A SURVEY ON JOB SEEKERS’ USAGE OF RECRUITMENT METHODS IN HONG KONG

A SURVEY ON JOB SEEKERS’ USAGE OF RECRUITMENT METHODS IN HONG KONG

Joseph Kim-keung Ho

Independent Trainer, Hong Kong, China

Abstract: Job search has been studied from the economic and sociological perspectives for quite some time; in the public domain, e.g., Youtbube.com, there is also literature on the topic mainly in the form of practical advice and newspaper news. Nevertheless, specific literature on job search in the Hong Kong setting is rare. This paper presents a brief review of the job search literature and findings on job seekers’ usage of recruitment methods in Hong Kong based on a Facebook-based questionnaire survey. No influential factors on job-seekers’ recruitment methods usage are identified in the survey exercise. Nevertheless, the paper is useful to stimulate further research on this topic and should also be considered as a follow-up paper on the writer’s recent work (Ho, 2015) on e-recruitment.

Key Words: a macro-framework on job search study; Facebook-based questionnaire survey; job search; job-seeking; multiple regression analysis; recruitment methods

INTRODUCTION

The topic of job search (e.g., study of job-seeking behaviors) comes to the attention of the writer in the process of researching on employee recruitment in the Hong Kong setting (Ho, 2015). Apparently, understanding the job-seeking behaviors of job seekers with different profiles can inform employee recruitment practices by recruiters. In Ho (2015), the topic of employee recruitment in the Hong Kong context, notably on e-recruitment, has been examined, which was informed by a Facebook-based questionnaire survey. This paper, making use of the same Facebook survey dataset of Ho (2015), studies the related topic of job-seeking behavior in Hong Kong. In this respect, it can be considered as a follow-up paper on Ho (2015). The first part of the paper is a literature review on job search and the second part provides some survey findings on job-seeking behavior in Hong Kong. The paper should be of value to both academics interested in the job search topic and job-seekers in general.

BASIC IDEAS UNDERLYING THE TOPIC OF JOB SEARCH

To prepare for the discussion, an introduction of the basic ideas on job search is necessary. Job search has been described as “the process that aims to match job seekers to suitable opportunities” (Green et al., 2011). Job search behavior is conceived as “the specific behaviours through which effort and time are expended to acquire information about labour market alternatives” (Bretz, Boudreau and Judge, 1994); it can be active or passive and can be measured in terms of intensity, i.e., time spent on job search, the number of job applications made and the number of job search methods employed (Green et al., 2011); Job search methods can be formal, e.g., using employment agency services, or informal, e.g., using personal contacts and referrals. Very often, these methods are complementary to each other. The topic of job search has been examined from two broad angles:

            From the academic angle, job search has been studied with economic, behavioural and sociological logic (Green et al., 2011; Zaretsky and Coughlin, 1995; Employment Relations, 1981; Schaffer and Taylor, 2012; Jensen and Jensen, 2006). It has also been examined in relation to employee recruitment, e.g., Tariq et al. (2014). A useful academic literature source on job search is the Journal of Vocational Behavior (Elsevier).

            From the non-academic angle, advice and best practices on job search are formulated and communicated to job seekers. Examples can be found from Youtube.com, e.g., UC Berkeley (2014), Mowlaee (2014) and InstruxionAE (2009). Newspapers also report on job search activities as news from time to time, e.g., Yiu (2011) and Yau (2009).

            Overall, the topic of job search is important for informing public policy formulation on unemployment alleviation and job-seeking, especially for the unemployed and fresh graduates. Figure 1 portrays a macro-framework on job search study that incorporates and organizes the main ideas from the literature in the form of a broad conceptual structure.

