Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using BDAC and business strategy alignment.

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Table 7
Equations for hierarchical modeling using partial least squares (PLS).
First-order BDAC model Second-order BDAC model Third-order BDAC model
y i = Λy.
ηj εi ηj = Γ.
ζκ
+ ζj ηj = β. ηj + Γ.
ζκ
+ ζj
y i ¼manifest variables (e.g., items of BDA
planning)
η j ¼first-order factors (e.g., BDA planning) η j ¼second-order factors (i.e., management, technology and talent
capability)
Λ y ¼loadings of first-order LVs
Γ ¼loadings of second-order LVs
η j ¼first-order LVs (e.g., BDA planning)
ζ κ ¼second-order LVs (e.g., BDA management
capability)
β η j ¼higher-order LVs with loadings (i.e., from first to the nth order,
except the highest order)
ε i ¼measurement error ζ j ¼error of first-order factors Γ ζ κ ¼the highest-order LV with loadings (i.e., third-order BDAC
construct)
ζ j ¼error of second-order factors
more on its own construct than on other constructs and, therefore,
all constructs share a substantial amount of variance with their
own items (Chin, 1998b; Fornell and Bookstein, 1982). In all cases,
the item’s relationship to its own construct has a shared variance
of more than 56% (i.e., 0.75*0.75), which is substantial in magnitude
in comparison with other constructs (Chin, 2010). Overall, the
measurement model was considered satisfactory due to the evidence
of adequate reliability (AVE40.50, CR40.80) and convergent
validity (loadings40.75), as shown in Table 8, and discriminant
validity ( AVE 4correlations), as shown in Table 9. The
first-order measurement model was thus confirmed as satisfactory:
it was then employed for testing the higher-order measurement
model and the structural model which is described in the
next sections.
5.2. Higher-order measurement model
In Table 10, the study estimated the measurement properties of
the higher-order constructs, that is, the third-order BDAC construct
and second-order management capability, technology capability,
and talent capability constructs. The third-order BDAC
construct consists of 44 items (16þ12þ16) of which 16 items
(4þ4þ4þ4) represent BDA planning capability, 12 items
(4þ4þ4) represent BDA technology capability, and 16 items
(4þ4þ4þ4) represent talent capability. As higher-order constructs
are reflective, the study confirmed that the loadings of
items of both the third-order BDAC construct and the second-order
constructs (management capability, technology capability, and
talent capability) are significant at po0.05.
The degree of variance of the third-order BDAC construct was
explained by its second-order antecedents, that is, BDAMAC (88%),
BDATEC (83%), and BDATLC (90%). Accordingly, the variances of the
second-order constructs were explained by their corresponding
first-order components (see Appendix 2). For example, the degree
of explained variance of BDAMAC was explained by BDAPL (82%),
BDAID (79%), BDACO (72%), and BDACT (85%). Similarly, BDATEC
was explained by BDACN (75%), BDACM (85%), and BDAMD (81%).
Finally, BDATLC was explained by BDATM (86%), BDATK (89%),
BDABK (87%), and BDARK (88%). All these path coefficients from
the first-order to the second-order to the third-order constructs
were significant at po0.01.
5.3. Structural model
To assess the validity of the structural model, the study estimated
the relationship between the higher-order BDAC and FPER.
The results provide a standardized beta of 0.709 for the BDAC–
FPER path in the main, thus supporting H1. In order to identify the
moderating effect of ACBSA on FPER, we applied PLS product-indicator
approach (Chin et al., 2003). We created the interaction
construct by multiplying the hierarchical BDAC construct with
ACBSA construct following the guidelines of Chin et al. (2003).
Thus, to estimate the interaction effect, we separately estimated
the influence of BDAC on FPER, ACBSA on FPER and the impact of
BDAC*ACBSA (interaction variable) on FPER. For the interaction
model, the results provide us a standardized beta of 0.261 for
BDAC–FPER (po0.01), 0.542 for ACBSA-FPER (po0.01) and 0.153
(po0.05) for BDAC*ACBSA-FPER respectively. We confirm the
significance of the moderator because the path coefficient of the
BDAC*ACBSA-FPER is significant, independently of BDAC–FPER and
ACBSA-FPER relationships in the interaction model (Henseler and
Fassott, 2010). According to the guidelines of Cohen (1988), the
size of the moderating effect is medium (f 2 ¼0.23) but significant
at po0.05. Thus, the results provide support for the H2. InTable
11, we evaluated the main model (m) with the interaction
model (i) using an incremental F test to investigate whether inclusion
of moderating variable (ACBSA) significantly enhances the
R 2 for FPER. The findings confirm a superior prediction power of
the interaction model, which is reflected in firm performance
(ΔR 2 ¼0.093, f 2 ¼0.23, po0.01). Similarly, we investigated the
impact of control variables (COVA) on FPER, however, the results
show insignificant impact of COVA as the R 2 change is very small
after including this construct in the model.
5.4. Additional analyses
The findings of the study confirm the impact of BDAC and
ACBSA on FPER, thereby ensuring the nomological validity of the
overall research model. The study also conducted few additional
analyses to ensure overall validity of the findings. First, to address
the concern of non-response bias, we checked the profiles of the
survey respondents and those on the panel in terms of organization
size and industry, and no non-response bias was found
through the chi-square tests (Kim et al., 2012). We also compared
the early (20%) and late (20%) response groups, and the paired t-
test did not provide any significant difference on the survey items
between two groups. Second, we checked common method variance
(CMV) by applying Harman’s one-factor test (Podsakoff and
Organ, 1986) on the 11 first-order constructs and the results did
not provide any significant common factor loading on all the
measures. Third, we applied Stone–Geisser's Q 2 to test the predictive
validity of the model (Akter et al., 2011). Using the crossvalidated
redundancy approach (omission distance¼7), this study
obtained a Q 2 0.503 for FPER, which adequately demonstrate the
predictive validity of the BDAC construct on FPER in the big data
environment (Chin, 2010). Finally, the study conducted power
analysis (1 β) to validate the empirical findings on the 152 responses
in the sample. Power (1 β) indicates the probability of
successfully rejecting a null hypothesis (Cohen, 1992). Using
G*Power 3.1.3 by Faul et al. (2009), the study conducted the power
test (post hoc) to estimate the validity of the hypothesized relationships.
Cohen (1988) suggests that a threshold level of 0.80 be