Introduction – The rationale and the research gap

This MA research project has two principal objectives. The first is the creation and analysis of a corpus of annual reports of Irish-registered companies, and examination of its most prominent lexical features. The second goal is the development of teaching resources informed by the corpus analysis, aimed at Business English teachers, learners as well as for non-native English-speaking professionals. It is also hoped that the research will inform and inspire the development of additional BE corpora and relevant corpus-based pedagogical materials. The materials that were developed are underpinned by the lexical approach and the template that is provided can be used by teachers and researchers for the creation of additional materials informed by the corpus data presented. The corpus, the findings, as well as teaching materials will be published online on a dedicated website. In an attempt to “narrow the gap between academia and the workplace” (Zhang, 2013:144), and inform the content of BE teaching and learning the available literature in the field, the findings, as well as possible anticipated limitations and considerations will be discussed in this thesis. 

It is commonly accepted that the English language has become the current Lingua Franca of business (Labrador and Ramon: 2020). Major multinational companies are mandating English as the common corporate language in an attempt to “facilitate communication and performance across geographically diverse functions and endeavors” (Neeley: 2012). 

Business professionals, managers, and leaders, in particular, working in a foreign language will typically need to develop, at some stage of their career, a refined knowledge of English. A corpus-based teaching resource can be used to inform teaching and answer many questions on what is perceived as key lexis and collocations required to achieve a high-level command of the target language. All of this linguistic information is important because choosing inappropriate words is very common among nonnative speakers of English and neither intuition nor introspection can accurately inform learners about the appropriate use of lexical items (Sardinha, 2000; Stewart, 2010). 

As studies (Walker 2011) reveal, non-native English-speaking professionals are conscious of their nonnative linguistic status and wish to improve the language they use at the workplace in order to perform more successfully in a business environment. Their command of English affects their standing, the impression they create, their message, and their overall professional success. In response to the need to help non-native English-speaking professionals improve their language use, this study has a twofold aim: to offer corpus-based insights into salient linguistic features of Business English and their context-specific use and to develop sample corpus-based Business English learning materials.

The study focuses specifically on 20 annual reports of 2019 published in Ireland by the twenty ISEQ-listed public companies (ISEC 20). It is evident from the literature that this genre has not been examined in the Irish context before. A previous study in the UK (Rutherford, 2005) conducted a corpus-based genre analysis of annual reports of UK companies by employing word frequencies to identify genre rules and analyse accounting narratives. To the best of the author’s knowledge, no Business English corpora have been created in the Irish context and the present study is the very first of its kind in Ireland.

Literature Review

This Chapter reviews the academic literature on corpus linguistics studies specifically in the context of BE, provides an overview of the use of corporate annual reports in ELT pedagogy and establishes the theoretical framework (i.e. lexical approach) which underpins the corpus-based materials which were developed.

2.1. Corpus Linguistics

2.1.1 Definition
Bowker and Pearson (2002:9) simply describe a corpus (pl. corpora) as “a large collection of authentic texts that have been gathered in electronic form according to a specific set of criteria”, or as McEnery and Baker (2017:1) put it, a corpus can be understood as “large bodies of machine-readable texts”. Corpora are digital files that can be analysed with the help of software known as corpus analysis tools or concordancers. Baker and Pearson (2002) point out that corpora are an extraordinary resource for linguists and language researchers. In a corpus investigation, small fragments of a text are examined, such as individual words or multi-word bundles, and multiple fragments can be examined simultaneously, therefore our interaction with corpora differs significantly from the way we interact with printed texts. Finally, it is important to note that a corpus is “not simply a random collection of texts” (Bowker and Pearson, 2002:11) but the texts in a corpus are selected according to “explicit criteria in order to be used as a representative sample of a particular language or subset of that language” (ibid.:11), i.e. of a particular subject field.

2.1.2 Types of corpora

Bowker and Pearson state: “There are almost as many different types of corpora as there are types of investigations” (2002:11). They subsequently identify types of corpora according to various criteria, such as the size, purpose, type of texts. The corpora are therefore classified as:

(a) General reference vs special purpose: general corpus refers to one representative of a given language as a whole and can therefore be used to make general observations about that particular language. A special purpose corpus focuses on a particular aspect of a language, such as the LSP of a particular subject field, a specific text type, language variety or the language used by members of a certain demographic group.

(b) Written vs spoken vs multimodal: a written corpus contains written texts, while a spoken corpus consists of transcripts of spoken material. Additionally, multimodal corpora combine different media.

(c) Monolingual vs multilingual: a monolingual corpus contains texts in a single language, a multilingual corpus contains texts in two or more languages.

(d) Synchronic vs diachronic: a synchronic corpus presents a snapshot of language use during a limited time frame, whereas a diachronic corpus can be used to study how a language has evolved over a long period of time.

(e) Open vs closed: An open (monitor) corpus is one that is constantly being expanded, a closed (finite) corpus is one that does not get augmented once it has been compiled.

(f) Learner corpus: A learner corpus is one that contains texts written by learners of a foreign language, created for the purpose of comparison and identification of the types of errors made by the learners. (ibid. 2002:11-13)

Following this taxonomy, a corpus compiled for the purpose of this thesis can be described as specialised, written, monolingual, synchronic and closed.

2.1.3 Corpus Linguistics
Corpus Linguistics (CL) is an approach or methodology for studying language use through the analysis of corpora. It is an empirical approach that involves studying examples of actual, authentic language, rather than hypothesizing about it. CL also makes extensive use of IT technology, which means that data can be analysed in ways that are not possible when dealing with printed material (Baker and Pearson, 2002:9).
What is worth mentioning is that CL first gained recognition among linguists about thirty years ago (Church, 1990) and the reason behind its sudden new popularity was the fact that “text was more available than ever before” (ibid.). Thirty years later, due to technological progress and accessible corpus tools, this relatively new area of linguistics has evolved into a rather “vibrant discipline” (Szudarski, 2017:1). Also Bowker and Pearson (2002:10) recognise the role of the constantly developing technology in the renewal of interest in CL. The emergence of new software programmes and corpus tools makes it easier to compile and consult electronic corpora, typically much larger than printed equivalents. Accordingly, electronic texts can be gathered and consulted in a much quicker manner than printed texts (Bowker and Pearson, 2002:11). As a result, “Corpora are becoming a very popular resource for people who want to learn more about language use” (ibid.:1).

2.1.4 General applications of CL in different fields
The increasing sophistication of CL methods and technology led to a rapid expansion in their use in the last three decades and to what has been deemed as a “remarkable renaissance” (Rutherford, 2005: 354; McEnery and Wilson, 2001: 1) of CL. Its development spread across the social and psychological sciences (McEnery and Wilson, 2001, ch. 4). Corpus linguistics methods have been applied in various disciplines and genre analysis (McEnery and Wilson, 2001: 117-119). Interestingly, they have also been employed in the examination of the pragmatics of questions in formal police interviews (Johnson (2002) cited by Rutherford, 2005). Depending on the researchers’ interests, CL approaches can be applied to a number of areas of linguistic study: language pedagogy, discourse analysis, translation studies, lexicography, LSP pedagogy, pragmatics, sociolinguistics, media and business discourse, literary or political linguistics (Bowker and Pearson, 2002:11, Szudarski: 2018). What is more, in the developing field of computational linguistics AI systems and language processing tools commonly adopt corpus-based resources (Bowker and Pearson, 2002:11). Also noteworthy is the use of corpora to assist historians to gain insights into societies of the past (McEnery, Baker, 2017:1). As Bowker and Pearson (ibid.) note: “corpora can be used by anyone who wants to study authentic examples of language use.”
As the physical constraints of printed media do not apply to electronic corpora and millions of words of running texts can be stored digitally, language corpora have the potential to be used more extensively than other resources. Their electronic form means that they are easily updatable and much more straightforward to consult than printed resources, which means conducting a search of a corpus can be done in seconds (Bowker and Pearson, 2002:18). Therefore, it is not surprising that CL research approaches have been applied in a wide range of disciplines, and have been used to investigate a broad range of linguistic issues.

2.2 Business English and Annual Reports
This section Discusses Business English and the genre of Annual Reports from a CL perspective.

 

2.2.1 Business English and its distinctive features
Business English is a broad concept within the ESP varieties, it is often used “as an umbrella term to refer to any interaction, written or spoken, that takes place in English, where the purpose of that interaction is to conduct business” (Nickerson and Planken, 2016:3). The main purpose of BE instruction is to communicate effectively in a professional context. As Frendo (2005:1) states, millions of people around the world use English daily in their business activities. Essentially, BE is communication with other people within a specific context and its main function is bringing “people together to accomplish things they could not do as individuals” (Frendo, 2005:1).

Due to its practical, task-oriented nature BE has a set of unique distinctive features that can be briefly summarised as:
(a) asymmetrical – business interactions are often a result of an unequal status of their participants (e.g. manager vs. trainee) which reflects the language and the communication strategies used;
(b) topic-centred and task-oriented – the language serves the purpose of accomplishing certain tasks in order to fulfil the organisation’s goals;
(c) standardised – the structure of the formal business interactions is typically ordered, it tends to progress through a number of stages and involves specific turn-taking rules;
(d) specialised – BE involves specific, professional lexis relevant to the participants’ specialism, the business discipline and the company’s core activity, making it distinctively different from the everyday English (Nickerson and Planken, 2006:42-43).