Regarding Figure 1, the factor of “Infrastructural support for job search” covers all the contemporary recruitment services available, including the e-recruiting methods that have been examined by Ho (2015). On the factor of “The job search process”, a major theory used is the Theory of Planned Behavior (e.g., Zikic and Saks (2009).). Boswell et al. (2006) is an academic work that deals with considerations related to both factors of “Profile of job seekers” and “Social and economic context” in Figure 1 while Côté et al. (2006) mainly focuses on the consideration of trait affect in “Profile of job seekers” and “Outcomes on job seekers”. Vansteenkiste et al. (2005) is an example of a study associated with “Outcomes on job seekers”, notably on the negative consequence of unemployment due to unsuccessful job search. Again, on the factor of “Outcomes on job seekers”, an example is job attainment (van Hooft et al., 2004). Finally, Green et al. (2011) provides a comprehensive study on job search, including the factors of “Infrastructural support for job search” and “Social policy on employment, unemployment and re-employment”, with particular attention on the supportive roles of the Internet and social networks to job seekers as well as policy context of Active Labour Market Policies.

            Job search literature in the Hong Kong setting, however, is not common, based on the recent Google Scholar search exercise by the writer. Two references in this regard are relevant; they are K.W. Lee’s (2008) B.Sc. thesis on Psychological Consequence of Unemployment among Hong Kong University Graduates and Lai and Chan (2002)’s article on “The effects of job-search motives and coping on psychological health and re-employment: a study of unemployed Hong Kong Chinese”. Nevertheless, the Facebook survey conducted by the writer, to be presented here, was not mainly targeted at university fresh graduates nor the unemployed. Rather, the survey respondents are mainly the writer’s previous and existing tertiary students, especially part-time students who have been working in the industry for quite some time. Some of the survey findings on recruitment have been reported in Ho (2015). In terms of the macro-framework (re: Figure 1), the present survey study primarily covers the “Profile of job seekers” and the “The job search process: existing practices” factors while Ho (2015) mainly examines the “Infrastructural support for job search” factor. The next section is going to present the survey findings on the topic of job-seeking behavior of respondents with diverse profiles.

THE FACEBOOK-BASED QUESTIONNAIRE SURVEY AND ITS MAIN FINDINGS ON RECRUITMENT METHODS USAGE

The Facebook-based survey was conducted by the writer with his Facebook friends from May 25 to 29, 2015. The survey made use of the free-of-charge survey tool offered by Kwiksurveys.com. There were 103 respondents to the survey out of a total 1,565 Facebook friends at the time of the survey. The questionnaire includes 20 questions. They are listed here, together with the basic statistics on them (Ho, 2015):

Question 1: What is your gender?

·       Male: 46 (44.66%)

·       Female: 57 (55.34%)

Standard deviation: 5.5

Responses:                 103

Question 2: What is your age?

·       18 to 27: 2 (1.94%)

·       28 to 37: 43 (41.75%)

·       38 to 47: 47 (45.63%)

·       48 to 57: 11 (10.68%)

·       58 to 67: 0 (0%)

·       68 or above: 0 (0%)

Standard deviation:             20.06

Responses:                 103

Question 3: What is your education background?

·       Not yet a degree-holder: 20 (19.42%)

·       Finished University Undergraduate Degree study: 59 (57.28%)

·       Finished Master Degree study: 23 (22.33%)

·       Finished Ph.D. Degree study (or equivalent): 1 (0.97%)

Standard deviation:             20.97

Responses:                  103

Question 4: What sector does your employer’s organization belong to?

·       Public sector: 18 (17.48%)

·       Private sector: 81 (78.64%)

·       NA: 4 (3.88%)

Standard deviation:             33.49

Responses:                 103

Question 6: What is the main function you are involved in in your organization?

·       Marketing and sales: 18 (17.48%)

·       Accounting and Finance (in-house): 38 (36.89%)

·       Admin and Human Resource (in-house): 5 (4.85%)

·       Production and/or Service Operations Management: 9 (8.74%)

·       IT and Computing (in-house): 13 (12.62%)

·       Design and Research & Development: 1 (0.97%)

·       Others or NA: 19 (18.45%)

Standard deviation:             11.27

Responses:                 103

Question 7: How would you describe your job position in your employer’s organizational hierarchy?

