Control variables

As discussed in Chapter Four (Section 4.5.2.3), this study used three multi-item control variables: market truculence, technological turbulence, and organisational slack. The first respondent answered questions regarding these control variables (Questionnaire A). Specifically, market turbulence was measured using three items in the reflective fashion. As

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shown in Table 5.15, the loadings for all items were greater than the recommended cut-off value (> 0.50), ranging from 0.85 to 0.95. The bootstrapped t-values for all items were greater than the recommended cut-off value (> ± 1.96), ranging from 3.56 to 4.97. Therefore, the results indicate that all items had satisfactory explanatory power. In addition, composite reliability and AVE of market turbulence were greater than the recommended cut-off values (CR= 0.93, AVE= 0.82).

Technological turbulence was measured using four items in the reflective fashion. As shown in Table 5.15, the loadings for all items were greater than the recommended cut-off value (> 0.50), ranging from 0.63 to 0.96. The bootstrapped t-values for all items were greater than the recommended cut-off value (> ± 1.96), ranging from 4.00 to 4.83. Therefore, the results indicate that all items had satisfactory explanatory power. In addition, composite reliability and AVE of technological turbulence were greater than the recommended cut-off values (CR= 0.90, AVE= 0.70).

Organisational slack was measured using three items in the reflective fashion. As shown in Table 5.15, the loadings for all items were greater than the recommended cut-off value (> 0.50), ranging from 0.71 to 0.96. The bootstrapped t-values for all items were greater than the recommended cut-off value (> ± 1.96), ranging from 4.11 to 4.60. Therefore, the results indicate that all items had satisfactory explanatory power. In addition, composite reliability and AVE of organisational slack were greater than the recommended cut-off values (CR= 0.89, AVE= 0.73).

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Table 5.15 – Result of outer-measurement model of multi-item control variables

Constructs Loading t-Value Market Turbulence (CR= .93, AVE= .82)

In our firm’s business environment:

MT1 ...customer needs and product preferences changed rapidly. .93 4.88

MT2 ...customer product demands and preferences were uncertain. .85 3.56

MT3 ...it was difficult to predict changes in customer needs and preferences. .95 4.97

Technological Turbulence (CR= .90, AVE= .70)

In our firm’s business environment:

TT1 ...it was difficult to forecast technology developments. .63 4.49

TT2 ...technology environment was uncertain. .96 4.00

TT3 ...technological developments were unpredictable. .75 4.83

TT4 ...technology environment was complex. .95 4.32

Organisational Slack (CR= .89, AVE= .73)

Our firm has:

OS1 …available resources for future projects. .88 4.60

OS2 …discretionary financial resources. .96 4.33

OS3 …manpower to work on special projects. .71 4.11

5.4.8. Convergent and discriminant validity Convergent validity

As discussed in Section 5.4.1, convergent validity and discriminant validity are used to examine the validity of a reflective outer-measurement model. Convergent validity represents the degree to which an item is associated with its respective construct (Hulland, 1999). The assessment of convergent validity is based on two criteria. First, Nunnally (1978) suggests that convergent validity of an outer-measurement model is satisfactory when composite reliability of all constructs within a model exceed 0.70 threshold (see also Henseler et al., 2009; Hair et al., 2011b). Second, Fornell and Larcker (1981) suggest that convergent validity of an outer-measurement model is satisfactory when AVE of all constructs within a model exceed 0.50 thresholds, meaning that the construct explains more than half of its items’ variance (see also Henseler et al., 2009; Hair et al., 2011b). As shown in Tables 5.7 to 5.15, the results of composite reliability (ranging from 0.87 to 0.95) and AVE (ranging from 0.54 to 0.52) of all constructs of interest (exploratory strategy, exploitative strategy, exploratory R&D, exploitative R&D, exploratory marketing, exploitative marketing, and new

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product performance, established product performance, firm performance, market turbulence, technological turbulence, and organisational slack) were greater than the recommended thresholds. Therefore, the results indicate satisfactory convergent validity for all constructs of interest.

