Stance Analysis of Corporate Social Responsibility Reports - Corpus-based Approach

1. STANCE ANALYSIS OF CORPORATE SOCIAL RESPONSIBILITY
REPORTS: CORPUS-BASED APPROACH
Fion Yau Wai
Submitted in partial fulfillment of the requirements for
the degree of Master of Arts in English for the Professions
Department of English
The Hong Kong Polytechnic University
May 3, 2006

2. ABSTRACT
Corporate social responsibility (CSR) goes beyond the bottom-line activities
(profitability) and serves as a public relations exercise of multinational
corporations. Based on an entire stance framework (Biber, 2004), this study
aims to research the major grammatical devices and semantic categories in a
specialized small CSR Corpus. Stance marker frequencies are assessed using
Wordsmith Tools to identify stance lexical items occurring in particular
grammatical frames. Overall, complement clauses are the most preferred
grammatical category of stance devices. Modals are also very common but
stance adverbials are relatively less frequent. In terms of semantic domains, the
use of factive (epistemic: certainty) stance markers has a much broader base
across grammatical categories, while the expressions of likelihood and attitudinal
stance are infrequently used. The Longman Grammar Written and Spoken
(LGWS) Corpus is used as a reference of modern general English. Interestingly,
the results indicate that the total frequency of stance markers in the CSR Corpus
(written text) is notably higher than the total frequency in conversation. In
comparison, the CSR, conversation and fiction registers favour verb +
complement clauses, while news shows a heavy reliance on adjective +
complement clauses. Noun + complement patterns are not so preferred in the
CSR reports but are moderately common in academic prose. There are
differences across registers in the preferred devices used to express stance which
may result from the specific communication purpose of the CSR reporting: a) to
enhance public trust and corporate branding by focusing on future events with
positive impact, b) to show ability to make contribution to both profit-making
activities and socially beneficial events, and most importantly, c) to express
commitment towards people, society and the natural environment to achieve
sustainable success under the trend of globalization.
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3. ACKNOWLEDGEMENTS
First and foremost, I would like to express my gratitude to my project supervisor,
the Acting Head of English Department, Dr Xu Xun-feng, for his advice and
guidance. I would also like to express my sincere thanks to the MAEP
Programme Leader, Prof Martin Warren and Research Centre Director, Prof
Winnie Cheng, who were my interviewers and offered me such a wonderful
language learning experience. I cannot miss the opportunity to give applause to
other experienced MAEP instructors – Dr Steven Evans, Dr Christopher Green,
Dr Gail Forey, Ms Kate Mead, Ms Pamela Smith, and Ms Vicky Man. My most
heartfelt appreciation is extended to all my classmates, especially Simon, Rimmy,
Ammy, Jennifer, Roger, Danny, Howard, Eva, Frederica and Alex for their
friendship and kindness in sharing insights and expertise in both classroom
discussions and social gatherings.
I truly want to pass my special thanks to those outstanding individuals who
inspired me to develop personal interests and get involved in community services
while I stayed in New York some years ago. Aslania, Leo, Evan, and Theresa
were my English teachers in some ESL Institutes; I respect their enthusiasm and
patience when teaching non-native English speaking students. Susan taught me
foundation concepts of NGOs and philanthropy. Ximena trusted my abilities to
help out in the Queens Chapter for fundraising supports. Tak Kato inspired me by
his passion to organize volunteering activities in the Japanese community. Last
but not least, Sofia, my mother-like landlord, taught me how to smile in rough
times of life in an unfamiliar foreign country.
I would also like to thanks my girlfriends – Georgia, Kennis, Robie and Kumi, for
their encouragement and inspiration to me to be an independent young woman.
My counsellor Martha and Christian fellows Morris, Grace and Candy are an
important reserve of inner-strength while I have suffered from emotional
frustrations in recent months. With their listening and prayers I am able to strive
for the completion of my very first research project.
Lastly, I am sure without the strong belief in higher education and generosity in
financial support from Mr. and Mrs. Yau, I cannot have the privilege in the past
years to pursue intellectual growth and personal advancement without family
burden. I dedicate my whole-hearted thankfulness to my beloved parents.
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4. LIST OF TABLES
Table 1 Statistical summaries of the CSR Corpus 30
Table 2 10 key words with the highest positive Keyness 31
Table 3 Cross reference of semantic classifications in two studies 37
Table 4 Frequency of grammatical constructions of stance markers 88
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5. LIST OF GRAPHS
Figure 1 Distribution of stance markers by major grammatical category 40
Figure 2 Semantic categories of modal verbs 42
Figure 3 Frequency of modal verbs in the CSR Corpus 42
Figure 4 Distribution of stance adverbials 51
Figure 5 Distribution of that-complement clauses controlled by a verb 62
Figure 6 Frequency of the most common factive verbs
controlling that-clauses 64
Figure 7 Breakdown of stance markers within the complement category
in the CSR Corpus 73
Figure 8 Semantic classifications of to-complement clauses
controlled by a verb 75
Figure 9 Distribution of common desire/intent/decision verbs 76
Figure 10 Frequency of prominent stance nouns 80
Figure 11 Frequency of prominent stance adjectives 83
Figure 12.1 Distribution of stance markers by major grammatical category
in different registers (based on LGSWE Fig 12.1) 91
Figure 12.2 Distribution of stance markers in the CSR Corpus 91
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6. TABLES OF CONTENTS
ABSTRACT······································································2
······································································
ACKNOWLEDGEMENTS ·························································
························································3
LIST OF TABLES ·································································4
································································
LIST OF GRAPHS································································5
································································
CHAPTER 1 INTRODUCTION ··················································9
·················································
1.1 R EASONS FOR C HOOSING THE R ESEARCH TOPIC················ ················ ···9
················ ················ ··
1.2 B ACKGROUND OF THE T OPIC ················ ································
················ ································10
1.3 R ESEARCH OBJECTIVES ················ ································ ····
················ ································ ····11
1.4 R ESEARCH QUESTIONS ················ ································ ·····
················ ································ ·····12
CHAPTER 2 LITERATURE REVIEW···········································
·········································· 13
2.1 STANCE ANALYSIS················ ················ ·························13
················ ················ ························
2.2 C ORPORATE SOCIAL RESPONSIBILITY ················ ················ ·········
················ ················ ·········17
2.2.1 Background and Definitions ················ ················ ··········
················ ················ ··········17
2.2.2 Benefits································ ················ ···········
································ ················ ···········19
2.2.3 Stakeholders and Performance Indicators················ ················21
················ ···············
2.2.4 Components ················ ································ ·······
················ ································ ·······22
2.3 C ORPUS L INGUISTICS ················ ································ ·····
················ ································ ·····23
2.3.1 Definitions of Corpus ································ ················23
································ ···············
2.3.2 Corpus-based Analysis················ ················ ···············
················ ················ ···············24
2.3.3 Corpora ················ ················ ···························25
················ ················ ··························
CHAPTER 3 RESEARCH METHODOLOGY····································27
····································
3.1 SOURCE OF D ATA ················ ················ ·························27
················ ················ ························
3.2 PROCEDURES AND T OOLS OF ANALYSIS ································ ·······
································ ·······28
3.3 WORDSMITH T OOLS ················ ································ ······
················ ································ ······29
3.3.1 Accuracy of Data ················ ································ ···
················ ································ ···31
3.3.2 Frequency Counts ································ ················ ···35
································ ················ ··
3.4 FRAMEWORK FOR THE S TANCE ANALYSIS ················ ·····················36
················ ····················
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7. CHAPTER 4 CORPUS FINDINGS AND COMPARISONS·························39
·························
4.1 M ODALS AND SEMI-MODALS ································ ····················40
································ ···················
4.1.1 “Will” ································ ································ ·
································ ································ ·43
4.1.2 “Can” ································ ································ ·
································ ································ ·45
4.1.3 “Must”················ ································ ················ ·47
················ ································ ················
4.2 STANCE ADVERBIALS ································ ················ ·········
································ ················ ·········50
4.2.1 “Really”················ ································ ················53
················ ································ ···············
4.2.2 “According to” ································ ················ ··········
································ ················ ··········54
4.2.3 “Mainly” ················ ································ ···············55
················ ································ ··············
4.2.4 “Sort of” ································ ················ ···············
································ ················ ···············57
4.3 T HAT-COMPLEMENT C LAUSES ································ ··············59
································ ·············
4.3.1 Definitions················ ································ ··············59
················ ································ ·············
4.3.2 That-complement clauses controlled by a verb ································ ·
································ ·60
4.3.2a “Recognize” also recognise ·················································
·················································65
4.3.2b “Know”·································································66
················ ················ ················ ················
4.3.2c “Show” ·································································67
················ ················ ················ ················
4.3.2d “Believe” ·······························································
·······························································69
4.3.2e “Ensure” ································································70
················ ················ ················ ···············
4.4 TO -COMPLEMENT CLAUSES ································ ················73
································ ···············
4.4.1 To-clauses controlled by a verb································ ··············74
································ ·············
4.4.1a “Want” ·································································
·································································76
4.4.1b “Aim” ··································································77
················ ················ ················ ················ ·
4.4.1c “Need” ·································································
·································································78
4.4.2 To-complement clauses controlled by a noun ································ ··
································ ··79
4.4.2a “Commitment” ···························································
···························································80
4.4.2b “opportunity” ····························································81
···························································
4.4.2c “Responsibility”··························································
··························································81
4.4.3 To-complement clauses controlled by an adjective ················ ··············
················ ··············82
4.4.3a “Able” ··································································83
················ ················ ················ ················ ·
4.4.4 Evaluative adjectives················ ················ ······················84
················ ················ ·····················
CHAPTER 5 DISCUSSION OF FINDINGS·······································
······································86
5.1 SUMMARY OF CSR CORPUS F INDINGS ················ ················ ·········
················ ················ ·········86
5.2 C OMPARISON BETWEEN THE CSR C ORPUS AND GENERAL ENGLISH ················ ···
················ ···89
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8. CHAPTER 6 CONCLUSION ···················································
···················································92
6.1 C ONNECTING CSR WITH STANCE ANALYSIS ················ ·····················92
················ ····················
6.2 IMPLICATIONS ················ ················ ····························93
················ ················ ···························
6.3 LIMITATIONS AND F URTHER S TUDY················ ····························95
················ ···························
BIBLIOGRAPHY ································································
······························································· 96
APPENDICES··································································101
··································································
APPENDIX 1 - INVOLVEMENT IN NGO S ················ ················ ·············
················ ················ ·············102
APPENDIX 2 WORDLIST (F IRST PAGE ONLY)················ ·······················104
················ ······················
Appendix 2.1 Statistics················ ················ ·····················104
················ ················ ····················
Appendix 2.2 Frequency················ ································ ···
················ ································ ···105
Appendix 2.3 Alphabetical································ ················ ··106
································ ················ ·
APPENDIX 3 - B IBER 2004: 113-15 APPENDIX : COMPLETE LIST OF FORMS INCLUDED IN THE
ANALYSES OF STANCE ················ ·······························
················ ·······························107
APPENDIX 4 – DOUGLAS B IBER’S E- MAIL AND ATTACHMENT ················ ············
················ ············111
APPENDIX 5 – CONCORDANCE LINES OF “WILL” FROM THE CSR CORPUS ················ ·117
················
APPENDIX 6 – CONCORDANCE LINES OF “CAN ” FROM THE CSR CORPUS ················ ··122
················ ·
APPENDIX 7 – CONCORDANCE LINES OF “M UST” FROM THE CSR C ORPUS ················
················126
APPENDIX 8 – CONCORDANCE LINES OF STANCE ADVERBIALS FROM THE CSR CORPUS ······
······127
APPENDIX 9 – CONCORDANCE LINES OF VERBS + THAT FROM THE CSR C ORPUS ············129
···········
APPENDIX 10 – CONCORDANCE LINES OF STANCE VERB + TO FROM THE CSR CORPUS ·······
·······132
APPENDIX 11 – CONCORDANCE LINES OF NOUN + TO FROM THE CSR CORPUS ··············135
·············
APPENDIX 12 FREQUENCY OF VARIOUS GRAMMATICAL CONSTRUCTIONS················ ·
················ ·137
Appendix 12.1 Semantic categories of modals and semi-modals ·······················138
······················
Appendix 12.2 Semantic Categories of Stance Adverbials ················ ············139
················ ···········
Appendix 12.3 Semantic Categories of That-Complement Clauses ·····················140
····················
Appendix 12.4 Semantic Categories of To-Complement Clauses ·······················142
······················
8

