Semantic fields of problem in business English: Malaysian and British journalistic business texts

Semantic fields of problem in business English: Malaysian and British journalistic business texts

Afida Mohamad Ali1

Abstract This paper reports on an LSP-based research project dealing with a contrastive analysis of business and management texts taken from the Malaysian business magazine Malaysian Business (MB) and its British counterpart, Management Today (MT). The objective of the research was to study the semantic fields and linguistic signals of Problem patterns in order to determine whether they display specific differences which can be ascribed to their linguistic and cultural contexts. The study adopted a corpus-based approach based on a corpus containing fifty feature-articles from each magazine. The text corpus was analysed according to Hoey’s Problem-Solution textual patterns and the corpus tool, Wmatrix, was used to identify the semantic fields in the Problem patterns. Key semantic fields were found for Problem in MB and MT compared with a normative corpus (the BNC Written Informative Sampler). 1. Introduction According to Flowerdew (2003), the Problem-Solution textual pattern regularly occurs in technical reports and other academic writing, where the author introduces a problem and then presents the main point of the paper as a solution. From her study on student and professional technical writing, it was apparent that little research has been carried out on the linguistic correlates of the Problem-Solution pattern through a genre-based approach or quantitative corpus analysis. This is especially true not just in the field of academic writing (EAP), but also in the field of ESP. It is particularly important in the field of Business English because teachers and learners need to comprehend and incorporate this pattern in order to observe the lexical features and semantic concepts that are characteristic of Problem and Solution in such texts. This study addresses this weakness through a contrastive corpus-based analysis of

1 English Language Department, Faculty of Modern Languages and Communication, University Putra Malaysia, 43400 UPM Serdang Selangor, Malaysia Correspondence to: Afida Mohamad Ali, e-mail: [email protected]

Corpora Vol. 2 (2): 211–239

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Malaysian and British journalistic business texts to identify the semantic fields denoting a Problem based on Hoey’s (1979, 2001) Problem-Solution rhetorical pattern, along with the lexico-grammatical patterns that occur in those semantic fields. 2. Review of the literature As the focus of the article is on Business English, specifically semantic fields, I will highlight the corpus research related to the semantics of Business English, mainly of Afida (2001), Nelson (2000) and Fox (1999). With regards to past research, two areas seem to be of importance here. First, semantic fields do not seem to have been the focus of any corpus-based analysis of business texts. Most studies have concentrated more on semantic associations or prosodies (Sinclair, 1991; Louw, 1993; Stubbs, 1995; Tribble, 2000; Hoey, 2003; Nelson, 2006). Semantic field theory holds that meanings represented in the lexicon are interrelated and cluster to form fields of meaning, for example, sprinting, trotting and jogging cluster into a field of running, which, in turn, group with many other verbs into a larger field of human motion (Malmkjaer, 2004: 340). Secondly, most of the lexis of business texts investigated by corpus linguistics techniques, which are, overall, positive in nature, has not been examined within the framework of Hoey’s Problem-Solution rhetorical pattern.

It has been noted that lexis referring to distinctly negative states, and words expressing deep, reflective and emotive feelings are used far less in business (Afida, 2001; Nelson, 2000; Fox, 1999). Afida’s (2001: 50) study found that the use of more positive expressions in business and management articles in MB signified an optimistic preoccupation of the writers which could either be inspirational or motivational to readers. Some of the words that connote positivism are ideal work experience, success, succeed, positive attitude and risk-free. Words that connote a negative sense were less frequent, e.g., perilous journey, stress, pressures, losses, agonising, risking, lose and traps. Nelson’s (2000) categorisation of business lexis discovered that words commonly used in business showed up clearly and formed a distinct semantic world of business. It was found that the lexis fell into a limited number of semantic categories. These categories included business people, companies, institutions, money, business events, places of business, time, modes of communication and lexis concerned with technology. Remarkably, the key lexis of Business English was found to be overtly optimistic in nature, with very few negative words featuring at all. Fox (1999) concentrates on words that signify concepts related to time, human propensities, value assumptions, spatiality, profit and productivity. She argues that these dominant conceptual areas of management language clearly reflect the professional and social

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priorities of management. This is in line with Irgl’s (1986, 1989) studies on lexis in business and economics text where he found a large range of synonyms used to express key concepts in the subject. As in the work of Fairclough (1989), Afida (2001) and Nelson (2000), Fox’s (1999) findings show that, in general, business management language has a higher preference towards positive concepts over negative ones, e.g., good, successful, goals, strive, win, as opposed to unsuccessful, bad, weak and old.

In their corpus-based research, Sinclair (1991) and Louw (1993) asserted that semantic association or patterning relating to positive and negative words was found to be used intentionally. For example, Stubbs (1995) discovered that the word cause tends to co-occur with negatively-associated words, e.g., accident, cancer and crisis, and that provide collocates with positive words, e.g., care, food and help, etc. Both Louw and Stubbs concluded that there is no linguistic theory that explains the collocation of words connoting negative or positive concepts. Moreover, this opinion was further expounded by Hoey (1997) who states that semantic prosody cannot be explained by looking only at collocations. Taking on a teaching-oriented approach, Hoey (2000) asserts that for a learner to learn a word, the best way is to learn it in context. Hence, this study maintains that it is not only semantic associations that are important, but also the communicative functions that are associated with certain semantic fields in Business English. In this case, Hoey’s (1979; 2000) Problem-Solution pattern in analysing textual patterns will be adopted and this will be discussed in the following section. 3. Problem-solution textual pattern The interaction between the reader and the text involves the reader asking questions about the text, and the writer, having presupposed these questions, providing the answers and information in the text and thereby creating a text which responds to the reader’s expectations. These repeated questions and answers by the reader and the writer construct structures and patterns in the text, i.e., Problem-Solution (Hoey, 1979).

Hoey’s (2001) argument concerning frequently-used text patterns is well accepted as these patterns appear in most texts from certain cultures. Many texts are primarily concerned with problems and their solutions, and evaluations of these solutions. This area of analysis was founded by Winter (1976), who discovered that many technical texts followed a pattern of ‘Solution-Problem-Solution-Evaluation’. Such a pattern normally appears in related clauses or sentences, having either a matching or logical sequence. For example, a question-answer pattern is a matching sequence, while a cause and effect relation is a logical sequence.