Discriminant validity

Discriminant validity represents the extent to which items of a construct are different from items of other constructs within a model (Hulland, 1999). Discriminant validity of an outer- measurement model is satisfactory when the variance shared between a construct and its items is higher than the variance shared between any two constructs (Fornell & Larcker, 1981). Drawing on Patterson and Smith (2003), O’Cass & Ngo (2007), and O'Cass, Ngo, and Heirati (2012), this study assessed discriminant validity using two criteria, the Fornell and Larcker’s (1981) criterion and Gaski and Nevin’s (1985) criterion. The Fornell and Larcker (1981) criterion suggests that when a construct shares more variance with its assigned items than with other constructs in the structural model discriminant validity exists. In statistical terms, the AVE of each construct should be greater than the construct’s highest squared correlation with any other constructs. The Gaski and Nevin’s (1985) criterion suggests that the discriminant validity among constructs can be satisfactory when the correlation between two constructs is not higher than their respective composite reliabilities (see also O’Cass, 2002).

As noted in Chapter Three (Section 3.3), this study seeks to articulate the extent that exploratory and exploitative capabilities enable a firm to implement its exploratory and exploitative strategies to enhance new product and established product performance. In addition, Chapter Four (Section 4.5.2.3) explained that this study employed three respondents from each firm to answer three separated questionnaires. Specifically, Questionnaire A

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includes items related to exploratory strategy, exploitative strategy, firm performance, and control variables. Whereas, Questionnaires B and C are related to constructs (i.e., exploratory R&D, exploitative R&D, exploratory marketing, exploitative marketing) that enable a firm to implement its exploratory and exploitative strategies with respect to the selected new product and established product, respectively. Given hypotheses suggested in Chapter Three (Section 3.3), questions in Questionnaires A and B are related to hypotheses 1a, 1b, 2, 5a, 6a, 7a, and 7b, whereas questions in Questionnaires A and C are related to hypotheses 3a, 3b, 4, 5b, 6b, 8a, and 8b. To this end, this study merged Questionnaire A with Questionnaire B and Questionnaire A with Questionnaire C to develop two independent data sets.

The first data set (Data Set I) is related to the extent that a firm deploys its business- level capabilities to implement exploratory and exploitative strategies to enhance new product performance and firm performance. The second data set (Data Set II) is related to the extent that a firm deploys its business-level capabilities to implement exploratory and exploitative strategies to enhance established product performance and firm performance. Analyses of variance (ANOVA) indicated no significant differences between the respondents in Questionnaire A and B on their designated title (F= 1.21) and education level (F= 0.89), indicating the appropriateness of merging Questionnaire A with Questionnaire B (see De Luca & Atuahene-Gima, 2007). In the same vein, there are no significant differences between the respondents in Questionnaire A and C on their designated title (F= 1.01) and education level (F= 0.69), indicating the appropriateness of merging Questionnaire A with Questionnaire C. Drawing on De Luca and Atuahene-Gima (2007), this study assessed the convergent validity of these two data sets separately by assessment of two independent outer- measurement models.

As shown in Table 5.16, the first outer-measurement model encompassed constructs of interest measured in Questionnaires A and B (Data Set I). In this outer-measurement model,

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the square root of the AVEs (the off-diagonal elements which ranging from 0.73 to 0.90) were greater than all individual correlations (ranging from -0.09 to 0.50) (Fornell & Larcker, 1981). In addition, no individual correlation (which ranging from -0.09 to 0.50) was higher than its respective composite reliabilities (ranging from 0.88 to 0.93) (O’Cass, 2002). Following Fornell and Larcker’s (1981) criterion and Gaski and Nevin’s (1985) criterion, the results indicate satisfactory discriminant validity of all constructs in the first outer- measurement model. In addition, the possibility of multicollinearity among all constructs was assessed following Cohen et al. (2002). The maximum variance of inflation factor score was 1.93 lower than the cut-off value of 5 recommended by O'Brien (2007). Therefore, it can be concluded that multicollinearity was not evident.

Table 5.16 – Evidence of discriminant validity for the constructs in Data Set I

CR 01 02 03 04 05 06 07 08 09 10 11