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Factors Motivating Business Students To Move Commerce Essay

Today, employees still act in accordance with employers’ legal obligations and try not to attract special attention from local independent business owners. This paper's main aim is to take a fresh look into graduates’ expectation on the factors motivating employees when considering switching jobs if employed and to identify the level of interest as they commence and develop entrepreneurial ventures. The study principally examined why employees decide to become entrepreneurs, therefore, seven main issues associated with graduates' enterprises as they established, operated and grew their businesses, these being chosen following a process of brainstorming with a pilot group of graduate entrepreneurs.

Review of Literature

Overview

A number of researchers have attempted to consider factors such as gender, grade point average, duration and field of study and entrepreneurial family background as important factors affecting students’ perception and attitudes towards the prospect of new own business formation, and some of these factors clearly enhance or inhabit such tendency (Oakey, Mukhtar and Kipling, 2002). However, Cooper, Woo, and Dunkelberg (1989) suggested that the various objectives identified by other researchers can be reduced to three factors: challenge, wealth and autonomy. Using cluster analysis Woo, Cooper, and Dunkelberg (1991) identified two types of entrepreneurs depending upon their purposes at the time of commencing the business: firstly "self-regulating" who placed high priority on not having to work for others, and secondly, “company-men." who builds the organization.

Motivation

Generation of start up ideas have been explored by a number of researchers. Opportunity recognition is dependent on whether the entrepreneur was extrinsically stimulated. A leading entrepreneurship text has recognised the “important implications for entrepreneurs who need to be creative in their thinking” and of the concept that creativity can be learned or enhanced (Timmons & Spinelli, 2008). The three types of opportunities identification to the field of entrepreneurship as established by Sarasvathy, Dew, Velamuri, and Venkataraman (2003) are recognized, discovered and created. There are various motives to start a new venture. According to Amit, McCrimmon, Zietsma and Oesch (2001), money is important but not necessarily most important. They argue that some of the key non-monetary motives for starting up a business include the wish to be independent and the combination of work and household responsibilities. These start-up motives may have important consequences for the degree of (over)optimism that characterizes (promising) entrepreneurs. For example, if an entrepreneur is mainly driven by wealth creation, it may be expected that (s)he is more likely to be disappointed if the turnover in the first year is relatively low. If the entrepreneur is driven by the wish to be independent, (s)he may be unpleasantly surprised by the strong reliance upon a limited number of clients or the bank. If the primary start-up motive is exploiting a perceived opportunity, the entrepreneur may be faced with other people who came up with the same idea or possibly an overestimated market demand for the (new) product.

Gilad and Levine (1986), agreed in their analysis on intrinsic and extrinsic that there are discrimination between start-up motives. Intrinsic motives include the desire for independence and combining work with care for family members. Entrepreneurs who are driven by such motives will probably be less inclined to set unrealistically high pecuniary goals. Extrinsic motives include two categories: pull and push factors. An opportunity of perceived profit is an important pull factor of entrepreneurship, while (the threat of) unemployment is a well-known push factor. Regarding the exploitation of opportunities, Hayward, Shepherd and Griffin (2006) argue that overoptimistic founders will commit too many resources to the opportunities that are the bases of their ventures. If entrepreneurs are ‘blinded’ by their own ideas and fail to adequately assess the competition and the (potential) problems to transform the opportunity into a profitable venture, over optimism is around the corner.

The creation of a new organization, however, is contingent upon the belief that self-employment promises more expected utility than either employment within an existing organization or unemployment (Douglas & Shepherd, 2000; Van Praag & Cramer, 2001).

Methodology

Gartner (1989) proposed that a common limitation of studies into the predictors of entrepreneurial intentions is the failure of investigators to choose samples that are (1) comprised solely of people who are serious about entrepreneurship and (2) who are in the process of making the decision to become involved in creating a new business.

Krueger, Reilly and Carsrud (2000) find that studies comprising samples of upper-division college students can uncover job-related preferences at a time when respondents are struggling with important career decisions. Therefore, it is acceptable and appropriate to investigate entrepreneurial intent utilizing a sample of upper-class college students. (Brice and Nelson, 2008), it is important to note that the population of interest in their study consists of individuals who perceive that they will become entrepreneurs and not necessarily only those who will actually become entrepreneurs. This difference is significant because while actions has been demonstrated to be predicted by intentions. Therefore, the focus of this research remains at the entrepreneurial intentions level of analysis.

The sample chosen consists of postgraduate and undergraduate business degree program students who were nearing graduation. When students contemplate graduation, they may also develop immediate career plans and long-range goals. The respondents are those from the business disciplines because, based on their discipline interest, they have already decided to pursue business-related careers. For that reason, a homogeneous sampling of university college students was included in this study.

In this study, we follow the method tested by Brice and Nelson. This study sample consisted of 200 students from University Colleges in Malaysia who participated utilizing a structured questionnaire data collection methodology. Subjects consisted of final (3rd) year business undergraduates and final year Master of Business Administration (MBA) students in the concentrations of management.