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The communication of problem recognition, solutions and their evaluation is an issue of importance to all of us: we usually describe events in the order in which they occur, so the conventional order of the four parts of the meta-structure is ‘Situation-Problem-Solution-Evaluation’ (Jordan, 1984). This structure need not occur in this exact order but it gives coherence between sentences whereby the occurrence of one part tends to trigger the incidence of another element (Jordan, 1984). For example, a problem begs a solution and a negative evaluation creates a problem. Apart from maintaining coherence, this pattern follows the natural time-sequence of presenting high-priority information in a sensible order, while the effective use of signals for each part helps the reader to understand the type of information given and how it relates to other items in a text (Jordan, 1984). The Problem-Solution pattern is shown below with an example taken from my research data. Sentence numbers are added for ease of reference:

(1) Banker-turned-property developer Ahmad Zaidi Hamidi has a huge task at hand as chairman of Syarikat Perumahan Negara, the government’s full-fledged property developer in the making. (2) Wasting no time, he has completely revamped his company…

(Malaysian Business)

According to Hoey, a sentence that signifies a problem – defined as a condition that entails a response – contains lexical items that evoke a negative evaluation in the reader’s mind. Jordan (1984: 20) defines it as ‘any form of dissatisfaction or other stimulus that makes us want to improve a situation’. From the example above, sentence one sets the stage for the story, but with the words huge task, this sentence brings to mind a problematic situation. Following Hoey’s pattern, a problem may generate a response in the reader with the expectation of a certain action, or a solution to the problem, and this can be seen in sentence two. However, if another sentence precedes the problem sentence but without suggesting any expectations, then it functions as a Situation or the setting of the topic at hand. It is the writer’s choice to encode a particular situation as a problem and readers can sense the writer’s intention brought forward through the chosen linguistic signals (Jordan, 1984). These signals make the identification of a text-pattern possible.

Previous studies have found that there are two main ways to detect a Problem – causal relations and negative lexical signals (Crombie, 1985; Scott, 2000; Flowerdew, 2003). Problem statements are commonly found in some types of Cause-Effect relations like Reason-Result, Means-Result, Grounds-Conclusion, Means-Purpose, Condition-Consequence (Crombie, 1985). Causative verbs like create, cause, pose, present, become and due to indicate a possible future problem arising. For example, ‘Shipping lines encounter


inefficiency at ports and this causes delay in their daily business’ (MB). Causative verbs tend to collocate with lexical signals like nouns with a negative semantic prosody (Crombie, 1985). These nouns indicate a Problem and are generally negative words. Evaluative words like going bankrupt, failure, loss, downfall and less successful may trigger gloomy thoughts in the reader’s mind. Similarly, non-evaluative problematic issues like poverty, war, disease, demonstration, strike or attack also seem to evoke a depressing reaction in readers. In the same line as Martin (2000), Hoey (2001) contends that if the word suggests a negative evaluation, it is an ‘evoking signal’. Conversely, inscribed2 signals are explicitly-encoded evaluations, e.g., problem and trouble (Martin, 2000).

There is, however, little corpus-based work using the Problem-Solution rhetorical pattern, and most of it has been conducted only on newspaper texts (Scott, 2000) or technically-oriented reports (Flowerdew, 2003). Using a small-scale corpus of feature articles, Scott (2000) looked at the key words problem and solution by comparing the corpus with a reference corpus. Using WordSmith Tools (Scott, 1996), his study found that the usage of problem was restricted at a local level and that the word appeared as key in only three articles. In a comparative analysis of the Problem-Solution pattern in a student and professional corpus of technical writing, Flowerdew (2003) applied Martin’s (2000; 2003) systemic-functional APPRAISAL system which analyses the interpersonal and evaluative meanings of words and codes them as inscribed and evoking signals. Her findings revealed a higher usage of evoking lexis for Problem in the professional corpus while the student corpus preferred inscribed lexis. Also, the word problem was frequently found in the causal category of Reason-Result and collocated highly with causative verbs.

By taking into account previous studies of the Problem-Solution pattern, this paper further explores Business English by taking a different angle. Like Flowerdew (2003), this study used Martin’s inscribed and evoking categories, and Nelson’s (2000) concept of semantic categories. However, whereas Flowerdew and Nelson relied on a keyword analysis using WordSmith tools, this study used a program called Wmatrix (Rayson, 2005) which categorises lexis into semantic fields. This study also focussed on the Problem category and not on the Solution, an area which has been similarly addressed by Flowerdew and Nelson. After presenting the methodology, I will show that key semantic fields were found to denote the Problems which are (intentionally and significantly) foregrounded in business discourse.

2 Martin (2000) presents a similar interest on evoked and inscribed lexis using the APPRAISAL system as a means of classifying evaluative language.

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4. Methodology 4.1 Combining an LSP and corpus-based Approach It is worth mentioning that a corpus-based and a Language for Special Purposes (LSP) methodology have similar orientations. A corpus-based approach has similar aims to Hoffmann’s (1991) views on LSP text analysis. Corpus work can be seen as an empirical approach, in which the starting point is authentic data. The method is, therefore, inductive in that statements of a theoretical nature about the language or the culture are arrived at from observations of real cases. The examination of language information leads to the formulation of a hypothesis to account for these facts, which in turn leads to a generalisation based on the evidence of the repeated patterns in the concordance. The final step is the unification of these observations in a theoretical statement (Tognini-Bonelli, 2001). LSP research also analyses texts, but these texts are of a specialised, scientific and professional nature. This specialised text analysis should also take into account other foreign languages in order to maximise results and contribute towards future research in LSP. In this study, the corpus, which consists of articles from Malaysian Business (MB) and Management Today (MT), is made up of journalistic texts reporting on various business topics in Malaysia and Britain. 4.2 The specialised corpora: Malaysian Business and Management Today Analysis of the semantic fields of Problem and Solution was carried out on a corpus of business articles taken from MB and MT created as part of my doctoral research. The entire corpus comprises one hundred feature articles which were selected randomly, i.e., fifty articles are taken from each magazine in order to achieve representativeness in terms of corpus size. Using simple random sampling, a list of all the articles’ titles taken from the year 2001 to 2002 were produced from both magazines. Using a random number generator (Wiersma, 1995), fifty articles were chosen to form the representative sample. The MB corpus consists of approximately 60,000 words while the MT corpus contains 100,000 words (see Table 1). The feature articles from both magazines relate to areas such as banking and finance, corporate management, economy, enterprise and industry. A feature normally appears in newspapers or newsmagazines, and deals with a wide range of topics, including events, people, politics, lifestyles and social trends (Tiernan, 2005). A feature usually contains the writer’s opinions with a fairly serious and comprehensive analysis of a topic and will give statistics, examples, quotes and opinions. The topics in


