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My Disssertation

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

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My Disssertation

  1. 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. 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. 2
  3. 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. 3
  4. 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 4
  5. 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 5
  6. 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 ················ ···················· 6
  7. 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 7
  8. 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. 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. 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. 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. 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? 12
  13. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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