501.1
20
3,594
179.7
Soft disp.
60
25,237
420.6
59
13,824
234.3
MED/COM
20
6,356
317.8
20
3,470
173.5
EEN
20
4,833
241.7
19
2,771
145.8
BIO
20
6,740
337.0
19
3,864
203.4
Hard disp.
60
17,929
298.8
58
10,105
174.2
All totals
120
43,166
359.8
117
23,929
204.5
Table 1. Acknowledgement corpus (20 DA from each discipline) vs. Hyland’s corpus
(2003)
Note: APL: Applied linguistics, BUS: Business studies, PBA: Public administration, MED:
Medical science (Present)/ COM: Computer science (in Hyland’s), EEN: Electronic
engineering, BIO: Biology.
3.2 Analysis
The texts were analysed for their generic structure and linguistic realisation in terms of
structural moves/steps, sentence patterns of expressing thanking acts, and lexical choices in
modifying thanking acts. To investigate the generic structure of acknowledgements employed
by the Taiwanese authors, Hyland’s (2003) three-tier scheme of dissertation
acknowledgements was adopted. The texts were coded manually by the researcher after a
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research assistant and the researcher went through every four randomly-selected texts in each
discipline together (i.e. 24 texts in total, with an inter-coder reliability of 88.6%) to reach a
consensus of categorisation of moves and steps. The coding of the sentence patterns of
expressing thanking acts, which was adopted from Hyland and Tse (2004), also applied the
identical procedure as above, and the inter-coder reliability of this classification reached
91.2%. Regarding the lexical choices of realising thanking acts, a text analysis and
concordance programme WordSmith Tools v 5.0 (Scott, 2008) was used to count word
frequency and identify the keywords used in modifying and receiving thanking acts.
4 Results and Discussion
4.1 Generic structure
As Table 2 shows, the generic structure of this present corpus also largely follows Hyland’s
(2004a) three-tier model of DA. The thanking move is obligatory so all of the writers utilised
one step in this move at least once while the other two moves, namely the reflecting and
announcing moves, are apparently optional because only 26% and 46% of the DA
respectively include these two. Expressing gratitude for intellectual support, ideas, analyses,
and feedback, etc. from academic communities and for the encouragement, friendship,
sympathy, and patience etc. of non-academic associates is regarded as indispensable across
each discipline. However, to these writers, claiming responsibility for any flaws or errors in
their dissertations seems to be unnecessary; hence, not a single instance of Step 3.1 was
located in the present corpus. Though there is no great difference between the two science
areas in terms of move/step structure, some subtle variations were still found. For instance,
the reflecting move is used twice as often in the DA in the soft sciences, and there is also 20%
higher use of Step 2.1, introducing those to be thanked, compared with those in the hard
sciences. In particular, the discipline of public administration (PBA) has significantly higher
occurrences of each move and step (excluding Step 3.1) than any other discipline. It is
believed that the core of public administration is involvement in human relationships, and
thus writers in this discipline may tend to emphasise the assistance offered by various other
parties during their research journey. After all, public administration is closely associated with
interaction, communication, and human relationships, and DA rightly provide a chance to
display these functions. Besides, it is also predicted that writers in the soft sciences would
apply writing strategies more rigorously than students in the hard sciences. Step 2.1 in DA
serves as a topic sentence in writing a paragraph, by which authors introduce or summarise
the main idea of the entire paragraph to increase reading accessibility. Presenting the
participants to be thanked at the beginning helps readers determine the subject and
perspective of the paragraph. Thus, even though all the writers studied their PhD degrees in
the American educational system, they did not all strictly follow the general guidelines of
how to write a paragraph in academic texts.