Nelson’s findings (Nelson, 2006) correspond to these conclusions. His corpus analysis reveals that, first of all, the BE words and phrases represent a limited number of semantic categories and combinations of words, compared with everyday English. Additionally, BE lexis is predominantly related to business and it tends to be more positive in nature. Finally, adjectives in the BE largely refer to products and companies rather than people and they emphasise action rather than emotion. Furthermore, Nelson (2006) also highlights the importance of the concept of a business-specific semantic prosody understood as “the collocational meaning arising from the interaction between a given node and typical collocates” (McEnery and Xiao, 2006:5). It refers to the relationship of a given word to speakers and hearers, and is concerned with attitudes (Baker et al, 2006:144), and similarly to collocations, semantic prosody cannot be accessed via conscious introspection nor intuition by the non-native speakers (Sardinha, 2000). Nelson (2006) argues these characteristics should have direct implications for BE instruction and have to be taken into consideration when designing the relevant BE syllabus.

2.2.2. Authentic and corpus-informed materials in BE classroom
In the interest of narrowing the gap between teaching and professional business communication many scholars (Nickerson and Planken, 2016:44), Frendo 2005:40, Koester, 2006, Nelson, 2006) emphasise the importance of real language data in BE pedagogy. There are many reasons for using authentic materials in the BE classroom, with the term authentic referring to materials “not written for pedagogic processes” (Wallace, 1998: 145). Some crucial benefits are that the authentic materials allow the students to better understand what they will find in their prospective professional environments (Ruiz-Garrido and Palmer-Silveira, 2015). Authentic materials also appear to increase students’ motivation and interest (Breen 1985; Tomlinson 2001); resulting in eager meaningfully engaged learners (Apsari, 2014).
For good reasons, the discussion of authenticity in the classroom has also been re-energised by the availability of corpus data (O’Keeffe et all, 2007:26). Many researchers (Tsai, 2021; Skorczynska, 2010; Walker, 2011, O’Keeffe at al, 2007) suggest that the corpus evidence should be taken into consideration when deciding materials for BE instruction. The literature shows that the unnatural-sounding linguistic components in published textbooks do not quite reflect the complexity of real-life language use that a corpus investigation reveals. As an example, the results obtained from Skorczynka’s (2010) study of BE textbooks reveal a gap between the textbook and the corpus sample in respect of the metaphors. Her corpus investigation established that nearly a third of textbooks’ metaphors were never used. These findings demonstrate the importance of the corpus evidence, when selecting teachable material for BE instruction. In addition to that, Evans’s (2012) statement that: “Only the most deluded materials designer could imagine that BE materials can accurately reflect the complexities of a global, wired world” establishes the need for genuinely authentic materials in the BE instruction. Among the scarce innovative corpus-based BE textbooks Business Advantage (Koester et al. 2012) deserves attention. Also, in Investigating Workplace Discourse (Koester et al: 2006) the authors argue for a combination of quantitative corpus-based methods in BE pedagogy that would focus on investigating specific linguistic features in different genres and qualitative methods such as analysis of conversations.

Also, Frendo (2005: 45) calls for authenticity in the context of BE instruction, considering it a key issue when selecting teaching materials. The use of language corpora, he suggests, can be particularly useful, as it is “now relatively easy either to compile one’s own corpus of language or to gain access to huge, computerised language databases,” that can be accessed by both teachers and the learners. (Frendo, 2005:49)

2.2.3 Annual Reports – definitions, genre and functions
Annual Reports (ARs) are “published documents used by most public companies to disclose the important corporate information to shareholders and the general public”(Lu and Ren, 2021:84). Bhatia (2010:39) characterises their main purpose as “informing their shareholders about the performance and health of the company, specifically its successes and failures, current problems, and prospects for its future development.” Contemporary ARs are typically visually attractive and consistently branded promotional documents (Cao, et al 2012). Created by the insiders, ARs are designed to be “used by audiences of both insiders and non-experts” (Cao, et al, 2012). The primary audiences for ARs are shareholders and potential investors while other targeted audiences are employees, customers, suppliers, governments, contractors and the community. Furthermore, ARs may be of interest to researchers, as well as to BE teachers and learners (Cao, 2012, de Groot et al, 2011).

2.2.4 Annual Reports and Corpora – pedagogical applications in BE
As for the pedagogical value and practicality of the ARs in the BE classroom, Nickerson and Plankett (2015:97) consider them “common forms of promotional Business English texts” and consider their suitability and usefulness in BE pedagogy. They assert that “students at higher levels of language proficiency could easily work with texts like annual reports” (ibid. 2015:105) and that since annual reports are easily accessible they can readily provide practitioners with “a rich source of information to use with advanced Business English classes.” (ibid. 2015:105). Similarly Poole’s (2017) research demonstrates the value of AR in a corpus-based analysis in which he also advocates the use of “pedagogically-downsized specialized corpora”, that can be implemented in the BE classroom and also more broadly in ESP contexts.

In one of the most recent research projects, Lu and Ren (2021) offer a corpus-based analysis of the linguistic features of the specific section of ARs, namely Management Discussion & Analysis, of public companies in China, in which they compare linguistic practices of Chinese corporate writers with those of international norms. Authors argue that the findings on the linguistic differences between the narratives produced by the Chinese and American companies have implications for Business English pedagogy, specifically in the Chinese context.
Further details and the rationale for choosing Annual Report for this project are discussed in the Methodology Chapter, in section 3.3.

2.3 Corpus-based applications in BE
The literature provides various examples of adopting CL in language teaching and specifically in reference to BE.
Clifton and Philips (2006:76) argue that for the syllabus to have high surrender value “and not waste the learners’ time with language that is not pertinent to their discourse community”, it is essential for the instructor to carry out a language audit or needs analysis. Therefore, by building up a corpus that reflects the nature of the relevant interactions the instructor “can take a more objective look at what language is useful to the learner” (ibid.:76). This ensures that the syllabus and the teaching materials are based on the authentic linguistic material applicable to the particular discourse community and not on an intuition which tends to be false (ibid.).

Similarly to Nelson (2006) who provides corpus-based evidence that specific areas of lexis are characteristic of authentic BE and different from everyday English, Walker (2011) demonstrates the usefulness of a corpus-based analysis in teaching those unique aspects of vocabulary and collocations. His study applies specifically to answering learners’ questions about collocations and semantic prosody in the context of BE and reveals the effectiveness of corpus investigation in teaching senior managers in global companies to develop a more sophisticated command of what is perceived as the key lexis. A corpus-based investigation of the authentic text of a specific Business genre or collocational behaviour of key lexis can be used to answer specific lexical questions in a very precise and accurate way.

In the realm of BE pedagogy, the application of CL was also successfully verified by Tsai’s (2021) study. The research explores the effects of corpus consultation in a BE, more specifically Business Letters writing course. Her research provides evidence that corpus use can be a very beneficial learning tool for BE writing as it improves students’ writing quantity and quality in terms of lexical and syntactic complexity. She advocates “easy-to-use software tools for corpus analysis” for both learners and instructors and the popularisation of corpus application in BE writing and EFL overall.

2.4 CL and the lexical approach in materials development

2.4.1 Corpus-informed BE materials
Previous studies reveal that the majority of the available material does not reflect the findings of corpus-based research into business discourse (Hyland, 1994, Williams, 1988, as cited by Walker, 2011). Textbooks have been criticised for their choice of unnatural-sounding linguistic components and business meetings have been shown to contain “a high degree of linguistic complexity not reflected in the (teaching) resources” (Walker, 2011). Researchers believe that CL can help bridge the differences between instruction and practical communication. Corpora in addition to their application in producing grammars and dictionaries can be effectively used to produce textbooks and teaching materials by providing authentic language examples for material developers (Nickerson and Planken, 2016:173, Cotos, 2017). Teachers themselves can successfully use concordancer software to develop exercises that “prompt students to test linguistic hypotheses, notice contextual meanings, examine collocations” (Cotos, 2017:7). Koester (2014), whose work on Business English textbooks already featured CL, advises supplementing materials with corpus-based examples that show how learners can employ various language functions to improve their overall performance in negotiations (Koester, 2014:175-6).

A recent, and undoubtedly interesting example, and also a compelling reflection of the increasing popularity of application of corpora in teaching, is a project coordinated by Le Foll (2021). The outcomes are presented as an online step-by-step guide for teachers on using online and corpus resources. Created with a contribution of pre-service trainee teachers from Osnabrück University (Germany) as an Open Educational Resource the project aims to empower ELT teachers to design their own, authentic, corpus-based lessons.

2.4.2 The Lexical approach
The lexical approach was first introduced and described by Lewis (1993) and is specified as a method of teaching foreign languages based on the premise that “language consists of grammaticalised lexis, not lexicalised grammar” and “the grammar/vocabulary dichotomy is invalid; much language consists of multi-word ‘chunks’” (Lewis, 1993, vi). The approach rests on the idea that an important part of learning a language is built upon “being able to understand and produce lexical phrases as chunks” (Selivan, 2012). Learners first internalise chunks and can then extract regularities from them, much in the same way as native-speaker children master the English grammar without explicitly attending to the tense rules (ibid.). A lexical syllabus is based on the principle that it is beneficial to teach the most frequent words in a language first as these words have a wide variety of uses, so students will acquire the flexibility of language, while also covering the main points of grammar in a language without having to memorise a large vocabulary (Baker et al, 2006:107). The frequency information of the target language can be provided by a corpus analysis.