a feature are sometimes challenging and may be manipulative, which correspond to its communicative purposes – to inform, entertain and persuade. The style of a feature includes a mix of emotional content, factual and major arguments; emotive words to convey attitude; imaginative language to make descriptions interesting; a story or line or argument which may or may not be logical; selective use of facts; artwork – illustrations, photographs and graphs; quotations or comments by important people and humour (Tiernan, 2005). The use of informal, colloquial language and first-person narration are used to establish a personal tone. Attractive features like relevant jargon add authenticity to information and opinions, while anecdotes help to maintain reader interest and facts help to validate the writer’s viewpoint. Moreover, rhetorical questions and emotive words are also used to elicit a personal reaction from the reader while the effective use of metaphors and description captures the reader’s imagination, and reports of direct speech personalise the topic.

In terms of representativeness, both magazines were chosen because of their similar, specialised (local business) informational content, their intentional focus and wide readership, so that a valid contrastive study of the articles could be conducted. High distribution can be seen to reflect the size of the company and can be regarded as a factor for representativeness (Flowerdew, 2003). These magazines have the highest coverage in terms of circulation and are the longest-running business magazines in their respective countries. Table 1 presents the background details for both magazines. The specialised corpus was statistically compared with the one-million BNC Written Informative Sampler (BNCWInf). The BNC Sampler Corpus is a subcorpus of the British National Corpus, consisting of approximately one-fiftieth of the whole corpus, that is, two million words. It is divided equally between spoken and written texts. The reference corpus consists of nine text categories (number of words are provided for each category), i.e., informative (779,027), pure science (32,974), applied science (117,685), social science (29,868), world affairs (277,128), commerce and finance (92,057), arts (51,645), belief and thought (43,626), and leisure (134,044). By using the written sampler as the general corpus, the semantic fields and linguistic signals of Problem and Solution in the specialised business discourse of MB and MT can be compared with those of the more general language. 4.3 Corpus software: Wmatrix Hoffmann (1991: 159) claims that, ‘the outcome of text-linguistic research into LSP is an important prerequisite of informational and documentational work, particularly if it is combined with automatic language data processing, or in

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other words, corpus linguistics’. In relation to this, Wmatrix (Rayson, 2005) was used to quantitatively analyse the semantic fields (identified by their

Malaysian Business (MB) Management Today (MT)

Established 1972 1970

Circulation (per annum) 540,000 100,464

Audience Captains of industries, managers, political leaders and decision makers.

Managers, chairmen, chief executives and senior directors.

Aim

To serve readers by helping them make educated and informed investing decisions by keeping abreast with significant developments in listed companies.

Management Today is about the way you work and what you're worth and how you advance your career and still have a life. About how you handle your people, and best practice and the digital economy.

Topic

Features analysis of significant news happenings in the local business scene, socio-economic genre dealing with the economy and stock market, modern society, and information technology.

Features modern business practices and trends with aspects of general management.

Table 1: Background of Malaysian Business (MB) and Management Today (MT)

frequency of occurrence in a specialised corpus relative to their frequency in a more general corpus) in each text and in each corpus. Wmatrix provides a web interface to the UCREL3 Semantic Analysis System (USAS) and Constituent Likelihood Automatic Word-tagging System (CLAWS) corpus annotation

3 UCREL (University Centre for Computer Corpus Research on Language) is a research centre at Lancaster University, specialising in the automatic or computer-aided analysis of large bodies of naturally-occurring language. Its work focusses on modern English, early modern English, modern foreign languages, and minority, endangered and ancient languages.

http://www.lancs.ac.uk/


tools, and standard corpus-linguistic methodologies such as frequency lists and concordances. The first stage of annotation involves CLAWS,4 a part-of-speech tagger which assigns a part-of-speech (POS) tag or grammatical word classes to every word in running text with about 97 percent accuracy (Garside and Smith, 1997), e.g., ‘NN1’ for singular common noun and ‘VM’ for modal auxiliaries.

The analysis of the concepts signalling Hoey’s categories, e.g., Problem-Solution was facilitated by SEMTAG, a semantic tagger. SEMTAG assigns a semantic field tag to every word in the text with about 92 percent accuracy. The POS-tagged text is then fed into SEMTAG,5 which assigns semantic tags representing the general-sense field of words from a lexicon of single words and a list of multi-word combinations called idioms, e.g., ‘as a rule’. Currently, the lexicon contains nearly 37,000 words and the idiom list contains over 16,000 multi-word units (Archer et al., 2002). An idiom list enables the corpus tool to identify any idiomatic expressions, usually non-decompositional sequences, and to assign a special set of tags to the words in that particular idiomatic phrase to denote a part-of-speech relation above the level of the word (McEnery and Wilson, 1996: 122). Items not contained in the lexicon or idiom list are assigned a special tag, Z99. Antonymity of conceptual classifications is indicated by +/− markers on tags, e.g., A15+ (Safety) as opposed to A15- (Danger). Comparatives and superlatives receive double and triple +/− markers respectively, e.g., larger (N3.2++) and largest (N3.2+++). The lexicon and idiom list are updated as new texts are analysed (Rayson and Wilson, 1996). SEMTAG can be used to raise hypotheses or simply to confirm them (see Thomas and Wilson, 1996). For example, the semantic tag A15- which refers to the concept of Danger, reveals words which evoke a negative or dangerous situation signifying a Problem, e.g., danger and risk. From here, frequency lists and text concordances can be obtained. The log-likelihood statistic (LL) is employed by Wmatrix; only items with a LL value of more than seven are considered to be statistically significant, since 6.63 is the cut-off for 99 percent confidence of significance.6