·       Senior management: 15 (14.56%)

·       Middle management: 46 (44.66%)

·       Junior management: 29 (28.16%)

·       Not applicable or no idea: 13 (12.62%)

Standard deviation:             13.22

Responses:                  103

Question 8: Is your organization a local one or a foreign one?

·       It is a local organization: 53 (51.46%)

·       It is a foreign organization: 39 (37.86%)

·       It is complicated: 10 (9.71%)

·       Not applicable or no idea: 1 (0.97%)

Standard deviation:             21.09

Responses:                  103

Question 9: Does your employer organization rely on printed newspapers for employee recruitment for vacancies of job similar to yours?

·       Yes, it strongly relies on them: 6 (5.94%)

·       Yes, it mildly relies on them: 31 (30.69%)

·       It does not rely on them: 55 (54.46%)

·       Not applicable or no idea: 9 (8.91%)

Standard deviation: 19.7

Responses:                  101

Question 10: Does your organization rely on third-party job boards, e.g., recruitment.com.hk, jobsDB.com and ClassifiedPost.com to recruit employees for vacancies of job similar to yours?

·       Yes, it relies on them a lot: 46 (44.66%)

·       Yes, it mildly relies on them: 41 (39.81%)

·       Basically, it does not rely on them: 10 (9.71%)

·       Not applicable/ no idea: 6 (5.83%)

Standard deviation:             17.89

Responses:                 103

Question 11: Does your organization rely on its own website to recruit employees for vacancies of job similar to yours?

·       Yes, it relies on them a lot: 26 (25.24%)

·       Yes, it mildly relies on them: 30 (29.13%)

·       Basically, it does not rely on them: 37 (35.92%)

·       Not applicable/ no idea: 10 (9.71%)

Standard deviation:             9.91

Responses:                 103

Question 12: Does your organization rely on social media platforms, e.g., Facebook.com and Linkedin.com to recruit employees for vacancies of job similar to yours?

·       Yes, it relies on them a lot: 6 (5.83%)

·       Yes, it mildly relies on them: 11 (10.68%)

·       Basically, it does not rely on them: 66 (64.08%)

·       Not applicable/ no idea: 20 (19.42%)

Standard deviation: 23.77

Responses:                  103

Question 13: Does your organization rely on traditional recruitment agencies/ head-hunters to recruit employees for vacancies of job similar to yours?

·       Yes, it relies on them a lot: 32 (31.37%)

·       Yes, it mildly relies on them: 37 (36.27%)

·       Basically, it does not rely on them: 25 (24.51%)

·       Not applicable/ no idea: 8 (7.84%)

Standard deviation: 10.97

Responses:                  102

Question 14: Does your organization rely on existing employees’ referrals to recruit employees for vacancies of job similar to yours?

·       Yes, it relies on them a lot: 31 (30.1%)

·       Yes, it mildly relies on them: 42 (40.78%)

·       Basically, it does not rely on them: 22 (21.36%)

·       Not applicable/ no idea: 8 (7.77%)

Standard deviation: 12.46

Responses:                  103

Question 15: Does your organization rely on social media platforms, directly or indirectly, to do job applicants’ screening for vacancies of job similar to yours?

·       Yes, it relies on them a lot: 5 (4.9%)

·       Yes, it mildly relies on them: 27 (26.47%)

·       Basically, it does not rely on them: 47 (46.08%)

·       Not applicable/ no idea: 23 (22.55%)

Standard deviation:             14.92

Responses:                  102

Question 16: Do you rely on social media platforms for your own job-seeking purpose?

·       Yes, I rely on them a lot: 16 (15.53%)

·       Yes, I mildly rely on them: 37 (35.92%)

·       Basically, I do not rely on them: 41 (39.81%)

·       Not applicable/ no idea: 9 (8.74%)

Standard deviation: 13.55

Responses:                  103

Question 17: Do you rely on potential employers’ organizational websites for your own job-seeking purpose?