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Table 2. Percentages of acknowledgements with each step by discipline
The argument that writers in the soft sciences are prone to construct more and generically
more complex acknowledgements than the hard science students is also proposed by Hyland
and Tse (2004). Tables 3 and 4 respectively compare the current corpus with Hyland’s
(2004a) in terms of acknowledgements with each step and the average number of steps per
text by discipline. Generally, in both corpora, the soft science students tended to use Move 1
and Step 3.1 much more frequently than the hard science students did. Yet, still some major
variations exist between the two contexts. Firstly, in the present corpus, Step 2.4 seems to be
obligatory with a 100% occurrence, and Step 3.2 has a much higher appearance rate than in
Hyland’s corpus. In contrast, Step 3.1 is not identified at all in the current corpus. The
possible explanations can be that the Taiwanese writers in the present study studied their PhD
degrees in the US while the Hong Kong writers in Hyland’s corpus studied in their home
country (i.e. Hong Kong), and studying in a foreign country made the Taiwanese writers
depend much more on the moral and spiritual support of friends, colleagues, family or
religious beliefs. Hence, after successfully completing their degrees, these students would be
apt to dedicate their dissertations to those who had provided such spiritual assistance and
moral support. Due to a similar reason, the average occurrence of moves/steps in the
Taiwanese corpus is even higher than that in Hyland’s corpus (2004a) as the Taiwanese
students might have relatively more people to be thanked. Other possible reasons may be that
Taiwanese students tend to consider DA as a very formal genre, and they have a cultural
expectation of expressing gratitude for any assistance, both of which may contribute to the
detailed and elaborate production of their DA (Cheng, 2012). Though the students in the soft
disciplines tended to produce more steps than those in the hard disciplines in both corpora,
interestingly the lowest average of the present corpus in electronic engineering is close to the
highest average of Hyland’s corpus in applied linguistics, i.e. 7.3 vs. 8.5. Thus, it is assumed
that the variation of contexts in which PhD students study also affects the average number of
steps produced.
Soft disciplines
Hard disciplines
APL
BUS
PBA
All
MED
EEN
BIO
All
Total
1 Reflecting Move
20
15
70
35
15
15
20
17
26
2 Thanking Move
Step 2.1
55
45
100
67
40
60
45
48
58
Step 2.2
100
100
100
100
100
100
100
100
100
Step 2.3
100
100
100
100
100
95
100
98
99
Step 2.4
100
100
100
100
100
100
100
100
100
3Announcing Move
Step 3.1
0
0
0
0
0
0
0
0
0
Step 3.2
50
45
50
48
60
30
40
43
46
LSP Journal, Vol.3, No.2 (2012) / http://lsp.cbs.dk
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Soft disciplines
Hard disciplines
Present
Hyland’s
Present
Hyland’s
Total
1 Reflecting Move
35
26
17
13
23
2 Thanking Move
Step 2.1
67
39
48
19
43
Step 2.2
100
100
100
100
100
Step 2.3
100
75
98
59
83
Step 2.4
100
77
100
66
86
3 Announcing Move
Step 3.1
0
11
0
3
2
Step 3.2
48
4
43
2
24
Table 3. Comparison of percentages of acknowledgements with each step by soft and
hard disciplines
Note: Hyland’s study combines both master and doctoral dissertations
Discipline
Present
Hyland’s
Overall
APL
10.0
8.5
9.3
BUS
10.3
3.7
7.0
PBA
14.3
4.8
9.6
Soft disp.
11.5
5.7
8.6
MED/COM
9.1
5.3
7.2
EEN
7.3
4.6
6.0
BIO
11.0
5.8
8.4
Hard disp.
9.1
5.2
7.2
All totals
10.3
5.5
7.9
Table 4. Text complexity: average number of steps per text by discipline
Note: differences in summed totals due to rounding
Tables 5 and 6 respectively show the average frequency with which steps occurred in each
discipline and a comparison with Hyland’s (2004a) figures. The rankings of moves/steps
produced from the highest to the lowest in the two different sciences are identical. The top
two frequently-produced steps are Step 2.2 and Step 2.4, which suggests that academic and
emotional assistance are mostly valued and appreciated by the writers. Hyland’s comparison
also shows the same tendency. However, comparatively, the soft science DA still exhibit a
higher frequency of each move/step than those from the hard sciences. The results support
both Giannoni’s (2002) and Hyland’s (2004a) observations that writers from the hard
disciplines tend to construct less complex acknowledgements in academic texts.
