Proceedings of the 12th European Conference on Knowledge ...

Thi Hai Hang Nguyen et al
The variable “harmfulness level <strong>of</strong> high staff turnover” (H1) was measured by a single question. The
variable “effectiveness <strong>of</strong> record system” was measured by average <strong>of</strong> two related questions basing
on <strong>the</strong> usage <strong>of</strong> job-related reports and <strong>the</strong> consulting sources for management in <strong>the</strong> firm. O<strong>the</strong>r
variables were measured by different constructs namely (1) “Knowledge sharing level <strong>of</strong> working
environment” (9 items adopted from Yang (2007), (2) “training effectiveness” (5 items); those are
needed to check <strong>the</strong> reliability. The full questionnaire is available from <strong>the</strong> contact author.
4.4 Contact method& sample size
In January 2010, <strong>the</strong> questionnaire were delivered by post and via emails to more than 500
companies randomly selected in <strong>the</strong> Yellow Pages <strong>of</strong> Ho Chi Minh City, but more than 50% <strong>of</strong> emails
failed to reach <strong>the</strong>ir destination (i.e. incorrect email addresses) and <strong>the</strong> general response rate was too
low. The researchers <strong>the</strong>n proceeded to carry out <strong>the</strong> survey via available networks to contact
CEOs/business owners <strong>of</strong> SMEs. In this second round, <strong>the</strong> questionnaires were sent to more than
300 CEOs/business owners, and <strong>the</strong> response rate was quite positive – 87 responses were received
(~29%). Of <strong>the</strong>se, 71 sets <strong>of</strong> data met requirements for analysis. With respondent permission, 25
visits to companies were made to obtain a better view <strong>of</strong> what was really going on inside those firms.
4.5 Data analysis methods
A fuzzy method and a chi-square test were employed to analyze collected data. Linear Discriminant
Analysis (LDA) was also used to classify <strong>the</strong> classes <strong>of</strong> firms in this research. While <strong>the</strong> chi-square
test and LDA are quite popular in economic and management research, fuzzy sets are still not used
<strong>of</strong>ten despite <strong>of</strong> <strong>the</strong>ir advantages such as providing a more nuanced representation <strong>of</strong> categorical
concepts by permitting degrees <strong>of</strong> membership in sets ra<strong>the</strong>r than binary in-or-out membership, and
<strong>the</strong> possibility <strong>of</strong> addressing both diversity and ambiguity in a systematic manner (Ragin and
Pennings, 2005). Most <strong>of</strong> <strong>the</strong> primary data collected from <strong>the</strong> survey in this research is categorical,
and somehow presents imprecise concepts such as <strong>the</strong> level <strong>of</strong> administrative management, or
sharing culture and working environment evaluation, etc. The rules to assess <strong>the</strong> criteria with
imprecise dependencies among variables in <strong>the</strong> research are fuzzy. Thus, <strong>the</strong> fuzzy method was
selected for this study.
5. Survey findings
5.1 Sample description
Primary data was obtained from 71 sets <strong>of</strong> responses. All firms in this study are ranked as SMEs
according to <strong>the</strong> Vietnamese Government <strong>of</strong>ficial definition. The industry types <strong>of</strong> SMEs are described
in table 1. The average age among firms in <strong>the</strong> sample is 6.3 years old. 58 out <strong>of</strong> 71 firms are locally
owned firms (81.6%). Eleven firms are owned and operated by foreigners. And only two <strong>of</strong> <strong>the</strong> 71
firms are state-owned.
Industry types Number <strong>of</strong> firms Percentage
Service 54 79.2%
Construction & Real Estate 06 8.5%
IT 06 8.5%
Trading 05 7.0%
Forwarding 16 22.5%
Tourist agency 12 16.9%
Consultancy 02 2.8%
O<strong>the</strong>r 07 9.9%
Manufacturing or Mixed 17 23.8%
Garment& textile 04 5.6%
Furniture & handicraft 03 4.2%
Construction materials 04 5.6%
Food processing 03 4.2%
O<strong>the</strong>r 03 4.2%
Table 1: Description <strong>of</strong> SMEs in <strong>the</strong> sample
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