To test the above hypotheses, a rigorous relationship measuring statistical approach is required. Correlation coefficient analyses such as Pearson’s correlation, Spearman’s correlation, Gamma correlation and Kendall’s tau are intended to measure the strength of the relationship between two variables. Kendall’s tau, unlike other correlation approaches, has an intuitively simple interpretation that employs an algebraic structure. Noether (1981) suggests that Kendall’s tau is one of the best approaches to measure the strength of the relationship. Echoing the same sentiment, Terziovski and Guerrero (2014) advise the use of Kendall’s tau-b as a more robust correlation coefficient under a wide variety of data distribution. Since, as described in Chapter 8, both the independent variables and the dependent variables are nonparametric ordinal variables and as this research has a wide range of data distribution that tries to measure the strength of relationships between a HRIS-enabled HR practices and the HRM performance, the Kendall tau-b correlation is used (Morganet al., 2013; Terziovski and Guerrero 2014).
Assumption: The main assumption for Kendall tau-b correlation is that the data are at least ordinal (Morganet al., 2013, p.143).
In order to conduct the Kendall tau-b correlation, based on the survey questionnaire design described in Chapter 7, Research Methods: Measures (Table 7.1), ten separate analyses are needed. For the ease of understanding, these ten analyses are categorized into three sections; the first section includes four analyses that test the strength of relationship between the HRIS- enabled HR transactional practices and the HRM performance; the second section includes next four analyses that test the strength of relationship between the HRIS-enabled HR traditional practices and the HRM performance; and the last section includes two analyses that test the strength of relationship between the HRIS-enabled HR transformational practices and the HRM performance. Table 9.1 summarizes these details:
Table 9.1 - The impact of HRIS-enabled HR practices on HRM performance
A
n
al
ys
is Independent variable Dependent variable
Association between HRIS-enabled HRtransactionalpractices and HRM performance 1 HRIS-enabled HR transactional day-to-day
record keeping practices such as entering payroll information, employee status changes,
etc.(Question 1)
HRM performance that is measured by overall employee satisfaction, motivation, presence (obverse of absenteeism) and retention (obverse of
turnover).
(Question 6) 2 HRIS-enabled HR transactional day-to-day
record keeping practices such as entering payroll information, employee status changes,
etc.(Question 1)
HRM performance that is measured by overall employee involvement, trust, commitment and social climate between workers and management.
3 HRIS-enabled HR transactional benefit administration practices such as, overseeing the
health insurance coverage, administering investment and retirement program,etc.
(Question 2)
HRM performance that is measured by overall employee satisfaction, motivation, presence (obverse of absenteeism) and retention (obverse of
turnover).
(Question 8) 4 HRIS-enabled HR transactional benefit
administration practices such as, overseeing the health insurance coverage, administering
investment and retirement program,etc.
(Question 2)
HRM performance that is measured by overall employee involvement, trust, commitment and social climate between workers and management.
(Question 9)
Association between HRIS-enabled HRtraditionalpractices and HRM performance 5 HRIS-enabled HR traditional management
practices such as recruitment, selection, training, promotion and compensation.
(Question 3)
HRM performance that is measured by overall employee satisfaction, motivation, presence (obverse of absenteeism) and retention (obverse of
turnover).
(Question 10) 6 HRIS-enabled HR traditional management
practices such as recruitment, selection, training, promotion and compensation.
(Question 3)
HRM performance that is measured by overall employee involvement, trust, commitment and social climate between workers and management.
(Question 11) 7 HRIS-enabled HR traditional management
practices such as performance management, rewards, career development and
communication.
(Question 4)
HRM performance that is measured by overall employee satisfaction, motivation, presence (obverse of absenteeism) and retention (obverse of
turnover).
(Question 12) 8 HRIS-enabled HR traditional management
practices such as performance management, rewards, career development and
communication.
(Question 4)
HRM performance that is measured by overall employee involvement, trust, commitment and social climate between workers and management.
(Question 13)
Association between HRIS-enabled HRtransformationalpractices and HRM performance 9 HRIS-enabled HR transformational
management practices such as strategic planning, organizational development, knowledge management and change
management.
(Question 5)
HRM performance that is measured by overall employee satisfaction, motivation, presence (obverse of absenteeism) and retention (obverse of
turnover).
(Question 14) 10 HRIS-enabled HR transformational
management practices such as strategic planning, organizational development, knowledge management and change
management.
(Question 5)
HRM performance that is measured by overall employee involvement, trust, commitment and social climate between workers and management.
(Question 15)
Correlation coefficient values (i.e. the strength of association between two variables) are determined between minus one and plus one scale (i.e. -1 to +1 scale) used by Pearson correlation. The positive correlation suggests that the variables are perfectly linear by an increasing relationship and the negative correlation suggests that as the variables are perfectly linear by a decreasing relationship (Morgan et al., 2013, and Bolboaca and Jäntschi, 2006). Morganet al.,(2013, p.145) suggest that:
If the association between variables is weak, the value of the statistic will be close to zero and the significance level (Sig.) will be greater than .05, the usual cut-off to say
that an association is statistically significant. However, if the association is statistically significant, the p-value will be small (<.05).
The effect size, in other words the strength of relationship in the analyses is interpreted, as cited by Morganet al.(2013, p.102), based on Cohen (1998) and Vaske, Gliner and Morgan (2002) table given below:
Table 9.2 - Interpretation of the strength of a relationship (effect size) General interpretation of the
strength of a relationship r² r
Much larger than typical .49 ≥ |.70|
Large or larger than typical .25 |.50|
Medium or typical .09 |.30|
Small or smaller than typical .01 |.10|
Note: ‘r’ family values can vary from 0.0 to ± 1.0, but except for reliability (i.e.the same concept measured twice), ‘r’ is rarely above .70.