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Soft disciplines
Hard disciplines
APL
BUS
PBA
All
MED
EEN
BIO
All
Total
1 Reflecting Move
0.2
0.05
0.7
0.32
0.15
0.15
0.2
0.17
0.24
2 Thanking Move
Step 2.1
0.55
0.45
1.35
0.78
0.4
0.6
0.65
0.55
0.67
Step 2.2
4.4
4.25
5.5
4.72
3.7
3.25
4.65
3.87
4.29
Step 2.3
1.8
1.8
2
1.87
1.8
0.95
2.25
1.67
1.77
Step 2.4
2.6
3.2
4.25
3.35
2.45
2
2.8
2.41
2.88
3 Announcing Move
Step 3.1
0
0
0
0
0
0
0
0
0
Step 3.2
0.5
0.45
0.5
0.48
0.6
0.3
0.4
0.43
0.46
Table 5. Relative frequency of steps in each text by discipline
Soft disciplines
Hard disciplines
Total
Present
Hyland’s
Present
Hyland’s
Present
Hyland’s
1 Reflecting Move
0.32
0.3
0.17
0.2
0.24
0.2
2 Thanking Move
Step 2.1
0.78
0.4
0.55
0.2
0.67
0.3
Step 2.2
4.72
1.6
3.87
1.7
4.29
1.7
Step 2.3
1.87
1.2
1.67
0.9
1.77
1.0
Step 2.4
3.35
1.2
2.41
1.0
2.88
1.1
3 Announcing Move
Step 3.1
0
0.1
0
0
0
0.1
Step 3.2
0.48
0.1
0.43
0
0.46
0.1
Avg. per text
11.52
4.9
9.1
4.1
10.31
4.6
Table 6. Comparison of relative frequency of steps in each text by soft and hard
disciplines
Note: Hyland’s study combines both master and doctoral dissertations
4.2 Participants acknowledged
Table 7 shows the percentages of gratitude expressions toward different individuals. Overall,
other academic teachers were most frequently thanked, followed by family members,
committee members, colleagues, advisors, friends, institutions, study participants, and
religious beliefs. However, there is a slight difference between the two science areas. In the
soft sciences, family members are most frequently acknowledged, while other academic
teachers are most usually thanked in the hard sciences. It is supposed that hard research
usually involves much collaborative team work; thus, naturally other academic teachers’
assistance was highly appreciated. In contrast, many social science studies relied on the
researchers themselves alone and thus emotional support, in particular from family members
and friends, would become relatively more highly valued. Besides, some variations also exist
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across disciplines. Participants in the research were fairly crucial in the applied linguistics
discipline, especially concerning language teaching topics; therefore, they enjoyed higher
occurrences of acknowledgement than in other disciplines. Another example is the gratitude
to institutions. Most of these Taiwanese who studied medical science and biology were
supported by third parties either at home or in the targeted countries. Apparently, the hard
science PhD students had more chances of obtaining scholarships or sponsorship than the soft
science PhD students, which means institutions receive greater appreciation in the above two
hard disciplines. This situation, that discipline affects who should be acknowledged in DA,
was also evidenced in Scriverner’s (2009) research.
Addressees
Disp.
AD
OT
CM
CO
FM
IN
FD
PA
RL
APL
11.96
16.85
11.41
16.30
12.50
7.61
11.41
11.96
0
BUS
9.10
18.19
17.05
9.10
19.31
8.00
11.36
4.00
4.00
PBA
6.52
20.43
18.26
10.00
23.48
8.26
7.39
3.91
1.73
Soft
8.98
18.64
15.76
11.70
18.81
7.97
9.83
6.44
1.86
MED
12.50
15.48
11.90
19.64
18.45
9.52
7.14
4.76
0.60
EEN
16.26
17.89
17.07
8.94
22.76
3.25
7.31
5.69
0.81
BIO
8.64
24.86
15.14
11.89
17.30
9.19
8.65
3.78
0.54
Hard
11.98
19.75
14.50
13.87
19.12
7.77
7.77
4.62
0.63
Totals
10.32
19.14
15.20
12.66
18.95
7.88
8.91
5.63
1.31
Table 7. Percentages (%) of gratitude expressions toward different addressees
Note: Differences in summed totals due to rounding; AD: Advisor, OT: Other teacher, CM:
Committee, CO: Colleague, FM: Family, IN: Institution, FD: Friend, PA: Participant, RL:
Religion
Interestingly, advisors were much less thanked in the present study compared with other
studies examining Taiwanese writers’ DA constructed in Taiwan. Both Cheng’s (2012) and
Yang’s (2012a) investigations show that advisors were always highly and firstly
acknowledged by Taiwanese writers in dissertation acknowledgements. Socio-cultural
differences of advisor-advisee relationships in the two contexts can contribute to this
diversity. In Confucian societies such as Taiwan, Japan, Korea, and China, the role of
advisors is always authoritative and powerful. Advisors are not only the experts in the
researched fields but can make crucial decisions on failing or passing PhD candidates’
dissertations (Cheng, 2012; Krase, 2007; Li, 2005). The hierarchy of advisor-advisee is
strictly obeyed and thus Taiwanese writers in Taiwan would “view advisors as indispensible
addressees and always place them at the initial position of acknowledgements” (Cheng, 2012:
14). Nevertheless, in western academic culture, advisors are regarded as joint partners rather
than authorities. Advisorship seemingly emphasises the cultivation of the independent ability
of carrying out research, and thus mutual growth and enhancement confine the relationship
with advisees (Cheng, 2012; Krase, 2007). Indeed, socio-cultural expectations, academic
conventions and disciplinary variations all affect who should be thanked in priority in
dissertation acknowledgements.