When describing the lexical approach, the term ‘chunking’ has to be elaborated on, as described by Lewis (1993:121). It refers to the way in which lexical items are naturally stored in the memory i.e. redundantly, not only as individual morphemes, but as parts of phrases, and as longer memorised chunks of speech. Lexical items are retrieved from memory in pre-assembled chunks. Native speakers retain many chunks and as a result of combining them fluency is achieved. This process has direct implications for language learning as chunking significantly reduces processing challenges, since “tremendous demands are made upon students as they attempt to re-create language from scratch” (Lewis, 1993:121).

2.4.3 Rationale behind lexical approach
Clifton and Philips (2006) argue that the lexical approach can be an effective pedagogic tool for three main reasons. Firstly, the lexical approach disputes “an overemphasis on teaching single, decontextualized words”, claiming it may “hinder the development of an L2 lexicon and deny the learner the possibility of rapid and fluent use of the L2” (ibid.:75). Another argument is that “it is more effective for the learner to learn the whole and break it into its parts than to build up to the meaning of a lexical chunk by focusing on the separate parts”(ibid.:75). They argue that by promoting the more frequent chunks within the target genre/discourse, the teachers will significantly contribute to developing learners’ fluency and accuracy. As errors in collocations are particularly frequent among language learners, an approach that specifically targets collocations rather than isolated vocabulary helps to address this “problematic area of language learning” (ibid.:76).

In addition to the evidence suggesting that the lexical approach is an effective way of BE instruction, corpus-driven approaches are argued to provide samples of the authentic language to be used in the actual instruction (McEnery and Wilson, 2001, Bowker sand Pearson, 2002). Nickerson and Planken (2016:173) emphasise the importance of corpus-based studies as input for the course and teaching materials development, not only because corpora can provide authentic language examples for course developers, but also because such studies have uncovered fundamental differences between Business English and general English. Clifton and Philips (2006) also endorse the use of lexical and CL approaches suggesting a data-driven lexical approach working on the basis that the teacher, whose authority comes from the linguistic evidence of the corpus drawn from the Target Discourse Community, can gain legitimacy and authority on the analysed specialised language. The corpus and further analysis of the most frequently occurring lexical items should be the fundamental resource in bridging the gap “between learners’ specialised needs and the instructor’s limited knowledge of the language of a particular discourse community”(ibid.:73).

 

Methodology

 

This chapter describes the process of creation of the Annual Reports of Irish Companies (ARIC) corpus and also discusses tools and types of linguistic analyses that were applied.

3.1 Corpus design and data collection
The corpus Annual Reports of Irish Companies (ARIC) which was built for the aims of the present study is composed of 20 Annual Reports (ARs) of public companies listed on Euronext, previously known as the Irish Stock Exchange, that in 2018 merged with this cross-border European stock exchange (operating markets in Amsterdam, Brussels, Dublin, Lisbon, London, Milan, Oslo, and Paris). The companies chosen for this project comprise the list of ISEQ 20, which constitutes a benchmark stock market index that tracks companies trading on the Dublin stock exchange. ISEQ 20 is the index of 20 public companies with the highest trading volume and market capitalisation contained within the Irish Stock Exchange Quotient (ISEQ) Overall Index (Euronext Dublin, 2021, Investopedia, 2020).
Three main considerations were taken into account in corpus design:

(a) authenticity: selecting texts for corpus containing authentic materials representing the actual language that BE learners and users will encounter;

(b) pedagogical value and credibility: finding trustworthy resources that present instructional benefits. This is based on the premise that the language of the ARs offers insights on how the language is used in the context of business as it comprises specific lexis and unique phrases typical to this setting. As Nickerson and Planken, (2015: 105) observe, ARs offer vast pedagogical contributions in the advanced BE environment,

(c) diversity: the annual reports have multiple authors, which sustains linguistic variety and supports avoiding focusing on idiosyncratic language,

(d) availability: reports identified for this project are publicly accessible on the respective companies’ websites and easily available to download as pdf documents. For practical reasons, the reports for the year 2019 have been selected, as full reports for the year 2020 had not yet been made available by all the companies subject to this investigation at the time of commencement of this research project, i.e. April 2021. The examined documents provide a lexically rich material and are substantial in size. The number of pages of the ARs ranges from 81 (Kingspan, Glenveagh) to 389 pages (AIB).

3.3 Rationale for choosing Annual Reports
There are many reasons to justify the decision to focus on the genre of Annual Reports (ARs) for this project. ARs are described as “corporate disclosure documents” (Nickerson et al, 2016) and admittedly the genre has attracted considerable academic attention in recent years (de Groot, 2012). In respect of authenticity and reliability ARs “are the most important external documents and the most used channels for communication between organisations and stakeholders” (Cao et al., 2012), they are therefore an effective tool for promoting the company. As annual reports “are the main channel that companies use to communicate with stakeholders” (Moreno, Casasola, 2015), it can be implied that they receive plenty of attention from internal departments and external consultancies, and as a consequence, they are subject to careful scrutiny.
As regards their availability, ARs are readily accessible online documents in a pdf format, which also makes them a preferred and a very practical choice for Business English research. The fact that they have already received a lot of attention from acclaimed linguists (Bhatia, Cao et al, de Groot, et al.) is evidential of their linguistic and pedagogical value.

3.4 Text Identification
Contrary to Rutheford (2005), whose study examines solely ARs’ narratives with the objective of contributing to a better understanding of the genre on its own, this study’s ambition was to conduct a linguistic analysis that would inform the design of BE teaching materials. Therefore the focus is on linguistic items contained within the reports which bear pedagogical importance.

Given this objective, the corpus consists of the nearly full content of the analysed annual reports. Therefore among the material included were: (a) captions and notes within graphical material; (b) tables standing alone from the main text – non-numerical details; (c) slogans, quotes, and mission statements; (d) headlines and repeated headings and main material, (b) tables, lists, and bullet-pointed material. The full list of annual reports used to build the corpus of Irish Annual Reports corpus can be found in Table 1. The corpus creation procedure was first tested by working with only one report to start with (Kerry Group); subsequently, the following process was undertaken for the rest of the material. All the reports were downloaded in the form of PDF files from the respective companies’ websites (available in the Investor Relations section). The reports were then individually submitted to the optical character recognition (OCR) process using “OCR My Pfd” software in order to transfer them from PDF to text format (.txt). Once in text format, the reports were edited manually to correct any errors resulting from the ORC. Manual editing involved: deleting/removing the title and footnotes, names of the company, as well as numerical data such as results, percentages, and indicators.

As Sinclair (2004) points out: “It is important to avoid perfectionism in corpus building. It is an inexact science, and no one knows what an ideal corpus would be like.” Accordingly, he advises to treat the results more as indicative data, rather than definite. In line with his comment, creating the best corpus possible was attempted in the given circumstances, nevertheless, the creation of the ARIC Corpus took several attempts.
As to the size of a corpus, Bowker and Pearson (2002:10) observe that there are no “hard and fast rules about how large a corpus needs to be”. By the end of the process the target corpus had 918,436 tokens; understood as “the number of words in a text or corpus” (Milton, 2009: 9) or “single linguistic units” (Baker et al, 2006: 161) and 16,057 types of words, referring to “the number of different words” or “the total number of unique words” used in the corpus (Milton, 2009:9, Baker, 2006:162).
As mentioned previously the corpus comprises the reports written by multiple and different authors – often marketing agencies. This and also the fact that the discussed ARs represent companies belonging to different industries, prevents the analysis of idiosyncratic language. Table 1 lists out companies, whose ARs were used in this study, as well identifies their respective numbers of tokens and word types:

Table 1: Details of the specific reports, number of tokens, word types and business sector.

 

In content analysis it is common to “increase the efficiency of word searches by excluding from the text to be examined frequently occurring words and other language units of no significance to the analysis” (Rutherford, 2005:361), and so to focus on content words, understood as “a set of words in a language consisting of nouns, adjectives, main verbs and adverbs” (Baker et al, 2006: 47). A similar procedure was adopted here. The Whole 1990s BNC Stop List (Scott, n.d.) was used to eliminate the function words, also referred to as ‘grammatical words’, understood as “set of words consisting of pronouns, prepositions, determiners, conjunctions, auxiliary and modal verbs” (Baker et al, 2006: 76). Additionally, the most prominent function words, free-standing letters, companies’ names, and the name of the months appearing more frequently than other months’ names, and frequently occurring abbreviations in the target corpus were added to the list of words which were eliminated. These are listed in Table 2. Therefore, excluded from the analysis were: (a) frequently occurring structural (aka function) words (such as articles, conjunctions, pronouns, and common, auxiliary verbs (Milton:2009: 12;) (b) days and months of the year; (c) numerical denominations; (e) identifiable company and product names; (f) individual names of the board members and directors (g) addresses, as well as any geographic names. The full Stop List used in the study is available to view in Appendix 2. Details regarding content material can be found in the table below:

 

Table 2 Content excluded and included in the corpus.

