Employers’ Perception on the Antecedents of Graduate Employability for the Information Technology Sector

Sehgal, Nidhi; Nasim, Saboohi

doi:10.1007/978-981-13-9640-3_5

Nidhi Sehgal¹² &
Saboohi Nasim¹³

Part of the book series: Flexible Systems Management ((FLEXSYS))

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Abstract

This chapter aims to analyze the perceptions of the employers in the Information Technology (IT) sector in India on the antecedents of graduate employability. With an increased emphasis on organizational flexibility in today’s volatile and complex business environment, the employability of the workforce has gained crucial significance. Flexibility has been acknowledged as a predictor of organizational performance (Sushil, Global J Flex Syst Manag 16(4):309–311, 2015) and its strategic driver (Sharma et al., Global J Flex Syst Manag 11(3):51–68, 2010). Flexible strategies and business plan often demand the need to scale up the quality of manpower or shift the required skill set to swiftly adapt to the market changes accordingly. This flexibility is not confined to the quantity of manpower only but also encompasses the quality of skills deployed by the manpower (Srivastava, Global J Flex Syst Manag 17(1):105–108, 2016). Therefore, it is imperative for the potential job seekers to understand and continuously adapt to the changing knowledge and skill requirements of the employers to develop and maintain their employability. The employers in this dynamic sector demand a range of knowledge, skills, and other attributes from potential job seekers. However, the graduates passing out of higher education institutions fail to meet these expectations of the employers. Therefore, the sector is struggling with the challenges of talent crunch and qualitative demand–supply mismatch of manpower. The identification of factors that influence graduate employability is based on literature review. This chapter is empirical and examines the perceptions of the employers on the factors that impact employability and validates the association between the research constructs. Opinion surveys are used to elicit responses from a sample of 236 respondents, i.e., technical/HR personnel at the middle-level/upper middle-level management positions spanning across 71 reputed IT companies in India. These respondents are actively involved in the staffing of graduates seeking technical jobs in IT sector. The perception of these employers has been investigated using bivariate and multivariate analysis techniques. The key insights drawn in this chapter enable potential job seekers to clearly understand the employer demands in the IT sector and equip themselves with the required knowledge and skills. This also contributes to enhancing the manpower flexibility in organizations. The chapter has significant implications for the policy-makers and key stakeholders to bridge the employability gap in this sector.

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Authors and Affiliations

Zayed University, Dubai, United Arab Emirates
Nidhi Sehgal
Faculty of Management Studies and Research, Aligarh Muslim University, Aligarh, India
Saboohi Nasim

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Nidhi Sehgal
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Saboohi Nasim
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Correspondence to Nidhi Sehgal .

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Delhi School of Management, Delhi Technological University, New Delhi, India
P.K. Suri
Delhi School of Management, Delhi Technological University, New Delhi, India
Rajan Yadav