·       Yes, I rely on them a lot: 14 (13.86%)

·       Yes, I mildly rely on them: 56 (55.45%)

·       Basically, I do not rely on them: 25 (24.75%)

·       Not applicable/ no idea: 6 (5.94%)

Standard deviation: 18.99

Responses:                   101

Question 18: Do you rely on job boards for your own job-seeking purpose?

·       Yes, I rely on them a lot: 21 (20.39%)

·       Yes, I mildly rely on them: 50 (48.54%)

·       Basically, I do not rely on them: 23 (22.33%)

·       Not applicable/ no idea: 9 (8.74%)

Standard deviation:             14.99

Responses:                  103

Question 19: Do you rely on friends’ referrals for your own job-seeking purpose?

·       Yes, I rely on them a lot: 16 (15.53%)

·       Yes, I mildly rely on them: 52 (50.49%)

·       Basically, I do not rely on them: 31 (30.1%)

·       Not applicable/ no idea: 4 (3.88%)

Standard deviation: 17.92

Responses:                   103

Question 20: How would you describe your job-seeking behaviour?

·       An active job seeker: 40 (39.22%)

·       A passive job seeker: 43 (42.16%)

·       Not a job seeker at all: 11 (10.78%)

·       Not applicable/ no idea/ it is complicated: 8 (7.84%)

Standard deviation: 16.07

Responses:                   102

The basic statistics of the survey can also be found in Ho (2015). Readers interested in Facebook-based questionnaire survey research method are referred to Ho (2014) for a more detailed discussion. The main findings^[1] as related to job seekers and their reliance on recruitment methods are provided as follows:

Finding 1: (re: survey questions 16, 17, 18, 19 and 20): A refined grouping of figures on the job-seeking behavior of respondents with diverse job-seeking orientations is shown in Table 1. [Note: only those who replied with “strongly rely on” are counted in Table 1.]

Table 1

	Active job seekers	Passive job seekers	Non-job seekers
Total number	40	43	11
Strongly rely on social media platforms for job-seeking (question 16)	10 (25%)	3 (7%)	2 (18%)
Strongly rely on employers' websites for job-seeking (question 17)	11 (28%)	2 (5%)	1 (9%)
Strongly rely on job boards for job-seeking (question 18)	13 (33%)	5 (12%)	2 (18%)
Strongly rely on friends' referrals for job-seeking (question 19)	8 (20%)	7 (16%)	1 (9%)

Table 1 indicates that active job seekers make more use of all the recruitment methods for job-seeking than others. That is, their intensity of job search is higher than that of the passive and non-job seekers. Also, the job-seeking behavior of passive job seekers and non-job seekers are largely similar.  Note that the % figures in the table are based on the total figures in the first row of their respective columns. For example, the figure of 25% in the second row of the column of “Active job seekers” is 10/40 = 25%.

Finding 2: (re: survey questions 7, 16, 17, 18 and 19): A refined grouping of figures to study the job-seeking behavior of respondents with different seniority in organizations is presented in Table 2. [Note: only those who replied with “strongly rely on” are counted in Table 2.]

Table 2

	Senior management	Middle management	Junior management
Total number	15	46	29
Rely on social media platforms for own job-seeking (question 16)	2 (13%)	5 (11%)	6 (21%)
Rely on potential employers’ organizational websites for own job-seeking (question 17)	1 (7%)	6 (13%)	6 (21%)
Rely on job boards for own job-seeking (question 18)	4 (27%)	8 (17%)	7 (24%)
Rely on friends’ referrals for own job-seeking (question 19)	6 (40%)	6 (13%)	3 (10%)

Table 2 indicates that respondents who belong to junior management rely on social media platforms and potential employers’ organizational websites more than those in senior and middle management positions. And those in senior management position rely on friends’ referrals for job-seeking much more than those in middle and junior management positions.