4 CLAWS was developed at the University of Lancaster (Garside et al., 1987). The latest version of CLAWS is CLAWS7, with more than 146 tags (Garside and Smith, 1997: 108). 5 This automatic semantic analysis of texts relates to content analysis which is concerned with the statistical analysis of, primarily, the semantic features of texts (Wilson and Rayson, 1993). This means that hypotheses about the semantic content of texts can be generated and tested with reference to standard text norms (Wilson and Rayson, 1993). 6 This means that the sample is significant to represent the population. In other words, it allows only a 1 percent error. Thus, the result is highly significant (Siegel, 1988).

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For the analysis, all the sentences in the articles were manually identified by the researcher based on Hoey’s Problem-Solution pattern and saved in separate files for Problem and Solution. Using the computer, clauses in the filename ‘Problem’ were uploaded into Wmatrix and compared with the reference corpus, the BNC Written Informative Sampler (BNCWinf), to identify the dominant semantic fields existing in the Problem clauses in MB and MT. The same process was undertaken for clauses signifying a Solution. From here, the key words in those semantic fields can be derived. This enabled me to look at the dominant words, along with their contexts (using concordances) signifying the semantic fields of Problem and Solution in MB and MT. Therefore, sufficient examples can be called up for investigation of the linguistic structures realising this particular pattern. Using Hoey’s framework, I addressed the following questions: Is there a significant difference in the semantic fields of Problem in MB and MT compared to the BNCWInf? And what are the dominant semantic fields along with the evoking and inscribed words that signal a Problem in MB and MT? In the following section I present the results and discussion for the above questions. 5. Results and analysis 5.1 Semantic fields of Problem in MB and MT compared with the BNCWInf A comparison of MB and MT revealed only two significant negative semantic fields denoting a Problem – Weak and Affect-Cause/Connected. For Weak, MB (0.08 percent) has an overuse compared with MT (0.01 percent) (LL=10.22, p<0.01). Correspondingly, for Affect-Cause/Connected, MB (0.78) showed an overuse in relation to MT (0.31 percent) (LL=43.92, p<0.001). I will discuss the two fields (Weak and Affect-Cause/Connected) by placing them under negatively-inscribed/evoking words and causation, respectively. The small number of significant semantic fields for Problem between MB and MT was expected as the comparison was made on the Problem category for both magazines. However, a significant result was that when MB and MT were compared with the general reference corpus, BNCWInf, where results showed thirteen and fifteen semantic categories, respectively, relating to Problems in the business world (see Tables 2 and 3). The common key semantic fields in both MB and MT were Ability: Failure, Negative, Difficult, Evaluation: Bad, Danger, Affect: Cause/Connected and Violent/Angry. (MT has three categories for Evaluation which refer consecutively to the basic adjective, its comparative and superlative e.g., bad, worse, worst.) For analysis, I have categorised the fields into 1) Negation, 2)


Negatively-inscribed or evoking words, and 3) Affect: Cause: Connected. Each of these will now be analysed in turn.

Semantic field Example MB (percent)

BNCWInf (percent) LL

Ability: Failure failure, fails, unproductive 0.40 0.04 159.42

Negative not, no, n’t 1.23 0.61 73.18

Money: Debts losses, debt(s) 0.49 0.16 65.43

Difficult crisis, problem, difficult 0.51 0.19 55.62

Evaluation: Bad bad, flaw, severe 0.24 0.06 51.74

Weak weakness, weak 0.08 0.01 20.46

Danger risk(s) 0.12 0.04 18.77

Affect: Cause/Connected

due to, reason, causes 0.78 0.51 17.48

Money: Poor poor, non-profit 0.08 0.02 13.75

Measurement: Slow slowdown, slower, sluggish 0.08 0.02 13.53

Uncertainty doubt 0.11 0.04 10.51

Violent/Angry hit, fallouts 0.27 0.16 10.07

Weakness weakest 0.02 0.00 8.06

Table 2: Thirteen key semantic fields characteristic of Problem clauses in MB compared with the BNC Sampler Written Informative (BNCWInf)

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Semantic field Example MT (percent)

BNCWInf (percent) LL

Negative not, n’t, no 1.36 0.61 178.10

Ability: Failure failed, failure, lost 0.28 0.04 147.71

Competition rival(s), competitive, adversaries 0.15 0.03 70.30

Difficult problem(s), difficult 0.36 0.19 30.61

Worry, concern stress, worry, trouble 0.20 0.08 28.98

Affect: Cause/Connected

get, reason, because of 0.31 0.51 26.25

Evaluation: Bad serious, doomed 0.14 0.06 21.97

Evaluation: Bad (comparative) worse, disastrous 0.04 0.01 18.96

Discontentment disappointing, frustrating 0.06 0.01 18.46

Evaluation: Bad (superlative) worst 0.04 0.01 16.26

Evaluation: False lie, unthinkable, dishonest 0.08 0.03 13.53

Violent/Angry hit, fallout, aggressive 0.25 0.16 11.77

Foolish ludicrous, irresponsible 0.04 0.01 10.91

Danger risk(s), gamble 0.08 0.04 9.92

Sad suffer, grief, grim 0.12 0.07 9.07

Table 3: Fifteen key semantic fields characteristic of Problem clauses in MT compared with the BNC Sampler Written Informative (BNCWInf)