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4.3 Gratitude expressions
According to Hyland and Tse’s (2004) categorisation, there are five main types of patterns
used to express gratitude in thanking acts, Move 2. They are, nominalisation (N) (e.g. My
sincere thanks go to…), performative verb (V) (e.g. I thank…), adjective (A) (e.g. I am
grateful to …), passive (P) (e.g. Appreciation is given to …), and bare mention (B) (e.g. X is
very helpful in …). Table 8 exhibits the occurrence percentages of patterns expressing
gratitude in the present study by discipline and an overall comparison with Hyland’s (2004a)
findings. Generally, there is no difference in pattern ranking between the soft and hard
sciences. The performative verb pattern was used most commonly, while the passive pattern
was used the least by these Taiwanese students. Using the performative verb pattern always
begins with the subject I and this suggests a very direct authorial voice which “was
particularly marked in the science and engineering texts” (Hyland, 2004a: 266). Similarly, in
the present corpus, the disciplines of medical science and engineering also show this
tendency, where the performative verb pattern was used more frequently than in other
disciplines, particularly the soft disciplines. The least use of the passive pattern is not unusual
as Chinese is regarded as a language without voice category. Passive voice in Chinese is
expressed in a covert way instead of a marked way, which possibly makes Chinese-speaking
writers feel uneasy about using passive voice in constructing English DA (Zhao & Jiang,
2010).
Discipline
Patterns
N
V
A
P
B
APL
16.13
55.38
18.28
4.84
5.38
BUS
13.45
50.29
11.70
2.92
21.64
PBA
10.82
35.06
17.32
1.30
35.50
Soft disp.
13.27
45.92
15.99
2.90
21.94
MED
5.00
55.90
13.04
3.11
22.99
EEN
15.20
59.20
14.40
4.80
6.40
BIO
12.50
42.93
17.93
7.61
19.02
Hard disp.
10.64
51.70
15.32
5.32
17.02
All totals
11.96
48.81
15.66
4.11
19.48
Hyland’s
33.66
33.70
15.41
10.96
6.79
Table 8. Occurrence percentages (%) of patterns expressing gratitude in the present
corpus
Note: Differences in summed totals due to rounding; N: Nominalisation, V: Performative-
verb, A: Adjective, P: Passive, B: Bare mention
The bare mention pattern, signifying a more implicit and reserved thanking act, was ranked
the second highest in the present corpus; the ranking of two extreme ends of thanking acts
(i.e. explicitness in V pattern vs. implicitness in P pattern) as the consecutive first and second
places cannot be found in other similar studies where Chinese-speakers’ DA were analysed
(e.g. Hyland, 2004a; Zhao & Jiang, 2010). It is believed that the interwoven complexity of
socio-cultural perspectives and habits of English use contributes to this cause. Zhao and
Jiang’s (2010) observation suggests that Chinese-speaking students in China, as an EFL
context, are more reserved when expressing their feelings and emotions; thus, the bare
LSP Journal, Vol.3, No.2 (2012) / http://lsp.cbs.dk
62
mention pattern is most commonly used in their corpus. In contrast, Chinese-speaking
students in Hong Kong, where English is used as a second and one of the official languages,
used the bare mention pattern least but the performative verb pattern far more in Hyland’s
(2004a) corpus. It can be predicted that the longer Chinese-speaking students are exposed to
an English-speaking environment, the more likely it is that they would express their gratitude
explicitly. Hence, the Taiwanese students in the present study, who had been educated in an
EFL context for a long time and then studied for their PhD in an English-speaking context,
would display these two seemingly opposite preferences. In addition, the passive pattern was
used almost twice as often in the hard disciplines than in the soft disciplines. Academic
training of using the passive voice to represent objectivity in the hard sciences probably
caused this variation.