9. CHAPTER 1 INTRODUCTION
1.1 Reasons for Choosing the Research Topic
Corporate Social Responsibility (CSR) is a topic which integrates business and
the community and is closely in related to my previous workplace and volunteer
experiences. I worked in the sales and marketing departments of both profit
making business corporations and non-governmental organizations for several
years. Also, I have participated in a number of voluntary activities in local
charities in New York and Hong Kong in the past four years (Appendix 1).
Inspired by many kind-hearted people, my concern towards community affairs has
been raised. CSR discusses how “a corporate” can take up responsibility to social
good and thus have a positive impact on the world. I find it a fascinating idea
that the vision of successful businesses are able to stretch beyond profit-making
and extend to human rights, environmental awareness, stakeholder relationships,
corporate governance and charitable giving. One may find that CSR reports
have become a new corporate communication channel in recent years, especially
on the World Wide Web. Recent studies have suggested that CSR reports can be
effective media for companies, multinational corporations in particular, to
communicate the core values and the brand to their target audience (stakeholders)
9

10. - investors, customers, employees, suppliers, and non-governmental organizations
all over the world; ultimately, to enhance corporate branding and increase market
share (Maitland, 2006; Ernst & Young, 2006; Pricewaterhouse Coopers, 2006;
KPMG, 2006 - details in section 2.2.2 Benefits). I find it interesting to learn
more about this topic in terms of its linguistic features and semantic
classifications.
1.2 Background of the Topic
The positive impact of a company on the wider community is known as its
“Corporate Social Responsibility” (CSR). There is no standard definition of CSR,
but one commonly used definition is from World Business Council for Sustainable
Development (2006):
Corporate Responsibility is the commitment of business to
contribute to sustainable economic development, working with
employees, their families, the local community and society at large to
improve their quality of life.”
10

11. The major components of CSR include environment, labour issues, human rights,
community involvement, business ethics and charitable giving. CSR Initiatives
are increasingly important in many European countries such as Denmark,
Germany and France, as well as the USA and the UK. Some recent research
reports that more than 50 percents of the top 250 companies of the Fortune 500
issued separate CSR reports and are very keen to promote the CSR initiatives
(KPMG, 2006). Leading multinational corporations have shown that when CSR is
undertaken strategically it can bring significant benefits to the company including
building business and branding at the global level. A brand can be defined as “a
mixture of tangible and intangible attributes, symbolized in a trademark, which, if
properly managed, creates influence and generate value” (Clifton and Maughan,
2000: vii). It is believed that with the cooperation of the global community, the
corporations will expect to achieve long term economic success.
1.3 Research Objectives
My present research study focuses on Stance Analysis. Stance is speakers’ and
writers’ expression of “personal feelings, attitude, value judgments or
assessments” (Biber et al., 1999:966). The purpose of this dissertation is to
11

