Estructura y redacción

In advance of the analysis, cautions against generalising the research results into the general BELF job interview communications and its ESP curriculum are merited, specifically in terms of data collection.

First of all, the data gathered for this thesis is from a company located in the Middle East and from applicants from four different Asian countries. Therefore, the cultural

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perceptions and communicative behaviours are different from other BELF communicative situations such as those of Europe, America and/or Africa.

Second, even though a considerable effort has been made to diversify applicants’ job interview samples from different BELF regions, since the company participating in this research is just one Middle Eastern ship-repair company, the company’s values, business standards and culture are largely reflected in the selection process. In this sense, the findings cannot be applied to all other BELF job interview communications held in different sectors of business.

Finally, the amount of data analysed for this research is 40 samples in total, respectively 20 samples from each group. Since building a spoken corpus requires a considerable amount of human labour and time, a more extensive corpus, which would ensure enhanced representability of data, is not fully available from a practical perspective for a personal research project. Considering that this is a small and specialised corpus designed for the analysis of specific structural and linguistic features in a certain communicative situation (or a job interview setting in a multicultural BELF context), however, the result drawn from this corpus can be quite reliable in discussing pedagogical implications.

Considering all the limitations that this research entails, the discussion and pedagogical implications to be made throughout this paper are relevant for a multi-cultural BELF job interview situation, unless otherwise stated.

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CHAPTER 4 ANALYSIS: CONTEXTUAL STRUCTURE

4.1 Introduction

In this chapter, the various types of contextual structures of job interviews will be compared and analysed across two groups in advance of an analysis of its textual structures and linguistic features. As mentioned in Section 3.5.1, contextual structures here refers to the outside frame of the job interview in terms of examining the discourse from an extra linguistic structural point of view. That is, it is not directly related to the interactants’ linguistic aspects, but rather to the non-linguistic traits of job interview contexts. The discussions following will be conducted in terms of four major considerations: interview time, overall token distributions, turn-taking and contextual situations.

4.2 Contextual Structure of CELF-JOIN

4.2.1 Interview Time

As discussed earlier, the overall recording time of the successful group (hereinafter, SG) was 1.24 times longer in duration than that of the unsuccessful group (hereinafter, UG), amounting to approximately six hours and twenty minutes for SG and five hours and ten minutes for UG. The longer interview duration of SG also applied to interaction time – respectively around five hours and forty minutes for SG and four hours and forty minutes for UG – which solely includes direct verbal interactions between interviewers and interviewees and excludes the time for contextual situations, such as discussions between interviewers, written technical tests and interruptions by other staff members during the interview. Detailed

84 information on this is presented in Table 14.

Table 14. Comparison of interview time between SG and UG

SG UG

Recording time (hr: min.: sec.) 6:23:39 5:10:13

Interaction time (hr: min.: sec.) 5:43:41 4:40:50

Average recording time

per person (min.: sec.) 19:13 15:30

Average interaction time

per person (min.: sec.) 17:11 14:02

No. of tokens per minute

during interaction 146.02 124.72

SG’s time was also 1.24 times longer in duration for recording (19 minutes 13 seconds vs. 15 minutes and 30 seconds), and 1.22 times longer for interaction time (17 minutes 11 seconds vs. 14 minutes and 2 seconds) compared to UG. In all respects, the amount of the interaction for SG was around 20% higher than that of UG.

Furthermore, in terms of the number of tokens used during the interaction time, SG produced 17.70% more tokens than UG, respectively using 146.02 and 124.72 tokens per minute on average. Considering the fact that the same interviewers were involved for each group, it is reasonable to say that the gaps seen in the average tokens between the two groups were mostly caused by the applicants’ different speaking styles, rather than those of the interviewers. Even though more investigation on this is needed, and will be provided in the following linguistic analysis sections, possible interpretations of this in light of previous literature (Scheuer, 2001; Kerekes, 2006, 2007; Lipovsky, 2008) are also available. Previous studies have demonstrated that successful candidates have a strong tendency to volunteer more information in order to actively elaborate their answers, whereas unsuccessful applicants

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are likely to be less sensitive to this, and use a more lax mode of speaking. That is, the successful candidates in this research were more likely to be actively involved in the interactions by promoting themselves in a tight-speaking manner, compared to the unsuccessful candidate group. This seems to ultimately contribute to increasing or decreasing the informational density in SG and UG respectively, within an equal interaction time (i.e. per minute).