When the patterns used and individuals acknowledged in thanking acts were cross-compared,
some subtle differences between the sciences were identified. Firstly, it is found that family
members and other academic teachers are the two major addressees, with 40% being thanked
with performative-verb use in both science areas; however, there is a relatively high usage of
the bare mention pattern in thanking family members in the soft science DA and other
academic teachers in the hard science DA, as Table 9 shows. The results are different from
what Hyland and Tse (2004) argue in that the bare mention pattern, as a low-key way of
expressing thanks, is usually over-represented in offering gratitude for resource support.
Probably, the Taiwanese students were more emotionally reserved than the Hong Kong
students. Other subtle variations include the hard science students tending to use adjective
patterns to appreciate both moral and academic help (i.e. FM and OT) while their soft science
counterparts mainly use it to appreciate academic assistance (i.e. AD, OT and CM). Besides,
hard science students used a passive pattern to thank committee members and institutions,
while soft science students used it to thank various addressees (i.e. AD, OT, CO and PA
mainly). This difference between a widespread distribution and a concentrated distribution of
individuals thanked using different patterns substantiates the argument that writers in the soft
sciences are apt to use a greater variety of patterns than those in the hard sciences (Hyland &
Tse, 2004). Furthermore, the types of research in the various disciplines may also account for
this difference. That is, research participants were acknowledged more frequently in the soft
disciplines while institutions were more commonly thanked in the hard disciplines.
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63
Addressees
AD
OT
CM
CO
FM
IN
FD
PA
RL
TOTALS
Nominalisation
SOFT
13.9
19.4
12.5
2.8
18.1
8.3
16.7
6.9
1.4
100
HARD
12
22
8
18
10
8
8
14
0
100
TOTAL
13.1
20.5
10.7
9.0
14.8
8.2
13.1
9.8
0.8
100
Performative-verb
SOFT
10.6
19.3
17.9
8.0
19.7
6.6
9.1
6.9
1.8
100
HARD
13.4
20.9
11.7
14.2
21.8
4.6
7.5
4.6
1.3
100
TOTAL
11.9
20.1
15.0
10.9
20.7
5.7
8.4
5.8
1.6
100
Adjective
SOFT
12.4
22.4
15.7
7.9
11.2
7.9
10.1
11.2
1.1
100
HARD
16.2
14.7
25
8.8
20.6
4.4
7.4
2.9
0
100
TOTAL
14.0
19.1
19.7
8.3
15.3
6.4
8.9
7.6
0.6
100
Passive
SOFT
14.3
28.6
7.1
14.3
7.1
7.1
0
14.3
7.1
100
HARD
12.5
12.5
50
0
0
16.7
4.2
4.2
0
100
TOTAL
13.2
18.4
34.2
5.3
2.6
13.2
2.6
7.9
2.6
100
Bare mention
SOFT
3.6
17.9
15.7
13.6
25.7
10.7
7.9
2.9
2.1
100
HARD
5
25
10
12.5
21.3
17.5
6.25
2.5
0
100
TOTAL
4.1
20.5
13.6
13.2
24.1
13.2
7.3
2.7
1.4
100
Table 9. Percentages (%) of thanking patterns used to thank different addressees by
soft and hard disciplines
Note: Differences in summed totals due to rounding
4.4 Lexical choices to realise and modify the thanking acts
Table 10 shows the lexical choices used to realise the thanking acts between soft and hard
science authors. The results demonstrate that there is no significant difference between the
two groups, and that both of them tended to use verbs to express gratitude. However, this
tendency is completely opposite to Cheng’s (2012) claim that Taiwanese students tend to use
more noun forms to express their thanks than native English speakers, who employ more verb
forms. One possible explanation is that the authors in the present study were more or less
assimilated into the academic conventions and language use in an English-speaking
environment, though this assimilation may be either purposeful or unintended.