5.1.1 Negation In the Problem clauses, negation or negated sentences can be seen as an evoking signal. There is higher occurrence of this field (Z6) in the Problem clauses in MT (1.36 percent) compared to the BNCWInf (LL>15.13, p<0.001). Similarly, MB (1.23 percent) shows a significant difference compared to the BNCWInf (LL>15.13, p<0.001). The top three types of negation for this semantic field are not, its contracted form n’t and no negation. Quirk et al. (1994) state that not negation is mainly used in formal English rather than its contracted form n’t, which is used more in informal English. Based on the results for negatives, not negation seemed to be the dominant form in MB and MT. However, its contracted form n’t appeared to have a higher frequency in MT (173 times) than in MB (150 times). This suggests that MT has a more informal style than MB, and shows the Malaysian magazine’s preference for a formal, journalistic style.7 No negation was found to be considerably rarer than not negation in both MB and MT, and this echoes other corpus-based studies on negation (see Wilson, 1991; Tottie, 1991, 1987; Nelson, 2000). Negation in Problem clauses from MB and MT is mainly concerned with a deficiency or a lack of resources, solutions or expertise in dealing with a Problem, the failure of certain individuals, plans and actions to succeed or follow through, and also evaluation of the state of business companies.

There are several dominant negation structures signalling a Problem in MB and MT which may also contain negative evoking noun or verb phrases (underlined) as in the following. (i) not + verb in a cause and consequence/effect structure: Managers who do not continue to learn and widen their knowledge and expertise will, thus, lose their credibility with regard to their leadership.

An official contacted at PNHB says the conditional offer was deemed unfair and did not follow government directives, leading to the rejection.

7 Contractions are one of the markers for an informal writing style. The overall results for contractions show a significant difference that MT (0.39 percent) is higher than MB (0.10 percent) (LL = 135.73, p<0.001). According to Rayson and Garside (2000), the log-likelihood is a reliable statistical test to compare corpora that are not balanced in terms of their size. The reliability of the corpus comparison is strengthened by taking into account the representativeness, homogeneity and comparability of the two compared corpora.

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(ii) not + verb + adverb This negation structure is used to evaluate a failure of a certain business practice (underlined noun phrases), for instance: A spate of unfavourable corporate manoeuvering and the de-privatisation of several entities do not augur well for the already weak stock market.

…they are still holding back on R&D expenditure and this does not bode well for our future competitiveness.

(iii) not + main verb + noun A Problem may also be an absence of essential facilities or resources. The example below shows the negation or non-existence of ‘computer facilities’ in schools which is an obstacle in achieving an IT (information technology) literate population: …a total of 5,010 or 69.5 per cent of primary and 758 or 46.2 per cent of secondary schools do not have computer facilities.

(iv) not + adverb + adjective A similar problem signalled by negation is seen in the cause and consequence construction below, where there is a lack of certain traits in people, countries, business plans, services, products or certain principles or regulations not being followed, for example, sentences with this structure: …Malaysians are not very conversant with the trends in the tech industry, especially those related to the dotcom business.

The countries were not politically and socially cohesive and so were vulnerable to external intervention.

As long as corporate decisions are not adequately transparent and corporate governance not adhered to, small investors would eventually end up taking higher risks relative to their returns.

(v) not + (be) + (ART) + adjective + noun A Problem may also be a negative evaluation of the state of a business:


There is a lot of uncertainty involved and Utama is not an easy party to deal with.

Time dotCom Bhd is not a great stock. By all accounts, they are not good statistics for an industry used to double-digit growth in the boom years of the 1990s.

Since its inception in 1991, THUB, then known as Pembinaan Seleksi Sdn Bhd, had not been seeing a rosy bottomline.

The negated sentences contain mostly negative evoking nouns and verb phrases (underlined) indicating a Problem and the negation further helps to strengthen this notion. However, the negated sentences also appear in the cause and effect (consequence) structure which Crombie (1985) identifies as a signal of a Problem. 5.1.2 Negatively-inscribed and evoking words Comparing the results between MB and MT from Tables 2 and 3, we can see that both contain more negative semantic fields than the BNCWInf. The fields include: Ability: Failure (X9.2-), Competition (S7.3-), Difficult (A12-), Evaluation: Bad (A5.1-, A5.1--, A5.1---), Weakness (S1.2.5-), Danger (A15-), Worry, concern (E6-), Discontentment (E4.2-), Fear/shock (E5-), Foolish (S1.2.6-), Evaluation: False (A5.2-), Uncertainty (A7-), and Violent/Angry (E3-).8 However, due to space limitations, I will discuss only some of the fields previously mentioned with concordanced examples.

Failure: This is the most significant semantic field in that the percentage in MB (0.40 percent) is higher compared with the BNCWInf (0.04 percent; LL=159.42, p<0.001). Similarly, there was an overuse of this field in MT (0.28 percent) in relation to the BNCWInf (0.04 percent; LL=147.71, p<0.001). The most frequent word in this field was the negative evaluative noun failure(s). It appeared most dominant in MB while the verb failed was top in MT. In MB, the key words used were inscribed ones, consisting mainly of failure(s), failed, fails, its near-synonym floundered and the negative input of a word signalled by the prefix in– or un–, (unproductive, unsuccessful). The

8 The semantic tags show semantic fields which group together word senses that are related by virtue of their being connected at some level of generality with the same mental concept. The semantic tags were developed by Lancaster University Computer Corpus Research Group, (UCREL). A tag that begins with A refers to general and abstract terms, X refers to psychological states, actions and processes, S refers to social actions, states and processes, E refers to emotion.

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noun failure in MB mainly occurred in premodified noun phrases preceded by nouns, e.g., corporate, bank and others, and fell under the cause-effect category of problem signal (Crombie, 1985) as shown in the concordance below:

misadventures and bad management that results in failure . Unlike the consequences of

The downstream consequences of a major corporate failure can bring unproportionate hardship and

uncommon factor cited by many corporates for their failure . It is true that the crisis had adversely

true that the main cause of many a corporate failure was bad management . In many cases

business of underwriting risks . Bank failure is not something new .

Compared with MB, MT used a wider range of negatively-inscribed

lexis indicating the field of Failure which, unlike MB, included colloquial words like screw up, make a hash of, mess up and cropper. This reflects the informal style of MT compared to MB.