They were appropriate primarily because their academic concentration implied that they had serious interest in pursuing a business career. The main themes covered by the survey questions include firm and owner characteristics; interest to start-up; motivation to switch jobs; career preferred timing and industry; medium for seeking employment: desire and likelihood of rewards and opportunity; criteria of choosing employers. The two researchers’ contacted students directly via targeted groups of respondents list originating from the Faculty of their academic major program.

Five questions adapted from Chen, Greene, and Crick (1998) was used to assess start up intentions. Responses were gathered on a 5-point Likert scale and total scale score was obtained by averaging the five questions. Brice and Nelson have reported a Cronbach's alpha of 0.92 for this scale, which implies strong reliability. Information pertaining to each respondent's age, gender, and class was obtained to use as control variables in the analysis. Each of these control variables was recorded as non-continuous, categorical predictors.

No. Description

1. Employment with established firms associated with independent business start-ups (IBS) EMP → BS

2. Motivations to switch job if employed in established firms associated with independent JMOT→BS

business start-ups (IBS)

Table 1: Hypotheses

Analysis and Results

Once all the related information from the respondents was entirely obtained, the students' motives leading to start-up were analyzed. From the mean of all motivation constructs, it could be argued that the main motive for start-up is the need for achievement (average value = 14.3), followed by economic reasons (mean value = 12.89) and the need for independence (average value = 12.89). In order to establish instrument reliability, Cronbach’s coefficient alpha was computed. The reliability coefficient was 0.71 which indicates that the instrument was reliable in its measurement of determinants for start ups. Data reduction technique is used to unfold the information embedded in our data.

Hypothesis 1: Intention for independent business start-ups is higher than joining established firms

Variable

Mean

Std Deviation

Parameters

Respondent

Frequency

Percent

Gender

1.4541

0.49916

male

107

54.6

female

89

45.4

Total

196

100

Expected Salary

3.0765

0.99705

Less than RM 2000

2

1.0

Rm2001 - 5000

65

33.2

Rm5001 – Rm10000

64

32.7

Rm10001 – Rm15000

46

23.5

More than Rm15000

19

9.7

Total

196

100

Age

4.5969

0.62903

Above 51yrs

1

0.5

31 to 40yrs old

9

4.6

26 to 30yrs old

57

29.1

Below 25yrs

129

65.8

Total

196

100

Table 2: Demographics

After elimination of subjects with survey questionnaires were only partially completed, the final sample totalled 196 students. As shown in Table 2, this sample was equally represented between the genders, consisting of 107 (54.6%) males and 89 (45.4%) females. Subjects were primarily graduating undergraduate business students (65.8%) and graduating postgraduate students (34.2%). In fact, there were 129 bachelor degree students who aged below 25 years than MBA students who aged 26 and above. The majority of subjects were expecting salary between RM5001- RM15000 (56.2%) which is not in accordance or earnable with employment even in established firms.

Statistic

Std. Error

Sig.

N Valid

Missing

Mean

196

0

2.08

.078

5% Trimmed Mean

2.01

Median

2.00

Variance

1.183

Mode

1

Std. Deviation

1.088

Test of Normality (Kolmogorov-Smirnov)

0.233

0.000

Table 3: Statistics for Exploring Entrepreneurial Sector

Since the mean, median and mode values are very close to each other, it shows the data is symmetrical. The mean for the 196 students is 2.08 with a standard deviation of 1.088. The Trimmed mean value of 2.01 is similar to the mean above. Hence, shows there are no outliers in the data set. In this survey, since the sample size is 196, the Kolmogorov-Smirnov test is used. The p-value of the test is less than 0.001. Hence, the data is not distributed normal.

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Very Keen

77

39.3

39.3

39.3

Keen

52

26.5

26.5

65.8

I'm open to any opportunity

46

23.5

23.5

89.3

May consider it

16

8.2

8.2

97.4

Not interested at all

5

2.6

2.6

100.0

Total

196

100.0

100.0

Table 3.1: Intention of exploring into entrepreneurial sector

Of the 196 students, 77 (39.3%) very enthusiastic towards start-ups, 52 (26.5%) eager to start-up, 46 (23.5%) open to any opportunity, and 16 (8.2%) to consider start-up option. Out of total, 5 (2.6%) prefer employment.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.819

Bartlett's Test of Sphericity

Approx. Chi-Square

383.666

df

15

Sig.

.000

Table 4: KMO and Bartlett's Test

Since the correlation value is within 0.5 to 0.8, start-up intention among degree students is said to correlate “adequately” with at least one other variables in the construct. In this survey, the KMO value is 0.819, which is considered good.

Bartlett's test of sphericity is used to analyse whether the correlation matrix is an identity matrix. Identity matrix can be ruled out if the p-value of the test is less than 0.05 (Karuthan and Krishna, 2009). In this model, since the p-value is less than 0.001, the researcher proceeds with factor analysis.

Since the researcher wanted to study the underlying construct among the six variables: Curiosity, Interest, Consideration, Preparation, Setting Up and Start-up Timing. This is a single underlying concept; therefore, it is called the “Start-up Intent Structure”. Since the “Start-up Intent Structure” varies from person to person, it is a variable too. However, it cannot be measured by physical means. Hence, it is called a latent variable or just factor. The model for “Start-up Intent Structure” is given in Figure 4.