12. research the prominent lexical and grammatical stance devices used in the CSR
reports issued by the world’ leading financial services corporations.
s
The research hypothesis is that the multinational corporations’ CSR practice and
their CSR components are strongly correlated with their stakeholders, business
strategies and corporate branding; therefore, the most frequently occurring stance
devices should be aligned to these three corporate core values.
1.4 Research Questions
This study aims to answer the following questions:
i.) What are the major grammatical devices and semantic categories
used to express stance within and across the chosen corporate social
responsibility reports?
ii.) What are the similarities and differences between present-day general
English and my own specialized Corporate Social Responsibility (CSR)
Corpus in terms of uses and patterns of prominent stance devices?
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13. CHAPTER 2 LITERATURE REVIEW
This section first outlines previous studies of stance analysis in regards to
grammatical constructions and semantic classifications. It then reviews the
definitions, benefits and key issues of corporate social responsibility. Finally, the
important aspects in corpus linguistics including definitions of corpus,
corpus-based analysis, and corpora used in the present research are discussed.
2.1 Stance Analysis
Over the past few decades, linguists have evaluated texts on emotion, attitude,
commitment, certainty and doubt of writers or speakers under different labels and
research objectives. For example, “Evaluation” (Hunston, 1994; Hunston and
Thompson, 2000), “Intensity” (Labov, 1984), “Affect” (Ochs, 1989),
“Evidentiality” (Chafe, 1986; Chafe and Nichols, 1986), “Hedging” (Holmes,
1988; Hyland, 1996a), “Appraisal” (Martin, 2003; White, 2006), “Modalization”
(Halliday, 1994) and “Stance” (Barton, 1993; Beach and Anson, 1992; Biber and
Finegan, 1988, 1989; Biber et al., 1999; Conrad and Biber, 2000; Precht, 2000).
In this present study, I focus on reviewing the recent studies of “Stance” by the
corpus linguists - Douglas Biber, Edward Finegan, Kristen Precht and Susan
13

14. Fitzmaurice. I find their findings are more relevant and valuable resources
which can help me to develop my own research on stance devices in the Corporate
Social Responsibility (CSR) Corpus.
Biber and Finegan (1988), in their first empirical study on writers’ (speakers’)
expression of attitudes, feelings, judgments, or commitment, explore various
speech styles of spoken and written English as marked by stance adverbials. All
occurrences of stance adverbials are identified in the LOB and London-Lund
corpora (410 texts of written and spoken British English). The adverbials
marking stance are divided into six semantic categories: honestly, generally, surely,
actually, maybe and amazingly adverbials. In their next research paper, Biber
and Finegan (1989) extend the investigation to adjectival, verbal, and modal
markers of stance, focusing on the lexical and grammatical encoding of
evidentiality and affect in English. The stance markers are expanded and divided
into 12 categories: (1) affect, (2-4) certainty adverbs, verbs and adjectives, (5-7)
doubt adverbs, verbs, and adjectives, (8) hedges, (9) emphatics, (10-12) modals
marking possibility, necessity, and prediction.
14

15. Biber et al. (1999: Chapter 12) develop a general framework of stance which falls
into three major semantic categories: epistemic, attitude, and style. Epistemic
markers express the speaker’s judgment about the certainty, reliability, and
limitations of the propositions, as well as the source of knowledge. Attitude
stance markers convey the speaker’s attitude or emotions about the proposition’s
content. Style markers describe the manner of speaking. Hunston and
Thompson (2000:5) suggest epistemic stance is roughly modalization in
Halliday’s terms and attitudinal stance is roughly appraisal in Martin’s terms.
The analyses are carried out on the Longman Spoken and Written English (LSWE)
Corpus (contains c. 40 million words in overall texts). In his recent research,
Biber (2004) explores historical change in the preferred grammatical marking of
stance by examining the entire system of stance devices. The findings are
concluded through corpus-based analysis of the written and speech-based registers
(drama, letters, newspapers and medical prose) in the ARCHER Corpus, tracking
the patterns of change from 1650 to the present.
15

16. Precht’ (2000) Ph.D. dissertation also attempts to examine the entire system of
s
devices used to mark stance, including modality, adverbials, and complement
clause constructions. The Longman Corpus of Spoken and Written English
(LSWE), 31 million words in conversations, meetings, news and academic writing
in British and American English, was used to conduct a comprehensive study of
patterns of individual stance markers in modern general English. In Precht
(2003), the study aims to compare stance differences in British and American
conversations among friends, family and colleagues in terms of evidentiality and
affect. The analysis found more than 1,400 different stanced words in English,
and yet English speakers use only about 150 words for ninety percent of their
stance expression. She argues that the expression of stance is shaped by culture
and custom, that is, English speakers are socialized to use particular stance
markers in particular way (Precht, 2003:240)
In contrast, Fitzmaurice’ (2003) study concerns the semantic domains of
s
epistemic and attitudinal stance of personal letters produced by a network of early
eighteenth-century English writers. The research focuses on modal verbs and
stance complement constructions such as stance verbs with the first person subject
16

17. in order to explore the grammatical realization of speaker involvement in
epistolary language. In general, previous studies have focused primarily on
tracking historical changes and examining the registers of personal letters, drama,
medical prose, conversations, news, fictions, and academic prose, but no study has
investigated particular stance devices taking on a more specialized business report
on social responsibility, which is a new corporate communication tool towards
globalization.
2.2 Corporate Social Responsibility
2.2.1 Background and Definitions
According to Anderson, Jr. (1989:6), the concept of social responsibility has been
with us since the beginning of mankind and has slowly evolved to its present state.
The history and development of social responsibility emerged as early as in
5000B.C.-550A.D in Egypt, Babylonia China and Greece (1989:30-33).
However, the first comprehensive approach to modern era social responsibility
was not ushered until 1953 with the publication of Howard R. Bowen’ book
s
Social Responsibilities of the Businessman. Social responsibilities were defined
as “the obligation of businessmen to pursue those policies, to make those
17

18. decisions, or to follow those lines of action which are desirable in terms of
objective and values of our society” (Bowen, 1953 cited in Anderson, Jr. 1989).
Considering the participants and components involved in social responsibility,
Anderson, Jr. (1989: 9) defines social responsibility as “the obligation of both
business and society (stakeholders) to take proper legal, moral-ethical, and
philanthropic actions that will protect and improve the welfare of both society and
business as a whole; all of this must be accomplished within the economic
structures and capabilities of the parties involved.”
Taking the realist view, the UK government’ Department of Trade and
s
Industry-sponsored Corporate Responsibility Group defined CSR as: “The
management of an organization’ total impact upon both its immediate
s
stakeholders and upon the society within which it operates. CSR is not simply
about whatever funds and expertise companies choose to invest in communities to
help resolve social problems … it is about the integrity with which a company
governs itself, fulfils its mission, lives by its values, engages its stakeholders,
measures its impacts and reports on its activities.” (UK Government’s
18