Difficulty: For this field, MB (0.51 percent) displayed an overuse in relation to the BNCWInf (0.19 percent; LL=55.62, p<0.001). Similarly, MT (0.36 percent) had a higher occurrence compared to the BNCWInf (0.19 percent; LL=30.61, p<0.001). I have chosen to look at the top three words for this semantic category, i.e., the inscribed nouns problem(s) and crisis, and the evaluative adjective difficult. The most dominant word for this semantic field in MB is the noun crisis, as can be seen in the examples below:

is but lo and behold , the moment one uninvited crisis rolls in , the normally cool manager becomes

epreciated ringgit . Just before the crisis of 1997 , there was a tapering of exports in the

market meltdown in the aftermath of the economic crisis that afflicted the region in mid 1997 . d higher

an economy , after a brief recovery from the 1997 crisis , is now staring again at a slowdown or even

their failure . It is true that the crisis had adversely affected the performance of all

Most of the negatively-inscribed signals were premodified noun

phrases with modifying adjectives economic, uninvited or the determiner the preceding the nouns which mainly referred to one particular event or cause, e.g., ‘the 1997 economic crisis’. However, the word crisis in MT was used without referring to a particular event but to a condition or consequence, e.g.,


‘drama into a crisis, it is now in a state of crisis and was set to herald a real crisis’.

The main signal for a problem is indicated by the inscribed noun problem, mainly in premodified noun phrases. In MT it was the most dominant word for this semantic field (Difficult) e.g., their worst problem, age problem, a trivial problem, the physical problem of screen size. When premodified by evaluative adjectives, problem may be cataphoric and anaphoric; for example, the problem in this sentence refers forward to the reason: ‘But there was a problem: the cabin audio equipment wasn’t working and no one on board was qualified to repair it’.

Apart from problem(s), another signal was the evaluative adjective difficult + [to + verb] which was the third most used in MB but came in second in MT. It is used for negative evaluation of a proposition (i.e., plan, deal, feedback), for example in the concordance below from MB:

already in ‘ tuition fees to the market ’ . It can be difficult estimating how cash-rich a company is.

, although there was a hotline , it was quite difficult to get a response or to get the right person to a

hiking prices continuously will be increasingly difficult to execute , ’ says Malaysia Street .

‘ The ruling in some ways made it difficult for us to proceed with the whole deal .

and merchant bankers feel it will be increasingly difficult for the company to secure a rescuer .

Evaluation – Bad: In MB, this field was found to be significant with a

frequency of 0.24 percent compared with the BNCWInf (0.06 percent; LL=51.74, p<0.001). However, for MT there were three significant fields for this category (A5.1---, A5.1--, A5.1-) (p<0.001), which refer to the comparison of adjectives, e.g., the superlative worst, comparative worse and basic adjective bad. This may reveal that MT is more descriptive and uses a variety of words to evaluate negatively the problems. For the semantic field A5.1--- the superlative worst, is used as an evaluative adjective mostly in premodified noun phrases with negative evoking words – worst excesses, worst recession, worst nightmare, worst culprits and inscribed noun e.g., worst problem. Other near synonyms denoting the quality ‘Bad’ are adjectives and adverbs negatively evaluating causes and effects of business conditions, products and information, e.g., bad, flaw, severe, poor, dire and detrimental:

, is because KTMB ’s own locomotives are in bad shape and the cost of maintaining the fleet is high

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stand that Shrewsbury and his wife had long been in bad financial shape . Dabasir had fallen int

it is the combination of business misadventures and bad management that results in failure .

the main cause of many corporate failure was bad management . In many cases , badly managed

dampened . Lower consumer spending means bad news for companies like Jaya Jusco Stores Bhd

Debts: In MB, this semantic field appeared significant (0.49 percent)

compared to the BNCWInf (0.16 percent; LL=65.43, p<0.001). Words like losses, debts, spending, overheads and bankrupt are evoking signals of problems in the business field, e.g., ‘During the recession almost all private property developers and construction companies suffered losses’. This field, however, does not appear significant in MT.

Weakness: The Weakness domain (semantic field S1.2.5-), as opposed to Toughness, appeared significant in MB (0.08 percent) compared with the BNCWInf (0.01 percent; LL=20.46, p<0.001). The words making up this category mainly consisted of inscribed nouns, verbs and adjectives. For example, the top word in this category was the adjective weak in premodified noun phrases and the evaluative noun weaknesses:

took the financial crisis of 1997 to lay bare the weaknesses of the economy . But since late last

to avoid responsibilities or to cover up their weaknesses . Thus we see leaders who change the

Nathan admits that the company still has many weaknesses . ‘ There is absolutely no doubt

Danger: One of the practices in business is assessing probable risks or

dangers and avoiding them. The results showed that MB had a slightly higher percentage of these (0.12 percent) in relation to the BNCWInf (0.04 percent; LL=18.77, p<0.001). This field was also overused in MT (0.08 percent) compared to the BNCWInf (0.04 percent; LL=9.92, p<0.01). It revolved around risky and dangerous situations where the top two words used in MB and MT were inscribed words, e.g., noun/verb risk(s) and the noun danger. However, MT used a more varied lexis for this field, e.g., jeopardy and gamble. For example, the concordance below from MB shows the various occurrence of risk as a noun in premodified noun phrases:

be expected to absorb some exposure to currency risk that Proton has to deal with from time to time As als of savings deposits are withdrawn , the liquidity risk increases rapidly . Similarly , losses


postmodified noun phrases:

ood times were not proportionately balanced by the risk they undertook in periods of turmoil .

P 50 ) Often , they are incapable of assessing the risk of their investments . EVA P 51 ) Equally , the

non-participating shareholders do not realise the risk of their investments until the debt bubble bursts .

ities in banks erode stockholders funds and pose a risk to depositors on the safety of their savings .

and also as a verb:

wiped out . A large number of employees risk the prospect of being laid off from work .