Finding 3: (re: survey questions 16, 7, 1, 2, 20 and 3; Formula 1): The finding makes use of Excel’s regression function to conduct a multiple regression analysis on the survey data. In this case, the dependent variable (y1) is “Reliance on social media platforms for own job-seeking” (re: survey question 16). There are five independent variables x1 to x5, which come from survey questions 7, 1, 2, 20 and 3. To conduct the multiple regression analysis, the survey answers from the respondents are converted into scores as follows:

Reliance on social media platforms for own job-seeking (re: survey question 16):

Rely a lot:      3

Rely mildly: 2

Do not rely:   1

Job position in organizational hierarchy (re: survey question 7):

Senior management:           3

Middle management:          2

Junior management:            1

Gender (re: survey question 1):

Male:              1

Female:          0

Age group (re: survey question 2):

18 to 27:          22.5

28 to 37:          32.5

38 to 47:          42.5

48 to 57:          52.5

58 to 67:          62.5

Activeness in job-seeking (re: survey question 20):

An active job seeker:           3

A passive job seeker:           2

No a job seeker:                    1

Education background (re: survey question 3):

Finished Ph.D. Degree study:                    4

Finished Master Degree study:                 3

Finished Undergraduate Degree study:  2

Not yet a degree-holder:                            1

Formula 1

Reliance on social media platforms for own job-seeking (y1) = a + b1 x (x1: job position in organizational hierarchy) + b2 x (x2: gender) + b3 x (x3: age group) + b4 x (x4: activeness in job-seeking) + b5 x (x5: education background)

            From the Excel multiple regression analysis report (re Appendix 3a), the independent variables’ b values can now be incorporated into Formula 1 as follows:

Reliance on social media platforms for own job-seeking (y1) = 2.2760 - 0.2125 x (x1: job position in organizational hierarchy) -0.0177 (x2: gender) + 0.0062(x3: age group) + 0.0465 x (x4: activeness in job-seeking) - 0.2236 x (x5: education background)

Interpretation: The regression formula indicates that job-seekers in more senior job positions, who are male and who possess higher education background slightly rely less on social media platforms for job-seeking than others; also job-seekers who are active in job-seeking and who have a higher age tend to slight rely more on social media platforms for job-seeking than others. As the p-values of all the independent values in Formula 1 are higher than 5%/2 (the critical value), these figures are not able to reject the null hypotheses that all these independent values have a b value of zero (i.e., no positive nor negative correlation with the dependent variable y1).

Formula 2

Reliance on potential employers’ organizational websites for own job-seeking (y1) = a + b1 x (x1: job position in organizational hierarchy) + b2 x (x2: gender) + b3 x (x3: age group) + b4 x (x4: activeness in job-seeking) + b5 x (x5: education background)

Finding 4: (re: survey questions 17, 7, 1, 2, 20 and 3; Formula 2): In this case, the dependent variable (y1) is “Reliance on potential employers’ organizational websites for own job-seeking” (re: survey question 17). There are five independent variables x1 to x5, which come from survey questions 7, 1, 2, 20 and 3, see Formula 2 below. 

            From the Excel multiple regression analysis report (re Appendix 3b), the independent variables’ b values can now be incorporated into Formula 2 as follows:

Reliance on potential employers’ organizational websites for own job-seeking (y2) = 2.0968 – 0.1723 x (x1: job position in organizational hierarchy) – 0.1618 x (x2: gender) + 0.0020 x (x3: age group) + 0.2137 x (x4: activeness in job-seeking) – 0.1612 x (x5: education background)

Interpretation: The correlation pattern between the dependent variable y2 and the independent variables of x1 to x 5 is very similar to that of Formula 1; again, as all the p-values of x1 to x5 are larger than 5%/2 (the critical value), the null hypotheses that the b values of x1 to x5 be zero cannot be rejected.

Formula 3

Reliance on job boards for own job-seeking (y3) = a + b1 x (x1: job position in organizational hierarchy) + b2 x (x2: gender) + b3 x (x3: age group) + b4 x (x4: activeness in job-seeking) + b5 x (x5: education background)

Finding 5: (re: survey questions 18, 7, 1, 2, 20 and 3; Formula 3): The finding applies Excel’s regression function to conduct a multiple regression analysis on the survey data. In this case, the dependent variable (y1) is “Reliance on job boards for own job-seeking” (re: survey question 18). There are five independent variables x1 to x5, which come from survey questions 7, 1, 2, 20 and 3. The conversion exercise on responses into scores is the same as that of Finding 3.