 

What is more, files that appeared corrupted, i.e. they included whole phrases spelt together and not separated, were either remedied by manual editing, i.e. separation, (Dalata report), or deleted, as in the case of Smurfit Kappa’s AR, where frequent occurrence of corrupted files could be observed as the chains of capital letters creating nonsense, unidentifiable strings of letters and characters such as: PLOOLRQDQGDUHVXPPDULVHGLQ or Êøçì÷Ìòððì÷÷è. The report was attempted to be OCR-ed 3 times using different formats and software, with no apparent results. Corrupted lines were deleted due to time constraints. Considering that these represent 1% of the running text (0.6%), which is not statistically significant, this has no real impact on the overall quality of the corpus.

Other manual editing consisted of separating the words or phrases that mistakenly occurred together in the OCR process, and words the text editor would not recognise as standard English and automatically highlighted. Whenever in doubt as to correctness of the word, the AR pdf document was consulted and the original, version was adopted (e.g. businessto-busines vs. business-to-business).

3.5 The Software
The ARIC corpus was built using AntConc software (Anthony, 2020), a freely available and versatile corpus tool. AntConc is a “multi-platform, multi-purpose corpus analysis toolkit, designed specifically for use in the classroom” (Anthony, 2004) and it hosts a comprehensive set of tools including a concordancer, word and keyword frequency generators and tools for clusters/lexical bundle analysis. This section mentions the linguistic analyses applied in the present study and identifies tools that facilitated each investigation. The manual (Anthony, 2014) and various video tutorials (Antony, 2014, Bednarek 2011) were consulted for training purposes. Therefore, the following tools were used to identify the following linguistic features of ARs:
(a) the Word List Tool was used to identify the 50 most frequent content words,
(b) the Keyword List Tool facilitated the top 50 key content words analysis,
(c) the Collocates Tool identified the 20 most significant collocations of the 50 most frequent content words,
d) the Clusters/N-Grams Tool was consulted to identify the 50 most frequent 4-word clusters.

3.6 Types of corpus-based linguistic analysis
This section Examines in detail the steps involved in the different types of analysis of the lexical features of ARIC.

3.6.1 Frequency
The first step of the corpus analysis was to obtain a frequency list of content words in the corpus using AntCon Word List Tool (Anthony, 2020). The BNC-based stop list (Scott, n.d.) which contained frequent function words was used to exclude these items from frequency analysis. This excluded word sequences consisting of entirely grammatical/function words from the investigation. In addition, proper nouns, personal names and abbreviations were removed manually from this list if they occurred in high frequency, details are discussed in section 3.4.
Firstly, the txt. files comprising the 20 edited ARs were uploaded to the concordancer to create the corpus. Subsequently the Stop List (Scott, n.d.) was uploaded and the Tool Preference settings were adjusted to allow for the use of the Stop List. The Word List tool was run and the output consisting of 16,057 entries was saved into a dedicated folder.
The current research, for practical reasons focuses on the 50 most frequent content words, and this frequency analysis has a dual purpose, it serves as a basis of understanding which vocabulary should be prioritised in the BE instruction, and as a starting point for further investigations.

3.6.2 Keywords
The keywords analysis allows for identification of words that are unusually frequent (or infrequent) in the target corpus in comparison with the words in a reference corpus. This allows identification of characteristic words in the corpus which may play a major role in BE and BE acquisition. For the purpose of this research project the Brown corpus (1979) was used as a reference and uploaded into the tool settings. This identification involved the automatic comparison of word lists using the AntConc Keyword Tool (Anthony, 2020), a feature available within the concordancer. The output of the analysis – the positive key-word list of 1,729 entries was also saved in the dedicated folder.

3.6.3. Collocations
The process of collocations investigation proved more time consuming and required manual editing. Following the recommendations on the frequency analysis, as described in section 4.3, collocations of the target ARIC corpus in this project were analysed from the perspective of the 50 most frequent content words as search words/nodes. Again, for practical reasons, the focus here was specifically on the 20 top collocates of the top 50 highly frequent content words.

As to the statistical measure, the Mutual Information was applied, understood as “a measure that compares the probability of finding two items together and of finding each item on its own ” and is used as a measure of the strength of a collocation (Baker et al, 2006:120). The Mutual Information score (MI) of 3, which is typically considered “a strong collocate” (Hunston, 2002:75, cited by Ackerman and Chen, 2013). However, the information on statistical significance has not been included in the table to focus on the frequency data.

The window span of 5 (L and R) which is also a “common setting”, and also a default feature of AntConc (Antony, 2014, Bendarek, 2011), which indicates how many words to the left and right from the search word was applied. The span of five words for examination on either side of the node/search word is not only quite typical but also, the wider the span, may lower the relevance and “less useful information emerges when searching for collocates beyond the four or five-word span” (Kostopoulou, 2015:75). The cut-off point, understood as the minimum collocate frequency, was set to 2, following Bendarek’s (2011) recommendation.
The first step of the analysis was to obtain a list of content words in the corpus using AntConc concordancer software (Anthony, 2020). The previously created list of the 50 most frequent content words of the ARIC corpus served as as the basis of the subsequent analysis of the collocates of these highly frequent words. Similarly to the two previous analyses, an output file corresponding with each word was saved in the dedicated folder (total 50 txt. files.) A list of these collocations constituting 1,000 entries was compiled and is available in the Appendix 3.

Next the most significant lexical collocations were manually selected to inform the development of the teaching materials; following the advice of Ackerman and Chen (2012:1) who argue that “only with human intervention can a data-driven collocation listing be of much pedagogical use while still taking advantage of statistical information to help identify and prioritise the corpus-derived collocational items that traditional manual examination is unable to manage”.

3.6.4 Four-word clusters
As Biber et al (2005:376) write: “the actual frequency cut-off used to identify lexical bundles is arbitrary”, therefore for practical reasons for the present study, a frequency approach was used to identify the 50 most frequent four-word sequences. Similarly to the previous analyses, AntConc software was used specifically its Clusters/N-Grams tool. This analysis was independent from the previous content word and keyword analyses. A total of 9,380 clusters were identified with the minimum frequency setting of 10, that is, clusters that occur at least 10 times within the target corpus. The output was saved and subsequent manual elimination of clusters that did not consist of 4 meaningful words, such as of the group’s, in the group’s took place, due to their little pedagogical value. Finally, the list of 50 meaningful phrases is presented in Appendix 3.

3.7 Materials development
The target corpus was analysed and the learning objectives were defined in terms of acquisition of the most frequently occurring lexical items presented in the context of the business genre of AR. The findings informed the development of teaching resources presented as worksheets with a variety of language tasks and exercises that will potentially allow the teachers to further develop their own corpus-based resources to assist learners in acquiring the respective lexical items (vocabulary, collocations and four-word clusters).
Additionally, elements of the genre approach, as recommended by Frendo (2005:81), were integrated into the content of some of these materials and activities for BE learners. He suggests genre-based teaching may particularly benefit more advanced learners of BE. In a genre approach, learners are given the chance to study model texts and to base their own production on what they have noticed.

Materials created in this project may be seen as a component of a bigger syllabus/programme rather than being stand-alone and were developed to accommodate teaching on the CEFR level B2+, as they are aimed at the learners who “are highly competent in his or her own first language and will often be striving for the same level of competency in English foreign language (…)” (Walker, 2011). This description reflects the CEFR global scale descriptor of this level according to which a learner on level B2 is classified as an independent user who:

“Can understand the main ideas of complex text on both concrete and abstract topics, including technical discussions in his/her field of specialisation. Can interact with a degree of fluency and spontaneity that makes regular interaction with native speakers quite possible without strain for either party. (…)” (CoE, 2010).

 

Presentation of Findings and Discussion

 

In this section an overview of the results and further discussion in the context of pedagogical value of the four corpus analyses are presented. The creation of the teaching materials is further discussed. Due to space constraints, the sample outputs are presented in Tables 3-6, however, full details are available in Appendices 1-4.

4.1 The fifty most frequent content words
The frequency analysis underpins the majority of the analytical work that is carried out within the remit of CL. The results of the frequency analysis are word lists, compiled by frequency counts of each word in a corpus, and can be further used to derive keyword lists and collocational data. Creating frequency word lists is typically the starting point of any corpus-based analysis (Baker at al., 2006:76).

What can be immediately observed from the current frequency analysis, is the fact that the most frequent words of the target corpus are highly topic-specific, that is they all strictly relate to business, which corroborates Nelson’s (2006) findings discussed in section 2.2.1 as regards the specificity of lexis in BE. The link between the used vocabulary and phrases in the discipline of business clearly demonstrates that business-specific vocabulary dominates in business texts and confirms the need for top-specific language instruction.

In terms of grammatical classification, it can be observed that the most frequent content words of the analysed corpus are predominantly nouns (group, year, risk, company, board, committee), which reveals that nominalisation is extensively used in ARs. Adjectives are the next common part of speech (financial, net, fair, key, capital, annual, strategic, tax, income) followed by participles (continued, including, consolidated, based). As can be seen in Table 3 which lists the 20 most frequent words (the full list of 50 words is available in Appendix 1), a strong and evident association of entries with the field of business can be observed.

Table 3 The 20 most frequent content words.

 

 

The rationale behind frequent analysis and creating the frequency list follows the logic of Milton’s Frequency Model of vocabulary learning (Milton, 2009:25), founded on evidence that “there is a strong relationship between a words frequency and the likelihood that a learner will encounter it and learn it” and that “the more frequent a word is, the more likely it is to be learned, as a general rule.”(Milton, 2009:27). The usefulness of frequency data corpus analysis generally, is that “it identifies patterns of use that otherwise often go unnoticed by researchers” (Biber et al, 2005:376).