Appendices

1.1 Annexure I: Results of Correlation Analysis

Correlations
		AVGPISCS	AVGPISPC	AVGPISCT	AVGPISTI	AVGTSTM	AVGTSTK	AVGOK	AVGE	AVGPIS	AVGTS
AVGPISCS	Pearson Correlation	1	0.683^**	0.728^**	0.773^**	0.727^**	0.722^**	0.721^**	0.735^**	0.891^**	0.761^**
	Sig. (2-tailed)		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	236	236	236	236	236	236	236	236	236	236
AVGPISPC	Pearson Correlation	0.683^**	1	0.808^**	0.814^**	0.800^**	0.770^**	0.775^**	0.775^**	0.884^**	0.824^**
	Sig. (2-tailed)	0.000		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	236	236	236	236	236	236	236	236	236	236
AVGPISCT	Pearson Correlation	0.728^**	0.808^**	1	0.829^**	0.852^**	0.813^**	0.737^**	0.766^**	0.917^**	0.874^**
	Sig. (2-tailed)	0.000	0.000		0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	236	236	236	236	236	236	236	236	236	236
AVGPISTI	Pearson Correlation	0.773^**	0.814^**	0.829^**	1	0.819^**	0.795^**	0.772^**	0.795^**	0.945^**	0.846^**
	Sig. (2-tailed)	0.000	0.000	0.000		0.000	0.000	0.000	0.000	0.000	0.000
	N	236	236	236	236	236	236	236	236	236	236
AVGTSTM	Pearson Correlation	0.727^**	0.800^**	0.852^**	0.819^**	1	0.817^**	0.760^**	0.795^**	0.874^**	0.955^**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000		0.000	0.000	0.000	0.000	0.000
	N	236	236	236	236	236	236	236	236	236	236
AVGTSTK	Pearson Correlation	0.722^**	0.770^**	0.813^**	0.795^**	0.817^**	1	0.796^**	0.805^**	0.848^**	0.951^**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000		0.000	0.000	0.000	0.000
	N	236	236	236	236	236	236	236	236	236	236
AVGOK	Pearson Correlation	0.721^**	0.775^**	0.737^**	0.772^**	0.760^**	0.796^**	1	0.785^**	0.822^**	0.816^**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000		0.000	0.000	0.000
	N	236	236	236	236	236	236	236	236	236	236
AVGE	Pearson Correlation	0.735^**	0.775^**	0.766^**	0.795^**	0.795^**	0.805^**	0.785^**	1	0.841^**	0.839^**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.000		0.000	0.000
	N	236	236	236	236	236	236	236	236	236	236
AVGPIS	Pearson Correlation	0.891^**	0.884^**	0.917^**	0.945^**	0.874^**	0.848^**	0.822^**	0.841^**	1	0.903^**
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000		0.000
	N	236	236	236	236	236	236	236	236	236	236
AVGTS	Pearson Correlation	0.761^**	0.824^**	0.874^**	0.846^**	0.955^**	0.951^**	0.816^**	0.839^**	0.903^**	1
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	N	236	236	236	236	236	236	236	236	236	236

**Correlation is significant at the 0.01 level (2-tailed)

1.2 Annexure II: Results of Regression Analysis

1.2.1 Regression Analysis of Macro Variables

Model summary
Model	R	R-square	Adjusted R-square	Std. error of the estimate
1	0.841^a	0.708	0.706	0.49960
2	0.861^b	0.742	0.740	0.47028
3	0.869^c	0.755	0.752	0.45929

^aPredictors: (Constant), AVGPIS
^bPredictors: (Constant), AVGPIS, AVGTS
^cPredictors: (Constant), AVGPIS, AVGTS, AVGOK

ANOVA^d
Model		Sum of squares	df	Mean square	F	Sig.
1	Regression	141.333	1	141.333	566.225	0.000^a
	Residual	58.408	234	0.250
	Total	199.740	235
2	Regression	148.210	2	74.105	335.075	0.000^b
	Residual	51.530	233	0.221
	Total	199.740	235
3	Regression	150.800	3	50.267	238.286	0.000^c
	Residual	48.941	232	0.211
	Total	199.740	235

^aPredictors: (Constant), AVGPIS
^bPredictors: (Constant), AVGPIS, AVGTS
^cPredictors: (Constant), AVGPIS, AVGTS, AVGOK
^dDependent variable: AVGE

Coefficients^a
Unstandardized coefficients		Standardized coefficients	t	Sig.
B	Std. error	Beta	t	Sig.
−0.033	0.147		−0.223	0.823
0.870	0.037	0.841	23.795	0.000
0.037	0.139		0.269	0.788
0.466	0.080	0.450	5.805	0.000
0.400	0.072	0.433	5.576	0.000
0.043	0.136		0.318	0.751
0.366	0.083	0.354	4.391	0.000
0.322	0.073	0.349	4.387	0.000
0.197	0.056	0.210	3.504	0.001

1.2.2 Regression Analysis of Micro Variables of Personal and Interpersonal Skills

Model summary
Model	R	R-square	Adjusted R-square	Std. error of the estimate
1	0.795^a	0.632	0.631	0.56041
2	0.825^b	0.681	0.678	0.52311
3	0.840^c	0.706	0.702	0.50302
4	0.844^d	0.712	0.707	0.49877