In MB, danger occurs as a noun, evaluating a Problem (in bold) as dangerous:

macro demand for K-workers in the country and the danger that brain drain poses to the country 's long-

09 ) There are many more such groups which are in danger of being sidelined by the K-economy

cent in 2000 . ‘ Repegging or all outright float is a danger at this point as it could generate a fallout

There was a very strong tendency for the words risk and danger to be

used with pre- and postmodified noun phrases signifying a Problem situated either to the left or right of the node word. Analysis of these words showed the potential usefulness of knowing the Problem terminologies associated with business and can contribute to the Business English classroom.

Worry: Analysis of this field showed a significantly high occurrence in MT (0.20 percent; LL=28.98, p<0.001), but not in MB. A possible reason for this may be the formality of MB which avoids matters that deal with emotions. The top three most dominant words in MT were stress, worry and trouble. A concordance of the dominant word stress in MT for this field is as follows:

her was an alcoholic . There ’s no doubt the stress can be very harmful to your life and that of your

that he would alienate the staff and create stress within the business with his vision of where he ’s

most say they experience above-average stress levels , few believe they are at the point of burn

way up the greasy career pole , long hours , stress and corporate entertaining make a healthy diet as

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, relating to exercise and diet . Stress has been called the plague of modern living

As seen in the concordance above, the noun stress is seen as harmful

and used as a negatively-evoking noun; it functions as both cause and consequence. It is seen as a Problem that is harmful to health and work life, and occurs in a sentence which also contains other negative lexical signals, e.g., harmful, alienate, greasy career pole, long hours and plague. In MT, all the sentences using stress or other inscribed words in this category, e.g., worry, trouble (‘get into trouble’, ‘ran into trouble’) collocate with another Problem signalled by either inscribed or evoked lexis or negation and thus make it easy to identify it as a Problem:

can not be managed . Many people worry about whether to tell others about their idea .

they quake . Company bosses also worry about globalisation and the sheer media maze .

of boss management . Some bosses worry that their staff are not working hard enough .

Competition: Competition from rivals in the business world can be

considered a threat or an obstacle. According to Nelson (2000), it has negative connotations based on a study involving the Business English Corpus – the toughness of the competition – unbridled, fierce and aggressive competition being examples. This field appeared significant only in MT (0.15 percent) when compared to the BNCWInf data (0.03 percent; LL=70.30, p<0.001), where words like rival(s), competitive and adversaries evoke a problem. Interestingly, sentences like ‘…that Sainsbury’s would soon be swallowed up by a rival grocer’ and ‘Potential predators include Delhaize of Belgium and the Dutch chain Ahold’ are metaphorical, where the rivals are portrayed as predatory animals,9 and so evoke a negative evaluation.

Discontentment: This field also displayed significantly in MT (0.06 percent) in relation to the BNCWInf (0.01 percent; LL=18.46, p<0.001). This domain includes words referring to Sadness or Discontentment. Most of the words in this domain used the negative prefix dis–, consisting mainly of evaluation using negative adjectives premodifying the nouns, e.g., ‘more disappointing figures’, ‘towards the disappointing figure of’, ‘a lot of disappointed people’; and sentiments using single negative adjectives e.g., demoralized, fed up or nouns such as customer dissatisfaction and regrets.

9 This confirms the studies done on the growing use of metaphorical expressions using animal imagery in business and economics discourse (White, 2003; Henderson, 2000; Fox, 1999).


All in all, the words in the semantic categories discussed above do not by themselves signal the Problem. Most of the Problem clauses also contain other signals. Thus, a typical Problem contains multiple items like negation, and most of the negative words seem to be inscribed which were mainly nouns, adjectives and verbs. For example, in ‘There is a lot of uncertainty involved and Utama is not an easy party to deal with’, where the negative inscribed noun uncertainty and negation help to signal a Problem. As found previously, the cause and consequence structure is also a signal of a Problem (Crombie, 1985). The field of Cause (A2.2) was found to be significant in both MB and MT when compared to the BNCWInf. I discuss this in the next section. 5.1.3 Affect: Cause/Connected (Causation) According to Flowerdew (2003), when an explicit causative verb collocates with a negatively-inscribed word, the verb has a negative semantic prosody, that is, it suggests some type of adverse incident. This was also confirmed in the author’s research e.g., ‘Shipping lines encounter inefficiency at ports and this causes delay in their daily business’ (MB). The cause is something that brings about an effect or a result, e.g., ‘Works at the tunnel portal will create a noise problem’. Verbs like cause, lead to, bring, become, pose, incur and others signal causality where the Problem is exacerbated.

As seen in Tables 2 and 3, the semantic field Affect: Cause/Connected (A2.2) appeared underused in MT (0.31 percent) compared with the BNCWInf (0.51 percent; LL=26.25, p<0.001) with dominant words such as reason and because of. However, MB has an overuse of this field (0.78 percent) compared with BNCWInf (0.51 percent; LL=17.48, p<0.001) with words like due to, reason and causes. Stubbs (1995) has pointed out that the verb cause collocates with words that indicate undesirable things, such as illnesses and natural or economic disasters. Following Sinclair (1991) and Louw (1993), he associates undesirable things with the semantic prosody of the verb cause, and suggests that a true definition of the word should not be ‘make something happen’ but ‘make something bad happen’. My findings confirm Stubbs’s notion where cause collocated with adverse situations, and this was similar for due to:

ty capital , but end up with a diminished capital due to mismanagement . A poor performance

go ahead with the privatisation of KTMB , largely due to concerns over its viability . It is

RB-Hicom failed to come to a mutual agreement due to a price dispute What could be

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MB Banking Group fell apart at the eleventh hour due to disagreements over which bank would ng lines encounter inefficiency at ports and this causes delay in their daily business . In turn

from the lack of awareness by the public on the causes of failures . As long as corporate

The erosion of confidence by the public in a bank causes abnormal withdrawals of savings .

activities . Yet , at times , the causes of major bank failures have not always been

sulting work , I have found that one of the basic causes for the failure of change programmes is the

6. Discussion The comparison of the semantic fields of Problem in MB and MT, in conjunction with the comparison of the BNCWInf has revealed different aspects of journalistic style and business English. Based on the analysis, three main observations can be made, relating to causation, inscribed vs. evoking items and journalistic style.