19. Department of Trade and Industry, 2006)
The terminology used in relation to corporate responsibility and for reporting on
CR performance is varied. Companies may refer to sustainability, sustainable
development, corporate citizenship, corporate social responsibility and corporate
responsibility.
2.2.2 Benefits
The current climate is positive for CSR; in particular, following corporate
scandals such as Enron, Anderson and Worldcom, there is a greater recognition by
businesses that CSR can help to restore public trust in the corporate world
(Hancock, 2005: 7). Numerous research and surveys reported positive findings
that CSR strategy could externally help to managing the effects of globalization,
cutting environment cost, building long-term sustainable success, raising
productivity and competitiveness, while internally improving employee
recruitment/retention and enhancing customer loyalty. Research published by
the UK’ Institute of Business Ethics, comparing companies in the FTSE250,
s
provided strong evidence that “those clearly committed to ethical behaviour
19

20. perform better financially over the long term than those lacking such a
commitment” (Source: Alison Maitland, Financial Times, 3 April 2003). 94% of
senior executives from 147 of the Global 100 Companies believed the
development of a Corporate Social Responsibility (CSR) strategy can deliver real
business benefits (Ernst & Young, 2006). 71% of CEOs would sacrifice
short-term profitability in exchange for long-term shareholder value when
implementing a sustainability programme (Pricewaterhouse Coopers, 2006).
KPMG International Survey of Corporate Responsibility Reporting 2005 reflected
the growing importance within the business community of corporate
responsibility as the key indicator of non-financial performance, as well as a
driver of financial performance since 1993. Key results are reported:
- 52 percent of the top 250 companies of the Fortune 500 (Global 250,
G250) and 33 percent of the top 100 companies in 16 countries
(National 100, N100) issued separate CR reports.
- Reporting in the financial sector has increased dramatically among both
G250 and N100 companies. The financial sector shows a 138 percent
increase in reporting activity since 2002.
20

21. - 80 percent G250 companies are in electronics & computers, utilities,
automotive and oil & gas sectors. 50 percent N100 companies are in
utilities, mining, chemicals, oil & gas, forestry and paper & pulp sectors.
- The top two countries in terms of separate CR reporting are Japan (80
percent) and the UK (71 percent). Other countries include Canada,
France, Germany and USA.
- Main CSR topics are: environmental issues (85 percent), social issues
(66 percent) and economic issues (61 percent)
2.2.3 Stakeholders and Performance Indicators
According to Cooper (2004: Chapter 4), the key stakeholder groups of social
responsible corporations include (but are not limited to): shareholders, investors,
managers, employees, customers, suppliers, and the environment. The purpose
of the CSR reporting is to reflect the social performance and accountability of
corporations to the stakeholder groups. Traditionally, annual reports and
financial statements provide financial information to users in order to understand
the profitability, efficiency, liquidity and financial strength of the corporation.
However, to cover a wider scope of contributions (business, community and
21

22. environment), the corporate social responsibility reports intend to provide
non-financial measures and multi-dimensional performance measurement
frameworks to target stakeholders (Cooper, 2004:1-2). Key indexes are the Dow
Jones Sustainability Index (DJSI), the FTSE4Good Index and the Environment
Index (formerly known as the BiE Index).
2.2.4 Components
Some of the key CSR issues that social responsible corporations and their
stakeholders interested in are: Environment (climate change and companies’
reductions of CO2 emissions), Human Rights, Arts & Education, Health & Safety,
Volunteering, Employee Relations, Community Financing, Risk Management,
Charitable Giving, and Corporate Governance (Hancock, 2005:47, 88-89).
22

23. 2.3 Corpus Linguistics
2.3.1 Definitions of Corpus
Sinclair (1991:171) defines a corpus as a collection of naturally-occurring
language texts, chosen to characterize a state or variety of a language. In modern
computational linguistics, a corpus typically contains many millions of words: this
is because it is recognized that the creativity of natural language leads to such
immense variety of expression that it is difficult to isolate the recurrent patterns
that are the clues to the lexical structure of the language.
According to the Oxford Companion to the English Language, ed. McArthur &
McArthur (1992), Corpus is from Latin corpus body. The plural is usually
corpora. A corpus is (1) a collection of texts, especially if complete and
self-contained: the corpus of Anglo-Saxon verse (2) in linguistics and
lexicography, a body of texts, utterances, or other specimens considered more or
less representative of a language, and usually stored as an electronic database.
Currently, computer corpora may store many millions of running words, whose
features can be analyzed by means of tagging (the addition of identifying and
23

24. classifying tags to words and other formations) and the use of concordancing
programs. Corpus linguistics studies data in any such corpus.
2.3.2 Corpus-based Analysis
According to Biber, Conrad & Reppen (1998:4), the essential characteristics of
corpus-based analysis are:
- it is empirical, analyzing the actual patterns of use in natural texts;
- it utilizes a large and principled collection of natural texts, known as a
“corpus”, as the basis for analysis;
- it makes extensive use of computers for analysis, using both automatic and
interactive techniques;
- it depends on both quantitative and qualitative analytical techniques.
Corpus-based analysis studies the use of language characteristics by considering
the relevant “association patterns” (Biber, Conrad & Reppen, 1998:4). By
investigating the use of a linguistic feature, the analysis can look at
“lexical-lexical” association patterns such as three nearly synonymous words big,
large, and great and consider the collocations of these three words. We can also
24

25. investigate “lexical-grammatical” associations such as comparing the verbs that
are most commonly used with that-clauses versus to-clauses (for example, think
co-occurring with that-clauses versus want commonly co-occurring with
to-clauses). The use of a linguistic feature can be studied in terms of its
non-linguistic associations. The analysis can look at the frequency and
distribution of different kinds of complex structures across spoken and written
registers. Research also compares the linguistic features of second-language
learners and native-speakers. Corpus-based techniques also allow investigations
of individual author styles or the historical development of language use. The
results of large-scale studies of use are helpful for language teaching purposes to
help design classroom activities or workplace training (Biber, Conrad & Reppen,
1998: 9-12).
2.3.3 Corpora
A corpus is a large and principled collection of natural texts. Three well-known
and publicly available corpora are used in the present research: FLOB (for
reference) and LGSWE (for modern general English comparison).
25

26. The FLOB (Freiburg Lancaster-Oslo/Bergen) Corpus - British English, 1991
which contains approximately 1 million words taken from various types of written
text ranging from press, general prose, learned writing and fiction.
The LSWE (Longman Spoken and Written English) Corpus – American and
British English since 1980’ which contains 40 million words in 37,244 texts
s
which taken from 4 registers: Conversation (CONV), Fiction (FICT), News
(NEWS) and Academic Prose (ACAD).
26

27. CHAPTER 3 RESEARCH METHODOLOGY
3.1 Source of Data
Text-based data examined in this study was obtained from the World Wide Web,
commonly the “Corporate Social Responsibility” section of the chosen
companies’ websites. Therefore, the source of data is convenient and reliable.
The websites contained the latest CSR reports of four leading multinational banks
when the data was collected in December 2005. The banking sector was chosen
because financial services are closely associated with everyone, ranging from a
local individual retail banking customer to multinational corporate and
institutional investment clients and even the central government of all nations.
With an international network, it is extremely important for the banking and
financial services organizations to communicate effectively with global internal
and external audiences, especially the professional audience with growing
influential power such as socially responsible investment (SRI) analysts (investors)
and non-governmental organizations (NGOs). In this sense, corporate social
responsibility reports in the banking sector, which convey a specific
communication purpose to align readers with accountability, sustainability and
corporate governance, is more essential than in other industries. The four chosen
27