To sum up, both in terms of quantity and quality of time, SG yielded more productive and positive outcomes than UG by having longer interview durations, or promotional opportunities, and further by making their communication more informative based on intensely organised self-advertisement within the restricted time given. In the next section, the tokens used throughout the interactions between the two groups, and further between the interviewers and applicants, will be closely observed in order to compare and analyse their distributional differences across the whole corpus.

4.2.2 Token Distributions

Overall, CELF-JOIN, which is comprised of 20 sets of applicant data for each group, contains 85,214 tokens. The size of SG (50,188 tokens, 58.9% of the total corpus) was 1.43 times bigger than that of UG (35,026 tokens, 41.1%). The detailed organisation of CELF- JOIN according to the participants in each group can be visualised as per Figure 9. The participants are divided into three groups: interviewers, applicants and others (e.g. staff who assisted in the job interview process but did not influence the interactions). However, since the portion of ‘others’ in this corpus is extremely small (0.13% in SG and 0.06% in UG out of the total corpus), and therefore does not seem to have any significant meaning for the

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discussion, it is not included in all of the tables and figures provided in the following sections.

Figure 9. Detailed organisation of CELF-JOIN according to participants

In term of the overall corpus, the group of participants that occupied the biggest proportion of discourse was the SG applicants (38.11%), followed by the UG applicants (23.61%). The proportional gap between these two groups was 14.50%, whereas the gap between the interviewers was only 3.23% (respectively 20.66% in SG and 17.43% in UG). This means that the successful candidates produced 1.62 times more tokens than the unsuccessful candidates did, whereas the interviewers for the two groups displayed differences of only 1.19 times in their token distribution. In other words, while the interviewers conducted the interviews using smaller numbers of token differences (19% gap) in the case of both SG and UG, the applicants’ reactions in each group differed considerably (62% gap). Higher token usage occurred in SG compared to UG, when providing answers. This is supported by Scheuer’s (2001) study, which revealed that the ‘felicitous candidates’

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produced almost twice the number of words than ‘infelicitous applicants’ did in response to questions. This coincides with the previous discussion, which suggested that successful applicants engaged in the interview process in a quantitatively more active way both in terms of total token production and token production per minute. This leads to the conclusion that the size of corpus between the two groups is largely dependent on the amount of tokens produced by each applicant group, rather than by the interviewers.

Finally, Figure 10, shown the average token distribution ratio between the interviewers and candidates within each SG and UG group, clearly shows the successful candidates’ higher token occupancy rates during the interactions, compared to those of the interviewers.

Figure 10. Token distribution rates of interactions between interviewers and applicants

Out of 2,510 tokens per interview case on average for SG, the interviewers used 880 tokens at a rate of 35.06%. However, the successful candidates produced 1,624 tokens, amounting to 64.70%, which means that they spoke 1.85 times more than their interviewers did throughout

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the interactions. In UG, on the other hand, the number of tokens that the candidates and interviewers produced per interview case was 1,006 (57.44%) and 743 (42.40%); in other words, a token production rate only 1.36 times higher was observed for the candidates compared to the interviewers. The successful candidates’ significantly higher domination of the interactions implies that the power of job interviews does not always lie on the side of the interviewers, despite the fact that they are regarded as having a full control over the conversation; rather, the power can considerably vary according to how the applicants approach the interactions, in terms of active and aggressive attitudes.

To sum up, the successful candidates’ intense engagement during interactions is closely connected to the success of the job interview. Longer interactions seem to increase the possibility of applicants promoting their qualifications and skills in depth, and the duration depends considerably on the applicants’ interactional styles and attitudes. This implies that it is important to increase learners’ awareness of this aspect by informing them that interactional style and attitude can be considered a major criterion for successful and unsuccessful interview outcomes. However, systematically applying this into an actual language teaching classroom requires further discussion to identify what, exactly, brings about these quantitative differences between the two groups in terms of structuring their answers using effective and strategic choices of lexical items.

4.2.3 Turn-taking

Turn-taking refers to ‘speech exchange systems’ between interlocutors (Sacks, Schegloff & Jefferson, 1974, p. 696), which relates to who speaks first and next, and how they take turns. In this study, a ‘turn’ is determined by the start of any utterance made by the next

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speaker as the previous speaker finishes his/her speech or takes a brief or lengthy pause during the turn, as exemplified below.

Example 1. The principle of turn-taking

1. I1: You’re very (.) you know, varied- (1) various experience (.) and the: among

here, (1) er: which company and which position (.) make you most- most- mostly proud of yourself and er: good achievement.

