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GUIDED BY: PREPARED BY:
Mr. P.P. Varma Rima Chaturvedi
DGM ( NW- Pers.) SSMV JUNWANI, BHILAI
 My primary attention and focus is directed towards the
HRIS users working in various departments of Bhilai
Steel Plant.
HUMAN PROFILE OF BSP
More than machinery and processes, it is the men ( in
number 32000 approx.) i.e. the engineers, technicians,
skilled and unskilled workers behind them that
constitute the flesh and blood of this plant.

1.) To understand the HRIS of BSP.
2.) To identify and evaluate the reasons of
establishment of HRIS in BSP.
3.) To suggest recommendations and strategies for
BSP regarding its HRIS.
RESEARCH PLAN
Research Design Descriptive
Research Method Survey
Survey Instrument Questionnaire
Sample Unit HRIS Users of BSP
Sampling plan Judgement
Sample Size 33
Male respondents 24
Female respondents 9
Total age-groups 4
HYPOTHESIS FORMULATION
H0: Human Resource Information System (HRIS) is well
implemented in BSP.
For analysis of collected data SPSS is used. K-cluster
means and ANOVA tables are used for interpretation of
analysed data.
4 age-groups considered are 21-30, 31-40, 41-50 & 51-
60.
X1 to X12 factors are: User friendly, compatible, reliable,
efficient, secure, flexible, maintenance, clarity,
consistency, stability, accuracy & validity respectively.
51-60 AGE GROUP WHICH ARE 41-50 & 31-40 AGE GROUPS
MALES STRONGLY AGREE WITH WHICH ARE MALES ARE
ALL THE FACTORS OF HRIS. Final Cluster Centers AGREED & STRONGLY
Clusters AGREED.
 1 2 3
 X1 1 2 1
 X2 1 2 1
 X3 1 2 1
 X4 1 2 1
 X5 1 2 1
 X6 2 2 1
 X7 1 2 1
 X8 1 2 1
 X9 1 2 1
 X10 2 2 1
 X11 1 2 1
 X12 1 2 1
 G 2 2 2
 A 4 3 2
 ANOVA

Mean Square df Mean Square df F Sig.
 X1 1.628 2 .154 30 10.568 .000
 X2 2.263 2 .104 30 21.818 .000
 X3 1.374 2 .141 30 9.761 .001
 X4 1.273 2 .133 30 9.545 .001
 X5 2.222 2 .096 30 23.077 .000
 X6 2.255 2 .258 30 8.746 .001
 X7 3.495 2 .096 30 36.294 .000
 X8 2.828 2 .074 30 38.182 .000
 X9 3.040 2 .030 30 102.614 .000
 X10 2.919 2 .074 30 39.409 .000
 X11 3.040 2 .030 30 102.614 .000
 X12 3.040 2 .030 30 102.614 .000
 G .251 2 .201 30 1.243 .303
 A 10.051 2 .348 30 28.868 .000
 The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize
the differences among cases in different clusters. The observed significance levels are not corrected for this
and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal. GENDER IS
INSIGNIFICANT WHILE REST OF THE FACTORS ARE FOUND TO BE SIGNIFICANT.
1. Gender does not affect the use of HRIS in BSP.
2. Age groups and all the factors X1 to X12 are found to be
significant after using K cluster analysis of variance.
3. There are 3 major age clusters mainly (31-40), (41-50) &
(51-60).
4. Major age cluster is 51-60.
5. According to the representative age group, HRIS is very
much used in BSP and it is being used for more than 5
years.
6. Users adopt changes in HRIS very quickly. Moderate
training is required by the user to be an expert of HRIS.
7. Overall implementation of HRIS is found to be

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Major project hr

  • 1. GUIDED BY: PREPARED BY: Mr. P.P. Varma Rima Chaturvedi DGM ( NW- Pers.) SSMV JUNWANI, BHILAI
  • 2.  My primary attention and focus is directed towards the HRIS users working in various departments of Bhilai Steel Plant. HUMAN PROFILE OF BSP More than machinery and processes, it is the men ( in number 32000 approx.) i.e. the engineers, technicians, skilled and unskilled workers behind them that constitute the flesh and blood of this plant. 
  • 3. 1.) To understand the HRIS of BSP. 2.) To identify and evaluate the reasons of establishment of HRIS in BSP. 3.) To suggest recommendations and strategies for BSP regarding its HRIS.
  • 4. RESEARCH PLAN Research Design Descriptive Research Method Survey Survey Instrument Questionnaire Sample Unit HRIS Users of BSP Sampling plan Judgement Sample Size 33 Male respondents 24 Female respondents 9 Total age-groups 4
  • 5. HYPOTHESIS FORMULATION H0: Human Resource Information System (HRIS) is well implemented in BSP. For analysis of collected data SPSS is used. K-cluster means and ANOVA tables are used for interpretation of analysed data. 4 age-groups considered are 21-30, 31-40, 41-50 & 51- 60. X1 to X12 factors are: User friendly, compatible, reliable, efficient, secure, flexible, maintenance, clarity, consistency, stability, accuracy & validity respectively.
  • 6. 51-60 AGE GROUP WHICH ARE 41-50 & 31-40 AGE GROUPS MALES STRONGLY AGREE WITH WHICH ARE MALES ARE ALL THE FACTORS OF HRIS. Final Cluster Centers AGREED & STRONGLY Clusters AGREED.  1 2 3  X1 1 2 1  X2 1 2 1  X3 1 2 1  X4 1 2 1  X5 1 2 1  X6 2 2 1  X7 1 2 1  X8 1 2 1  X9 1 2 1  X10 2 2 1  X11 1 2 1  X12 1 2 1  G 2 2 2  A 4 3 2
  • 7.  ANOVA  Mean Square df Mean Square df F Sig.  X1 1.628 2 .154 30 10.568 .000  X2 2.263 2 .104 30 21.818 .000  X3 1.374 2 .141 30 9.761 .001  X4 1.273 2 .133 30 9.545 .001  X5 2.222 2 .096 30 23.077 .000  X6 2.255 2 .258 30 8.746 .001  X7 3.495 2 .096 30 36.294 .000  X8 2.828 2 .074 30 38.182 .000  X9 3.040 2 .030 30 102.614 .000  X10 2.919 2 .074 30 39.409 .000  X11 3.040 2 .030 30 102.614 .000  X12 3.040 2 .030 30 102.614 .000  G .251 2 .201 30 1.243 .303  A 10.051 2 .348 30 28.868 .000  The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal. GENDER IS INSIGNIFICANT WHILE REST OF THE FACTORS ARE FOUND TO BE SIGNIFICANT.
  • 8. 1. Gender does not affect the use of HRIS in BSP. 2. Age groups and all the factors X1 to X12 are found to be significant after using K cluster analysis of variance. 3. There are 3 major age clusters mainly (31-40), (41-50) & (51-60). 4. Major age cluster is 51-60. 5. According to the representative age group, HRIS is very much used in BSP and it is being used for more than 5 years. 6. Users adopt changes in HRIS very quickly. Moderate training is required by the user to be an expert of HRIS. 7. Overall implementation of HRIS is found to be