^aPredictors: (Constant), AVGPISTI
^bPredictors: (Constant), AVGPISTI, AVGPISPC
^cPredictors: (Constant), AVGPISTI, AVGPISPC, AVGPISCS
^dPredictors: (Constant), AVGPISTI, AVGPISPC, AVGPISCS, AVGPISCT

ANOVA^e
Model		Sum of squares	df	Mean square	F	Sig.
1	Regression	126.250	1	126.250	401.997	0.000^a
	Residual	73.490	234	0.314
	Total	199.740	235
2	Regression	135.982	2	67.991	248.470	0.000^b
	Residual	63.758	233	0.274
	Total	199.740	235
3	Regression	141.037	3	47.012	185.799	0.000^c
	Residual	58.703	232	0.253
	Total	199.740	235
4	Regression	142.274	4	35.569	142.977	0.000^d
	Residual	57.466	231	0.249
	Total	199.740	235

^aPredictors: (Constant), AVGPISTI
^bPredictors: (Constant), AVGPISTI, AVGPISPC
^cPredictors: (Constant), AVGPISTI, AVGPISPC, AVGPISCS
^dPredictors: (Constant), AVGPISTI, AVGPISPC, AVGPISCS, AVGPISCT
^eDependent variable: AVGE

Coefficients^a
Model		Unstandardized coefficients		Standardized coefficients	t	Sig.
Model		B	Std. error	Beta	t	Sig.
1	(Constant)	0.624	0.142		4.389	0.000
1	AVGPISTI	0.691	0.034	0.795	20.050	0.000
2	(Constant)	0.358	0.140		2.558	0.011
	AVGPISTI	0.423	0.055	0.486	7.637	0.000
	AVGPISPC	0.345	0.058	0.380	5.964	0.000
3	(Constant)	−0.019	0.159		−0.119	0.906
	AVGPISTI	0.281	0.062	0.323	4.533	0.000
	AVGPISPC	0.308	0.056	0.339	5.479	0.000
	AVGPISCS	0.275	0.062	0.254	4.470	0.000
4	(Constant)	−0.023	0.158		−0.145	0.885
	AVGPISTI	0.226	0.066	0.259	3.404	0.001
	AVGPISPC	0.256	0.061	0.281	4.222	0.000
	AVGPISCS	0.247	0.062	0.228	3.961	0.000
	AVGPISCT	0.140	0.063	0.158	2.230	0.027

^aDependent variable: AVGE

1.2.3 Regression Analysis of Micro Variables of Technical Skills

Model summary
Model	R	R-square	Adjusted R-square	Std. error of the estimate
1	0.805^a	0.648	0.647	0.54810
2	0.840^b	0.705	0.703	0.50278

^aPredictors: (Constant), AVGTSTK
^bPredictors: (Constant), AVGTSTK, AVGTSTM

ANOVA^c
Model		Sum of squares	df	Mean square	F	Sig.
1	Regression	129.444	1	129.444	430.889	0.000^a
	Residual	70.296	234	0.300
	Total	199.740	235
2	Regression	140.841	2	70.420	278.577	0.000^b
	Residual	58.899	233	0.253
	Total	199.740	235

^aPredictors: (Constant), AVGTSTK
^bPredictors: (Constant), AVGTSTK, AVGTSTM
^cDependent variable: AVGE

Coefficients^a
Model		Unstandardized coefficients		Standardized coefficients	t	Sig.
Model		B	Std. error	Beta	t	Sig.
1	(Constant)	0.757	0.131		5.765	0.000
1	AVGTSTK	0.721	0.035	0.805	20.758	0.000
2	(Constant)	0.444	0.129		3.443	0.001
	AVGTSTK	0.418	0.055	0.467	7.573	0.000
	AVGTSTM	0.358	0.053	0.414	6.715	0.000

^aDependent variable: AVGE

1.2.4 Regression Analysis of Controlled Impact of all Micro Variables

Model summary
Model	R	R-square	Adjusted R-square	Std. error of the estimate
1	0.871^a	0.758	0.751	0.46017

^aPredictors: (Constant), AVGOK, AVGPISCS, AVGPISCT, AVGPISPC, AVGTSTK, AVGTSTM, AVGPISTI