28. CSR reports are published by the world’ leading financial services companies.
s
Downloaded versions of the reports come from the links:
HSBC Corporate Social Responsibility Report 2004
http://www.investis.com/reports/hsbc_csrr_2004_en/report.php?type=1&page=1
Citigroup Citizenship Report 2004
http://www.citigroup.com/citigroup/citizen/community/data/citizen04_en.pdf
Standard Chartered 2004 Corporate Social Responsibility Report
http://www.standardchartered.com/corporateresponsibility/site/pdf/S_Chartered_CR_2004.pdf
Deutsche Bank Corporate Social Responsibility Report 2004
http://www.db.com/en/downloads/responsibilty/CSR_Report_Single_pages_en_0705.pdf
3.2 Procedures and Tools of Analysis
A corpus-based analysis allows a combination of computational, quantitative and
qualitative methods. The study began with computational and quantitative
analysis. First, the corporate social responsibility reports from the corporate
websites of the four chosen banks were downloaded and saved. All files were
in .pdf format, which only allowed read and print functions to be used. Secondly,
the Scansoft PDF Converter Professional 3.0 was used to convert the PDF files to
Microsoft Word documents and all files were saved as .doc format. The layouts
28

29. of both written texts and graphics were slightly changed after conversion;
however, the advantage is that copy and paste functions are allowed. Thirdly, the
main texts were carefully copied and pasted in new word files; all photographs,
tables, charts, and description of these graphics were ignored. Fourthly, each
document was created in plain text format: hsbc.txt, citigroup.txt, scb.txt and
deutsche.txt and saved in drive c:wsmith4/text. Fifthly, Oxford Wordsmith Tools
4.0 (Details in 3.3) was used and the 4 texts were chosen to create wordlists,
concordances and keywords. A specialized small corpus, CSR Corpus, was
generated as an effective analysis tool in order to answer the research questions.
Finally, findings from the CSR Corpus were interpreted and compared with a
reference corpus of general English, the LSWE Corpus, to complete the
Qualitative Analysis.
3.3 Wordsmith Tools
Wordsmith Tools (version 4.0) is an integrated suite of programs for looking at
how words behave in texts. The Wordlist lets users see a list of all the words or
word-clusters in a text. Concord gives users a chance to see any word or phrase
in context so that collocations and clusters can be seen. With Keywords tool,
29

30. users can find the key words in a text. When opening the Wordsmith Tools
Controller, the four corporate social responsibility reports in plain text format -
hsbc.txt, deutsche.txt, scb.txt and citigroup.txt were chosen to generate word lists.
The word lists in “alphabetical”, “frequency” and “statistics” formats were
displayed in Appendix 2. Tokens in individual text ranges from 15,705–28,216
words. Total tokens of 4 texts are 88,841 and key statistics are summarized in
Table 1 (Details in Appendix 2.1).
Table 1 – Statistical summaries of the CSR corpus
Banks Text File File Size Tokens Types
(running (distinct words)
words) in Text
HSBC Hsbc.txt 98,313 15,705 2,563
Citigroup citigroup.txt 161,494 24,919 3,346
Standard Scb.txt 121,921 20,001 2,759
Chartered
Deutsche deutsche.txt 176,441 28,216 4,095
Bank
Overall CSR corpus 558,169 88,841 6,924
The newly created CSR Corpus word lists as csr_four banks.lst was saved. Next
a key word list was made to compare the CSR corpus wordlist with a reference
corpus wordlist. In this present paper, Flob Corpus (a large word-list with 1
million words of written British English in 1991) is used as a benchmark to
30

31. compare with the CSR corpus. The KeyWords list identifies and ranks words
that occur with a noticeably higher frequency in the CSR corpus compared with
the frequency of occurrence in the reference corpus. A word which is positively
key occurs more often than would be expected by chance in comparison with the
reference corpus in the CSR corpus (Table 2).
Table 2 10 key words with the highest positive Keyness
N Key word Freq. % RC. Freq. RC. % Keyness P
1 OUR 1,392 1.57 990 0.07 4,869.31 000000
2 WE 1,289 1.45 2,699 0.18 2,690.39 000000
3 CITIGROUP 345 0.39 0 1,976.05 000000
4 BANK 412 0.46 141 1,748.71 000000
5 S 420 0.47 202 0.01 1,645.41 000000
6 DEUTSCHE 269 0.30 4 1,499.27 000000
7 AND 3,341 3.76 27,289 1.86 1,258.02 000000
8 EMPLOYEES 230 0.26 20 1,180.05 000000
9 ENVIRONMENTAL 261 0.29 71 1,158.40 000000
10 BUSINESS 337 0.38 305 0.02 1,077.57 000000
3.3.1 Accuracy of Data
Concord is a search word tool which shows concordance lines in chosen text files.
By using Concord, users are able to see many authentic examples of a word or
phrase and access information about collocates of the search word, dispersion
plots showing where the search word came in each file, and cluster analyses
showing repeated clusters of words (phrases) with frequency in concordance. In
the next chapter, Corpus Findings and Comparisons, Concord Tool is used very
31

32. often to search for the major grammatical devices used to express stance. A few
checking steps about the accuracy of data are worth mentioning here:
1) To identify stance adverbials in the CSR corpus, a word or phrase can be
typed in (e.g. surprisingly or according to). However, to identify
that-clause or to-clause controlled by a verb, I considered all word-forms
(called Lemmas). For example, a stance verb “know” also includes
knows, known, knowing, knew. In this case, I searched for “know* that”
and “knew that” to get accurate total frequency. When inappropriate
examples were found such as N1 “knowledge that”, the concordance was
deleted. The correct concordance was then be saved and printed.
N Concordance
1 stakeholders have perspectives and knowledge that we need and can learn
2 the global financial system from abuse. Knowing that our business benefits from
3 and can let Native American people know that there is help out there.”
2) In some circumstances, a word can be a verb or a noun (such as need).
When considering to-clauses controlled by a verb, I needed to check
every example of the concordance lines such as need* to and then delete
need or needs in noun form (N1 and N3 below). The correct
concordances were saved to get the accurate total frequency.
32

33. N Concordance
1 our future. We have talked about our need to focus on our long-term success,
2 and being informed when you need to make those decisions can help
3 in a number of areas, but stressed the need to report more on our financing
4 compete effectively and succeed, they need to continue to invest the time in
3) To avoid the risk of missing out any possible instances in the Concord,
when counting to-clauses controlled by a noun, both singular and plural
forms were considered (e.g. commitment to and commitments to;
opportunity to and opportunities to). With the same concern, to avoid
over counting of occurrences, I checked the concordance lines manually to
see whether they were verbs or nouns. For example plan to, only N1 and
N5 are in noun form, the deleted instances are verb forms, we plan to and
they plan to.
N Concordance
1 Developed a comprehensive Five Point Plan to foster greater appreciation of the
2 where we do business. We plan to achieve this goal by offering
3 best practices. In 2005, we plan to survey some of the nonprofit
4 of the world in late 2004. In Brazil, we plan to work with Inter-American
5 (currently 17 per cent). * Develop a plan to address perceived and any actual
6 views, and in the next few years we plan to grow this activity. The starting
7 now agree or strongly agree that they plan to work for the Bank in three years
4) Some words can be an adjective or adverb (such as hard). When
considering to-clauses controlled by an adjective, I checked all
concordance lines. N1, N4 and N5 were deleted because hard is
employed as an adverb in the phrase we work hard. The correct
33

