corpora journal Afida_5feb08

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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, ManagementToday (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 corpuswas analysed according to Hoeys 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 authorintroduces a problem and then presents the main point of the paper as a

solution. From her study on student and professional technical writing, it wasapparent 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, MalaysiaCorrespondence to: Afida Mohamad Ali, e-mail: [email protected]

Corpora Vol. 2 (2): 211239


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A. Mohamad Ali212

Malaysian and British journalistic business texts to identify the semantic fields

denoting a Problem based on Hoeys (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,

trottingand joggingcluster into a field of running, which, in turn, group withmany 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 Hoeys 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). Afidas (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 andtraps. Nelsons (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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Semantic fields of problem in business English 213

priorities of management. This is in line with Irgls (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), Foxs (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, weakand 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, cancerand crisis, and that provide collocateswith positive words, e.g., care, food and help, etc. Both Louw and Stubbsconcluded 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 isto 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,

Hoeys (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 textwhich responds to the readers 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).

Hoeys (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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A. Mohamad Ali214

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 ofanother 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 governments

full-fledged property developer in the making. (2) Wasting no time, he hascompletely 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 readers 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 Hoeys 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 theproblem sentence but without suggesting any expectations, then it functions as

a Situation or the setting of the topic at hand. It is the writers choice to encode

a particular situation as a problem and readers can sense the writers 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


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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, downfalland less successfulmay trigger gloomy thoughts in the

readers mind. Similarly, non-evaluative problematic issues like poverty, war,disease, demonstration, strike or attack also seem to evoke a depressingreaction 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., problemand 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. UsingWordSmith Tools (Scott, 1996), his study found that the usage ofproblem was

restricted at a local level and that the word appeared as key in only threearticles. In a comparative analysis of the Problem-Solution pattern in a student

and professional corpus of technical writing, Flowerdew (2003) applied

Martins (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 causalcategory 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 Martins inscribed and evoking

categories, and Nelsons (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 theAPPRAISAL system as a means of classifying evaluative language.


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A. Mohamad Ali216

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-basedapproach has similar aims to Hoffmanns (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 researchin LSP. In this study, the corpus, which consists of articles from MalaysianBusiness (MB)and Management Today (MT), is made up of journalistic textsreporting on various business topics in Malaysia and Britain.

4.2The 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 magazinein 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 writers opinions with a fairly serious and comprehensive analysis

of a topic and will give statistics, examples, quotes and opinions. The topics in


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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 writers 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 readers 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 thearticles 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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A. Mohamad Ali218

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, chiefexecutives and senior directors.

Aim

To serve readers by helping

them make educated and

informed investing decisionsby keeping abreast with

significant developments inlisted companies.

Management Today is about the

way you work and what you're

worth and how you advanceyour career and still have a life.

About how you handle yourpeople, and best practice and thedigital 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, andinformation 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/http://www.lancs.ac.uk/


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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 Hoeys 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 inthe 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., dangerand

risk. From here, frequency lists and text concordances can be obtained. Thelog-likelihood statistic (LL) is employed by Wmatrix; only items with a LL

value of more than seven are considered to be statistically significant, since6.63 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 Hoeys 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 MBand 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 Hoeys

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


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Negatively-inscribed or evoking words, and 3) Affect: Cause: Connected.

Each of these will now be analysed in turn.

Semantic field ExampleMB

(percent)

BNCWInf

(percent) LL

Ability: Failurefailure, fails,

unproductive0.40 0.04 159.42

Negative not, no, nt 1.23 0.61 73.18

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

Difficultcrisis, problem,

difficult0.51 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

dueto, reason,causes

0.78 0.51 17.48

Money: Poor poor, non-profit 0.08 0.02 13.75

Measurement: Slowslowdown, 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 ExampleMT(percent)

BNCWInf(percent)

LL

Negative not, nt, no 1.36 0.61 178.10

Ability: Failure failed, failure, lost 0.28 0.04 147.71

Competitionrival(s), competitive,

adversaries0.15 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

of0.31 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

Discontentmentdisappointing,frustrating

0.06 0.01 18.46

Evaluation: Bad

(superlative)worst 0.04 0.01 16.26

Evaluation: Falselie, unthinkable,

dishonest0.08 0.03 13.53

Violent/Angry hit, fallout, aggressive 0.25 0.16 11.77

Foolishludicrous,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)


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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, p15.13, p


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A. Mohamad Ali224

(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 wellfor 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 facilitiesinschools 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 donot 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 inthe tech industry, especially those related to the dotcom

business.