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64
Lexical items
Soft
%
Hard
%
Total
%
Noun
gratitude
57
10.34
57
11.75
114
11
thanks
88
15.97
65
13.40
153
14.77
appreciation
45
8.17
44
9.07
89
8.59
gratefulness
2
0.36
0
0
2
0.19
indebtedness
1
0.18
0
0
1
0.10
debt
4
0.73
5
1.03
9
0.87
Verb
thank
193
35.02
194
40
387
37.36
appreciate
18
3.27
12
2.47
30
2.90
appreciated
8
1.45
4
0.82
12
1.16
acknowledge
20
3.63
14
2.89
34
3.28
recognize
0
0
2
0.41
2
0.19
owe
16
2.91
8
1.65
24
2.32
Adjective
gratefulness
60
10.89
44
9.07
104
10.04
Indebted
22
3.99
24
4.95
46
4.44
thankful
15
2.72
11
2.27
26
2.51
appreciative
2
0.36
0
0
2
0.19
obliged
0
0
1
0.21
1
0.10
Total
551
100
485
100
1036
100
Table 10. Frequency of lexical realisations of explicit thanking acts for the soft and
hard disciplines
Note: Differences in summed totals due to rounding
The word and keyword analysis show some slight variations between the two science areas.
Table 11 shows the numbers of distinct (different) words used, while Table 12 exhibits the
total numbers of keywords generated when the two corpora were compared with the BNC
(British National Corpus, a daily spoken and written English corpus), respectively. It suggests
that firstly, soft science students might have a better command of English vocabulary; thus,
they tended to use more distinct words than the hard science students did. Yet, relatively
higher TTR (distinct words/total running tokens) in hard science DA implies that these
authors are used to writing shorter sentences but with higher lexical density; in other words,
their writing style may be more concise and straightforward. In addition, keyword use also
demonstrates that soft science students employed comparatively more overused keywords
than their hard science counterparts did, suggesting that the lexis used in soft science DA
contained fewer daily words and their lexical choices better represent the main features of
aboutness and keyness (Archer, 2009; Baker, 2009; Scott & Tribble, 2006) in this genre.
LSP Journal, Vol.3, No.2 (2012) / http://lsp.cbs.dk
65
Discipline
Distinct
words (DW)
TTR
(DW/tokens)
Sentences
Means in
words
Standard
deviation
APL
1,885
23.81
510
15.52
13.14
BUS
1,779
24.38
563
12.96
10.30
PBA
2,145
21.40
647
15.49
10.59
Soft disp.
1936
23.20
573
14.66
11.34
MED
1,691
26.60
460
13.82
10.49
EEN
1,216
25.16
466
10.37
9.26
BIO
1,642
24.36
719
9.37
9.05
Hard disp.
1,516
25.37
548
11.19
9.60
All totals
1,726
24.29
561
12.92
10.47
Table 11. Distinct words, TTR, and sentence length of the present corpus
Soft disciplines
Hard disciplines
Total keywords
372
329
Overused keywords
328
292
Underused keywords
44
37
Table 12. Keywords of soft and hard disciplines with reference to BNC
As Appendix 1 lists, the selected overused keywords which were mainly employed in the
thanking acts also show some variations between groups. This wordlist contains the lexis,
modifying the types of assistance obtained from various addressees and the extent of the
author’s emotional state in expressing gratitude. On the one hand, it again corroborates that
soft science students were able to use a greater variety of words to modify their thanking acts,
while on the other hand, it displays subtle variations of priorities in acknowledging help in the
top 20 words of very high keyness between groups. For instance, hard science authors tended
to use unspecific words (e.g. support, assistance or help) more frequently, while soft science
authors would apparently identify their reasons for thanking more specifically. However, both
keyword lists mirror the same fact. That is, the key features of this genre address the issues of
what is to be acknowledged and how to magnify gratitude for academic and moral assistance.
Moreover, a large number of non-daily adjectives (esp. –ful, such as grateful, insightful,
helpful, thoughtful and superlatives such as deepest, endless, sincerest or foremost) make
dissertation acknowledgements a relatively formal genre (Hyland & Tse, 2004). Hence,
keyword analysis not only helps researchers ensure what DA is really about in a target
situation and its diversity across disciplines, avoiding trivia and insignificant detail (Scott &
Tribble, 2006), but also helps student writers to distinguish variations between texts,
determine the content of texts, and identify textual and rhetorical styles (Archer, Culper, &
Rayson, 2009; Baker, 2009).