O’Keefe, et al. (2007:33-37) demonstrate that the most frequent words constitute an essential part of the core vocabulary. They further argue that this type of information may offer a potential for pedagogy organised around lexis, adding that: “The single word has served us well, and will continue to do so” (O’Keeffe, et al., 2007: 58-59).

Teachers and syllabus designers, or materials writers armed with the complex information a frequency list provides, can produce and apply more practical vocabulary pedagogy which focuses on the specific educational needs of the students. In effect, this approach will result in naturally learners/users-centred methodology, and can be applied even at the elementary levels. (O’Keefe, et al, 2007:47)

4.2 Keywords
Keywords are defined as “those words which are identified by statistical comparison of a ‘target’ corpus with another, larger corpus, which is referred to as the ‘reference’ or ‘benchmark’ corpus” Evison’s (2010:127). A key word, according to Scott (2010:149), is a word which is found to occur with unusual frequency in a given text or set of texts. As such it may be found to occur much more frequently (positive keyword) than would otherwise be expected or much less frequently (a negative keyword). In order to know what is expected, a reference of some sort must be used, therefore a reference corpus has to be employed. A reference corpus is the basis of comparison in the keyword analysis, and according to Baker at al, (2006:137) would typically be “a larger set of texts drawn from a wider range of genres and/or sources”. For the purpose of investigation of English language, they recommend using the Brown family corpora, or sections on the British National Corpus. For the purpose of this research the Brown corpus of one million tokens (Nelson and Kucera, 1979) was used as a reference.

Similarly to frequency analysis, the top keywords identified here (Table 4) are highly topic-specific, i.e. they are strongly associated with a domain of business. The second observation is that the keyword list contains 82% (41) of the same content words, the difference being in their frequency of occurrence. Analogous to the previous analysis, the entries in table 4 have been limited to the top twenty, and the full list is available in Appendix 2. Out of the fifty analysed most frequent words and the 50 top keywords 9 (18%) of keywords are not appearing in the first 50 most frequent content words list. These include, ranked by frequency: lease, risk, strategic, shareholders, asset, accounting, corporate, impairment, strategy. This corroborates previous findings that the corpus contains highly specialised and business-specific vocabulary.

Table 4 The Top twenty (positive) keywords.

As keywords analysis allows us to gain insights into which vocabulary is typical of the genre, it also should be prioritised when teaching and planning the syllabus. Ideally, words from both frequency and keyword lists should be taken into consideration while designing the teaching materials.

4.3 Collocations
This study identified the twenty most frequent collocates of the 50 most frequent content words. The list contains 1,000 entries in total, which includes the node/search word and its collocates. Table 3 provides a sample output from the analysis, the full list is available in Appendix 3. Both the nodes/search words and their respective collocates are ranked by descending order of frequency. As discussed in section 4.1, frequency plays a significant role in vocabulary learning.

What can be immediately noticed is that the grammatical/function words appear as the top frequent collocates of the most frequent content words, combining into what is referred to as colligations (Baker at al, 2006:36) i.e. grammatical word combinations, for instance noun/verb + preposition, such as statement of, year of.

Considering that the focus of this research is on the lexical features of ARs, the function words – such as: articles (a, the), prepositions (of, to, in, for, on, with), and essentially colligations, were excluded from this analysis. The full list of collocations, including the colligates, i.e. the 20 collocates of the top 50 content words are provided in Appendix 3, whereas Table 5 presents a sample output of the lexical collocations extracted from the top 20 content words.

Lexical collocations, understood as collocations that do not contain grammatical elements (Bahns, 1993: 57), but two lexical words (noun + noun/verb/ adjective) where both words contribute to the meaning, appear to be much more diverse: group statement, financial risk, committee audit, strategic report, business group, fair value. The nodes examined in this work, i.e. the 50 most frequent content words of the corpus, are predominantly nouns (exceptions: financial, net, total, annual, new, including, recognised, fair). This shows that nominalisation is a key feature of ARs. The frequent noun + noun and noun + adjective combinations may also indicate that the nodes/search words somewhat moderate/influence their collocates.

What can be immediately observed is that the vast majority of the lexical collocates of analysed nodes are notably associated with the world of business, and specifically linked with the disciplines of finance and accounting (such as: financial statement, business report, company directors, net value, remuneration governance, share earnings, restricted cash, cash equivalents, internal audit, credit risk, profit tax, etc.).

Therefore, similarly to previous investigations, the lexical collations are highly discipline/theme specific. What is more, the majority of the lexical collocations emerge to form two-word compounds (compound nouns) that appear to carry a very specific meaning in the business context, that can be inferred only from this specific setting. What can be observed is that the node is described by its collocate and together they yield a new concept. This corresponds with Bauers’ (2019:1) explanation of compounding as the “word formation process of combining two or more elements, each of which is used elsewhere in the language as a word of its own right to form a new concept”. For most compounds in English, the first constituent is the modifier, whereas the second is referred to as the head, which tends to determine the class to which the compound belongs while the modifier adds the meaning of “specialisation”, e.g executive salary is a category of salary. (Dhar & van der Plas 2019).

Examples in the analysed corpus include financial statement, remuneration committee, ordinary shares, chief executive, share earnings, interest amortisation, executive salary, financial assets, directors report, business group, share dividend, cash equivalents, income statement.

Table 5: The lexical collocates extracted from the top twenty collocates of the 20 most frequent words.

 

Collocations, as described by Baker et al. (2006:36), refer to “the phenomenon surrounding the fact that certain words are more likely to occur in combination with other words in certain contexts” and a ‘collocate’ is essentially a word occurring within the neighbourhood of another word. Collocations, also described “as units of formulaic language” (Gablasova et al., 2017:155), have recently gained significance in the field of language learning and use (Gablasova et al, 2017). The focus on identifying collocations in this MA research project is dictated by the fact that “collocations describe the way individual words co-occur with others” (Lewis, 1993:93), and published literature on collocations indicates “fluent and natural production associated with native speakers of the language” (Gablasova et al, 2017:156). An observation that words exist “in the company of other words” (McEnery and Wilson, 2001:71) implies that this knowledge and awareness thereof is essential for the relevant and appropriate use and application of words.

One of the crucial developments in vocabulary research has been the Firthian approach of the word meaning (Firth:1935, as cited in O’Keeffe at al, 2006:59), arguing that the meaning of a word can be inferred from the the way it is combined with other words in actual use, more so than from the meaning it possesses in itself. While Bahns (1993:56) calls them “a neglected aspect of vocabulary teaching”, researchers have been increasingly interested in how words combine as pairs of collocations and how groupings of more than one word “have unitary meanings and specialised functions” (O’Keeffe et. al, 2006:59).

The emergence of Corpus Linguistics and corpus investigation allowed linguists to verify these mainly intuition-based concepts “in actual, attested language use on a larger scale.” (ibid.:59). Collocations have been rather prominent in corpus linguistics research, and the rationale behind it is that “corpora represent a rich source of information about the regularity, frequency, and distribution of formulaic patterns in language” (Gablasova et.al, 2017:156). Ackerman and Chen (2013:3) add that while “collocations can be instantly recognized by native speakers, they often remain difficult for learners to acquire and use properly”, possibly due to the fact, that collocations can contain “some element of grammatical or lexical unpredictability or inflexibility” (Nation,2001:324, as cited by Ackerman and Chen, 2013). Therefore, corpus investigation can act as “an objective frame of reference” (Bowker and Pearson, 2002:19).
The main conclusion from the current analysis is that these highly-technical and domain- i.e. business- specific collocations may directly inform the content of specialized BE vocabulary teaching. The fact that the identified collocations are highly specific may signify the importance of the specialised terminology in the BE pedagogy. What is more, in accordance with Chung and Nation (2003), learning the word’s meanings along with its common collocations significantly facilitates acquiring of technical vocabulary, therefore collocations should be incorporated into the BE syllabus. As O’Keeffe et al. summarise:
“For the learner of any second/foreign language learning the collocations of that language is not a luxury if anything above a survival level mastery of the language is desired, since collocation permeates even the most basic frequent words” (O’Keeffe et al, 2007:60).

 

4.4 Four-word clusters
This investigation identified the 50 most frequent word chunks typical to the genre of Irish AR. This section presents these and discusses their pedagogical application in the BE classroom. Word clusters can be described as multi-word sequences or ‘lexical bundles’ (Biber, et al, 2004:371) are also referred to as “any group of words in sequence” (Baker et al 2006:34) and not just single words, or collocations. Similarly to vocabulary and collocations, lexical bundles play an instrumental role in successful L2 learning (Biber et al.: 2004). For the purpose of this thesis the term lexical bundles, or more specifically: four-word clusters was adopted (Biber et al.: 2004).
What can be immediately observed from the investigation of the clusters in the target corpus, is that they consist of most of the highly frequent content words. This would include examples containing words such as: group, financial, statement, year, value, company, results, in the four-word expressions such as: the consolidated financial statements, for the year ended, the fair value of, for the financial year, cash and cash equivalents. The most evident conclusion that can be drawn from this investigation is that most frequent content words appear to occur frequently, and even form the most frequent four-word clusters. This is illustrative of how language systematically clusters into combinations of words, referred to as ‘chunks’ (O’Keeffe 2007:13) and may be indicative of the notion that language is pre-patterned.
Table 6: The twenty most frequent 4 word clusters (full list in Appendix 4)

Secondly the presence of some less specialised expressions also needs to be noted, the examples include predominantly prepositional phrases such as: in accordance with the, in respect of the, in relation to the, as a result of, set out in the, at the end of, at the date of, in line with the, as a result of, are set out in, as part of the. Although these phrases do not contain strictly business-specific content words, they still bear quite an official tone and relate to the formal register of the formal/conventional and legal language of business, also corroborating Bhatia’s (2010) findings related to the presence of the different types of discourse in the genre of Annual Reports.