L1

L6

L5

L4

L2

L3

Setting Up

Preparation

Consideration

Start-up Timing

Interest

Curiosity

Figure 4: Factor model for “Start-up Intent structure”

In Figure 4, one can visualize six simultaneous regression functions: Curiosity, Interest, Consideration, Preparation, Setting Up and Start-up Timing as the dependents and “Start-up Intent Structure” as the independent.

Curiosity = L1 × “Start-up Intent Structure” + e1

Interest = L2 × “Start-up Intent Structure” + e2

Consideration = L3 × “Start-up Intent Structure” + e3

Preparation = L4 × “Start-up Intent Structure” + e4

Setting Up = L5 × “Start-up Intent Structure” + e5,

Start-up Timing = L6 × “Start-up Intent Structure” + e6

where Li’s are called the factor loadings and ei’s are the error terms.

Component

Initial Eigenvalues

Extraction Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

3.109

51.811

51.811

3.109

51.811

51.811

2

1.019

16.981

68.792

1.019

16.981

68.792

3

.579

9.650

78.442

4

.573

9.555

87.997

5

.382

6.372

94.369

6

.338

5.631

100.000

Extraction Method: Principal Component Analysis.

Table 4.1: Total Variance Explained

In the table above, since there are 6 variables in this analysis, 6 components (or factors) are listed in the first column. The respective eigen values and percent of variance explained are provided in the next two columns. For Factor 1, the eigen value is 3.109 and the variance is 51.811% of the total variance. For factor 3, 4,5 and 6 the eigen value is less than the default value of 1. In the same table, under “Extraction Sums of Squared Loadings”, only two factors are listed, corresponding to the factors for which the eigen values is more than 1. Based on the cumulative % column, these factors explain 68.792% of the total variance in the 6 original variables. According to Karuthan and Krishna, (2009) established that, in social sciences, at least 50% of the total variance in the variables in analysis must be explained by the factor of factors. In this survey, a single factor extracted explains more than 50% of the total variance in the original variables.

Hypothesis 2: Motivations to switch job if employed in established firms associated with independent business start-ups

We use multivariate data analysis to recognize the association between different motivation variables. The analysis has shown the variables in a scatter plot and quantifying the strength of association using correlation analysis. An association is established, both empirically and theoretically, therefore we pursued to obtain a regression model. This model, used to predict the value on entrepreneurial intention (outcome), given the values on the motivations to switch job if employed variables (predictors).

Motivation for switching Job

N

Mean

Std. Deviation

Std. Error

95% Confidence Interval for Mean

Mean Rank

Lower Bound

Upper Bound

Looking to move up my career

1

1.00

.

.

.

.

34.00

Escape from office environment

48

1.85

.799

.115

1.62

2.09

82.20

Looking for challenges

61

2.52

1.120

.143

2.24

2.81

110.89

Join a established Organization

33

2.24

1.200

.209

1.82

2.67

97.58

More money

39

1.64

.873

.140

1.36

1.92

68.96

Better work life balance

4

2.50

1.732

.866

-.26

5.26

.

Not considering to Switch

6

2.00

1.549

.632

.37

3.63

.

No value

4

1.00

.000

.000

1.00

1.00

.

Total

196

2.08

1.088

.078

1.93

2.23

Table 5: Descriptive of Motivations for Switching Job

Table 6, the p-value for the Levene’s test for equality of variance is 0.000, which is less than 0.05. Thus, equality of variances is not assumed.

Exploring Entrepreneurial sector

Levene Statistic

df1

df2

Sig.

7.347a

6

188

.000

a. Groups with only one case are ignored in computing the test of homogeneity of variance for Exploring Entrepreneurial sector.

Table 6: Test of Homogeneity of Variances

Exploring Entrepreneurial sector

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

29.467

7

4.210

3.933

.000

Within Groups

201.227

188

1.070

Total

230.694

195

Table 7: One-way ANOVA

Table 7 depicts that the F-value is 3.933 and the degrees of freedoms are 7 and 188. The p-value of the test is 0.000, which is less than 0.05. The eta-squared, h2 =0.128, which is less than 0.15. In this case, the researcher has performed nonparametric test.

Test Statisticsa,b

Exploring Entrepreneurial sector

Chi-Square

20.348

df

4

Asymp. Sig.

.000

a. Kruskal Wallis Test

b. Grouping Variable: Motivation for switching Job

Ranks

Exploring Entrepreneurial sector

N

Mean Rank

Motivation for switching Job

Very Keen

77

109.37

Keen

52

77.53

I'm Open to any opportunity

46

83.51

May consider it

16

127.59

Total

191

Test Statisticsa,b

Motivation for switching Job

Chi-Square

18.920

df

3

Asymp. Sig.

.000

a. Kruskal Wallis Test

b. Grouping Variable: Exploring Entrepreneurial sector

Table 4. Scale unidimensionality test

Number of

Construct Item Chronbach efficient Factor

Number Alpha Factors Variance

Success 4 .7927 1 61.8

Self-rule 3 .7373 1 65.6

Monetary 4 .7644 1 59.0

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