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66
5 Conclusion
As discussed earlier in this study, acknowledgements are sophisticated and complex, and their
constructions are heavily affected by many factors such as academic conventions, author’s
language proficiency, socio-cultural expectations or even personal writing style. The present
study attempted to investigate a less attended variable influencing the construction of
dissertation acknowledgements, namely discipline variations. The results demonstrate that
though generally most EFL writers followed Hyland’s (2004a) three-tier model to compose
their acknowledgements, subtle differences exist between the two science areas in terms of
generic construction and lexical realisations in modifying thanking acts. It is believed that the
diversities of research per se (i.e. its epistemology, ontology and methodology) and writers’
exposure to an English-speaking environment together with the above factors could contribute
to the variations in constructing DA.
The pedagogical implication of this study for ESP practitioners is as follows. Compared with
other genres in academic texts, acknowledgements have received relatively less attention in
research (Hyland, 2004a) and furthermore, teaching how to write appropriate
acknowledgements is not well accommodated either. Rather than mimicking formulaic
structures and rhetoric, graduate students should be explicitly informed of the possible factors
which would affect how they employ thanking moves/steps, strategies and lexical choices
while constructing appropriate DA. In addition, listing word frequency and identifying
keywords used in various DA corpora can be helpful in presenting writers with possible
lexical choices and constraints in different settings, which serves as a reference to cater for
academic, linguistic, socio-cultural, disciplinary and contextual differences. Hence, ESP
instructors are advised to conduct genre-based writing instruction of this genre at both macro
(i.e. socio-cultural) and micro (i.e. linguistic) levels (Hyland, 2004b; Paltridge, 2001; Yang,
2012b) as it can assist PhD students in writing impressive and proper acknowledgements.
Additional analysis can be done to complement this research. A cross-cultural comparison can
be conducted to examine whether discipline variations exercise similar influences on DA
written by native English speakers. Furthermore, an intra-cultural analysis is also
recommended. Texts collected from other Chinese EFL learners (e.g. from mainland China)
who studied in the US, can be compared to ensure whether authors’ socio-cultural
backgrounds exercise greater influences on constructing DA than the variations in English
speaking environments, or vice versa. Finally, qualitative methods can be adopted into the
inquiries. To better realise the account of why authors choose certain arrangements and lexis
in different disciplines and to learn how they perceive themselves as writers of DA, continued
analysis such as interviews or ethnographic methods can be integrated into projects that
concentrate on corpus analysis.
LSP Journal, Vol.3, No.2 (2012) / http://lsp.cbs.dk
67
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7 Appendix
Appendix1. Overused keywords employed to modify thanking acts in two sciences with
reference to BNC
Soft disciplines
Hard disciplines
Key word
Keyness
Frequency
Key word
Keyness
Frequency
SUPPORT
849.3895264
190
SUPPORT
685.6174316
148
ENCOURAGEMENT
707.8490601
81
