Does institutional investor influence information technology investment decisions and corporate performance?

Figures & data

Table 1. Industry classification.

Industry	Observation
Construction	80
Consumer Products	460
Financial Services	194
Health Care	30
Industrial Products & Services	558
Plantation	96
Properties	206
Transportation & Logistics	40
Technology	362
Trading-Services	179
Total number of observations	2,205

Table 2. Variables definition.

	Description	Source
Dependent Variable
EVM	EVM stands for equity value multiple, a measure for firm performance using the book value and sales value.	Alghorbany et al. (2022), Nenkov (2019), Shittu (2015)
Independent variables
$\mathit{\text{ITINVEST}}$	ITINVST is equivalent to the natural logarithm for the amount of Net Cash for IT Investing Activities	Alghorbany et al. (2022), Mohd-Noor (2017).
Interacting Variables
$\mathit{\text{GLIC}}$	CLIC signifies the percentage of the ordinary shares maintained by each GLICs.	Abd Mutalib et al. (2016), Abdul Wahab et al. (2008).
$\mathit{\text{FOWF}}$	FOWF is the proportion of foreign shares divided by total shares outstanding in a firm in a year.	Musallam and Lin (2019), Ismail, et al. (2016), Ho et al. (2011).
Control variables
$\mathit{\text{BOSIZE}}$	BOSIZE is the sum of directors at a company.	Kamardin (2014).
$\mathit{\text{BIND}}$	BIND signifies the total number of independent non-executive directors divided by the total number of directors.	Haider et al. (2016).
$\mathit{\text{LEV}}$	LEV signifies the total debt to total equity.	Alghorbany et al. (2022), Kamardin (2014).
$\mathit{\text{FSIZE}}$	FSIZE signifies the logarithm of total assets.	Alghorbany et al. (2022), Kamardin (2014).
SGROWTH	SGROWTH signifies the change in sales revenue.	Alghorbany et al. (2022), Houqe et al. (2019).
MARKSH	MARKSH signifies square the market shares of all firms in a market and then summing the squares.	Alghorbany et al. (2022), Houqe et al. (2019).
FAGE	FAGE is the total number of firm (i) age.	Hamdan et al. (2019).
$\mathit{\text{INDUSTRY}}{D}_{\mathit{\text{it}}}$	INDUSTRYD Industry dummies are based on financial and non-financial firms.	Hamdan et al. (2019).

Table 3. Descriptive Statistics.

	Obs	Mean	Median	Std. Dev	Min	Max
Dependent variable
EVM	2,205	3.81	−0.32	1.08	−1.19	12.65
Independent variable
ITINVEST (log)	2,205	6.49	6.48	0.82	4.11	8.69
ITINVEST(Rm)	2,205	13,900,000	3,055,000	30,700,000	13,000	494,000,000
Interacting Variables
GLIC	2,205	5.41	0	13.23	0	81.74
FOWF	2,205	3.97	0	11.89	0	74.97
Control variable
BOSIZE	2,205	7.48	7	2.06	4	17
BOIND	2,205	0.49	0.47	1.29	.14	.90
LEV	2,205	0.43	0.14	2.55	0	64.35
SALEGROW	2,205	4.48	4.58	25.68	−93.17	99.61
FIRMSIZE(log)	2,205	5.60	5.46	0.86	3.92	8.02
FIRMSIZE(Rm)	2,205	3,765,836	292,000	11,700,000	8,298	106,000,000
MARSH	2,205	0.57	0.49	0.42	0.001	3.14

Table 4. Correlation matrix.

	Variables	1	2	3	4	5	6	7	8	9	10	11
1	EVM	1
2	ITINVEST	0.05	1
3	GLIC	0.01	−0.09	1
4	FOWF	0.28	0.06	−0.02	1
5	BOIND	0.02	−0.08	0.34	0.03	1
6	BOSIZE	−0.03	−0.03	0.31	0.04	0.60	1
7	LEV	−0.02	0.01	0.04	−0.02	0.03	0.05	1
8	FIRMSIZE(log)	0.23	−0.01	0.045	0.17	0.36	0.44	0.07	1
9	SALEGROW	0.01	0.02	−0.00	0.02	0.00	0.00	0.00	0.08	1
10	MARSH	−0.14	0.11	−0.11	0.03	−0.05	0.04	0.02	−.18	0.09	1
11	FIRMAGE	−0.09	−0.05	0.019	−0.02	0.22	0.16	0.05	0.21	−0.04	−0.04	1

Table 5. Regression Results for Equity Value Multiples.

Variable	coeff	t-value	VIF
L.EVM	0.70	8.53***	–
ITINVET	−0.40	−8.67***	1.02
$\mathit{\text{ITINV}}*\mathit{\text{GLIC}}$ (H1)	0.01	2.85***
$\mathit{\text{ITINV}}*\mathit{\text{FOWF}}$ (H2)	0.01	8.80***
BOSIZE	−0.01	−4.48***	1.79
BOIND	0.02	0.64*	1.66
LEV	−0.01	−2.29**	1.02
FIRMSIZE	0.89	10.49***	1.38
SALEGROW	−0.01	−2.54***	1.03
MARSH	0.31	3.76***	1.09
FIRMAGE	−0.03	−5.92***	1.10
_cons	−3.39	−7.99***	–
Industry Dummy	Yes	–	–
Mean VIF	–	–	1.2
No. of obs	1,974	–	–
No. of groups	231	–	–
Prob > chi2	0.00	–	–
Hansen J	0.32	–	–
AR(1)	0.01	–	–
AR(2)	0.57	–	–

Note: The coefficient values are presented with the t-statistics in parenthesis, * p < 0.10; ** p < 0.05; *** p < 0.01. EVMt-1: is a lagged dependent variable. The lagged value of the dependent variable is automatically added in STATA to account for any dynamic endogeneity present in the regression; ITINVEST is IT spending and is the independent variable.

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Data availability statement

I utilized data from my previous article in the current study, incorporating a moderator variable to examine the relationship. Sure, the data of the IT Investment variable, Firm Performance variables and control variables are the same, but the moderator variable did not exist in my previous article, here are the differences. The authors confirm that the data of this study are available within my previous article: https://doi.org/10.1080/23311975.2022.2055906.