2. P5-P-WA: <=>erm, well, I, well, I would say that (.) erm: my stint also: in:- in:- overseas and also here in the [place 1] contributed that to (.) er: (1) what I: er: become: at the moment so: I became more tolerant of other people: I: adjust very well: I thrive in a multi-cultural er: work setting: (1) and: I’m flexible: and: you know: you (.) I would say that erm: erm: I:(.)'ve accomplished a lot, (.) I: contributed a lot to the company, (.) erm: because I (.) initiated the [name1],

3. I1: <=>hm

4. P5-P-WA: er: it’s a system for HR: I also: have erm: revised the: manuals, policies and procedures for the company. (.) So: I could say that (.) I’m proudest of: my achieve<10>ment.</10>

5. I1:

<10>What</10> kind of HR system?

6. P5-P-WA: It’s an [name2] (.) system (.) that is yeah erm: fitted (.) to the company’z needs.

7. I1: <=>hm: Self developed?

8. P5-P-WA: Yes, (.) yes. (1)

9. I1: Alright. (3)

As illustrated above, new turns generally begin after the previous speakers’ speech is finished (turn 4, 5, 8 and 9). Besides this, several turns were initiated right after the other speakers’ turns were finished without any noticeable pause (turn 2, 3 and 7) and some others overlapped with the previous turns (turn 5). In addition, minimal responses during a short break (turn 3) also counted as a turn. All of the cases were regarded as one single turn in this study.

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was 5,121. Out of the total turns, 2,573 (50.24%) were taken by the interviewers, 2,526 (49.33%) by the applicants, and 22 (0.43%) by other staff. In a similar proportion, out of 3,705 turns in UG in total, 1,876 (50.63%) were taken by the interviewers, 1,810 by the applicants (48.85%) and 19 (0.51%) by others. Since conversation is established based on interactions, it is quite natural that the turns were evenly occupied and contributed by each interlocutor group at almost the same participation ratio (around 50%), even though there were sometimes more than two interviewers, and conversations (or turn exchanges) between the interviewers themselves took place in some of the cases.

When turns consisting of only minimal responses (hereinafter, TMRs) are considered (e.g. turn 3), however, the two groups reveal significant differences in their turn patterns. Prior to discussing the TMRs, the scope of minimal responses needs to be clarified for the subsequent consideration of a multi-cultural communicative environment. Generally, ‘minimal responses’ refer to the utterances of ‘a listener during a speech event to signal a certain level of engagement with the speaker’ (Fellegy, 1995, p.186), such as mhm, yeah and

hm. In Fishman’s (1978) study, however, the functions of minimal responses (i.e. yeah, umm,

huh and only that) were more broadly defined as those that are used to request clarification, give a sceptical response and reveal critical attitude, rather than merely to express active listenership, demonstrate a sense of support, and signal understanding and agreement. Therefore, the minimal responses need to be defined from a wider perspective, with broader categories than those suggested by Fishman. Considering that the CELF-JOIN deals with multi-cultural communications involving five different nationalities, furthermore, the minimal responses actually uttered by speakers do not exactly match those of previous studies. Under the diversified categorisations made on the basis of the major communicative functions suggested by Fishman (1978), therefore, various kinds of minimal responses uttered by

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different speakers with different cultural backgrounds were transcribed according to the VOICE transcription scheme with reference to their communicative functions. That is, the signals of active listenership in terms of a positive minimal response (e.g. yes, yeah, mhm), and also the signals for hesitations (e.g. er:, erm:), negative feedback (i.e. no), clarification (i.e. haeh?) and eliciting agreement (i.e. huh?).

In the following analysis, minimal responses which consist of a single turn, are only considered. The number and patterns of minimal responses throughout the corpus, regardless of whether they are sole components of one single turn (e.g. turn 3) or insertions in other speech (e.g. turn 7), will be discussed in more depth in the lexico-grammar analysis section of this paper. Here, therefore, the turns comprising only minimal responses will be examined in order to more closely consider the turn-taking patterns of the two different groups.

First of all, the percentage of TMR is 28.57% (1,463 out of 5,121 turns) in SG and 22.70% (841 out of 3,705 turns) in UG. SG showed relatively more TMR (a difference of 5.87%) compared to UG, as detailed in Table 15.