ENCOURAGEMENT
682.8666382
74
GUIDANCE
321.3438721
51
GUIDANCE
399.9871216
56
INSIGHTFUL
311.7173157
23
GRATEFUL
310.5231934
44
SINCERE
306.6517029
33
ASSISTANCE
236.3457947
40
SUGGESTIONS
293.0917358
43
PATIENCE
209.0389099
27
PATIENCE
237.2440186
32
SINCERE
201.7852478
22
LOVE
194.8524475
64
INSIGHTFUL
173.3990936
13
COMMENTS
181.4472809
36
SUGGESTIONS
168.7018585
26
INVALUABLE
180.2219238
24
ADVICE
156.47966
38
DEDICATION
172.7871704
22
HELP
146.996994
58
DEEPEST
153.5054932
19
LOVE
135.4749756
45
ALWAYS
153.3752899
74
CONTINUOUS
117.1722336
21
ADVICE
148.9017029
41
ESPECIALLY
100.0851669
34
UNCONDITIONAL
134.1839905
16
THANKFUL
90.87915802
11
ASSISTANCE
127.9447327
28
VALUABLE
88.89487457
19
VALUABLE
127.2196884
27
SPECIAL
86.58507538
34
HELP
124.7024384
61
COMMENTS
78.30434418
18
INSPIRATION
124.5999527
20
FRIENDSHIP
76.09213257
14
MENTORING
122.6819382
9
INVALUABLE
73.04397583
11
DEEPLY
117.2798538
25
ADVICES
69.79037476
5
ENDEAVOR
117.1376343
8
DEDICATION
69.70275879
10
FRIENDSHIP
109.5598755
20
GREAT
62.92067337
41
UNDERSTANDING
86.79158783
28
ENDEAVOR
57.78165054
4
GENEROUSLY
78.66903687
11
GENEROUS
57.55865097
12
CONTINUOUS
76.92755127
17
GENEROUSLY
57.4994545
8
ESPECIALLY
75.84360504
33
HELPFUL
56.65489578
13
TIMELY
72.33976746
10
DEEPEST
56.37428284
8
INSPIRED
70.67552185
15
INSPIRATION
55.31654358
10
ENDLESS
66.01902008
13
FOREMOST
52.72362137
8
MANY
63.89118195
70
ENDLESS
52.17824936
10
SINCEREST
63.66653442
5
MENTORING
52.12264633
4
HELPFUL
59.93144608
15
ALWAYS
51.8959198
36
EDITING
59.20835495
10
KNOWLEDGE
50.82569885
21
INSIGHTS
59.08026505
10
PRECIOUS
49.73204803
10
HEARTFELT
58.89132309
7
INSIGHTS
49.0126152
8
FEEDBACK
56.21112061
11
PROOFREADING
48.86028671
4
SHARING
52.1133728
13
EXPERTISE
48.12690735
11
GRACIOUSLY
51.12567902
6
SINCERELY
43.09775925
8
WONDERFUL
49.13499069
15
UNDERSTANDING
43.0551033
16
UNWAVERING
48.02345657
5
UNCONDITIONAL
42.77565002
6
CONSTRUCTIVE
47.44129944
9
CARE
38.07379532
23
GREATLY
47.06027985
13
OPPORTUNITY
36.8275032
15
LSP Journal, Vol.3, No.2 (2012) / http://lsp.cbs.dk
70
KNOWLEDGE
46.08368301
23
DEEPLY
35.86294556
10
GENEROUS
43.91822433
11
EDITING
33.46864319
6
SUPPORTIVE
43.52952576
8
WONDERFUL
31.43604469
10
DISCUSSIONS
42.98007202
12
SUPPORTS
30.26088524
7
PATIENTLY
42.39264679
7
SHARING
29.78320694
8
THOUGHTFUL
39.86115265
7
ENCOURAGING
28.92959976
8
WISDOM
39.50578308
9
STEADFAST
28.61402512
3
ENCOURAGING
39.46471405
11
CHALLENGING
27.90635681
6
TEACHING
38.33738708
17
HELPING
27.67040825
9
DATA
38.21007156
26
DISCUSSIONS
27.58444023
8
KINDNESS
38.16832733
7
TREMENDOUSLY
26.46580887
4
SUPPORTING
37.56524658
11
SUPPORTIVE
25.89236832
5
ADVICES
37.44016266
3
EXPERIENCES
25.84810257
8
PRECIOUS
37.18175125
9
INSPIRING
23.98058319
4
INSPIRING
36.7943573
6
CONSTANT
36.66481781
13
FELLOWSHIP
36.43498993
7
EXPERTISE
35.64498901
10
EXPERIENCES
35.23467255
11
SCHOLARSHIP
34.53067017
7
HUMOR
33.81703949
3
PERSEVERE
33.61241913
4
ENCOURAGEMENTS
33.44131851
3
POSSIBLE
32.55728149
30
GENEROSITY
32.44838715
6
FOREMOST
32.25347519
6
LOVING
32.2256012
8
COUNTLESS
32.00497437
6
INTERVIEWS
31.73026848
9
SUPPORTS
31.54142761
8
WILLINGNESS
30.70469666
7
PERSPECTIVES
30.2885685
6
CHALLENGES
29.88908958
7
GREAT
28.66644669
35
INTELLECTUAL
28.12630463
9
CONSTANTLY
27.94377899
9
SCHOLARLY
27.67696762
5
BLESSINGS
27.2507515
4
HELPING
26.62705612
10
PRICELESS
26.5873909
4
ENTIRE
24.90049171
10
TREMENDOUS
24.07941246
7
REWARDING
23.96828461
5
Note: Keywords are ranked from the highest keyness to the lowest.
***