            From the Excel multiple regression analysis report (re Appendix 3c), the independent variables’ b values can now be incorporated into Formula 3 as follows:

Reliance on job boards for own job-seeking (y3) = 1.6073 + 0.0680 x (x1: job position in organizational hierarchy) – 0.0524 x (x2: gender) – 0.0017 x (x3: age group) + 0.1499 x (x4: activeness in job-seeking) + 0.0067 x (x5: education background)

Interpretation: Female and younger respondents slightly rely more on job boards for own job-seeking than others; also, those in senior job positions and more active in job-seeking slightly rely more job boards than others. However, as the p-values of all the independent variables are larger than 5%/2 (the critical value), the null hypotheses that the b values of these independent variables being zero cannot be rejected.

Formula 4

Reliance on friends’ referrals for own job-seeking (y4) = a + b1 x (x1: job position in organizational hierarchy) + b2 x (x2: gender) + b3 x (x3: age group) + b4 x (x4: activeness in job-seeking) + b5 x (x5: education background)

Finding 6: (re: survey questions 19, 7, 1, 2, 20 and 3; Formula 4): The finding makes use of Excel’s regression function to conduct a multiple regression analysis on the survey data. In this case, the dependent variable (y1) is “Reliance on friends’ referrals for own job-seeking” (re: survey question 19).

            From the Excel multiple regression analysis report (re Appendix 3d), the independent variables’ b values can now be incorporated into Formula 4 as follows:

Reliance on friends’ referrals for own job-seeking (y4) = 1.4564 + 0.3486 x (x1: job position in organizational hierarchy) + 0.2899 x (x2: gender) – 0.0110 x (x3: age group) + 0.1477 x (x4: activeness in job-seeking) – 0.1145 x (x5: education background)

Interpretation: respondents who are more senior in organization, are male and are active job seekers slightly rely more on friends’ referrals for job-seeking than others. At the same time, those who have a higher age and higher education background slightly rely less on friends’ referrals than others. For independent variables x2 to x5, the p-values are higher than the critical value of 5%/2, thus the null hypotheses that the b values are zero cannot be refuted. As to the independent variable x1 (job position in organizational hierarchy), its p-value at 0.0042 is lower than 0.025 (5%/2). In this case, the null hypothesis of its b value being zero is rejected.

            The findings from the multiple regression analysis (i.e., findings 3-6 with Formulas 1 to 4) are only able to detect very weak patterns (i.e., small b values and p-values larger than 5%/2) on the correlation between the independent variables and dependent variables for the four multiple regression formulas. At best, these patterns, as revealed in the various b values and p-values, can serve as hypotheses to be examined in future research on job-seeking. On top of that, the sample size of the survey is small and the sample is not random. Thus, the external validity of the survey findings is quite low. Overall, the survey findings did not involve much theory-driven analysis and, based on correlation analysis, these findings are also not capable to establish cause-effect relationship among the variables.

Concluding remarks

As a research topic, job search is not a novel one. Nevertheless, given the inadequate academic literature on job search in the Hong Kong setting, the paper can still claim some useful contribution to this topic by offering some empirical and literature review findings on it. Such academic contribution is, however, confined to the components of “Profile of job seekers” and “The job search process” in terms of the macro-framework on job search study (re: Figure 1). Another thing to note is that, although no influential factors on job seekers’ usage of recruitment methods have been identified in the Facebook-based survey^[2], the findings on these factors offer ideas for formulating hypotheses on job search which can be examined in future research work. Finally, this paper discusses job-seeking behaviors in relation to recruitment methods. It elaborates on some of the ideas raised by Ho (2015) on e-recruitment in the Hong Kong setting, thus can be considered as a follow-up paper on Ho (2015).

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APPENDIX

Appendix 1: exporting survey data to Excel.