34. concordances were saved to get the accurate total frequency.
N Concordance
1 rates are high. While we work hard to prevent foreclosures, we cannot
2 the actual number of closings is hard to predict going forward. The
3 impossible to achieve. It is particularly hard to remain objective about Iraq.
4 the rise. Responsible selling We work hard to offer the right products to the
5 of our decisions and work hard to find solutions with our partners.
5) To avoid over counting of frequency of modal verbs such as may, May as a
calendar month (N24) and May as a person’ name (N22) were deleted in
s
the concordance examples.
N Concordance
21 estimate the cost of this programme may be up to US$7 million in the first
22 Paul¡s Girls¡ School, London) and Lord May (President of the Royal Society).
¦ ¦
23 as sponsoring ethical legislation which may have an effect on HSBC North
24 Forest Products Sector Guideline In May 2004, we issued a major new
34

35. 3.3.2 Frequency Counts
For the purpose of comparing my CSR corpus findings with LGSW corpus
findings across different registers, all frequency counts reported in this present
paper are normalized to a common basis, per million words of text (Biber et al.,
1999: 38). For example, the modal verb, Will occurs 240 times in the CSR
corpus (with total 88,841 tokens), which is equivalent to 2,701 times per million
words.
CSR Will: 240 tokens/88841 tokens x 1,000,000words = 2,701 per million words
LSWE Will: Average = 3,650 per million words
Conversation = 5,600 per million words
Fiction = 2,600 per million words
News = 4,200 per million words
Academic = 2,200 per million words
(from Biber et al., 1999, Figure 6.8 and Table 6.6)
35

36. 3.4 Framework for the Stance Analysis
The framework for the analysis of stance used in this present paper has been
adopted from Biber (2004:133) (Appendix 3). This general framework is used
because it contains a very large set of stanced words in general English (almost
700 lexical items). Stance devices are distinguished into three major grammatical
types: modal verbs, stance adverbials, and complement clause constructions. I
also use the Longman Grammar of Spoken and Written English (Biber et al., 1999:
especially Chapter 12) as a reference to compare stance marking in modern
general English. In terms of grammatical structures of stance devices, Biber et al.
(1999) and Biber (2004) are the same. In terms of semantic classifications,
however, labels and explanations vary. For example, in LGSWE, adverbial
stance markers are grouped into three major semantic categories: epistemic,
attitudinal, and style of speaking. In Biber’ (2004) research paper, four
s
semantic classes are named: factive, non-factive, attitudinal, and likelihood. To
avoid confusion in definitions and discussion of findings, I consulted Professor
Douglas Biber by e-mail on 17 March 2006 and he was extremely helpful and
provided a comprehensive list. The framework is mostly based on the LGSWE
(email and attachment in Appendix 4). Thus I was able to cross check the
36

37. semantic classifications in his two studies on stance analysis (Table 3). For
example, factive adverbial is equivalent to epistemic stance with certainty
meaning; and non-factive is related to manner/style of speaking.
Table 3 Cross reference of semantic classifications in two studies
(Biber, 2004 and Biber, 2005)
Grammatical Semantic Classifications according Semantic Classifications according
Constructions to Biber, 2004 to Biber’ E-mail 23 March 2006
s
A. Modals and
semi-modals Possibility/permission/ability Possibility/permission/ability
modals
Logical necessity/obligation modals Necessity / obligation
Prediction/volition modals Prediction / volition
B. Stance Adverbials Attitudinal adverbials Attitude
Non-factive adverbials Style
Factive adverbials Epistemic: Certainty
Likelihood adverbials Epistemic: Likelihood
C. Complement
clauses
That-complement - Controlled by a verb Stance verb + that-clause
clauses Non-factive verbs Style
Attitudinal verbs Attitude
Factive verbs Epistemic: Certainty
Likelihood verbs Epistemic: Likelihood
- Controlled by an adjective Stance adjective + that-clause
Attitudinal adjectives Evaluation adjectives
Attitude/Emotion adjectives
Likelihood adjectives Epistemic adjectives: Certainty
Epistemic adjectives: Likelihood
- Controlled by a noun Stance noun + that-clause
Non-factive nouns Communication (non-factual) nouns
37

38. Attitudinal nouns Attitude/perspective nouns
Factive nouns Epistemic nouns: Certainty
Likelihood nouns Epistemic nouns: Likelihood
To-complement - Controlled by a verb Stance verb + to-clause
clauses Communication/speech act verbs Speech act and other
communication verbs (non-factual)
Mental/cognition verbs Cognition/perception verbs
(likelihood)
Desire/intent/decision verbs Desire/intention/decision verbs
Modality/cause/effort verbs Verbs of causation/modality/effort
Probability/simple fact verbs Probability (likelihood) verbs
- Controlled by an adjective Stance adjective + to-clause
Certainty adjectives Epistemic (certainty/likelihood)
adjectives
Ability/willingness adjectives Ability or willingness adjectives
Personal affect adjectives Attitude/emotion adjectives
Ease/difficulty adjectives Ease or difficulty adjectives
Evaluative adjectives Evaluation adjectives
- Controlled by a noun Stance noun + to-clause
38

39. CHAPTER 4 CORPUS FINDINGS AND COMPARISONS
This chapter goes into depth in reporting the major grammatical devices used to
express stance. Grammatical stance devices include two distinct linguistic
components, one presenting the stance and the other presenting a proposition that
is framed by the stance (Biber et al., 1999:969). Three common devices: modals
and semi-modals, stance complement clauses (controlled by a verb, adjective or
noun) and stance adverbials will be discussed. Overall, stance markers are
common in the CSR Corpus. Complement clauses are the most favourable
grammatical categories of stance markers. Modals are also very common while
semi-modals are rarely used. Adverbial stance markers are considerably less
frequent than the other grammatical categories. In the following sections, the
structure, semantic meanings, and frequencies of the major grammatical devices
to mark stance are surveyed (Appendix 12, 12.1-12.4). The results are presented
in four sections: 4.1 examines prominent modal verbs and discusses the
importance of semantic functions. 4.2 lists the adverbial stance markers with each
semantic category explanation. 4.3 and 4.4 investigates uses and patterns of
that-complement and to-complement clauses respectively. At the end of each
section, the most common features in stance analysis are discussed and compared
39

40. with the relevant major findings in the LSWE Corpus (Biber, 2004:113-15 and
Biber et al., 1999: 483-97; 557-63; 647-55; 662-75; 699-724; 853-75; 966-86).
Figure 1
Distribution of stance markers by major grammatical category
frequency per million words
10000
8000
6000
4000
2000
0
Complement clauses Modals and semi-modals Stance adverbials
4.1 Modals and semi-modals
Briefly, in present-day English, there are three basic groups of modals that
perform three sets of semantic-pragmatic functions. These are modals of
prediction/volition, of obligation/necessity, and modals expressing
permission/possibility/ability (Biber et al., 1999:485). In the CSR Corpus,
modal verbs are extremely important. The modals marking prediction/volition
are the most common, mainly due to the contribution of will. The modals
marking possibility/permission/ability are also very common, mainly due to high
frequency of can. The modals marking necessity/obligation are the least
40

41. common among the three semantic categories (Figure 2).
As shown in Figure 3, will is extremely common in the CSR Corpus (2700 times
per million words); it also ranks first in terms of frequency among all modal verbs
in the LSWE Corpus. At the other extreme, the modal shall is relatively rare
(only over 20 times per million words), the same, it ranks last in the LSWE
Corpus (Biber et al., 1999:486). Can ranks second (almost 2000 times per million
words) in the CSR Corpus is also extremely common (places third in the nine
central modals in the LSWE Corpus). Must, should, may, would, could and
might are relatively common in the social responsibility reporting. Semi-modals
have to, (have) got to, and ought to have rare occurrence, the result is similar to
the LSWE Corpus findings because semi-modal verbs are considerably less
common than core modal verbs (Biber et al., 1999:487). In present-day English,
semi-modals are more common in conversation than in written registers. In the
subsequent discussion, only the most prominent modal verbs of each
subcategories of semantic meaning are included. The results are compared with
the relevant findings of the four registers in the LSWE Corpus (Biber et al.,
1999:486-96).
41