The first observation is that most of the Problem clauses use the cause-consequence pattern, where the cause and consequence are both Problems signalled mainly by premodified noun phrases with negatively inscribed and evoked nouns and adjectives, e.g., ‘A poor performance or breach of ethical practice can result in a great loss of credibility capital for a manager’. This finding confirms previous studies on Problem structures such as Crombie (1985) and Flowerdew (2003). In a cause-and-consequence sentence structure, causative verbs are usually used to indicate the Problem where they collocate significantly with negative propositions (Flowerdew, 2003). In the Problem category, the results for Negation revealed that MT has a higher frequency of the contracted form n’t compared with MB. I found that the negation structures also consist of other signals of Problem, e.g., cause and effect structures (Crombie, 1985) and negatively-evoking or inscribed words (Flowerdew, 2003; Hoey, 2001). Causation signals were found to be more frequent in MB compared to MT.

Secondly, based on the words signalling a Problem in the semantic fields, there were more negatively inscribed words for both magazines than evoking ones. Inscribed words are those that have explicit meaning where the writer inscribes the evaluation (Martin, 2000). This confirms the fact that a Problem is a negative evaluation of a proposition (Hoey, 1983). Therefore, the negative evaluation is given by the writer by the use of evaluative words which evoke a negative or positive evaluation in the reader’s mind. It is reasonable,


as pointed out by Hoey (2001) and Flowerdew (2003), that evaluation is evident in most parts of the text. From the study’s data, inscribed words like failure, crisis, problem, difficult, trouble, disappointing, dissatisfaction, discontented, fear, terrified, worst, bad, poor, weakness, ludicrous, suffer, risk(s), danger, uncertainty, turmoil and mismanagement have a clear negative sense in the reader’s mind. These words consisted mostly of nouns and adjectives which confirm Flowerdew’s (2003) findings that nouns and adjectives make up the inscribed signals for Problems. On the other hand, the study found that evoking signals for Problem are small in number, e.g., stress, competition, losses, debts, tussle and attacks.

The third observation is related to the journalistic style of the two magazines. I found that MT is more informal and descriptive in presenting the Problems. This can be seen in the use of contracted forms of negation, e.g., n’t, negative semantic fields like ‘Discontentment’, ‘Foolish’, ‘Sad’, ‘Violent/Angry’, ‘Worry’, ‘Danger’, ‘Fear’ and ‘Uncertainty’, and the use of colloquial words in the semantic field of Failure. By contrast, MB has a more formal style and is concerned with less emotive semantic fields like ‘Debts’, ‘Weakness’, ‘Money: Poor’ and ‘Movement: Slow’. This formality in MB is supported by Nelson’s (2000) view that the lexis of Business English is, to a large extent, formed from a limited number of semantic groups that create a ‘meaning world’ for business. This world is populated by business people, companies, institutions, hierarchy, money, business events and places of business, and is marked by its dynamic and non-emotive lexis. Colloquial words were also found in the semantic fields in MT’s Problems, signalling an informal style compared to MB.

There are several possible explanations for the results. First, the differences between MB and MT may be due to differing house styles as practised by both magazines. These house styles may influence the magazine writers’ way of writing and how it is presented to the expected audience. In addition, the differences between MB and MT may reflect the wider usage of English in both cultures where the process of ‘informalisation’ (Fairclough, 1994) has penetrated written discourse, mainly in MT. Conversational and informal styles are infused in the professional domain. Fairclough (1994: 7) states that, ‘the engineering of informality, friendship and even intimacy entails a crossing of borders between the public and the private, the commercial and the domestic, which is partly constituted by a simulation of the discursive practices of everyday life, conversational discourse’. A possible explanation for informalisation is that it is deliberately used to make writing (or speech) more accessible to an audience and also to maintain solidarity (Goodman, 1996).

Secondly, the different style may reflect socio-cultural differences between Malaysia and Britain where a conversational approach to conveying

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information about business in MT compared to the straightforward and formal style in MB implies that the UK is more socially relaxed than Malaysia. This may also suggest the influence of Malaysia’s society in that its bureaucracy maintains official and formal language use in its professional discourses. This gives the impression that Malaysia, having been colonised by Britain in the past, prefers to use formal English in order to project a professional and scholarly image through its discourses. 7. Conclusion This study has contributed to the ESP field in several ways. First, it investigates Business English – specifically the Problem element of Hoey’s Problem-Solution rhetorical pattern, which has not been explored by corpus linguistics methodologies in this ESP domain. It also introduces readers to a very useful semantic tagger in Wmatrix for identifying semantic fields. Moreover, it has heeded the notion of contrastive analysis as stressed by Hartmann (1980). A cross-cultural LSP/ESP text analysis can reveal culture-bound communication differences in written texts. For instance, even though business is purely a serious and professional matter, and readers would expect this in a business text, a specialised business magazine like MT can appear more informal than MB. The formality maintained in MB seems to be essential in projecting a professional and scholarly image. A problem arises when a student reads a magazine like MB or MT, and decides to follow the writing style of MT. Since formality seems to be the norm in journalistic writing in Malaysia, the student’s writing style might be discredited. This relates to the issue of incorporating L2 pragmatic norms and cultural values in an L1 environment (Li, 1998). Clyne (1981: 65) states that, ‘if culture-specific discourse structures really play an important role, they should occupy a prominent place in teaching programs’, especially languages for special purposes. Business journalism in magazines or newspapers can efficiently meet learners’ needs in that they can familiarise themselves with the field of study, such as marketing, economics, accounting and business management (see Boyle, 1981). The ‘polished and highly idiomatic language of business reporting, as well as the political background vital for understanding business writing’ can serve as a motivating factor, which strikes interest in learners (Navarat, 1989: 35). This research will add substantially to a growing body of literature on professional genres of Business English, mainly from L2 business registers, and will help to counter the problem of the loss of professional registers in non-native discourse communities as claimed by Swales (2000), Louhiala-Salminen (1996) and Nickerson (2005). Furthermore, it will be an


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Semantic fields of problem in business English: Malaysian and British journalistic business texts

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