Based on concordance lines below, it can be observed that these expressions are predominantly used to express agreement and compliance with rules, provisions, policies and terms:

Transactions involving derivatives are carried out in accordance with the Treasury policy.

We challenged Management on the disclosures, in particular, whether they are sufficiently clear in highlighting the exposures that remain, the significant uncertainties that exist in respect of the provisions and the sensitivity of the provisions to changes in the underlying assumptions.

The Second Line of Defence sets the frameworks and policies for managing specific risk areas, provides advice and guidance in relation to the risk and provides independent review and challenge and reporting on the company’s risk profile.

The estimated minimum time commitment set out in the terms of appointment is 30 to 60 days per annum including attendance at Committee meetings.

Additionally they tend to express and demonstrate security obligations, and cause and effect:

The Group has a low risk appetite for loss of confidentiality, integrity or availability of our information assets as a result of cyber events.

This was primarily driven by the ROI portfolio as a result of post model adjustments i.e. management adjustments as outlined on pages 97 and 98, resulting in a charge of € 82 million.

Most of these positions arise as a result of activity generated by corporate customers while the remainder represent trading decisions of the Group’s derivative and foreign exchange traders with a view to generating incremental income.

O’Keeffe at al. (2007:13) emphasise that “language systematically clusters into combinations of word ‘chunks’” and this can provide researchers with insights for teaching vocabulary and how “learners approach the task of acquiring vocabulary and developing fluency”. The recent body of evidence challenges the tradition that “language is strictly compositional, arguing instead that much of common everyday language use is composed of prefabricated expressions” (ibid.:376) and that “lexical bundles are stored as unanalysed multi-word chunks, rather than as productive grammatical constructions and do not present production or comprehension difficulties for speakers and listeners in classroom teaching (Biber at all, 2005: 400).”

This view is also endorsed by Lewis (1993: 96), he states that “correctly identified lexical phrases can be presented to L2 learners in identifiable contexts, mastered as learned wholes, and thus become an important resource (…).” Previous and current corpora investigations revealed (Biber, at al, 2004, McEnery and Xiao, 2006, Liu, 2012, O’Keefe at al, 2007, O’Donnell et al, 2012), that much of the linguistic output is composed of multi-word units rather than single words or two-word forms and it is evident that the collocational functioning of words expand beyond the two-word units. It is only with the empirical corpus analysis that the linguists came to understand the extent of the significance and prominence of the chunks/multi-word clusters. As O’Keeffe et al. (2007: 60) state “language is available for use in ready-made chunks to far greater extent than could ever be accommodated by theory of language that rested on the primacy of syntax.”

4.5 Materials informed by ARIC and contributions
The current thesis provides the description of the lexical items of the ARIC corpus and therefore aims at contributing to the current state of knowledge on BE in the Irish context by offering the empirical insights on the examined lexical features. Corpus-informed teaching materials for immediate use in the classroom are also provided. They aim to address the need of a context-specific BE instruction and were one of the core deliverables of the current study. The main focus here is on written materials that are presented in a form of worksheets containing language activities informed by the lexical analysis of ARIC corpus. The worksheets, available in Appendix 6, can be used immediately in the BE classroom, or can serve as templates for teachers and researchers to create comparable exercises and activities informed by other corpora.

The worksheets are organised around the lexical features that were investigated in this research, and are titled accordingly: (a) ‘Vocabulary Focus’ – this worksheet comprises activities designed to promote the acquisition of the most frequent content words and keywords in context, (b) ‘Collocations Focus’ – concentrates on the top lexical collocations of the top 50 content words, (c) ‘Lexical Bundles/Cluster Focus’ – promotes the awareness and acquisition of the top fifty four words clusters, and finally, (d) ‘Genre Focus” worksheet combines the elements of the genre approach with a specific instruction on language items introduced in the texts that were extracted from the corpus.

By organising the activities in this manner, the corpus-informed materials are designed to promote the lexical approach and they aim to achieve it by: (a) focusing on the frequency information – i.e. all materials are organised around the top 50 most frequent lexical features, (b) introducing the corpus-derived lexical features in their original BE context by presenting the concordance lines form the corpus, also referred to as the Key Word In Context (KWIC), (c) demonstrating the role of lexical chunks by introducing the concept of bundles/clusters and collocations in language activities.

In addition to that, the ARIC corpus can be further explored to create more specific topic-based modules organised around specialised vocabulary of specific sectors (banking, construction, retail, etc). Furthermore, the raw corpus data, i.e. lists can also constitute a basis for direct application of the corpus investigation by learners in Data Driven Learning (DDL), depending on other circumstances such as teachers’ corpus knowledge, availability of the specialised software, learner’s readiness to avail of corpus methods.

Additionally, the raw corpus data can serve as a foundation for the design of BE language testing. Corpus-based assessment tools and vocabulary tests customised specifically to the educational needs of BE students could be created. Such tests would focus predominantly on the learner’s specialised lexical knowledge and would incorporate features examined in the current research i.e. vocabulary, collocations, and bundles/clusters. Informed by empirical evidence, corpus-based tests would ensure the authenticity of language to be tested.

It should be noted that the present research aims at offering multiple contributions to the BE teaching field. Firstly, it provides empirical evidence on the business language of ARs in the Irish context. To the author’s knowledge it is the first analysis of this kind. One major contribution is the delivery of extensive insights into most prominent lexical features of Irish ARs. The corpus-derived, frequency-driven observations offer empirical information on lexical items that are representative of the language of business in Ireland.

Secondly, through the analysis of these functional, complex and ‘intertextual’ (Bhatia, 2010) documents the present study addresses the issue of lack of authentic language of Irish business environment in the current BE pedagogy. It can be said that the findings and the proposed materials offer an opportunity to expand on the empirically-based lexical knowledge of the practitioners and can support achieving fluency by the non-native English speaking professionals working in Ireland. By offering the insights on the language of the Irish ARs, this research can be considered as an initial step to address the lack of comprehensive information on the nature of language that non-native professionals encounter in the Irish workplace.

Finally, another important implication from this study is that it could potentially make a contribution to an area of teacher development. The findings can be equally applied to any specific domain of ELT. The study’s descriptions, the sample materials that can be considered as model templates, as well as the specialised corpus could be of benefit in teacher’s continuous professional development (CDP). It can offer an increased awareness of convenience and approachability of corpus pedagogy, that can be used in any specific language instruction. It has the potential of helping BE/ESP teachers enhance their teaching practice and can provide a direction for novice BE/ESP instructors.

4.6 Pedagogical Implications and other applications
The corpus analysis, as described in Chapter 4 revealed an array of language features characteristic of the language of business used in the genre of Annual Reports. The investigation of the Annual Reports of the twenty Irish-listed companies points to the conclusion that the vocabulary used in the analysed genre is notably discipline-specific, i.e. it is evidently associated with the different areas of the domain of business (e.g. finance and accounting). This section provides a short summary of findings, suggesting their relevance and applicability to the BE pedagogy.

First of all, it appears that the practical findings of this project may have direct application in BE instruction, perhaps the most obvious one being the design and development of teaching resources. The corpus-based results can readily serve as a foundation for the development of highly relevant and authentic resources, programmes, and syllabi customised to the language needs of the learners (i.e. corporate clients who would order such courses).

In general, it is safe to say that all the linguistic features identified and investigated in this project may be beneficial in the BE pedagogy. The content words are essential for effective language use in general, they are the building blocks of language, or: “the core or heart of language”, as Lewis (1993:89) remarked. Notably, the content words examined in the present research, that is the most frequent words and the keywords, appear to be highly associated with the discipline of business. Therefore, they could constitute a solid basis of a very specialised BE syllabus.

Acquiring and understanding collocations can be particularly beneficial when learning specialised use of the word that often involves a form that differs from its other uses. This appears to be the case particularly in the specialised business texts where lexical items often appear to acquire specialised meanings explainable from context alone. As Chung and Nation (2003) remark, “most technical vocabulary needs to be learned productively by learners specializing in that area and learning common collocations and grammatical patterns helps this.” Moreover, Lewis’s statement – that also constitutes one of the key principles of the lexical approach – that “collocation is integrated as an organising principle within syllabuses” (Lewis, 1993:vi) illustrates its pedagogical value.

In addition to the benefits of collocations, the significant four-word clusters/bundles appear to be highly applicable in the BE pedagogy – their use not only demonstrates users’ appropriate lexical knowledge, it also remarkably contributes to the more natural, native-like flow of the language use, described as fluency. The frequency learning model can be also applied to word clusters therefore, the principal recommendation resulting from this analysis would be incorporating the findings and the analysed four-word clusters into BE language teaching. Awareness and practical use of the clusters from the list produced as a result of the investigation, as well as functional familiarity with the way in which they are used in the authentic documentation may greatly assist the learners and professionals in more authentic use of language in professional settings (Liu, 2012). As O’Keeffe et al, (2007:61) remark, “chunks are ready for use at any moment, and do not need re-assembling every time they are used”, and so they considerably contribute to fluency, understood as smooth and effortless performance in a language.