42. Semantic subcategories
Prediction/volition (46%): Will, would, shall
Possibility/permission/ability (38%) Can, could, may, might
Logical necessary/obligation (11%) modals: Must, should
( 5%) semi-modals: have to, got to, and ought to
Figure 2
Semantic Categories of Modal Verbs
4000
word s
3000
frequency per million
2000
1000
0
Prediction Possibility/ Logical necessity/
/volition modals permission/ obligation modals
ability modals
Figure 3
Frequency of modal verbs in the CSR Corpus
frequency per million words
3,000
2,500
2,000
1,500
1,000
500
-
will can must should may have would could might shall got to* ought
to* to*
* Semi-modal verbs
42

43. 4.1.1 “Will”
Will is commonly used to mark logical prediction (about the future) and personal
volition. In the CSR Corpus, the report writers intend to project a positive future,
predict growth and development, and make a promise of continuous efforts on
good initiatives. 3-word clusters commonly found include: will continue to, we
will also, will help us, will be able, and we will report. All concordance
instances of will are displayed in Appendix 5.
Marking Logical Prediction:
2005 will be an important year in testing out our approach to understanding the
social and environmental risks of our supply chain. We will be learning important
lessons from the phase I countries that are leading this programme and attempting
to develop our systems accordingly. (N2 SCB)
Sustainable business will give Deutsche Bank a competitive edge and contribute to
shareholder value. (N29 Deutsche)
According to Collins Cobuild English Grammar (2005:221), will usually indicates
that we are talking about a future event or situation. Because these examples are
cited from the CSR Reports 2004, the report writers try to predict business
activities that are related to the banks and their shareholders in 2005.
43

44. Marking (Personal) Volition:
Deutsche Bank will continue with its efforts to establish good governance in the
frame-work of the Global Compact and contribute to the development of
mechanisms that will increase transparency and compliance with the GC principles.
(N24 Deuce)
Other sector guidelines will follow and we will continue to develop our expertise in
sector-specific risk management. (N62 HSBC)
Intentions are usually stated by using will, shall or must in a declarative sentence.
The subject is “I” or “we” (Collins Cobuild English Grammar, 2005:233). In the
examples above, the banks express a determined voice to continue their efforts to
improve corporate governance and risk management.
In the CSR Corpus, the volition/prediction modals are commonly used to mark
both logical prediction and personal volition. Will is extremely important as it
accounts for 90% of total frequency in that semantic category. Compared with
the LSWE Corpus Findings (Biber et al., 1999:495), volition/prediction modals
are used most of the time to mark prediction in academic prose. In conversation,
these modals are commonly used to mark both volition and prediction. Will is
also common with both meanings (and is often ambiguous).
44

45. 4.1.2 “Can”
Can is often used to express “possibility”, i.e. not as the speaker’ qualification of
s
a statement but as deriving from an inherent capacity of the subject. Can is used
to express permission. Can is also realized more specifically as the subject’s
ability in connection with dynamic situations and perceptions. In the CSR Corpus,
most of the concordance instances show can functions as a marker of logical
possibility (e.g. we can do better, we can do more) or ability (e.g. we can make an
even bigger contribution, we can make a big difference). However, in some
instances, it is also often unclear as to whether it marks logical possibility or
ability. In very rare occurrences, can is marking permission. All concordance
instances of can are displayed in Appendix 6.
Can marking possibility:
The considerations, which guide a bank in behaving responsibly, are somewhat
different from those of an industrial or manufacturing company. For instance, their
environmental impacts can be substantial and direct. (N42 SCB)
A company is only regarded as "committed to sustainability" or as "socially
responsible" if it can prove that it puts its sustainability principles into practice in
its day-to-day business. (N52 Deutsche)
45

46. The writer uses can in the first example to say that it is possible that something is
the case. The second example uses if-clause to indicate the possible
consequence of something happening.
Can marking ability:
We believe we have skills we can bring to make that mission a success. (N116 SCB)
We support hundreds of development programs, primarily in the areas where we think
we can do the most good: financial education, microfinance, training and technical
assistance programs, and management expertise. (N123 Citigroup)
In general, the report writer use can to show the banks’ ability to make an
improvement or have a positive impact.
Can ambiguously marking logical possibility or ability:
And it is only by making progress together that we can transform the world into a
global community - one in which everybody has opportunities that far outweigh the
risks involved. (N154 Deutsche)
Customers are automatically given a choice of donating a specific amount of pesos
which they can then approve or decline, or increase the amount of their donation.
46

47. The programme is regionalised so that customers can identify with the charities
involved. (N20 HSBC)
As with the usage in the CSR reporting, LSWE Corpus findings show that can in
academic prose commonly marks both ability and logical possibility but rarely
expresses permission. Logical possibility is also the predominant use of these
modals in conversation (Biber et al., 1999:491).
4.1.3 “Must”
Must is used extrinsically to indicate that a certain situation is necessarily real and
that this can be inferred from a set of facts. The modality involved here is thus a
combination of necessity and deduction. Must is used intrinsically to express
(personal) obligation (Biber et al., 1999:494). All concordance instances of must
are displayed in Appendix 7.
Marking necessity:
In addition, in the process of compiling information for this report, we now believe
that we will need to clarify some of our credit and risk policies to help transactors
and risk officers determine when a project must comply with our ESRM Policy and/
or the Equator Principles. (N11 Citigroup)
47

48. However, we also know that sustainable success must go hand in hand with the
highest standards of behaviour. It is particularly gratifying to note that, of the many
business awards won by HSBC during 2004, several cited our overall conduct and
our commitment to good governance. (N12 HSBC)
The writer uses must to say that it is necessary that something happens or is done,
in order that something else can happen (Collin Cobuild English Grammar,
2005:236). The two examples above show must is used for the expression of
extrinsic necessity to comply with certain policies/standards.
Marking obligation:
Our name, “Citigroup,” must inspire trust and confidence. (N2 Citigroup)
We must put our clients first, provide superior advice, products and services, and
always act with the highest level of integrity. (N27 Citigroup)
The examples show intrinsic obligation to set priority to their stakeholders, mainly
customers and employees.
48

49. The CSR Corpus finds obligation/necessity modals and semi-modals are less
common overall than the other modal categories. It may due to the fact that CSR
initiatives are voluntary exercises of the companies rather than compulsory acts
required by laws and regulations. In general English, must is the only modal
used commonly for both logical necessity and personal obligation. In LSWE
Corpus, must in conversation is used most of the time to mark logical necessity,
while it is somewhat more common to mark personal obligation in academic
writing (Biber et al., 1999:494).
49

50. 4.2 Stance Adverbials
Stance adverbials express the attitude or assessment of the speaker/writer with
respect to the proposition contained in the main clause (Biber et al., 1999:966).
Adverbials are one of the primary lexical markers of stance in English, and Biber
(2004) distinguishes four semantic classes of stance adverbials: attitudinal,
non-factive, factive, likelihood. By using Biber (2004:133-35) “Appendix:
Complete List of Forms included in the Analyses of Stance” (Appendix 3) as a
comprehensive checklist, the study attempts to investigate the preferred devices
used to mark stance in the CSR Corpus. Frequencies of the stance adverbials
occurring in the corpus are counted. Most concordance instances are factive
adverbials, while non-factive and likelihood adverbials have very similar
frequencies. Attitudinal adverbials show rare occurrences. The four categories
and all occurrences of stance adverbials identified in the CSR Corpus are listed
from high to low frequency.
Factive (38 %) : really, in fact, never, of course, actually, certainly, obviously,
indeed, inevitably
Non-factive (29 %) : according to, mainly, generally, accordingly, typically
50