The countries werenot 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:


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There is a lot of uncertainty involved and Utama is not

an easy partyto 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 betweenMBandMTfrom Tables 2 and 3, we can seethatbothcontain more negative semantic fields than the BNCWInf. The fieldsinclude: 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 thepercentage inMB(0.40 percent) is higher compared with the BNCWInf (0.04

percent; LL=159.42, p


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noun failure inMBmainly occurred in premodified noun phrases preceded bynouns, e.g., corporate, bankand others, and fell under the cause-effect categoryof 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-inscribedlexis indicating the field of Failure which, unlike MB, included colloquial

words like screw up, make a hash of, mess up and cropper. This reflects theinformal style ofMTcompared to MB.

Difficulty: For this field, MB (0.51 percent) displayed an overuse inrelation to the BNCWInf (0.19 percent; LL=55.62, p


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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 dominantword for this semantic field (Difficult) e.g., their worst problem, age problem,

a trivial problem, the physical problem of screen size. When premodified byevaluative adjectives, problem may be cataphoric and anaphoric; for example,the problem in this sentence refers forward to the reason: But there was aproblem: the cabin audio equipment wasnt 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 inMBbut 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 toget 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 afrequency of 0.24 percent compared with the BNCWInf (0.06 percent;

LL=51.74, p


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A. Mohamad Ali228

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 badmanagement

. In many cases , badly manageddampened . 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


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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 , thenon-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, dangeroccurs as a noun, evaluating a Problem (in bold) as dangerous:

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

09 ) There are many more such groups which are in dangerof

beingsidelinedby the K-economy

cent in 2000 . Repeggingor alloutrightfloat is a danger

at this point as it could generate a fallout

There was a very strong tendency for the words riskand dangerto beused 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


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A. Mohamad Ali230

, 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, allthe 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 worryabout 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 worrythat their staff are

not working hard enough .

Competition: Competition from rivals in the business world can beconsidered 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 competitionbeing examples. This field appeared significant only in MT (0.15 percent)when compared to the BNCWInf data (0.03 percent; LL=70.30, p


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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 ofuncertainty involved

and Utama is not an easy party to deal with, where the negative inscribednoun 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 bothMBandMTwhen 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 authors research e.g., Shipping lines encounter inefficiency at ports andthis causes delay in their daily business (MB). The cause is something thatbrings 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 andothers signal causality where the Problem is exacerbated.

As seen in Tables 2 and 3, the semantic field Affect: Cause/Connected

(A2.2) appeared underused inMT(0.31 percent) compared with the BNCWInf(0.51 percent; LL=26.25, p


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A. Mohamad Ali232

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, inconjunction with the comparison of the BNCWInf has revealed different

aspects of journalistic style and business English. Based on the analysis, threemain 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 ethicalpracticecan result in a great loss of credibility capital for a manager. Thisfinding 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 thatMThas a higher frequency ofthe contracted form nt compared with MB. I found that the negationstructures 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 readers mind. It is reasonable,


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as pointed out by Hoey (2001) and Flowerdew (2003), that evaluation is

evident in most parts of the text. From the studys data, inscribed words like

failure, crisis, problem, difficult, trouble, disappointing, dissatisfaction,discontented, fear, terrified, worst, bad, poor, weakness, ludicrous, suffer,risk(s), danger, uncertainty, turmoiland mismanagementhave a clear negative

sense in the readers mind. These words consisted mostly of nouns andadjectives which confirm Flowerdews (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 thatMTis more informal and descriptive in presenting theProblems. This can be seen in the use of contracted forms of negation, e.g., nt,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,MBhas a moreformal style and is concerned with less emotive semantic fields like Debts,

Weakness, Money: Poor and Movement: Slow. This formality inMB issupported by Nelsons (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 MTs 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 ofEnglish 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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A. Mohamad Ali234

information about business in MTcompared to the straightforward and formalstyle in MB implies that the UK is more socially relaxed than Malaysia. This

may also suggest the influence of Malaysias 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 andscholarly 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 Hoeys

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 beessential 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 students 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-specificdiscourse structures really play an important role, they should occupy a

prominent place in teaching programs, especially languages for specialpurposes. 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 businessregisters, 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


25/29


addition to the wealth of research pertaining to cross-cultural rhetoric, corpus-

based research and text linguistics.

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