Appendix 2: using Excel’s filtering function to analyze survey data.

Appendix 3a: the Excel multiple regression analysis report for Formula 1: Reliance on social media platforms for own job-seeking (y1).

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.288794343
R Square	0.083402173
Adjusted R Square	0.020621499
Standard Error	0.716942695
Observations	79
ANOVA
	df	SS	MS	F
Regression	5	3.414210455	0.68284209	1.328468912
Residual	73	37.52249841	0.51400683
Total	78	40.93670886
	Coefficients	Standard Error	t Stat	P-value
Intercept	2.275997513	0.582030227	3.91044556	0.000204392
Position in organizational hierarchy	-0.212497551	0.12825886	-1.6567865	0.101854902
Gender	-0.017703833	0.174335312	-0.1015505	0.919391911
Age group	0.006184707	0.012052317	0.51315497	0.609392443
Activeness in job-seeking	0.046478067	0.125205115	0.3712154	0.711552285
Education background	-0.223648479	0.136029416	-1.6441185	0.104451908

Appendix 3b: the Excel multiple regression analysis report for Formula 2: Reliance on potential employers’ own websites for own job-seeking (y2).

Standard Error	0.598881214
Observations	79
ANOVA
	df	SS	MS	F
Regression	5	4.501458566	0.900292	2.510162701
Residual	73	26.18208574	0.358659
Total	78	30.6835443
	Coefficients	Standard Error	t Stat	P-value
Intercept	2.096776318	0.484289605	4.329592	4.6813E-05
Job position in organizational hierarchy	-0.172350071	0.102960691	-1.67394	0.098422134
Gender	-0.161786147	0.147367992	-1.09784	0.275883317
Age group	0.002045723	0.010401332	0.196679	0.844625361
Activeness in job-seeking	0.213676906	0.104698214	2.040884	0.044878662
Educational background	-0.161185792	0.109661496	-1.46985	0.145899685

Appendix 3c: the Excel multiple regression analysis report for Formula 3: Reliance on job boards for own job-seeking (y3).

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.157521893
R Square	0.024813147
Adjusted R Square	-0.041980473
Standard Error	0.693479679
Observations	79
ANOVA
	df	SS	MS	F
Regression	5	0.89327328	0.178655	0.371489771
Residual	73	35.10672672	0.480914
Total	78	36
	Coefficients	Standard Error	t Stat	P-value
Intercept	1.607313681	0.568289558	2.828336	0.006036343
Job position in organizational hierarchy	0.068011297	0.128376637	0.529779	0.597872521
Gender	-0.052447336	0.173690672	-0.30196	0.763542471
Age group	-0.00168464	0.012169398	-0.13843	0.89027982
Activeness in job-seeking	0.149850336	0.121762822	1.230674	0.22239555
Educational background	0.006716574	0.127053188	0.052864	0.957984456

Appendix 3d: the Excel multiple regression analysis report for Formula 4: Reliance on friends’ referrals for own job-seeking (y4).

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.410327473
R Square	0.168368635
Adjusted R Square	0.112926544
Standard Error	0.642338441
Observations	81
ANOVA
	df	SS	MS	F
Regression	5	6.264976124	1.2529952	3.036837753
Residual	75	30.94490042	0.4125987
Total	80	37.20987654
	Coefficients	Standard Error	t Stat	P-value
Intercept	1.456419567	0.473822076	3.0737689	0.002946832
Position in organizational hierarchy	0.345856348	0.117168345	2.95179	0.004215086
Gender	0.289855911	0.15741217	1.8413818	0.069519731
Age group	-0.010953952	0.009850821	-1.1119836	0.269696966
Activeness in job-seeking	0.147734684	0.109789038	1.3456233	0.182478056
Educational background	-0.114531466	0.115192381	-0.9942625	0.32329212

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[1] The main findings are arrived at by using Excel’s data-filtering function and regression function on the survey data; see also Appendices 1 and 2.

[2] Failure to identify influential factors on job seekers’ usage of recruitment methods from the survey is nevertheless still a finding.