51. Likelihood (27 %) : sort of, kind of, perhaps, roughly, evidently, most cases,
possibly, probably
Attitudinal (6 %) : importantly, wisely
(Note: all individual stance adverbials occur less than 120 times
per million words)
Figure 4
Distribution of Stance Adverbials
frequency per million words
500
400
300
200
100
0
Factive Non-factive Likelihood Attitudinal
Biber et al., (1999: Chapter 10.3) also discuss stance adverbials. Their
investigation uses different labels, yet explains similar semantic domains.
Stance adverbials (Biber et al., 1999:854) fall into three major semantic categories:
epistemic, attitude, and style. Epistemic stance (certainty) is equivalent to
Factive adverbials; epistemic stance (likelihood) is equivalent to Likelihood
51

52. adverbials. Non-factive is similar to Style stance and Attitudinal is Attitude
stance.
Epistemic stance adverbials and attitude stance adverbials both comment on the
content of a proposition. Epistemic markers express the speaker’s judgment
about the certainty, reliability, and limitations of the propositions; they can also
comment on the source of the information. Attitude stance adverbials convey
the speaker’s attitude or value judgment about the proposition’s content. Style
adverbials, in contrast, describe the manner of speaking. In the CSR Corpus,
epistemic adverbials are much more common than attitude or style adverbials.
The higher overall frequencies of epistemic adverbials are: really, according to,
mainly.
Sub-categories of epistemic adverbials:
Doubt / certainty: maybe, perhaps, of course, certainly, probably
Actuality: really, in fact, actually
Imprecision: like, sort of, kind of
Source of Info: according to, evidently
Limitation: mainly, generally
52

53. 4.2.1 “Really”
Adverbials marking actuality are very common in conversation (Biber et al., 1999:
870) far more than in the registers of news, academic and fictions. Speakers
make a point of identifying propositions as factual or real, with the stance markers
actually and really. Biber’s (2004:127) study shows that really is the most
common among those factive adverbial forms such as undoubtedly, obviously and
certainly. Really can appear in initial, medial and final positions; where it occurs
medially, it is particularly difficult to analyze (Biber el at., 1999: 857). Certain
instances do seem clearly to have the epistemic stance meaning “in reality” or “in
truth” but some could be interpreted as intensifying a verb or adjective, with the
approximate meaning “very (much)” (:858).
In the CSR Corpus findings, really indicates the report writers’ perception of
actuality or reality of a proposition. A grammatical pattern of really + verb is
observed.
Organizing bread and water is a big problem, but what really keep us alive
and human is music and our culture."
Most investors do not think that they can really make a change. But they can.
53

54. I feel proud to lead a business which really believes in making a difference to
the communities we operate in, but we have to be realistic.
Two concordances are in interrogative form. All entries display really in medial
positions, zero percentage in initial or final position.
Was that really the right moment?
But is it really the challenge and the duty of journalists to educate?
N Concordance
1 printemps" with a troupe of young people - really does change the participants' lives. The
2 never be sure what will happen. Was that really the right moment? Or was it perhaps a
3 bread and water is a big problem, but what really keeps us alive and human is music and
4 agree that we set out to inform. But is it really the challenge and the duty of
5 just buy a "sustainability black box" without really knowing what they have just
6 Most investors do not think that they can really make a change. But they can. Those
7 I feel proud to lead a business which really believes in making a difference to the
8 carefully. We will want to be sure that it is really tackling the issue of avoidable
4.2.2 “According to”
According to is a stance adverbial that shows the source of knowledge. Precht
(2000:78) suggests the stance in these markers comes from the use of an authority
to show the reliability of the information and identify the perspective from which
information comes. According to finding in Biber et al. (1999: 868), there is a
higher frequency in news (especially in American news) than in conversation,
54

55. fiction, and academic registers. In the CSR Corpus findings, the pattern of
according to + NP is used to identify specific sources that range from specifically
named people and institutions to law, beliefs, and standards. The sentences
display a preference for according to in medial positions. A small number of
entries show according to in initial position.
For this reason, we have significantly broadened the scope of our sustainability
report once again and, to better orient our readers, have structured it according
to our four stakeholder groups: customers, staff, shareholders and society.
By designing financial services according to shariah (Islamic law), …
HSBC also operates according to certain Key Business Values:
* the highest personal standards of integrity at all levels;
* commitment to truth and fair dealing;
* hands-on management at all levels;
4.2.3 “Mainly”
With the same function as typically and in most cases (Biber et al., 1999:855),
mainly can mark limitations on a proposition. In the CSR Corpus, the pattern of
55

56. verb + mainly + preposition is observed, such as focus mainly on. All sentences
display a preference in medial positions.
Consumer protection (in the form of investor protection) focuses mainly on
product distribution, product transparency and the cost structure.
Our customers: more than two million small, medium-sized and middle-market
enterprises, including sole proprietors, partnerships, clubs and associations,
incorporated businesses and publicly quoted companies whose external
finance comes mainly from banks rather than capital markets.
Some 280,000 customers switched to online statements during 2004, resulting
in a reduction in paper use of approximately 52 tonnes and an annualised cost
saving of $591,000 (US$1.1million), due mainly to lower mailing costs.
Biber et al., (1999:858) suggests attitude adverbials tell of the writer’s or
speaker’s attitude towards a proposition typically conveying an evaluation, value
judgment, or assessment of expectation. In the CSR Corpus, the findings state
that attitude adverbials account for a low frequency of occurrence. Major
attitudinal adverbials recorded in Biber’s complete list (2004:133-35) such as
surprisingly, hopefully, amazingly, conveniently, etc. cannot be found.
56

57. 4.2.4 “Sort of”
Expressions typically used as stance adverbials of imprecision include sort of,
kind of, possibly, and probably. They have particularly high frequencies in
conversations (Biber et al., 1999:867). In the CSR Corpus, the two highest
overall frequency of likelihood adverbial are sort of and kind of. However, the
sentences do not include the stance meaning of imprecision (also called hedging);
instead they behave as species nouns (Biber et al., 1999:255). They are used to
refer not to the amount but to the type of entity or mass expressed by a following
of-phrase. They behave grammatically like ordinary countable nouns. Clusters
such as the sort of, the kind of, a kind of are identified.
There can be few Chief Executives who have the sort of support that I enjoy in
trying to carry forward a Corporate Responsibility programme which has
significant impact across all our operations.
Part of the Academy's basic philosophy is to create interdisciplinary groups
among the scholarship holders, fostering the kind of dialogue between the
musical and directing sides of opera that is often lacking in daily practice.
LSWE Corpus findings (stance adverbials across registers) in Biber et al.
(1999:981-83) show single adverbs are the most common category of stance
57

58. adverbials in all registers. Also, stance adverbials are most plentiful in
conversations, while they occur with moderate frequencies in the written registers.
The large majority of single adverbs are of epistemic meaning such as actually,
really, and probably. In the CSR Corpus, the results show no significant
differences. Typical stance adverbials focusing on style (commenting on the
manner of conveying the message) that are commonly used in conversations and
news such as frankly, honestly, quite simply, truthfully (Biber et al., 1999:857) are
absent in the concordance examples in the CSR Corpus.
58