ANOVA^b
Model		Sum of squares	df	Mean square	F	Sig.
s1	Regression	151.460	7	21.637	102.181	0.000^a
	Residual	48.280	228	0.212
	Total	199.740	235

^aPredictors: (Constant), AVGOK, AVGPISCS, AVGPISCT, AVGPISPC, AVGTSTK, AVGTSTM, AVGPISTI
^bDependent variable: AVGE

Coefficients^a
Model		Unstandardized coefficients		Standardized coefficients	t	Sig.
Model		B	Std. error	Beta	t	Sig.
1	(Constant)	0.018	0.146		0.126	.900
	AVGPISCS	0.138	0.060	0.127	2.297	0.023
	AVGPISPC	0.123	0.060	0.136	2.058	0.041
	AVGPISCT	−0.013	0.066	−0.015	−0.200	0.841
	AVGPISTI	0.121	0.063	0.139	1.909	0.057
	AVGTSTM	0.146	0.063	0.169	2.320	0.021
	AVGTSTK	0.203	0.061	0.227	3.337	0.001
	AVGOK	0.171	0.058	0.183	2.945	0.004

^aDependent variable: AVGE

Validated Macro-model of Research from Employers’ Perspective

Validated Model of Micro Variables of Technical Skills from Employers’ Perspective

Validated Model of Micro Variables of Personal and Interpersonal from Employers’ Perspective

Validated Micro-model of Controlled Impact of all Independent Variables from Employers’ Perspective

1.3 Annexure III: Results of Collinearity

1.3.1 Collinearity Results—Macro Variables

Variables entered/removed^b
Model	Variables entered	Variables removed	Method
1	AVGTS, AVGOK, AVGPIS^a		Enter

^aAll requested variables entered
^bDependent variable: AVGE

Coefficients^a
Model		Collinearity statistics
Model		Tolerance	VIF
1	AVGOK	0.295	3.390
	AVGPIS	0.163	6.153
	AVGTS	0.167	5.980

^aDependent variable: AVGE

Collinearity diagnostics^a
Model	Dimension	Eigenvalue	Condition index	Variance proportions
Model	Dimension	Eigenvalue	Condition index	(Constant)	AVGOK	AVGPIS	AVGTS
1	1	3.937	1.000	0.00	0.00	0.00	0.00
	2	0.044	9.462	0.80	0.09	0.00	0.02
	3	0.014	16.730	0.06	0.90	0.07	0.23
	4	0.005	27.368	0.14	0.01	0.93	0.75

^aDependent variable: AVGE

1.3.2 Collinearity Results—Micro Variables

Variables entered/removed^b
Model	Variables entered	Variables removed	Method
1	AVGTSTK, AVGPISCS, AVGPISPC, AVGTSTM, AVGPISTI, AVGPISCT^a		Enter

^aAll requested variables entered
^bDependent variable: AVGE

Coefficients^a
Model		Collinearity statistics
Model		Tolerance	VIF
1	AVGPISCS	0.360	2.780
	AVGPISPC	0.261	3.836
	AVGPISCT	0.196	5.113
	AVGPISTI	0.202	4.939
	AVGTSTM	0.202	4.946
	AVGTSTK	0.253	3.946

^aDependent variable: AVGE

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Sehgal, N., Nasim, S. (2020). Employers’ Perception on the Antecedents of Graduate Employability for the Information Technology Sector. In: Suri, P., Yadav, R. (eds) Transforming Organizations Through Flexible Systems Management. Flexible Systems Management. Springer, Singapore. https://doi.org/10.1007/978-981-13-9640-3_5

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Appendices

Appendices

1.1 Annexure I: Results of Correlation Analysis

1.2 Annexure II: Results of Regression Analysis

1.2.1 Regression Analysis of Macro Variables

1.2.2 Regression Analysis of Micro Variables of Personal and Interpersonal Skills

1.2.3 Regression Analysis of Micro Variables of Technical Skills

1.2.4 Regression Analysis of Controlled Impact of all Micro Variables

1.3 Annexure III: Results of Collinearity

1.3.1 Collinearity Results—Macro Variables

1.3.2 Collinearity Results—Micro Variables

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