Another observation from this study is that most frequent lexical words regularly appear in collocations and the most frequent four-words constructions. This points towards the fact, and perhaps confirms, that the English language has a pre-patterned nature. This in turn also verifies the requirement for a lexical approach in BE teaching which highlights ‘pedagogical chunking’ (Lewis, 1993: 120). Language systematically clusters into a combination of words which has implications in teaching. It may indicate how to deliver meaningful vocabulary lessons and how learners approach the task of acquiring vocabulary and developing fluency. (O’Keeffe, et al, 2007:13). Collocation and bundles/clusters may therefore be a useful starting point in language teaching and learning in the context of BE, and broader contexts.

This allows the conclusion that the corpus data and the features of the language examined in ARs of Irish companies, as revealed by this empirical examination, inform the content of BE instruction. The teaching materials that were developed based on corpus data extracted from the ARIC Corpus are focused on BE lexis which can help learners and non-native speaking professionals improve their vocabulary use. The above findings and materials could equally be incorporated in a lexical syllabus, as suggested by Sinclair and Renouf (1988, as in Baker et al., 2006) that can be built around frequency-based information, as derived from this corpus.
Additionally, the intention of the researcher is to make the results public on a dedicated website that will be developed as a post-research project. The BE teachers and material creators, could freely avail of the lists to create activities promoting the awareness and practical use of the content words, collocations, four-word clusters, which can help learners improve the language they use in annual reports.

It is hoped that the mentioned materials can support the teachers and learners alike in their BE learning journey and by offering this contribution to the BE field this project hopes to bridge a gap between workplace and the classroom.

4.7 Limitations

This study concentrated on the 20 Annual Reports of Irish Companies published for the year 2019 and it only investigated reports published in this single year. Therefore it is restricted to a very specific time and the sample of business text is therefore somewhat limited.
Additional limitation may stem from the fact only one genre of BE is examined; and also quite generally, without making any further divisions to subgenres, such as CEO’s statements, and without further dividing the analysis to business sectors.

In terms of the analysis, this research relies solely on frequency, mostly in the interest of consistency, and also because this approach has the advantage “of being methodologically straightforward” (Liu, 2016). This methodology, however, serves the purpose of this thesis, also taking into account its time constraints. Some possible lines of future work may include deeper analysis of collocations in terms of statistical significance and perhaps a further combined comparative analysis may be in order.

The results are intended to be published on the dedicated website, that is to be created specifically for this project, where the sample teaching materials will also be included as downloadable files. Due to time constraints the website development will take place as the last step of this project and is planned further ahead. In addition, the feedback from the non-native speaking professionals and BE teachers is yet to be sought to gain insights into the effectiveness and practicality of the developed materials, which bears potential for designing a corpus-informed BE course/programme, in the future. Despite the limitations, it is hoped that the empirical findings of this research constitute a considerable amount of pedagogically valuable information that could be utilised in BE instruction.

 

Ackermann, K., Chen, Y. (2013). Developing the Academic Collocation List (ACL) – A corpus-driven and expert-judged approach. Journal of English for Academic Purposes. 12. 235–247. 10.1016/j.jeap.2013.08.002. 

Anthony, L. (2004). AntConc: A Learner and Classroom Friendly, Multi-Platform Corpus Analysis Toolkit. IWLeL 2004: An Interactive Workshop on Language e-Learning 7 – 13. Retrieved on 30th July, 2021, from: http://www.laurenceanthony.net/research/iwlel_2004_anthony_antconc.pdf

Anthony, L. (2014). AntLab by Lawrence Antony [Tutorial series]. Retrieved on 25th June 2021, from: https://www.youtube.com/channel/UC5bgZtdgKyj5un66ZOGGN2A

Anthony, L. (n.d). Lawrence Anthony’s Website. Retrieved on 20th May, 2021, from: https://www.laurenceanthony.net/software/antconc/

Anthony, L. (2020). AntConc (Version 3.5.9) [Computer Software]. Tokyo, Japan: Waseda University. Available from https://www.laurenceanthony.net/software

Apsari, Y. (2014). The Use of Authentic Material in Teaching Reading Comprehension. Eltin Journal, Vol 2, no 2, pp. 88-94

Baker, J. McEnery, T., (2017). Corpus Linguistics and 17th-Century Prostitution. https://library.oapen.org/bitstream/handle/20.500.12657/31700/625761.pdf;jsessionid=9FB58CAC81030551D6EAB638E6B70EAB?sequence=1) 

Baker, P., Hardie, A., McEnery, T. Glossary of Corpus Linguistics (2006); Edinburgh University Press.  

Bahns, J. (1993). Lexical collocations: a contrastive view. ELT Journal 47(1) 56- 63. Oxford, Oxford University Press.

Bauer, L. (2017). Compounds and Compounding. Cambridge Studies in Linguistics. Cambridge Uni- versity Press.

Bednarek, M. (2011). Antconc – Collocation, CorpusLingAnalysis, retrieved on 24th June 2021, from: https://www.youtube.com/watch?v=IAPXSGcju0Q

Bhatia, Vijay. (2010). Interdiscursivity in professional discourse. Discourse and Communication – DISCOURSE COMMUN. 4. 32-50. 10.1177/1750481309351208. 

Biber, D., Conrad, S., Cortes, V., (2004). If you look at …: Lexical Bundles in University Teaching and Textbooks. Applied Linguistics. 25. 371-405.

Bowker, L., Pearson, J. (2002). Working with Specialized Language: A Practical Guide to Using Corpora. London, Routledge.

Breen, M. P. (1985). Authenticity in the language classroom. Applied Linguistics 6, 60-70.

Brown University, A Standard Corpus of Present-Day Edited American English, for use with Digital Computers (Brown). 1964, 1971, 1979.  Compiled by W. N. Francis and H. Kučera. Brown University. Providence, Rhode Island.

Cao J., Lixin, L., Wang, H. (2012). Lexical Features in Corporate annual report: a corpus based study. European Journal of Business and Social Sciences, 1 (9), pp 55-72.

Chung T. M. and Nation, P. Technical vocabulary in specialised texts. Reading in a Foreign Language 15 (2), 2003 ISSN 1539-0578

Chen, J. (2020). Euronext Dublin, Investopedia. Retrieved on 25th May 2021 from: https://www.investopedia.com/terms/i/irish-stock-exchange-ise.asp

Clifton, J., Phillips, D. (2006). Ensuring High Surrender Value For Corporate Clients And Increasing The Authority Of The Language Instructor: The Dividends Of A Data-driven Lexical Approach. The Journal of Language for International Business, 17(2), 72-81.

Cotos, E., (2017). Language for Specific Purposes and Corpus-based Pedagogy. In: Chapelle, C., Sauro, S. (Eds.) (2017). The handbook of technology and second language teaching and learning. Wiley-Blackwell. 

Council of Europe, (2021), Global scale – Table 1 (CEFR 3.3): Common Reference levels. Retrieved on 24th June, 2021, from:

https://www.coe.int/en/web/common-european-framework-reference-languages/table-1-cefr-3.3-common-reference-levels-global-scale

Dhar, P., van der Plas, L. (2019). Learning to predict novel noun-noun compounds. arXiv preprint arXiv:1906.03634.

O’Donnell, M., Scott, M.,  Mahlberg, M., Hoey, M. (2012). Exploring text-initial words, clusters and concgrams in a newspaper corpus. Corpus Linguistics and Linguistic Theory. 8. 10.1515/cllt-2012-0004. 

Erling, E. J., Thir, V. (2016). English Language, Section 12: English as a lingua franca. The Year’s Work in English Studies, 95, 130–169.

Euronext Dublin (2021), [website], retrieved on 25th May 2021, from:  https://www.euronext.com/en/markets/dublin

Evans, S. (2012). Designing email tasks for the Business English classroom: Implications from a study of Hong Kong’s key industries, English for Specific Purposes, 31(3), 202-212.

Evison, J., (2010). What are the Basics of Analysing Corpus? (122-135). In: O’Keeffe, A. McCarthy, M (Eds.) The Routledge Handbook of Corpus Linguistics. Taylor & Francis Group, 2010. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/ucd/detail.action?docID=496305.

Frendo, E., (2005). How to teach Business English. Pearson Education Ltd, 2009 impression. Harlow.

Gablasova, D., Brezina, V. and McEnery, T. (2017). Collocations in Corpus-Based Language Learning Research: Identifying, Comparing, and Interpreting the Evidence. Language Learning, 67: 155-17.

Gries, S. (2017) Corpus Approaches. In Dancygier, B., The Cambridge Handbook of Cognitive Linguistics, Cambridge University Press, 590–606

de Groot, E., Korzilius, H., Gerritsen, M., Nickerson, C. (2011). There’s No Place Like Home: UK-Based Financial Analysts’ Response to Dutch-English and British-English Annual Report Texts. IEEE Transactions on Professional Communication, 54(1), 1–17. https://doi.org/10.1109/TPC.2010.2099791

Koester, A. (2014). “We’d be prepared to do something, like if you say … ” hypothetical reported speech in business negotiations. English for Specific Purposes, 36, 35–46.