Table 15. Comparison of turns for minimal responses between SG and UG

SG UG TMR Turns in total Amount of TMR in the speakers’ total turns (%) TMR Turns in total Amount of TMR in the speakers’ total turns (%) Interviewers 807 2,573 31.36 421 1,876 22.44 Applicants 649 2,526 25.69 418 1,810 23.09 Others 7 22 31.82 2 19 10.53 Total 1,463 5,121 28.57 841 3,705 22.70

Interestingly, whereas the interviewers and applicants in UG showed similar TMR, respectively 22.44% and 23.09% (a difference of 0.65%) out of their total number of turns

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used in the interactions, the SG interviewers used 5.67% more TMR than their applicants during the interviews, at a ratio of 31.36% and 25.69%, respectively. That is, among the four participant groups (i.e. SG interviewers, SG applicants, UG interviewers, UG applicants), the interviewers in SG responded the most attentively and interactively, by allocating more than 30% of their turns to show active listenership to their interlocutors, or successful applicants. This implies that the interviewers’ attitudes towards the applicants in SG were generally more favourable and positive, and it also means that the successful candidates’ discourse, or self- promotion, was more attractive and informative for the interviewers in relation to their evaluations.

In addition, in terms of TMR distributions within each group, the SG interviewers’ higher participation was highlighted, as shown in Figure 11.

Figure 11. TMR distribution rates in each group

Out of the total 1,463 TMR in SG, the interviewers (807 turns, 55.16%) produced 10.80% more TMR than their applicants (649 turns, 44.36%), whereas both interviewers and applicants in UG recorded similar rates of TMR in their conversations (50.06% from the

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interviewers and 49.70% from the applicants). This suggests that stimulating a high level of interviewer engagement during interactions is very critical and highly relevant to successful job interview interactions.

The distribution of five detailed patterns of TMR, or positive and negative feedback,

hesitations, clarifications and eliciting agreement, gives a clear idea of the different minimal response usage between two groups, as shown in Table 16.

Table 16. Detailed patterns of TMR in terms of turn-taking strategies

Groups Types

of TMR

Interviewers Applicants

SG UG SG UG

TMR Percentage TMR Percentage TMR Percentage TMR Percentage

Positive feedback 768 95.17 384 91.21 615 94.76 380 90.91 Hesitations 23 2.85 14 3.33 14 2.16 23 5.5 Negative feedback 0 0 1 0.24 9 1.39 8 1.91 Clarifications 11 1.36 17 4.04 11 1.69 7 1.67 Eliciting agreement 5 0.62 5 1.19 0 0 0 0 Total 807 100 421 100 649 100 418 100

The most predominantly used TMR in all four interlocutor groups was positive

feedback, with the average rate of 93.01%. SG showed relatively more positive feedback (95.17% for the interviewers and 94.76% for the applicants) compared to UG (respectively 91.21% and 90.91%), with gaps of 3.96% and 3.85%. The areas showing higher rates in UG were hesitations and clarifications. In the case of hesitations, wherein a turn does not begin right after the other speaker’s turn is finished, due to uncertainty, embarrassment and/or long thought processes, the unsuccessful candidates (5.5%) produced 2.55 times more hesitations, compared to the successful candidates (2.16%). Also, in terms of clarifications, which involve asking other speakers to explain something more clearly and in more depth due to uncertainty relating to the information provided, the interviewers in UG showed the highest rate (4.04%)

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of the four interlocutor groups. That is, the interactions in SG featured a considerably higher level of positivity, with productive verbal signals that encourage the other interlocutor’s involvement, and this seemed ultimately to contribute to making exchanges more interactive and relational; as pointed out by Kerekes (2006), a mutually collaborative interactional style is a core of successful job interview interactions. In UG, on the other hand, even though

positive feedback took up a major part of the TMRs, two negative factors, hesitations and

clarifications, which are symbols of delayed responses and misunderstandings, showed that there is a certain level of interruption even in a natural and smooth communicative flow.

When TMRs are excluded from the total turns, furthermore, it is possible to observe how the actual communicative turns containing certain types of promotional content were exchanged between the interlocutors. The distribution of total turns, including and excluding TMRs between the two groups, is visualised in Figure 12 and 13.

Figure 12. Distribution of turns with and without TMRs in SG (turns for non-interviewers/- applicants (i.e. ‘others’) not specified)

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Figure 13. Distribution of turns with and without TMRs in UG (turns for non-interviewers/- applicants (i.e. ‘others’) not specified)

In SG, the number of interviewers’ turns was slightly higher than that of their applicants when TMRs are considered (50.24% and 49.33%, 0.91% difference). However, this distributional rate is reversed when TMRs are excluded from the total turns. That is, the SG applicants took more turns for information exchanges (51.31%), compared to the interviewers (48.28%), and this can be seen as counterevidence of higher occupancy of TMRs in the interviewers’ speech, as previously discussed. However, UG did not show significant differences in either case (with differences of 1.78% and 2.2%, including and excluding TMR,