Koester, A., Pitt, A., Handford, M., Lisboa, M. (2012). Business advantage: Theory, practice, skills (Intermediate level). Cambridge: Cambridge University Press.

Kostopoulou, S. (2012). Developing English language support for immigrant students in Irish post-primary schools: A corpus linguistics approach. (Unpublished)  Ph.D. Thesis in Applied Linguistics, University of Dublin, Trinity College.

Labrador, N. Ramón, N. (2020). Building a second-language writing aid for specific purposes: Promotional cheese descriptions. English for Specific Purposes, 60, 40-52,

Le Foll, E. (Ed.) (2021). Creating Corpus-Informed Materials for the English as a Foreign Language Classroom, A step-by-step guide for (trainee) teachers using online resources. https://dx.doi.org/10.5281/zenodo.4992504. Retrieved on 15th July 2021, from https://elenlefoll.pressbooks.com.

Lewis, M. (1993). The Lexical Approach, The State of ELT and a Way Forward. Thomson, Impression 2002.

Liu, D. (2012). The most frequently-used multi-word constructions in academic written English: A multi-corpus study. English for Specific Purposes, 31(1), 25-35.

McEnery, T. and Hardie, A. (2012). Corpus Linguistics: Method, Theory and Practice. Cambridge University Press, Cambridge

McEnery, T. and Wilson, A. (2001). Corpus Linguistics. Edinburgh University Press, Edinburgh.

McEnery, A., Xiao, Z.,  (2006). Collocation, semantic prosody and near synonymy: Across-linguistic perspective. Applied Linguistics, 27(1), 103-129.

Milton, James. (2009). Measuring Second Language Vocabulary Acquisition. 10.21832/9781847692092. Multilingual Matters.

Moreno, A., Casasola, A. (2015). Readability evolution of the narratives in the annual report. A longitudinal study on two Spanish companies. 30(2): 202-235 (Article first published online: December 9, 2015; Issue published: April 1, 2016)

Neeley, T. (2012). Global Business Speaks English. The Magazine Harvard Business Review. Retrieved on 24th April, 2021 from https://hbr.org/2012/05/global-business-speaks-english.

Nelson, M. (2000). Mike Nelson’s business English lexis site. Retrieved on: 31st July, 2021, from: http://users.utu.fi/micnel/business_english_lexis_site.htm.

 

Nelson, M. (2006). Semantic associations in Business English: A corpus-based analysis. English for Specific Purposes, 25(2), 217–234.

Nickerson, C., Planken, B. (2016). Introducing Business English. Routledge. 

Poole, R. (2017). “New opportunities” and “Strong performance”: Evaluative adjectives in letters to shareholders and potential for pedagogically-downsized specialized corpora. English for Specific Purposes, 47, 40-51.

Ruiz-Garrido, M., Palmer-Silveira, J. (2015). Authentic materials in the Business English classroom: Annual Reports. Kalbotyra / Linguistics. 67. 86. 10.15388/Klbt.2015.8944.  

Rutherford, B. A. (2005), Genre analysis of corporate annual report narratives. A Corpus Linguistics–Based Approach. Journal of Business Communication, 42 (4), 349-378.

Sardinha, T. (2000). Semantic prosodies in English and Portuguese: A contrastive study. Cuadernos de Filologı ́a Inglesa, 9(1), 93–110.

Scott, M., WordSmith Tools Manual, The whole 1990s BNC Stop List: retrieved on 26th May, 2021 from: https://lexically.net/downloads/version7/HTML/stop_lists.html, https://lexically.net/downloads/BNC_wordlists/English_stoplist.stp

Selivan, L. (2012). Why has the lexical approach been so long in coming? The Guardian. Retrieved on: 24th April 2021 from: https://www.theguardian.com/education/2013/mar/26/leixical-approach-revolution

Sinclair, J. (2004). Developing Linguistic Corpora: a Guide to Good Practice. Appendix: How to build a corpus. Retrieved on: 23rd June 202,  from: http://users.ox.ac.uk/~martinw/dlc/appendix.htm#section3.

Skorczynska – Sznajder, H. (2010). A corpus-based evaluation of metaphors in a business English textbook, English for Specific Purposes, Volume 29, Issue 1, 30-42,

Szudarski, P. (2018). Corpus Linguistics for Vocabulary: A Guide for Research. Routledge

Tomlinson,  B.  (2001).  Materials  development. The  Cambridge  guide  to  teaching English to speakers of other languages. Carter R. and  Nunan, D.  eds. Cambridge: Cambridge University Press. 66–71.

Tsai, Y. R., (2021). Exploring the effects of corpus-based business English writing instruction on EFL learners’ writing proficiency and perception. Journal of  Computing in Higher Education. https://doi-org.ucd.idm.oclc.org/10.1007/s12528-021-09272-4

Walker, C. (2011). How a corpus-based study of the factors which influence collocation can help in the teaching of business English. English for Specific Purposes. 30 (20), 101-112.

Wallace, M. J. (1998). Action Research for Language Teachers. Cambridge: Cambridge University Press.

Zhang, Z. (2013). Business English students learning to write for international business: What do international business practitioners have to say about their texts? English for Specific Purposes, 32, (3),144-156.

 

 

Conclusion

 

As mentioned in the introduction section, this project has two main deliverables. One is the creation and analysis of the corpus, and examination of the most common lexical features, namely most frequent words, keywords, most frequent four-word clusters and collocations. The second deliverable is the development of teaching resources that can be immediately used in the BE classroom that apply principles of the lexical approach.

The corpus created for the purpose of this study comprises nearly one million tokens, the analysed lexical features were: the 50 most frequent lexical words, 50 keywords and 20 collocates of the 50 top frequent content words/nodes, and the 50 most frequent four-word clusters. Previous research and the provided overview of the literature in the field have revealed the usefulness of the corpus analysis in linguistics and its pragmatic pedagogical applications.

This research has led to the following conclusions on the lexical features on Irish ARs: the language of the Annual Reports of Irish companies, effectively seen as a sample of language of Business in Irish context, is highly specific and the terminology used is notably specialised and business-oriented. Secondly, the collocations occurring in the ARs tend to form what is referred to as compound nouns, and essentially constitute terminology that is strongly embedded in the language of business. Additionally, the presence of the most frequent components in the collocations and clusters was noted, which confirms the notion that English language is patterned, which in turn validates the requirement for lexical approach in BE. (Sinclair, 1991, as in O’Keeffe et al., (2007:63), Lewis, 1993:91)

In terms of direction of the future of this project, the results are intended to be published on the dedicated website, where the sample teaching materials/templates will also be available for download. Further analysis of the AR genre can be pursued, analysing the ISEQ 20 list in consecutive years, thereby creating a richer, bigger corpus that is bound to reflect the language of Annual Reports of Irish companies over a definite period. This may offer deeper insights and may lead to a deeper understanding of the language of business in the Irish context and its lexical features. This in turn could inform and enrich the pedagogy of BE overall.

In this manner the analysed corpus can be investigated further in order to obtain a much more thorough understanding of the given texts, or in order to obtain more materials and information, such as data on semantic prosody or statistical significance analysis of collocations. Additional analysis may also be in order to investigate dispersion. i.e. “how evenly are syntactic constructions a, b, c distributed in a corpus” (Gries, 2017:597). Further investigations can be employed such as semantic analysis, which would require tagging the corpus and employing linguistic annotation. This would in turn necessitate additional software tools (e.g. Sketch Engine, or TagAnt). The potential analysis of tagged corpora could support the part-of-speech (POS) investigation which was not a focus of this research. In addition, the use of corpus linguistics techniques may be combined with discourse analysis of BE or ARs genre analysis. Additionally, the current corpora can be extended to include (a) more ARs of companies registered in Ireland – to obtain broader, more representative view o the BE in the Irish context, (b) ARs of the same companies, but in consecutive years – to reveal more characteristics and patterns of the language, (c) a comparative multilingual corpus approach could be applied – to facilitate translation of the ARs, or (d) parallel corpora can be created using ARs from other English-speaking countries, and finally (e) an equivalent of a ‘learner’ corpus can be examined in parallel/compared, i.e corpus of ARs created by companies using Business English as Lingua Franca. This paragraph does not exhaust the possibilities of more advanced analyses that can be further performed.

Similarly, this study can easily be replicated in many other disciplines and scenarios, and in any other context of language use and teaching. Taking the ESP route, different texts stemming from different fields can be investigated such as engineering, CS, marketing and communication, sales, yoga teaching etc. Likewise, further analysis of different genres of Business English in Ireland could take place, and texts such as emails, transcribed meetings, training sessions or webinars can easily be investigated and applied to inform creation of teaching materials, as demonstrated here. As it emerges from the current and previous research corpus analyses, corpus investigation is very useful and effective in helping to understand how language is used in specific contexts. More attention should be paid to the use of language corpora as reliable sources of information for the creation of syllabi and materials.

To conclude, it is hoped that the ARIC corpus can contribute to the pedagogy of BE as a whole and can assist ESP and BE teachers to master this field of study using CL and corpus analysis methodology. It is also conceivable that this thesis may inspire teachers and researchers to perform the corpus analysis themselves. It may be a first step towards deeper analysis of Annual Reports of Irish companies. The information derived from the corpora that result from this and similar projects could inform further creation of the authentic pedagogical materials and which can be made publicly available online or integrated into specific language programmes.